April 2026 arXiv papers — page 16

Berengere Evin, Dorian Gaboriau, Mathieu Segard, Sylvain Challet

This letter is presenting a simple but effective mechanism that explains why ,during tritium aging, metal tritides retain most helium-3 for years and then suddenly release massive amount. The mechanism is based on the hypothesis that dislocations blocking could explain the sudden change of behavior. The modeling of this phenomenon combine a mechanical and mi

Andrzej Derdzinski, Sinhwi Kim, JeongHyeong Park

We conjecture that any scalar-flat K\"ahler surface in which the Weyl tensor acting on 2-forms annihilates the Ricci form must be either Ricci-flat or locally isometric to a Riemannian product of two real surfaces with mutually opposite nonzero constant Gaussian curvatures. This amounts to the nonexistence of proper weakly Einstein anti-self-dual K\"ahler su

Michael Mitterlindner, Maximilian Graber, Regina Kratzer, Markus Reichhartinger

Detailed Computational Fluid Dynamics (CFD) simulations are too computationally expensive for the real-time control and design optimization of multiphase flow reactors. To address these limitations, we introduce CLARA, a software toolbox that automates the generation of Compartment Models (CM) via the unsupervised clustering of CFD data. Unlike previous stud

Jun Guo, Qiwei Li, Peiyan Li, Zilong Chen

We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruction) in a single framework, addressing the critical limitations of prior unified world models (e.g., UWM) that only model 2D pixel-space and fail to balance action efficiency and world modeling quality. To lev

Víctor López Oller, Boyuan Liu, Michela Mapelli, Stefano Rinaldi

Be X-ray binaries (BeXRBs) are key laboratories to constrain binary interaction processes such as mass transfer, angular-momentum transport, and natal kicks. The Small Magellanic Cloud (SMC), hosting a nearly complete and well-characterized BeXRB population, offers a unique opportunity to test these physical processes at low metallicity. We aim to identify t

Matthew Sutcliffe, Ravindra Mutyamsetty

The containment of malware in computing networks may be naturally formulated as a network influence minimisation problem, in which one seeks to limit the expected spread of an infection while balancing the operational cost of disabling network connections. Classical approaches often rely on Monte Carlo simulation of stochastic diffusion processes and greedy

Jungkai Chen, Yongnam Lee, Phin-Sing Soo

Motivated by DeVleming's work on moduli of surfaces in $\mathbb{P}^3$ and Chen-Hu-Jiang's work on moduli of threefolds with volume $2$ and geometric genus $4$, we study the deformation of pairs of $\mathbb{P}^3$ and hypersurfaces using the classification of $\mathbb{Q}$-Gorenstein degenerations of $\mathbb{P}^3$ with canonical singularities. We prove that if

Nadav Alon, Supratik Chakraborty, Alexandre Duret-Lutz, Dror Fried

LTLf synthesis under partial observability requires reasoning about unobservable environment variables, which is typically handled by constructing a belief-state DFA via subset construction that universally quantifies these variables. Existing approaches perform this construction as a separate step prior to game solving, often generating belief states that a

Akhmed Sakip, Erland Hilman Fuadi, Omar Sayedelahl, Zonghang Li

Training large language models requires jointly configuring two interdependent aspects of the system: the global batch size, which governs statistical efficiency, and the 3D parallelism strategy, which governs hardware throughput. Existing approaches make these decisions independently: optimization work adapts the batch size to track the evolving critical ba

Guodong Fan, Cuiyun Gao, Chun Yong Chong, Lu Zhang

The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation approaches often rely on static representations and historical usage data, which are insufficient for adapting to the dynamic and evolving nature of service ecosystems. Recently, large

Euclid Collaboration, H. Jansen, N. Martinet, S. Grandis

The Euclid Wide Survey (EWS) will cover the majority of the extragalactic sky with a resolution similar to the Hubble Space Telescope. This unprecedented data set will introduce a new era of precision cosmology. However, systematic effects need to be controlled better than ever. One of the sources of systematic uncertainties in weak gravitational lensing are

Jaeyong Lee, Heeju Kang, Ahra Cho, Baek Eunkyung

With the rapid spread of generative AI services, the token has gained value not only as a technical unit of language processing but also as an economic currency for accessing AI services. Major AI model providers have adopted token-based billing as their default service model, requiring users to purchase platform-bound, fixed token usage rights. However, the

