April 2026 arXiv papers — page 124

Rahul Nellissery Rajan, Rajesh Kumari Rajendran, Guillaume Boussinot, Kamal Sbargoud

We present a study of the melting dynamics of a two-phase eutectic solid. In situ, thin-sample experiments using a transparent eutectic alloy and two-dimensional phase field simulations calibrated for the very same alloy are combined to assess pattern formation during directional melting in a temperature gradient. Depending on the melting velocity $V_m$ and

Giuseppe Prete, Gaetano Zimbardo, Silvia Perri

Energetic particles are ubiquitous in space and astrophysical plasmas, and interplanetary shocks are widely regarded as one of the main particle accelerators in the heliosphere. Indeed, in-situ measurements typically show that energetic particle fluxes peak at the shock, indicating a local acceleration process. Furthermore, the time profile of energetic part

Federica Giacchino, Cristina Fernández-Suárez, Miguel Á Sánchez-Conde, M. Ángeles Pérez-García

In the 4FGL-DR4 point-source catalog of the Large Area Telescope (LAT) onboard NASA's Fermi Gamma-ray Observatory (Fermi-LAT), around a third of the sources are still unidentified (unIDs). In this work, we perform a detailed study of one of them, namely 4FGL J2112.5-3043. Only gamma-ray emission has been detected from this unidentified source, with no co

Xiaowei Wang, Ievgen I. Arkhipov, Quan Lin, Huixia Gao

Exceptional points (EPs) have attracted extensive research interest due to their intriguing properties. One of the hallmarks of EP physics is that dynamically encircling the EPs induces chiral mode switching, arising from the breakdown of adiabaticity due to the presence of a complex spectrum in the system's Hamiltonian. While such chiral mode behavior h

Cuong Hoang, Le-Minh Nguyen

The proliferation of financial misinformation poses a severe threat to market stability and investor trust, misleading market behavior and creating critical information asymmetry. Detecting such misleading narratives is inherently challenging, particularly in real-world scenarios where external evidence or supplementary references for cross-verification are

Navpreet Kaur, Shubham Sharma, Abi Jebarson A, Harleen Dahiya

We study the parametrization of the energy-momentum tensor for the case of a proton in momentum space in terms of gravitational transverse momentum-dependent distributions (TMDs). These gravitational TMDs are investigated with the inclusion of higher-twist contributions to predict the mechanical properties, specifically the transverse pressure and shear forc

S. S. Mirzoyan, A. Avagyan, V. G. Gurzadyan

Understanding the prevalence of disk-like galaxies at very high redshifts is crucial for constraining the early formation of angular momentum-supported structures. The advent of JWST now permits rest-frame UV and optical morphological studies deep into cosmic epochs where disks have traditionally been considered uncommon. We apply an identical denoising VAE-

Zhengqiang Li

The prime numbers and the non-trivial zeros of the Riemann zeta function are globally linked by the explicit formula of analytic number theory. Whether they share a hidden, scale-by-scale geometric symmetry has remained unexplored. We address this by measuring the joint fractal structure of a prime residue class (p=1,5,9,13 mod 16) and the zero distribution

Zheng Liu

Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transportation electrification. However, it is challenging due to the computational expense of finite element simulations and the limitations of data-driven models. This paper presents a physics-informed machine learn

Sarker Md. Sadman, Walter R. L. Lambrecht

Mg-IV-N\textsubscript{2} compounds with IV=Si, Ge, Sn are ultra-wide band gap semiconductors with various potential electronic and optoelectronic applications. They share the \textit{Pna}2\textsubscript{1} space group crystal structure. Here we present Density Function Perturbation Theory (DFPT) calculations of the vibrational modes of these materials. We fo

Daniel Bhatti, William J. Munro, Seungbeom Chin

Multipartite entanglement is an essential quantum resource for various distributed quantum applications. One promising method for preparing multipartite entanglement is to interfere independent photons at linear optical interference setups. While heralding the successful interference and thereby the state generation is often costly, postselecting entangled s

Huanhuan Wei, Yun Chen, Jing Tang, Yuangang Deng

We theoretically investigate a quadratic optomechanical system comprising a single-mode optical cavity linearly coupled to one mechanical resonator and quadratically coupled to a second resonator. By tuning the cavity detuning and optomechanical coupling strengths, we demonstrate the transition from optical bistability to multistability with up to seven stea

