April 2026 arXiv papers — page 134

Reo Yamaguchi, Takuma Sakurai, Kazuhiro Yamaki, Akinobu Irie

We demonstrate a rapid, maskless fabrication method for superconducting terahertz Josephson plasma emitters (JPEs) based on direct ultraviolet laser micromachining of Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi-2212) single crystals. Although machining debris is formed near the processed regions, uniform stacks of intrinsic Josephson junctions are preserved inside the

Arya G. Pour, Jun Ji, Linbo Shao

Nonreciprocal conversion is essential for protecting sources and enabling unidirectional signal routing in photonic, phononic, electronics, and quantum systems, yet conventional implementations rely on magnetic bias that could be challenging to integrate on chip. We propose a magnet-free scheme for frequency-domain nonreciprocity based on sequential, time-ga

Kazuki Ikeda, Yaron Oz

We derive the reduced Dirac Hamiltonian on the non-rotating BTZ background and use its redshift structure to construct a gauge-covariant single-band lattice model on the constant-time BTZ cylinder. In equal-area coordinates the AdS radius $L$ fixes the local Gaussian curvature, while the horizon radius $r_h$ fixes the throat size and the strength of the near

Argyrios Loules, Antonios Nathanail, Ioannis Contopoulos

We analyze data from a standard 3D general-relativistic magnetohydrodynamics (GRMHD) simulation, focusing on equatorial slices in order to examine the details and the evolution of the azimuthal structure of the accreting matter. During flux eruption events, the non-axisymmetric features of the equatorial inner accretion disk are considerably enhanced, with t

Miguel Angel Moreno-Mateos, Simon Wiesheier, Paul Steinmann, Ellen Kuhl

Highly compressible solids, such as foams, exhibit complex responses, including pronounced tension-compression asymmetry. Capturing such behaviors within unified hyperelastic frameworks remains challenging. Invariant-based hyperelastic models are commonly identified from standard tests such as homogeneous uniaxial tension/compression and simple shear, implic

Elizabeth Yunerman, Ellen Price, Karin Öberg

Protoplanetary disk ice lines shape a multitude of planet formation processes, setting the environmental composition through evolution. Ice line locations depend on molecular sublimation and deposition properties, but in dynamic disks where temperature and density structures change, so do the expected compositions of planets and planetesimals. In turbulent v

Zichao Chen, Shiyu Zhu, Kailin Xu, Ruwen Wang

Charge density waves (CDWs) are a widespread collective electronic order in quantum materials, furnishing key insights into symmetry breaking and competing phases. However, their dynamic control with external fields remains a pivotal challenge. Here, we report deterministic and hysteretic switching of unidirectional CDW orientation via in-plane magnetic fiel

Hongyu Chen, Peixin Qin, Ziang Meng, Guojian Zhao

Quantum geometry, a quantum mechanical quantity comprised of Berry curvature and quantum metric, describes the geometric structure of the electronic bands in solids. The correlation between nontrivial quantum geometry and quantum materials leads to new findings in condensed matter systems. Here we demonstrate that altermagnets, with spontaneously broken time

Michiel Cevaal, Thomas de Jong, Mircea Lazar

In this paper, we consider the design of Model Predictive Control (MPC) algorithms based on Mamba neural networks. Mamba is a neural network architecture capable of sub-quadratic computational scaling in sequence length with state-of-the-art modeling capabilities. We provide a consistent and complete mathematical description of the Mamba neural network is pr

Fabian Geyer, Friedrich Tuttas, Walter Fichter, Torbjørn Cunis

In the context of spacecraft attitude control, parametrizations such as direction vectors or quaternions are often used to avoid singularities in the attitude representation. This, however, complicates the stability analysis of the system since, given the additional unit constraints, the resulting dynamics evolve on non-contractible manifolds. In this paper,

Tomasz Kwapinski, Marcin Kurzyna, Luis E. F. Foa Torres

We investigate the emergence of charge waves and their temporal dynamics in one-dimensional Su-Schrieffer-Heeger (SSH) topological chains. Contrary to the conventional view that charge oscillations are suppressed in gapped topological systems with preserved chiral symmetry, we show that such oscillations can indeed occur. The general condition for an arbitra