Parham Oveissi, Ankit Goel

This paper presents a model-free, data-driven control synthesis method called dynamic mode adaptive control (DMAC) for systems whose mathematical models are unavailable or unsuitable for classical control design. The proposed approach combines data-driven dynamics approximation with adaptive control synthesis to enable online controller design using measured

Rodrigo Dal Bosco Fontana, Jeferson de Oliveira

In the present work, we study the causal structure of spherically symmetric black holes immersed in a Chaplygin-like dark fluid, emphasizing the impact of the fluid parameters on curvature and horizon formation. We show that the spacetime curvature is significantly stronger than in its similar counterpart, the Reissner-Nordstrom-de Sitter geometry with the s

Charles Chiang, Simret Gebreegziabher, Annalisa Szymanski, Yukun Yang

LLM-as-a-judge approaches have emerged as a scalable solution for evaluating model behaviors, yet they rely on evaluation criteria often created by a single individual, embedding that person's assumptions, priorities, and interpretive lens. In practice, defining such criteria is a collaborative and contested process involving multiple stakeholders with diffe

Shai Bagon, Matan Kichler, Mark Sheinin

Optical vibration sensing enables recovering the scene sound directly from the surface vibration of nearby objects, turning everyday objects into ``visual microphones''. However, most prior methods had focused on capturing the vibrations of specific objects with highly favorable vibration responses. These include objects where the surface vibrations are gene

Junyi Ye, Zheyi Li, Alexey Y. Nikitin, Franco Nori

We consider a two-dimensional wave system containing a subwavelength hole, such as an aperture in an interface supporting surface electromagnetic or acoustic waves, or an island in a fluid surface sustaining gravity-capillary waves. Recent studies have revealed the emergence of pronounced wave vortices around such structures, termed type-II vortices, in cont

Lara Gauder, Pablo Riera, Andrea Slachevsky, Gonzalo Forno

We introduce a toolkit for uncovering spurious correlations between recording characteristics and target class in speech datasets. Spurious correlations may arise due to heterogeneous recording conditions, a common scenario for health-related datasets. When present both in the training and test data, these correlations result in an overestimation of the syst

Adam Krafczyk, Klaus Schmid

In the research of automated program repair (APR), benchmark datasets consisting of known defects in combination with test suites that indicate the defects are of high importance. They allow for an evidence-based comparison of different APR approaches. In our own work on APR we found significant challenges when working with widely used defect datasets, which

Albert Saiapin, Kim Batselier

Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) are classical kernel-based models that offer principled uncertainty quantification and perform well on small- to medium-scale datasets. Alternatively, formulating the weight space learning problem under tensor ne

Domenico Cotroneo, Giuseppe De Rosa, Cristina Improta, Benedetta Gaia Varriale

Understanding how software defects manifest and evolve in production environments is critical for improving reliability. While previous research has largely focused on pre-release defects, the nature of residual faults, i.e., those escaping testing and surfacing post-release, remains poorly understood. This paper presents a large-scale characterization of pr

Serhii Zabolotnii, Viktoriia Holinko, Olha Antonenko

Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence, supervision, and operational boundaries of AI autonomy. This article proposes a practical framework for trustworthy clinical

Runzhou Wang, Shenglin Zhang, Wenwei Gu, Yongxin Zhao

Microservice root cause localization is fundamentally challenged by the inherent heterogeneity of cloud-native systems, which encompasses diverse observability data and multiple system entities. Existing approaches typically focus on only one aspect of heterogeneity and thus fail to capture its full diagnostic value. In this work, we systematically examine t

Théophile Dupré, Romain Couderc, Miguel Moleron, Axel Coulon

Conventional wavelet-domain methods for room impulse response denoising rely on thresholding detail coefficients, which is unsuited for low frequencies. In this work, we introduce a wavelet-based post-processing algorithm that extends denoising to approximation coefficients by means of sparse dictionary learning with a time-varying error tolerance. The propo

Anubhab Biswas, Lorenzo Zambon, Lorenzo Nespoli, Giorgio Corani

Forecast reconciliation adjusts independently generated forecasts so that they satisfy some known constraints. While probabilistic forecast reconciliation is well established for linear constraints, some practical forecasting problems involve nonlinear relationships among variables. In this paper, we address probabilistic forecast reconciliation with nonline