Bekir Can Lütfüoğlu

We investigate massive scalar perturbations and several characteristics of particle motion in the spacetime of regular black holes arising in four-dimensional quasi-topological gravity. Quasinormal modes are computed using high-order WKB approximations with Padé resummation and verified through time-domain integration. For moderate values of the scalar-field

Alberto Domínguez, Adithiya Dinesh, Elena Madero

Blazar gamma-ray variability is predominantly stochastic and well described by red-noise processes. However, a subset of sources shows quasi-periodic oscillations (QPOs) on year-long timescales, whose physical origin remains debated. In high-synchrotron-peaked (HSP) blazars, departures from a single power-law gamma-ray spectrum, manifested as high-energy upt

Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural

Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid-state materials. However, integra

Ilteris K. Turan, Sarker Md. Sadman, Walter R. L. Lambrecht

The electronic band structure of CaSnN$_2$ in the wurtzite-based $Pna2_1$ structure is calculated using the Quasiparticle Self-consistent (QS)GW$^{BSE}$ method, including ladder diagrams in the screened Coulomb interaction W$^{BSE}$ and is found to have a direct gap of 2.59 eV at Γ, which corresponds to blue light wavelength of 478 nm and makes it an attract

Claudio Bonati, Ettore Vicari

We address issues related to the presence of defects at finite-temperature topological transitions, in particular when defects are modeled in terms of further variables associated with a quenched disorder, corresponding to the limit in which the defect dynamics is very slow. As a paradigmatic model, we consider the classical three-dimensional lattice ${\math

Jiajun Chen, Jing Xiao, Shaohan Cao, Yuming Zhu

Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially under unstabilized conditions where platform jitter and the weak appearance of tiny objects jointly degrade tracking performance. To address this problem, we propose DeTracker, a joint-detection-and-tracking framewo

Ze-fan Wang, Lei Lei, Yi-zhong Fan

The Hubble parameter ($H(z)$) is a function of the redshift and a reliable measurement is very important to understand the expansion history of the Universe. In this work, we perform full-spectrum fitting using BAGPIPES on more than four thousand massive, passively evolving galaxies released by the DESI collaboration to estimate their cosmological-independen

Waleed Khalid, Dmitry Ignatov, Radu Timofte

Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability, performance, and structural novelty -- remains underexplored. We present a closed-loop architecture synthesis pipeline within the NNGPT framework, in which a code-oriented LLM evolves over 22 supervised fine-tuning cy

Mattia Ottoborgo, Daniele Rege Cambrin, Paolo Garza

Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct composition of ingredients. Standard training procedures are primarily based on cross-entropy and focus solely on fluency. Building on RECIPE-NLG, we investigate the use of several com

Raghuvir Duvvuri, Chandini Vysyaraju, Avi Goyal, Dmitry Ignatov

Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints require both effective architectures and efficient search methods. Large Language Models (LLMs) present a promising alternative to computationally intensive Neural Architecture Search (NAS), but their application to ar

Zhaoqiang Wang, Ruth R. Sims, Sheng Xiao, Ruixuan Zhao

High-speed optical imaging of dynamic neuronal activity is essential yet challenging in neuroscience. While calcium imaging has been firmly established as a workhorse technique for monitoring neuronal activity, its limited temporal resolution and indirect measurement restrict its ability to capture rapid inhibitory and excitatory events and subthreshold volt

M. Scialpi, F. Mannucci, Q. D'Amato, C. Marconcini

We present the first-year results of the MUSE Large Program "Cosmic Duets", whose goal is to obtain adaptive-optics assisted MUSE observations with an angular resolution of 0.1"-0.2" in order to provide integral-field spectroscopy of sub-arcsec separation dual and lensed active galactic nucleus (AGN) candidates. These observations reveal prev

Julian Boesl, Yu-Jie Liu, Frank Pollmann, Michael Knap

We construct parametrized isometric tensor network states -- referred to as skeletons -- that allow us to explore phases of abelian topological order and can be efficiently implemented on quantum processors. We obtain stable finite correlation length deformations of string-net fixed points, which are constructed both by conserving virtual symmetries of the t