Peter Samaha, Amine Torki, Ysaline Renaud, Sam Fiette

Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assem

Z. Randriamanakoto, M. Rakototafika, B. Mongwane, P. Väisänen

We conduct a photometric study of star clusters (or knots) in the collisional ring galaxy (CRG) Arp 147 to trace the star formation history across its empty ring. Using HST F450W, F606W and F814W images, we find that Arp 147 hosts 211 knots and six kpc-size clumps, nearly 60 per cent of which have ages below 10 Myr, and two thirds have masses above $\rm 10^{

Suman Kanungo, Pawan Kumar Mishra

In this paper, we investigate a class of critical Ambrosetti-Prodi type problems involving the sub-Laplacian on a Carnot group. Specifically, we consider \[ \left\{ \begin{aligned} -Δ_{\mathbb{G}} u &= λu + u_{+}^{2_{Q}^{*}-1} + f(ξ) \quad &&\text{in } Ω,\\[2mm] u &= 0 \quad &&\text{on } \partialΩ, \end{aligned} \right. \] where $Δ_{\mathbb{G}}$ is the sub-L

Stefano Vignolo, Luca Fabbri

By employing the polar re-formulation, we show that there are no solutions of the Dirac equations in spherical symmetry when the spinor is required to satisfy the same symmetries as the space-time via the Lie derivative.

Shamil Asgarli, Chi Hoi Yip

We study intersecting families of words from the Erdős-Ko-Rado perspective. When the alphabet size is $2$, a maximum intersecting family is not necessarily a star. However, we prove that every maximum $3$-wise intersecting family is a star. We also present a new proof of the known result for alphabets of size at least $3$: maximum intersecting families of wo

Wooin Lee, Hyun-Tae Kim

The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's size. Although light-state optimizers like SinkGD attempt to address this issue, we identify the embedding layer dilemma: these methods fail to handle the sparse, high-variance gradients inherent to embeddin

Yamato Miyatake, Parinya Punpongsanon

3D food printing enables the customization of food shapes and textures, but typically produces uniform taste profiles due to the limited diversity of printable materials. We present TastePrint, a 3D food printing system that achieves layer-wise spatial taste distribution by dynamically applying liquid seasonings with a programmable airbrush during fabricatio

Md Akib Haider, Ahsan Bulbul, Nafis Fuad Shahid, Aimaan Ahmed

Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competition, we present LoRA-MME, a Multi-Model Ensemble architecture utilizing Parameter-Efficient Fine-Tuning (PEFT). Our approach addresses the multi-label classification challenge across Java, Python, and Pharo by comb

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(10087 \pm 44) \times 10^6$ $J/ψ$ events collected with the BESIII detector at a center-of-mass energy of $\sqrt{s}=3.097$ GeV, the antihyperon-nucleon annihilation processes $\barΛ p \to K^+ π^+ π^- + kπ^0$ ($k=1,2,3$) are studied at an incident $\barΛ$ momentum of approximately 1.074 GeV/$c$. The reactions $\barΛ p \to K^+ π^+ π^- π^0$ and $\barΛ p

Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras

Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning, it does not explicitly optimize for linguistic competence. To bridge this gap, we propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next

Nikolaos Mitrakos, Maria Papageorgiou, T. Rick Perche, Marios Christodoulou

Detection of entanglement through the Newtonian potential has been claimed to support the existence of gravitons, by extrapolating to a thought experiment which demonstrates that complementarity and causality would be in conflict unless quantum fluctuations exist. We critically assess this consistency argument using scalar field models. We show that whether

A. A. Aczel, B. R. Ortiz, Y. Luo, G. Pokharel

Pyrochlore magnets of the form $R_2B_2$O$_7$, in which rare-earth ions on the $R$-site form a three-dimensional network of corner-sharing tetrahedra, provide a canonical setting for geometrical frustration. Ho-based pyrochlores host a dipolar spin-ice ground state, characterized by Ising moments constrained by the ice rules and elementary excitations analogo

Chubin Lin, Jiandong Chen, Huihui Wang, Yangyang Fu

This Letter uncovers five distinct charge transport modes and their transitions in dual-energy electron beam diodes. We via first-principle particle-in-cell (PIC) simulations establish that the specific mode (e.g., space charge oscillations) and the current transport characteristics are essentially governed by the interplay between the electron beam energy a