Giuseppe De Rosa, Pietro Liguori

Python's dynamic nature complicates testing and increases the possibility that some defects evade detection, so an effective fault prediction becomes essential. We examine whether post-release faults can be predicted using modern ML and DL. Using a balanced dataset of over 4,000 labeled faults with 83 product, process, statistical, and Python-specific metric

Sina Heidari, Dimitrios S. Nikolopoulos

Deep learning compilers and vendor libraries deliver strong baseline performance but their performance is bounded by finite, engineer-curated catalogs. When these omit needed optimizations, practitioners substitute hand-written CUDA or CUTLASS, demanding expertise in GPU microarchitecture and C++ template metaprogramming. Recent LLM-based agents target kerne

Longjun Xiang, Hao Jin, Jian Wang

The intrinsic anomalous Hall effect (IAHE) provides a powerful transport fingerprint of quantum magnets, with its linear and second-order responses distinguishing ferromagnets and $\mathcal{P}\mathcal{T}$-symmetric antiferromagnets, respectively. Altermagnets, as an emergent class of quantum magnets, have recently been shown to host a third-order extrinsic a

Madina Bazarova, Faruk Alpay

We introduce the finite-horizon first-order rank profile of a language $L \subseteq \Sigma^*$: the least quantifier rank needed by an $\mathrm{FO}[<]$ sentence to classify membership in $L$ correctly on all words of length at most $n$. The invariant measures quantifier depth only; formula size is deliberately not bounded. First, we prove a rank calculus that

Carson Yu Liu, Jun Cheng, Chien-Chun Chen, Steve F. Shu

Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean scalar despite its $2\pi$ periodicity, which can introduce wrapping artifacts, discontinuities at $\pm\pi$, and a misma

F. S. Luiz, P. N. Ferreira, M. C. de Oliveira

Compiling time-evolution operators of the form $U(t)=e^{-iHt}$ into hardware-native gate sequences is a central bottleneck for digital quantum simulation on noisy intermediate-scale quantum (NISQ) devices. Generic transpilation treats $U(t)$ as an arbitrary unitary, discarding the structure of Hamiltonian dynamics and producing circuits whose depth exceeds h

Yang An, Wen Ting Zhang, Yi He

Let $\mathcal{OP}_n$ be the monoid of all orientation-preserving full transformations on $X_n=\{1,\dots, n\}$ with the natural order. For $\alpha \in \mathcal{OP}_n$, let $F(\alpha)=\{y\in X_n: y\alpha=y\}$ and $F(n,m)=|\{\alpha:|F(\alpha)|=m\}|$. Umar posed the question about the number $F(n,m)$ of elements of $\mathcal{OP}_n$ with $m$ fixed points. In this

Mingwen Fei, Xiang Fei, Yadong Liu, Hao Wu

We study a thermodynamically consistent diffuse-interface model that describes the motion of two macroscopically immiscible, incompressible, and viscous Newtonian fluids with unmatched densities. This model is compatible with continuum mixture theory. It adopts a mass-averaged (barycentric) velocity so that the two-phase flow is quasi-incompressible: the vel

Ivan Proskurnin

In this paper we improve the previously achieved upper bound on the corank of an equivariantly stable singularity for a group of prime order. We also prove that the maximal corank of a simple $\mathbb{Z}_p$-invariant germ tends to infinity as $p$ increases and is asymptotically logarithmic, so the previously obtained bound is valid up to order of magnitude.

Lang Dong, Dechao An, Junxiang Wu, Tianle Wang

Proton therapy has been rapidly advancing due to its excellent conformal index, but its relatively low relative biological effect (RBE) has somewhat limited its therapeutic efficacy for certain tumors. To address this, we previously proposed a nitrogen-targeting Proton-Carbon-Alpha-Therapy (Proton-CAT) enhancement method. In this letter, we present combined

Hirak Kansara, Leo Guo, Wei Tan

The rise of machine learning and additive manufacturing has enabled the design of architected materials with tailored properties that surpass those of natural materials. Inverse design offers a data-efficient alternative to trial-and-error methods, yet most existing approaches depend on either large datasets or scarce high-fidelity data from simulations and