K. É. Gabányi, S. Frey, L. I. Gurvits, Z. Paragi

High-redshift radio galaxies can provide insights into the structure formation and galaxy evolution at earlier cosmological epochs. TGSSJ1530+1049 was selected as a candidate high-redshift radio galaxy. Subsequent observations with the James Webb Space Telescope (JWST) presented in a companion paper (Saxena et al., 2026) have shown that it is located at a re

Ashirbad Padhan, Jeanne Colbois, Fabien Alet, Nicolas Laflorencie

We investigate long-range resonances in quasiperiodic many-body localized (MBL) systems. Focusing on the Heisenberg chain in a deterministic Aubry-André potential, we complement standard diagnostics by analyzing the structure of long-distance pairwise correlations at high energy. Contrary to the expectation that the ergodic-MBL transition in quasiperiodic sy

Hiroto Takahashi, Jack Murphy, Mitikorn Wood-Thanan, Pascal Puphal

The kagome lattice of spin-1/2 copper atoms in herbertsmithite is conjectured to sustain a quantum spin liquid state with spinon quasiparticles. Ideally, the kagome crystal planes are each separated by a plane of spinless zinc atoms. However, in real crystals some spin-1/2 copper atoms substitute randomly onto these inter-kagome zinc sites. Here we reconcept

Longjun Xiang, Jinxiong Jia, Fuming Xu, Jian Wang

We propose a quantum geometric map for the magnetononlinear Hall effect (MNHE), the planar Hall effect (PHE), and the ordinary Hall effect (OHE). These magnetotransport phenomena originate from the bilinear charge current of Bloch electrons in electromagnetic fields, incorporating both spin Zeeman coupling and orbital minimal coupling to the applied magnetic

Zijian Zhao, Dian Jin, Zijing Zhou

Recently, Image-to-Music (I2M) generation has garnered significant attention, with potential applications in fields such as gaming, advertising, and multi-modal art creation. However, due to the ambiguous and subjective nature of I2M tasks, most end-to-end methods lack interpretability, leaving users puzzled about the generation results. Even methods based o

Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang

Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effe

Tobias Schreieder, Tim Schopf, Michael Färber

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability. However, the field is fragmented due to inco

Konrad Pawlik, Nicolas Laflorencie, Jakub Zakrzewski

The study of many-body localized (MBL) phases intrinsically links spectral properties with eigenstate characteristics: localized systems exhibit Poisson level statistics and area-law entanglement entropy, while ergodic systems display volume-law entanglement and follow random matrix theory predictions, including level repulsion. Here, we introduce the intera

Mohamed H. Abdullah, Nouran E. Abdelhamid, Rasha M. Samir, Gillian Wilson

We present a comprehensive study of the galaxy size-stellar mass relation (SMR) at low redshift (z <= 0.125), using a large spectroscopic sample from the SDSS-DR13 survey. Our goal is to investigate how environment affects galaxy structural properties across multiple spatial scales. Galaxies are classified by specific star formation rate, optical color, and

S. K. Roushon

We deduce that the fundamental groups of the orbit configuration spaces of an effective and properly discontinuous action of a discrete group on a connected aspherical 2-manifold, with isolated fixed points, fit into a four-term exact sequence. This comes as a consequence of the four-term exact sequence of orbifold pure braid groups ([18], [11] and [19]). Th

Ivan Novikau, Ilon Joseph

We propose an efficient block-encoding technique for the implementation of the Linear Combination of Hamiltonian Simulations (LCHS) for simulating dissipative initial-value problems. This algorithm approximates a target nonunitary operator as a weighted sum of Hamiltonian evolutions, thereby emulating a dissipative problem by mixing various time scales. We i

Petr Cintula, Filip Jankovec, Carles Noguera

This paper presents a unified algebraic study of a family of logics related to Abelian logic (Ab), the logic of Abelian lattice-ordered groups. We treat Ab as the base system and refer to its expansions as superabelian logics. The paper focuses on two main families of expansions. First, we investigate the rich landscape of infinitary extensions of Ab, provid

Karthik Singaravadivelan, Anant Gupta, Zekun Wang, Christopher J. MacLellan

Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but require extensive tuning and struggle with lifelong learning due to catastrophic forgetting and fixed capacity, while classical probabilistic models lack flexibility and adaptability to streaming data. We introd