N. T. Nguyen-Dang, N. Ota, N. Okabe, M. Oguri

This is the second paper in a series exploring the X-ray properties of galaxy clusters optically selected by the Subaru Hyper Suprime-Cam (HSC) survey, using data from the SRG/eROSITA Final Equatorial-Depth Survey (eFEDS). We aim to investigate scaling relations between observable cluster properties and mass, and to study the radial X-ray profiles of a large

Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, Nikolaos Aletras

Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled

Marcell Bartos, Johannes Köhler, Florian Dörfler, Melanie N. Zeilinger

Standard model-based control design deteriorates when the system dynamics change during operation. To overcome this challenge, online and adaptive methods have been proposed in the literature. In this work, we consider the class of discrete-time linear systems with unknown time-varying parameters. We propose a simple, modular, and computationally tractable a

Raymon S. Watson, Karen V. Kheruntsyan

Advances in experimental control of interacting quantum many-body systems with multiple tunable parameters-such as ultracold atomic gases and trapped ions-are driving rapid progress in quantum thermodynamics and enabling the design of quantum thermal machines. In this work, we utilize a sudden quench approximation as a means to investigate the operation of a

Josiel Mendonça Soares de Souza, Miguel Quartin

Posterior inference on the more than a dozen parameters governing a gravitational wave (GW) event is challenging. A typical MCMC analysis can take around $100$ CPU hours, and next generation GW observatories will detect many thousands of events. Here we present a thorough comparison of the accuracy and computational cost of the Fisher Matrix, Derivative Appr

Neeraj Kumar, Ankur Srivastav, Phongpichit Channuie

In this article, we explore the Rényi law constraints on black hole merger in Gauß-Bonnet (GB) gravity. Specifically, we consider the case of static solutions in five-dimensional (5D) Anti-de-Sitter (AdS) spacetime and study the constraints on merger of two equal mass black holes. We calculate the general Rényi entropy expression and utilize it to study the

Tommaso Bambagiotti, Luca Gallerani, Andrea Mentrelli, Andrea Giusti

The quantum description of a gravitationally collapsed ball of dust proposed in Ref.~\cite{Casadio:2023ymt} is characterised by a linear effective Misner-Sharp-Hernandez mass function describing a matter core hidden by the event horizon. After reviewing the original model and some of its refinements, we investigate the quasi-normal mode spectrum of the resul

Mohammed Daba, Jing Qiu

Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite Category-level perceptual gene

Jonathan M. O. Massey, Alexander J. Smits, Beverley J. McKeon

Wall-pressure fluctuations beneath turbulent boundary layers drive noise and structural fatigue through interactions between fluid and structural modes. Conventional predictive models for the spectrum--such as the widely accepted Goody model (\textit{AIAA Journal} 42 (9), 2004, 1788--1794)--fail to capture the energetic growth in the {low-frequency range} th

Amy Rafferty, Rishi Ramaesh, Ajitha Rajan

Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespread clinical adoption remains limited due to opaque model decision-making. In prior work, we introduced ClinicXAI, a human-centric, expert-guided concept bottleneck model (CBM) designed for interpretable lung cancer diagnosis. We now extend that approach and pre

Arpan Bhattacharyya, Saptaswa Ghosh, Sounak Pal, Anandu Vinod

In this paper, as an application of the `Complexity = Volume' proposal, we calculate the growth of the interior of a black hole at late times for finite cutoff JT gravity. Due to this integrable, irrelevant deformation, the spectral properties are modified non-trivially. The Einstein-Rosen Bridge (ERB) length saturates faster than pure JT gravity. We com

Zachary G. Nicolaou, Hangjun Cho, Yuanzhao Zhang, J. Nathan Kutz

Glasses are traditionally characterized by their rugged landscape of disordered low-energy states and their slow relaxation towards thermodynamic equilibrium. Far from equilibrium, dynamical forms of glassy behavior with anomalous algebraic relaxation have also been noted, for example, in networks of coupled oscillators. Due to their disordered and high-dime