Stefan Arnold

Differential Privacy (DP) for text matured from disjointed word-level substitutions to contiguous sentence-level rewriting by leveraging the generative capacity of language models. While this form of text privatization is best suited for balancing formal privacy guarantees with grammatical coherence, its impact on the register identity of text remains largel

Michael Kunzinger, Moritz Reintjes, Roland Steinbauer, Inés Vega-González

We prove a low-regularity version of Hawking's singularity theorem for Lorentzian metrics in $W^{1,p}$ with Riemann curvature in $L^p$, where $p>2n$ and $n$ the dimension of spacetime. This extends previous results beyond the Lipschitz regime. Under suitable lower Ricci bounds and upper mean curvature assumptions, expressed in terms of temporal functions, we

Huy Phan, Ievgeniia Kuzminykh, Bogdan Ghita

In the rapidly evolving field of software engineering, the skills required of graduates entering the job market are constantly changing. Several studies have identified a gap between the skills taught in university curricula and those demanded by the software engineering industry. This chapter investigates the technical skill and expertise gap between higher

Michal Valko, Nuno C. Marques, Marco Castelani

This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard m

Saber Zerhoudi, Michael Granitzer, Jelena Mitrovic

Training trustworthy agentic LLMs requires data that shows the grounded reasoning process, not just the final answer. Existing datasets fall short: question-answering data is outcome-only, chain-of-thought data is not tied to specific documents, and web-agent datasets track interface actions rather than the core retrieval and synthesis steps of a RAG workflo

Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas, Pietro L. Campos

With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the continuous aspect of recommendation environments, these systems increasingly rely on contextual multi-armed bandits (CMAB) to deliver personalized and real-time suggestions. A critical yet underexplored component in

Jillian Ross, Eric So, Andrew W. Lo

Financial misconceptions carry direct economic costs, from panic selling to equity market avoidance, yet they are notoriously resistant to correction. Traditional financial literacy interventions are constrained by cost, reach, and a persistent gap between knowledge and behavioral change. Across three pre-registered studies, we find that purposefully designe

Yang An, Wen Ting Zhang

Let $\mathcal{PO}_n$ be the monoid of all order-preserving partial transformations on $X_n=\{1,\dots, n\}$ with the natural order, and let $\mathcal{O}_n$ and $\mathcal{POI}_n$ denote its submonoids of order-preserving full and injective partial transformations, respectively. For each transformation $\alpha\in\mathcal{PO}_n$, write the random variables $Y(\a

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with retrieval-augmented generation (RAG) remains fundamentally misaligned. Current RAG systems optimize for providing context before reasoning begins, while reasoning models require evidence injection during multi-

Matías I. Caruso, Javier Fernández, Cora Tori, Marcela Zuccalli

In this note we survey some of our results on the Lagrangian reduction of discrete-time mechanical systems (DMSs). It is intended as an introduction to the general ideas that we used in the reduction of DMSs with nonholonomic constraints, DMSs with external forcing, as well as a theory of reduction by stages for such systems. This line of work was inspired b

Gary P. Zank, Xiaocan Li, Krishna Khanal, Alphonse C. Sterling

Low-frequency turbulence in the solar chromosphere remains poorly understood. We address 1) the sources of low-frequency turbulence that potentially heat the chromosphere, and 2) how turbulence is transported and dissipated throughout the chromosphere and lower corona. We use particle-in-cell simulations to investigate mixed polarity magnetic fields correspo

Francisco C. E. Lima, Fernando M. Belchior, Allan R. P. Moreira

In this work, we construct a double O(3)-sigma model minimally coupled to a Maxwell field in (2+1)-dimensional spacetime and investigate the existence of self-dual magnetic vortex solutions. An analysis of the Bogomol'nyi-Prasad-Sommerfield (BPS) property reveals that both sigma fields belong to the same topological sector and that the potential assumes a pe

Chuanqi Zheng, Chushan Li, Ke Huang, Chenyu Zhang

Moir\'e superlattices based on rhombohedral multilayer graphene have emerged as a highly tunable platform for engineering correlated topological phases. Here, we systematically investigate the transport properties of the hole-doped side in rhombohedral tetralayer graphene/ hexagonal boron nitride (hBN) moir\'e superlattices across a range of twist angles and