Andre Bacellar

In law, regulatory regimes for pharmaceuticals and software security, newer authorities can revoke older established ones even when semantically distant. We call this CAR: retrieving the currently active authority frontier for a semantic anchor q, that is, front(cl(A_k(q))). This differs from finding the most similar document by relevance score: argmax_d s(q

Evan Gibson Smith, Jacob Whitehill, Fatemeh Ganji

Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost for deployment on resource-constrained hardware. However, existing SNN quantization evaluation focuses almost exclusively on accuracy, overlooking whether a quantized network preserves the firing behavior of i

Xinxuan Wang, Youngmin Park, Bryce Morsky

The spread of infectious disease is strongly influenced by social dynamics. In addition to infection risk, individuals vaccination decisions depend on prevailing social behavior: high infection levels and widespread vaccination can increase vaccine uptake, which in turn suppresses infection. This feedback can generate sustained oscillations in disease preval

W. Burstein, A. Iosevich, A. Sant

We establish a connection between analytic number theory and computational learning theory by showing that the M\"obius function belongs to a class of functions that is statistically hard to learn from random samples. Let $\mu_R$ denote the restriction of the M\"obius function to the squarefree integers in $\{1,\dots,R\}$. Using a recent lower bound of Pande

S Ganesh

Many body gravity (MBG) is a novel modified theory of gravity formulated in a 5-D space-time-temperature framework, in which the variation in temperature is recast as a variation in the 5-D metric. Previous work on MBG has shown that it can reproduce galaxy rotation curves, radial acceleration relation and the weak gravitational lensing of the bullet cluster

Yechan Kim, Hye-Sung Lee

Ultralight dark matter searches widely assume that signals are monochromatic, with a single frequency set by the mass. This assumption is generally violated in the presence of field mixing, even when the constituent fields have similar frequencies. Instead, dark matter signals can exhibit a two-timescale structure with intrinsic slow modulation. We demonstra

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Richard Messerly

We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale Dee

John Driscoll, Sigal Gottlieb, Zachary J. Grant, César Herrera

A mixed accuracy framework for Runge--Kutta methods presented in [Grant, JSC 2022] has been shown to speed up the computation in diagonally implicit Runge--Kutta (DIRK) methods by using less expensive low accuracy approaches for the implicit stages. This theory included both smooth and nonsmooth perturbations, and subsequent work focused primarily on the cas

Nina Żukowska, Wolfgang Stammer, Bernt Schiele, Jonas Fischer

Transparency of neural networks' internal reasoning is at the heart of interpretability research, adding to trust, safety, and understanding of these models. The field of mechanistic interpretability has recently focused on studying task-specific computational graphs, defined by connections (edges) between model components. Such edge-based circuits have been

Elsayed Mohammed, Omar Mashaal, Alec Digby, Pasquale Leone

Angle-of-arrival (AoA) estimation is a crucial function in wireless communications used for localization, beam-forming, interference management, and other applications. Deep learning (DL) solutions have been proposed for AoA to mitigate limitations of traditional AoA estimation techniques such as sensitivity to noise and the inability to generalize across di

Weixiang Shen, Bailiang Jian, Jun Li, Che Liu

Tool-augmented large language model (LLM) agents can orchestrate specialist classifiers, segmentation models, and visual question-answering modules to interpret chest X-rays. However, these agents still solve each case in isolation: they fail to accumulate experience across cases, correct recurrent reasoning mistakes, or adapt their tool-use behavior without

Qing Yan, Wenyu Yang, Yufei Wang, Wenhao Ma

Traditional esports scouting workflows rely heavily on manual video review and aggregate performance metrics, which often fail to capture the nuanced decision-making patterns necessary to determine if a prospect fits a specific tactical archetype. To address this, we reframe style-based player evaluation in esports as an Inverse Reinforcement Learning (IRL)

Jillian Fisher, Jennifer Neville, Chan Young Park

A common approach to personalization in large language models (LLMs) is to incorporate a subset of the user memory into the prompt at inference time to guide the model's generation. Existing methods select these subsets primarily using similarity between user memory items and input queries, ignoring how features actually affect the model's response distribut