Fatih Dinc, Ege Cirakman, Bariscan Kurtkaya, Mert Yuksekgonul

Abrupt learning is a common phenomenon in recurrent neural networks (RNNs) trained on working memory tasks. In such cases, the networks develop transient slow regions in state space that extend the effective timescales of computation. However, the mechanisms driving sudden performance improvements and their causal role remain unclear. To address this gap, we

Patrick P. Potts

The theory of quantum thermodynamics investigates how the concepts of heat, work, and temperature can be carried over to the quantum realm, where fluctuations and randomness are fundamentally unavoidable. These lecture notes provide an introduction to the thermodynamics of small quantum systems. It is illustrated how the laws of thermodynamics emerge from qu

Adam Kmec, Lionel Mason, Romain Ruzziconi

We extend Penrose's quasi-local mass definition to include higher-spin charges associated with the celestial $Lw_{1+\infty}$ symmetries and relate them to traditional definitions of multipoles. The resulting formulae provide explicit expressions that can be computed on finite 2-surfaces, given a choice of null hypersurface. They yield a geometric definition

Hong Zeng, Jiangtao Luo, Yongyi Ran

Low Earth Orbit satellite networks pose significant challenges to multi-hop semantic transmission because rapidly changing topology, link variability, and queue dynamics make end-to-end performance jointly depend on routing, relay processing, and semantic payload adaptation. Existing studies usually optimize routing or semantic transmission separately and ar

Jonathan Ramírez, Gustavo Melgarejo

We investigate an extended hybrid metric--Palatini theory defined by an arbitrary function $f(R,\mathcal{R},\hat{Q},Q,\mathcal{Q})$, where $R$ and $\mathcal{R}$ are the metric and Palatini scalar curvatures, and $Q=R_{μν}R^{μν}$, $\mathcal{Q}=\mathcal{R}_{(μν)}\mathcal{R}^{(μν)}$, and $\hat{Q}=R_{μν}\mathcal{R}^{(μν)}$ are the metric, Palatini, and mixed qua

Run Wang, Victor J. B. Jung, Philip Wiese, Sebastian Frey

Biosignals exhibit substantial cross-subject and cross-session variability, inducing severe domain shifts that degrade post-deployment performance for small, edge-oriented AI models. On-device adaptation is therefore essential to both preserve user privacy and ensure system reliability. However, existing sub-100 mW MCU-based wearable platforms can only suppo

João Pedro Breveglieri da Silva, Dmitri Vassilevich

If an operator $H$ anticommutes with a chirality operator $Γ_*$ such that $Γ_*^2=1$, the null space of $H$ can be decomposed in a direct sum of two spaces having positive and negative chiralities, respectively. When both spaces are finite dimensional, one can define an index, $\mathrm{Ind}(Γ_*,H)$, as the difference of dimensions of these two spaces. The key

Kunal Dutta, Agastya Vibhuti Jha, Haotian Jiang

A classical result of Steinitz from 1913 \cite{Ste13}, answering an earlier question of Riemann and L\'evy (e.g., \cite{Lev05}), states that for any norm $\|\cdot\|$ in $\mathbb{R}^d$ and any set of vectors $v_1, \cdots, v_n \in \R^d$ satisfying $\sum_{i=1}^n v_i = 0$, there exists an ordering $\pi: [n] \rightarrow [n]$ such that every partial sum along this

Robert Zhang, Eric Hayden Campbell, Dixin Tang, Isil Dillig

Predicate pushdown is a long-standing performance optimization that filters data as early as possible in a computational workflow. In modern data pipelines, this transformation is especially important because much of the computation occurs inside user-defined functions (UDFs) written in general-purpose languages such as Python and Scala. These UDFs capture r

Seungbum Jo, Srinivasa Rao Satti

Range minimum queries (RMQs) are fundamental operations with widespread applications in database management, text indexing and computational biology. While many space-efficient data structures have been designed for RMQs on arrays with arbitrary elements, there has not been any results developed for the case when the alphabet size is small, which is the case

Tanmay Srivastava, Amartya Basu, Shubham Jain, Vaishnavi Ranganathan

We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI. As agents evolve from reactive to always-listening assistants, they face a core privacy risk (of capturing non-consenting speakers), which makes their social deployment a challenge. To overcome this, we imp