Ning Liu

We formulate a variational principle for non-relativistic quantum mechanics inspired by Gauss's principle of least constraint. We define a quantum constraint functional as the probability-weighted square deviation between the actual motion and the unconstrained motion that would arise from external forces alone. In this functional, the quantum potential play

Victor Buchstaber, Mikhail Kornev, Vladimir Rubtsov

We show that the associativity condition of the universal symmetric 2-algebraic 2-valued group defined by the Buchstaber polynomial admits several mutually equivalent interpretations from the viewpoints of the Chazy equation, Gauss-Manin connections, Dubrovin-Frobenius structures, and the quantum Yang-Baxter equation. These results place the universal 2-valu

Tianhang Zhang, Jiawei Wang, Jialin Liu, Jingtian Wei

Penning traps, renowned for their unparalleled precision in determining fundamental properties such as mass and magnetic moments, are cornerstone instruments in modern physics. Their applications span from nuclear structure studies to stringent tests of quantum electrodynamics and CPT invariance. Although Penning traps have been demonstrated for fundamental

Zheng-Chuan Wang

There exists a geometric phase for a quantum state during the adiabatic evolution of the system. If the adiabatic procedure happens between the system and the environment interacting with it similar to Born-Oppenheimer (BO) approximation, we can introduce a temperature into the environment, which can be regarded as in an equilibrium state. Then a temperature

Bin Ye, Ruitao Chen, Lei Yin

We present a genuinely non-radial quantum-mechanical route by which $\pi$ emerges from equatorial localization on the sphere. For the highest-weight branch of spherical harmonics, this localization is captured by a natural geometric rigidity index, whose exact finite-quantum-number value is a Wallis partial product. The mechanism is realized in two settings:

Sergej Stanovcic, Daniel Sliwowski, Dongheui Lee

Annotating long-horizon robotic demonstrations with precise temporal action boundaries is crucial for training and evaluating action segmentation and manipulation policy learning methods. Existing annotation tools, however, are often limited: they are designed primarily for vision-only data, do not natively support synchronized visualization of robot-specifi

Ruijie Li, Yue Xiao, Shuaixin Yang, Gang Wu

In this paper, a novel pinching antenna-aided spatial multiplexing (PASM) architecture is conceived, which intrinsically amalgamates the benefits of flexible radiating element placement with radio-frequency (RF) chain transmission. Specifically, we leverage the deterministic phase variation along dielectric waveguides as a zero-power phase-control mechanism,

Jianmin Guo, Yao Du, Yizhen Yu, Yong Zou

We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machine can be trained from time series collected from a few sampled states of the two systems. We show that the trained mach

R. D. H. Hinlopen, C. Putzke, L. Holeschovsky, R. Nicholls

The superconducting diode effect (SDE) allows polarity-dependent critical currents when time-reversal and current-inverting spatial symmetries are broken. Superconducting diodes show promise for applications, but inversion asymmetry is usually encoded in sample geometry or non-centrosymmetric crystals, rendering them static circuit elements. Here we demonstr

Eliya Naomi Aharon, Meytal Grimland, Avi Segal, Loona Ben Dayan

Effective mental health counseling is a complex, theory-driven process requiring the simultaneous integration of psychological frameworks, real-time distress signals, and strategic intervention planning. This level of clinical reasoning is critical for safety and therapeutic effectiveness but is often missing in general-purpose Large Language Models (LLMs).

Meysam K. Rezaeian, Can Shao, Jürgen Herzler, Mustapha Fikri

The transition from gaseous precursors to nascent solid particles and their subsequent structural maturation were investigated in single-pulse shock-tube experiments using ex situ Fourier-transform infrared (FTIR) and Raman spectroscopy of sampled products. A mixture of 2% toluene in argon was pyrolyzed at around 2.0 bar with temperature plateau times of 2.0

Fuxin Guan, Nanyu Chen, Zemeng Lin, Wange Song

Synthetic complex-frequency excitations have emerged as a powerful tool for loss compensation and resolution enhancement. We show that, ideally, these excitations allow for the complete offsetting of intrinsic damping over long evolution times, governed by a universal inverse-time scaling law for residual damping under Nth-order synthetic illumination. Howev

I. V. Leonov

Using DFT+DMFT we study the normal-state electronic structure and magnetic correlations of the recently discovered alternating single-layer bilayer Raddlesden-Popper nickelate La$_5$Ni$_3$O$_{11}$ (1212-LNO). Our results exhibit qualitative differences for the structurally distinct single-layer and bilayer Ni ions, implying the importance of confinement and