Stavros Kassinos

Physics-informed neural networks (PINNs) are often selected by a single scalar loss even when the quantity of interest is more specific. We study a hybrid design in which the governing PDE residual remains automatic-differentiation (AD) based, while finite differences (FD) appear only in a weak auxiliary term that penalizes gradients of the sampled residual

Mark de Berg, Prosenjit Bose, Leonidas Theocharous

Many algorithmic problems can be solved (almost) as efficiently in metric spaces of bounded doubling dimension as in Euclidean space. Unfortunately, the metric space defined by points in a simple polygon equipped with the geodesic distance does not necessarily have bounded doubling dimension. We therefore study the doubling dimension of fat polygons, for two

Marco Buongiorno Nardelli

\textit{Ab initio} pseudo-atomic orbital (PAO) Hamiltonians express the electronic structure of a solid in a compact, localized basis that spans the same Hilbert space as a conventional Slater--Koster tight-binding model, thereby providing an exact \textit{ab initio} representation without any loss of accuracy. Building on this correspondence, we develop an

Yuzhu Chen, Vishal P. Patil, David Saintillan

Self-propelled particles can navigate complex environments, including viscous fluid interfaces with curved geometries. In this work, we study the emergent dynamics of a suspension of self-propelled particles confined to a stationary curved viscous interface. The evolution of the particle configurations is modeled using the Fokker-Planck equation on the curve

Dalibor Stevanovic

This paper studies the 2021 U.S. inflation forecasting failure. I show that the failure was primarily driven by sample composition rather than functional-form misspecification: estimation samples dominated by the Great Moderation underweight supply-shock regimes, and expectations anchored to that regime were slow to recognize the shift. Three historically in

Mohsen Haghighat, Ali Dehghanian, Levi Smith

This paper presents the experimental verification of a planar guided-wave terahertz (THz) spoof surface plasmon polariton (SSPP) bandpass filter (BPF) using a coplanar stripline (CPS) with internal grooves and periodic gaps. The proposed BPF operates by combining the low-pass behavior from the SSPP's band edge and the high-pass behavior from the gaps that ac

Saumik Narayanan, Raja Panjwani, Siddhartha Sen, Chien-Ju Ho

AI systems are increasingly used to assist humans in sequential decision-making tasks, yet determining when and how an AI assistant should intervene remains a fundamental challenge. A potential baseline is to recommend the optimal action according to a strong model. However, such actions assume optimal follow-up actions, which human decision makers may fail

Marián Poppr

Given a thin torus $T_K$ around a knot $K\subset \mathbb{R}^3$, we construct Morse models of cord algebra $Cord(T_K)$ with $\mathbb{Z}$ and loop space coefficients. Using the Multiple time scale dynamics we identify $Cord(T_K; \mathbb{Z})$ with $Cord(K; \mathbb{Z})$. In combination with the works of Cieliebak-Ekholm-Latschev-Ng and Petrak this indirectly rel

Leonardo Blas, Robin Jia, Emilio Ferrara

Large language models (LLMs) emulate a consistent human-like behavior that can be shaped through activation-level interventions. This paradigm is converging on additive residual-stream injections, which rely on injection-strength sweeps to approximate optimal intervention settings. However, existing methods restrict the search space and sweep in uncalibrated

Michael Dougherty, Kaiyi Fang, Yunting Jiang, Edgar Lin

For each finite configuration of distinct points in the plane, there is an associated lattice of noncrossing partitions. When these points form the vertices of a convex polygon, the result is the classical noncrossing partition lattice, which is enumerated by the Catalan numbers and satisfies many other useful properties. In this article, we examine three va

Aditi Agrawal, Celine John Philip, Giancarlo K. Sagastume, Marcus A. Battraw

Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthetic limbs, virtual reality, and household electronics. Unfortunately, current sEMG technology does not always perform consistently across users because individual differences such as age and body mass index, am

Atrey Desai, Sathvik Nair

For humans, filler-gap dependencies require a shared representation across different syntactic constructions. Although causal analyses suggest this may also be true for LLMs (Boguraev et al., 2025), it is still unclear if such a representation also exists for language models trained on developmentally feasible quantities of data. We applied Distributed Align

Michael Dougherty, Gina Root

Each finite configuration of points in the plane determines a corresponding lattice of noncrossing partitions. When these points form the vertex set of a convex polygon, the associated lattice is the classical noncrossing partition lattice (introduced by Kreweras in 1972), which makes many appearances in combinatorics and geometric group theory. If, on the o

Firas Ben Hmida, Philemon Hailemariam, Kashif Ali Khan, Birhanu Eshete

Deep neural networks (DNNs) remain largely opaque at inference time, limiting our ability to detect and diagnose malicious input manipulations such as adversarial examples. Existing detection methods predominantly rely on layer-local signals (e.g., activations or attribution scores), leaving cross-layer information flow and execution structure under-explored

S M Raihanul Alam, Md Dilshadur Rahman, Md Naimul Hoque

Visualizing narratives is useful to writers to reflect on unfinished drafts and identify unintentional biases and inconsistencies. Literary scholars can use the visualizations to identify nuanced patterns and literary styles from written text. Current narrative visualization is limited to representing character and location co-occurrences in a timeline, omit

Ruiyi Zhang, Peijia Qin, Qi Cao, Li Zhang

AI models underpin modern intelligent systems, driving advances across science, medicine, finance, and technology. Yet developing high-performing AI models remains a labor-intensive process that requires expert practitioners to iteratively design architectures, engineer representations, implement training pipelines and refine approaches through empirical eva

Deyuan Qu, Qi Chen, Takayuki Shimizu, Onur Altintas

Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed reactions can increase collision risk. We propose CooperDrive, a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions. Co

Rakesh K Jha

We investigate the behaviour of quantum fields in null-shifted Rindler wedges and analyse the particle spectra perceived by accelerated observers associated with these null deformations. Unlike the standard Unruh effect, our analysis compares two accelerated frames connected by a null displacement. We consider both massive scalar and Dirac fields, constructi

Biwei Dai, Po-Wen Chang, Wahid Bhimji, Paolo Calafiura

Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high-order statistics and machine learning (ML) techniques have been applied to weak lensing data to extract the

Emanuel Teixeira Martins, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Rodolfo S. Villaça

Quick and accurate emergency handling in Disaster Decision Support Systems (DDSS) is often hampered by network latency and suboptimal application accuracy. While Federated Learning (FL) addresses some of these issues, it is constrained by high communication costs and rigid synchronization requirements across heterogeneous convolutional neural network (CNN) a

Xiaolei Diao, Fausto Giunchiglia

Recent advances in data-centric artificial intelligence highlight inherent limitations in object recognition datasets. One of the primary issues stems from the semantic gap problem, which results in complex many-to-many mappings between visual data and linguistic descriptions. This bias adversely affects performance in computer vision tasks. This paper propo

Thales Sales Almeida, Giovana Kerche Bonás, Ramon Pires, Celio Larcher

Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select relevant evidence, and synthesize complete answers. While recent benchmarks evaluate web-browsing and agentic tool use, multilingual settings, and Portuguese in particular, remain underexplored. We present \text

Sangeeta Chowdhary, Ryan Swann, Sean Siddens, Muhammad Osama

Modern GPUs adopt chiplet-based designs with multiple private cache hierarchies, but current programming models (CUDA/HIP) expose a flat execution hierarchy that cannot express chiplet-level locality or synchronization. This mismatch leads to redundant memory traffic and poor cache utilization in memory-bound workloads such as LLM inference. We present Fleet

Georgios Spanodimos, Guanyu Qian, Xiaodan Cui

This paper presents a hybrid switched-capacitor converter (HSCC) with a novel multi-mode modulation (3M) scheme for wide-range voltage regulation in 48-V automotive power systems. By introducing a three-state operating sequence beyond the conventional 2:1 resonant operation, the proposed converter achieves variable step-down conversion ratios while preservin

Ju-Young Yoon, Nuno Cacoilo, Advait Madhavan, Jabez J. McClelland

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. A challenging threshold to cross for scaling sMT

Tasmia Jannat, Michael Gowanlock, Satish Puri

The growing volume of data in scientific domains has made spatial query processing increasingly challenging due to high data transfer costs across the memory hierarchy and limited memory bandwidth. To address these bottlenecks and reduce the energy consumed on data movement, this work explores Processing-in-Memory (PIM) systems by executing range queries dir