Yuwei Wu, Yichen Zhao, Dexter Ong, Vijay Kumar

Approximating collision-free space is fundamental to robot planning in complex environments. Convex geometric representations, such as polytopes and ellipsoids, are widely employed due to their structural properties, which can be easily integrated with convex optimization. Iterative optimization-based inflation methods can generate large volume polytopes in

Tanveer Ahmad

We study a projection-type gradient flow for equality-constrained maximisation of a smooth bilinear control objective on $\mathcal{H}=L^2(0,T;\mathbb{R})$, eliminating Lagrange multipliers through an $(M{+}1)\times(M{+}1)$ moving Gram matrix $\Gamma(s)_{\ell\ell'}=\int_0^T S(t)\,c_\ell(s,t)\,c_{\ell'}(s,t)\,\mathrm{d}t$. The flow generates monotonic ascent i

Sergio Iserte, Rafael Mayo, Enrique S. Quintana-Ortí, Antonio J. Peña

Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource allocation policy. However, the non-negligible additional development effort this solution imposes has constrained its adoption by the scientific programming community. In this wor

Eder Kikianty, Luan Naude, Mark Roelands, Christopher Schwanke

In this paper we develop a theory of integration for locally band preserving functions, introduced by Ercan and Wickstead, on Dedekind complete $f$-algebras. Specifically, we construct Darboux and Riemann integrals and show that they are equal. We then connect the theory of integrable functions to the theory of order differentiable functions, introduced by t

Jinze Li, Yang Zhang, Xin Yang, Jiayi Qu

Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended histories. However, existing agent memory systems are fundamentally constrained by text-context budgets: storing or revisiting raw trajectories is prohibitively token-expensive, while summarization and text-on

Sota Hanai

In this thesis, we study the collective modes induced by the chirality of elementary particles in magnetized media and their implications for neutron star phenomenology. We theoretically predict that the chiral magnetic wave can arise in quark matter inside neutron stars, resulting in the emergence of novel types of seismic oscillations and associated gravit

Sunan Zhao, Yunpeng Wang, Huiyu Yang, Zhihong Guo

Accurate and efficient prediction of three-dimensional (3D) wall-bounded turbulent flows poses a significant challenge for machine learning methods, particularly in scenarios where flow field data are limited. Physics-informed neural operator (PINO) combines neural operator and physics constraint methods, and shows great potential for solving a wide range of

Alessandro Simoni, Riccardo Catalini, Davide Di Nucci, Guido Borghi

Depth ambiguity and joint uncertainty are the two main obstacles in obtaining accurate human pose predictions by 2D-to-3D lifting methods proposed in the literature. In particular, these issues are caused by 2D joint locations that can be mapped to multiple 3D positions, inducing multiple possible final poses. Following these considerations, we propose lever

Jakob Fehle, Nils Constantin Hellwig, Udo Kruschwitz, Christian Wolff

Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-based and instruction-tuned models. This work presents a multilingual evaluation of state-of-the-art ABSA approaches across seven languages (English, German, French, Dutch, Russian

Arseny Pantsialei, Nicholas Sedlmayr

We consider a general model of a Luttinger liquid with broken parity and time reversal symmetry, but with their composite symmetry intact. Such a scenario can be due to a combination of torsion and a Zeeman field in nanowires, or a result of bringing different helical Luttinger liquids into proximity. The broken symmetries result in a band structure with no

Chen Song, Vladimir Cvetkovic, Angela Fontan, Rong Su

The theory of planned behavior (TPB) is one of the most influential frameworks in social psychology, stating that a person's behavior is driven by intention, which is primarily shaped by attitude, subjective norms, and perceived behavioral control. Despite its strong empirical support, TPB remains a static conceptual framework without explicit mathematical f

Tarlan Hasanli, Shahbaz Siddeeq, Bishwash Khanal, Pyry Kotilainen

Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM-based approaches typically use tests as auxiliary inputs rather than enforceabl

Yuanze Hu, Gen Li, Yuqin Lan, Qingchen Yu

Multimodal large language models (MLLMs) have achieved impressive progress on general multimodal tasks, yet they remain brittle on dial-based measurement reading. In this paper, we study this problem through controlled benchmarks and feature-space probing, and show that current MLLMs not only achieve unsatisfactory accuracy on dial-based readout, but also su