Fortunatus Aabangbio Wulnye, Justice Owusu Agyemang, Kwame Opuni-Boachie Obour Agyekum, Kwame Agyeman-Prempeh Agyekum

Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things (IoT) environments, particularly as data poisoning attacks increasingly threaten the integrity of model training pipelines. This study evaluates the susceptibility of four widely used classifiers, Random Forest, Gradient Boosting

M. Dembecki, J. Schabesberger, M. Bissolo, A. Thurn

The large-area synthesis of high-crystalline-quality two-dimensional (2D) materials is at the core of novel material integration for semiconductor technology. This effort relies on developing fabrication and characterization techniques that can uncover the material's intrinsic properties by preserving its pristine conditions. In this article, we present an a

Sang-Il Han

We present an empirical study of whether hierarchically structured, shared-weight recurrence can match the representational quality of independent-layer stacking in a Transformer-based language model. HRM-LM replaces L independent Transformer layers with a two-speed recurrent pair: a Fast module operating at every step for local refinement, and a Slow module

Vekil Bekmyradov, Noah C. Pütz, Thomas Bartz-Beielstein

Large Language Models (LLMs) have achieved impressive results on public benchmarks, often leading to claims of advanced reasoning and understanding. However, recent research in cognitive science reveals that these models sometimes rely on shallow heuristics and memorization, taking shortcuts rather than demonstrating genuine cognitive abilities. This paper i

D. Bazeia, A. C. Lehum

We develop a rigid $\mathscr{N} =(1,1)$ superspace framework for spatially inhomogeneous impurity deformations in $D=1+1$ dimensions by embedding the impurity profile into a real background superfield (spurion). This spurionic completion provides a manifestly supersymmetric description at the level of the action and offers a systematic route to identify whic

Chao Lu, Pooja Rao, Muralikrishnan Gopalakrishnan Meena, Kalyana Chakaravarthi Gottiparthi

The Variational Quantum Linear Solver (VQLS), a hybrid quantum-classical algorithm for solving linear systems, faces a practical scalability bottleneck: the Linear Combination of Unitaries (LCU) decomposition requires $O(L^2)$ circuit evaluations per optimizer iteration, where $L$ can grow to $4^n$ in the worst case for an $n$-qubit system. We address this c

Yassine Habchi, Hamza Kheddar, Muhammad Ali Qureshi, Mohamed Seghier

Portable medical imaging (PMI) has emerged as an important solution for point-of-care diagnosis in emergency, rural, and resource-limited settings where conventional imaging infrastructure is not readily available. Modalities such as portable computed tomography, portable magnetic resonance imaging, portable ultrasound, and wireless capsule endoscopy improve

Ivan Ternovtsii, Yurii Bilak

Sparse Mixture-of-Experts (MoE) models scale parameters while fixing active computation per token, but the specialization of individual experts remains opaque. In a companion paper we showed that routing topology is quality-neutral: five structurally different configurations converge to statistically equivalent language modeling quality. Here we show that ex

Felipe Parodi, Jordan Matelsky, Melanie Segado

Zero-ablation -- replacing token activations with zero vectors -- is widely used to probe token function in vision transformers. Register zeroing in DINOv2+registers and DINOv3 produces large drops (up to $-36.6$\,pp classification, $-30.9$\,pp segmentation), suggesting registers are functionally indispensable. However, three replacement controls -- mean-sub

Manuel Kunzler, Sascha Bruk, Max Kaiser, Martin Gurka

Stereolithography (SLA) and Tailored Fiber Placement (TFP) were combined to fabricate shape memory alloy hybrid composites (SMAHC) featuring a three-layer structure and exhibiting out of plane bending deformation when activated, in a fully integrated, additive manufacturing process. SMA wires as active elements were attached to a textile reinforcement layer,

Harini Dandu

Mobile applications rely on complex backends that introduce significant security risks, yet developers often lack the tools to assess these risks effectively. This paper presents AndroScanner, an automated pipeline for detecting vulnerabilities in Android application backends through combined static and dynamic analysis. AndroScanner extracts backend API cal