Wenjie Zhang, Qianglong Dai, Xiaoli Xu, Ruoguang Li

Integrated sensing and communication (ISAC) holds great promise in expanding the applications of wireless communication networks. However, in current communication-centric systems, the time-frequency resources available for sensing may be limited, and also usually non-uniformly and sparsely distributed across the time-frequency domain. Such a non-uniformity

Andrea Cattaneo

In this paper we study the class of \emph{split Nakamura manifolds}, which are a type of solvmanifolds generalizing Nakamura's threefold, defined as quotients of the semidirect product $\mathbb{C}^n \rtimes_\rho \mathbb{C}$ by a lattice. We discuss their de Rham and Dolbeault cohomology, with emphasis on the degeneration of the Fr\"olicher spectral sequence

A. Neuhaus, P. Gessler, P. Dreher, D. Janoschka

Topology describes properties of physical systems that remain constant under continuous deformations. For infinite vector waves, global topological invariants in position space are typically associated with periodic patterns. We demonstrate that even for aperiodic Helmholtz-decomposable wave fields, possessing only the wave's intrinsic periodicity, a topolog

Daniel Fortunato, José Campos, Rui Abreu

Coverage criteria play a central role in assessing test adequacy in classical software, yet their effectiveness for quantum programs remains poorly understood and largely unexplored. In this paper, we propose six quantum-tailored criteria - condition, decision, and path coverage, and their probabilistic variants - adapted from their classical counterparts. W

F. Mekhaldi, C. I. Paleari, A. M. Smith, A. Aldahan

The Carrington event of 1859 CE is considered as one of the largest geomagnetic storms of the observational era, and often used as a benchmark for a worst-case scenario. Yet, there exists no robust evidence of an associated solar energetic particle event of a significant magnitude, based on measurements of cosmogenic radionuclides 10Be and 14C from ice cores

Jatin Bhusal, Nancy Mahatha, Aayush Acharya, Raunak Regmi

As Competency-Based Education (CBE) is gaining traction around the world, the shift from marks-based assessment to qualitative competency mapping is a manual challenge for educators. This paper tackles the bottleneck issue by suggesting a "Human-in-the-Loop" benchmarking framework to assess the effectiveness of multiple LLMs in automating secondary-level mat

C. Martínez-Sebastián, G. Holgado, S. Simón-Díaz, F. Martins

Context. Growing evidence is revealing the crucial role of binarity in massive star evolution. This affects evolution models and demands a refinement of the available observational constraints. Aims. To investigate the possible evolutionary origins of a sample of 117 Galactic O-type stars with luminosity classes V to III and projected rotational velocities b

Martin J. Renner, Edwin Peter Lobo, Arturo Konderak, Remigiusz Augusiak

It is a well-established fact that some quantum correlations can be nonlocal, meaning that they cannot be described by a local hidden variable model. Certain quantum correlations have a form of nonlocality so strong that they cannot be reproduced even by models having an arbitrarily small local hidden variable component. These correlations are called fully n

Gota Morishita

Federated learning (FL) trains a shared model from updates contributed by distributed clients, often implicitly assuming that contributing clients are representative of the target population. In practice, this representativeness assumption can fail at two distinct stages, inducing selection bias. First, eligibility rules such as device constraints, software

Sakshi Jain, Y. M. Borse, R. Barabde

The problem of characterizing graphs with a prescribed number of main eigenvalues is a long-standing problem in spectral graph theory. Although some constructions are known, only a few produce infinite families of simple connected graphs with exactly $s \ge 2$ main eigenvalues. Zero-divisor graphs form a well-structured class of algebraic graphs whose spectr

Poul Erik Nissen, Anish Amarsi

Differential abundances of C, O, Mg, Al, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Zn, Y, and Zr were determined from high signal-to-noise VLT/UVES spectra for 25 turnoff stars with -2.4 < [Fe/H] < -1.3. Effective temperatures were obtained from profiles of the H_beta line and surface gravities via Gaia parallaxes. The analysis of the spectra were based on 1D model

Jan Korbel, Artemy Kolchinsky, Sarah A. M. Loos, Gonzalo Manzano

Stochastic thermodynamics is a framework for describing non-equilibrium processes at the level of fluctuating trajectories, where the state of a system evolves as a stochastic time series, allowing thermodynamic quantities such as work, heat, and entropy production to be defined along individual realizations rather than at the ensemble level only. Over the p

Per Arne Rikvold

To construct dispersion relations for diffusion or oscillation processes on random networks, it is necessary to obtain effective length scales for the eigenvectors of a graph Laplacian matrix, whose eigenvalues represent inverse time scales. For this purpose, we adapt a method originally introduced in condensed-matter physics to estimate correlation lengths

Žiga Babnik, Fadi Boutros, Naser Damer, Deepak Kumar Jain

Face Recognition (FR) is used in a variety of application domains, from entertainment and banking to security and surveillance. Such applications rely on the FR model to be robust and perform well in a variety of settings. To achieve this, state-of-the-art FR models typically use expressive adaptive margin loss functions, which tie the feature norm to concep

Antonio Castaldo, Maria Carmen Staiano, Johanna Monti, Sheila Castilho

Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retrieving and filtering data from general corpora. We use the r

Enrique Artal Bartolo

The embeddings of complex plane projective curves in the plane are a cornerstone of the topological study of algebraic varieties. In this work, we deal with the local and global aspects of these embeddings, with a special attention to its historical progress.

Arsen Panas, Volodymyr Tkachuk

In this article, we derived a rigorous lower bound on the ground-state energy for a class of one-dimensional quantum systems in deformed space with minimal coordinate and momentum uncertainties, representing the absolute minimum energy that is physically attainable. We considered a harmonic oscillator in such a space and calculate its ground-state energy. We

Bowei He

Mobile agent systems are emerging as a key paradigm for enabling intelligent applications on edge devices and in AIoT ecosystems. However, their scalability is fundamentally constrained by limited on-device computation and fragmented intelligence across devices. In this work, we propose a unified research agenda for scaling mobile agent systems along two com

Zijian Yang, Yulin Shao, Fen Hou, Shaodan Ma

Sparsity in the delay-Doppler (DD) domain enables efficient channel estimation, but the realization-wise sparsity level is rarely known in advance, and it fluctuates. What if we could estimate the channel without ever knowing how many delays or Dopplers are active? This paper answers that question. We propose a sparsity-agnostic structured estimator that req

Marcus Haywood-Alexander, Gregory Duthé, Eleni Chatzi

Digital twins provide a powerful paradigm for diagnostic and prognostic tasks in the monitoring and control of engineered systems; however, their deployment for complex structures remains challenged by model-form uncertainty, arising from unknown nonlinear dynamics, and by sparse sensing. These limitations hinder reliable online state estimation using either

Karol Dobiczek, Maciej Mozolewski, Szymon Bobek, Michał Szafarczyk

Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature attribution methods are limited by the inherent difficulty in mapping abstract waveform fluctuations to physical anatomical pathologies. To resolve this, we propose a

Rongliang Fu, Yi Liu, Qiang Xu, Tsung-Yi Ho

Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential for core algorithm enhancement remains untapped. We introduce MappingEvolve, an open-source framework that pioneers the use of LLMs to directly evolve technology mapping code. Our me

Krishna Ronanki, Beatriz Cabrero-Daniel, Tomas Herda, Stefan Sitkovich

Context: Large language models (LLMs) are observed to have a significant positive impact on various software engineering (SE) activities. With improved accessibility, the adoption of powerful LLMs in industry has surged recently. However, there is a lack of actionable best practices for the efficient and responsible adoption of LLMs within industrial softwar

Hyunsung Yoon, Sungju Ryu, Jae-Joon Kim

As the size of Deep Neural Networks (DNNs) increases dramatically to achieve high accuracy, the DNNs require a large amount of computations and memory footprint. Pruning, which produces a sparse neural network, is one of the solutions to reduce the computational complexity of neural network processing. To maximize the performance of the computations with suc

Vinayak S. Pawar, Siba Prasad Das

We study thermodynamic properties, in particular the Temperature~(T), Angular velocity~($\Omega_h$) and Entropy~(S) of the of magnetically charged slowly rotating (with rotation parameter $a \lsim 0.10$) Kerr black-hole(BH) with the inclusion of cosmological constant ($\Lambda$) in the background of nonlinear electrodynamics (NLED). At first we calculated th