Mohammad R. Abu Ayyash

We present Three-Phase Transformer (3PT), a residual-stream structural prior for decoder-only Transformers on a standard SwiGLU + RMSNorm + RoPE + GQA backbone. The hidden vector is partitioned into N equally-sized cyclic channels, each maintained by phase-respecting ops: a per-channel RMSNorm, a 2D Givens rotation between attention and FFN that rotates each

Sergey M. Zagorodnyuk

In this paper we study higher-order difference equations which can be written as follows: $$ \mathbf{J} (y_0,y_1,...)^T = \lambda^N (y_0,y_1,...)^T, $$ where $\mathbf{J}$ is a $(2N+1)$-diagonal bounded banded matrix ($\mathbf{J}=(g_{m,n})_{m,n=0}^\infty$, $| g_{m,n} |< C$, $C>0$; and $g_{k,l}=0$ if $|k-l|>N$), $y_j$s are unknowns, $\lambda$ is a complex para

Amirhossein Taherpour, Alireza Sadeghi, Georgios B. Giannakis

Scalable estimation of quantum states with readout errors is a central challenge in large multiqubit systems. Existing overlapping-tomography methods improve scalability by working with local subsystems, but they usually assume known or separately calibrated measurements. At the same time, readout-estimation methods model measurement errors without enforcing

Luigi Borasi, Francesco Carlo De Vecchi, Stefania Ugolini

A criterion for proving a strong form of propagation of chaos on the path space, known as entropy chaos, for a general interacting diffusion system is proposed. Our analysis focuses on the class of conservative diffusions introduced by Carlen, which are characterized by infinitesimal characteristic pairs, that is, a time-marginal probability density and a cu

Conan M. Liptrott, Sandra C. Chapman, Bogdan Hnat, Nicholas W. Watkins

A scale-by-scale analysis of energy flux in the turbulent cascade can be performed using the spatially filtered magnetohydrodynamic (MHD) equations, while the gradient tensor invariants are widely used to characterize the structure of velocity and magnetic fields. Physical mechanisms responsible for energy flux require specific field configurations whose str

Isabel Hernández, Laiz Valim da Rocha, Rodrigo Lucas Rodrigues

The paper is devoted to the description of the varieties of complex 5-dimensional nilpotent Jordan superalgebras. We find all representatives for the isomorphism classes, using the Jordan normal form, results of simultaneous matrix triangularization, the Jordan-Kronecker theorem for a pair of skew-symmetric bilinear forms and similar arguments developed for

Sudeepta Mondal, Soumalya Sarkar

Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture the fine spatio-temporal scales which govern the evolution of these systems. However, these simulations are often high-fidelity in nature, and can be computationally very expensive. Hence, generating data fr

P. Punta, J. A. Lay, A. M. Moro, J. Lois-Fuentes

Recently, a semi-microscopic structure model has been presented to study the structure of a weakly-bound, two-body nucleus with a deformed core, including Pauli-blocking effects. The model has been successfully applied within the adiabatic distorted wave approximation (ADWA) reaction framework to study the reactions $^{16}$C(d, p)$^{17}$C, restricting the an

Muhammad Imam Luthfi Balaka, Raul Castro Fernandez

Data analysts working with relational data often start with vague or underspecified questions and refine them iteratively as they explore the data. To support this iterative process, we demonstrate Pneuma-Seeker, a system that reifies a user's information need as explicit, inspectable relational specifications, enabling iterative refinement of the informatio

Chayansudha Biswas

Platonov in 1991 conjectured that adjoint groups are rational as varieties over arbitrary infinite fields, and as a consequence have weak approximation. The rationality part of the conjecture was disproved by Merkurjev in 1996, but the question about weak approximation remained open. We settle this in the negative.

Sanjeev Panta, Rhett M Morvant, Xu Yuan, Li Chen

Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multimedia data sources. We introduce M3R, a Meteorology-informed MultiModal attention-based architecture for dire

Ivan Ternovtsii, Yurii Bilak

Sparse Mixture-of-Experts (MoE) architectures employ increasingly sophisticated routing mechanisms -- learned routers, multi-hop trajectories, token-dependent gating. We ask: does routing topology actually determine language modeling quality? We build a geometric MoE (ST-MoE) using cosine-similarity routing against learned centroids in a low-dimensional spac