Carlos A. Cadavid, Juan D. Velez, Sergio Lenis

We study the long time behavior of the heat equation on the spherical Poincare dodecahedral space and introduce a spectral selection property P, asserting that for a dense open set of initial data, the solution eventually becomes a minimal Morse function. We first establish an obstruction principle. If the first positive eigenspace of the Laplace Beltrami op

Pengcheng Wang, Jerry Huang, Jiarui Yao, Rui Pan

Language-model agent systems commonly rely on reactive prompting, in which a single instruction guides the model through an open-ended sequence of reasoning and tool-use steps, leaving control flow and intermediate state implicit and making agent behavior potentially difficult to control. Orchestration frameworks such as LangGraph, DSPy, and CrewAI impose gr

Vladimir Kalušev, Branko Brkljač, Milan Brkljač

The paper presents design and prototype implementation of an edge based object detection system within the new paradigm of AI agents orchestration. It goes beyond traditional design approaches by leveraging on LLM based natural language interface for system control and communication and practically demonstrates integration of all system components into a sin

Anna Dornhaus, Joanna Masel

Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses

Anosh Arshad Sundhu, Aysegül Kahraman, Spyros Chatzivasileiadis

The increasing penetration of distributed energy resources (DERs) is transforming distribution networks into actively managed systems, introducing challenges related to voltage regulation, thermal loading limits, and operational security. This paper presents the development and implementation of a real-time Digital Twin (DT) for security assessment and coord

Daniel Alpay, Diana Barseghyan, Baruch Schneider

It is well known that the spectrum of the Dirichlet Laplacian for a two-dimensional waveguide, which is a local deformation of a straight strip, is unstable with respect to waveguide boundary deformations. This means that, when the waveguide is a straight strip, the spectrum of the Dirichlet Laplacian is purely essential. On the other hand, local boundary pe

Bernardo Flores

Bayesian nonparametric mixture models provide a flexible framework for data analysis but are often hindered by the computational expense of traditional inference methods like MCMC. A fast, recursive algorithm proposed by Newton (2002) offers a practical alternative, yet its formal connection to Bayesian inference and its theoretical properties remain only pa

Iris Zheng, Guojun Tang, Alexander Doronin, Paul Teal

We present a multispectral extension to 3D Gaussian Splatting (3DGS) for wavelength-aware view synthesis. Each Gaussian is augmented with spectral radiance, represented via per-band spherical harmonics, and optimized under a dual-loss supervision scheme combining RGB and multispectral signals. To improve rendering fidelity, we perform spectral-to-RGB convers

Mahdi Tavakol, Jinke Chang, Cyril Besnard, Gabriel Landini

Hydroxyapatite (HAp) is the primary mineral component of various mineralized tissues in the human body, including bone and teeth, where it performs critical roles of structural support and load transmission. In the context of dental health, the two most crucial properties of HAp are mechanical stability, which ensures resistance to forces, and chemical stabi

Elkin Ramírez, Bartosz Protas

In this investigation, we conduct a systematic computational search for potential singularities in 3D Navier-Stokes flows on a periodic domain $\Omega$ based on the Ladyzhenskaya-Prodi-Serrin conditions. They assert that for a solution $\mathbf{u}(t)$ of the Navier-Stokes system to be regular on an interval $[0,T]$, the integral $\int_{0}^T \|\mathbf{u}(t)\|

Neil B. Manson, Morgan Hedges, Michael S. J. Barson, Carlos A. Meriles

The nitrogen-vacancy (NV) center in diamond enables optical initialization and readout of its electronic spin, forming the basis of a wide range of quantum sensing and metrology applications. A central challenge in such measurements is the coexistence of two charge states, NV- and NV0: While detection protocols rely on the spin-dependent properties of NV-, f

DEnise Riquelme-Vasquez, Rolf Guesten, Mark R. Morris, Andrwe I. Harris

Context. Sagittarius C (Sgr C) is a massive, relatively quiescent complex at the western edge of the Galaxy's Central Molecular Zone (CMZ). While the Sgr B2 region has been extensively studied, Sgr C has received comparatively less attention. Aims. We aim to characterize the kinematics and physical state of the gas in Sgr C using spatially and velocity-resol

Farzaneh Jafari, Stefano Berretti, Anup Basu

We introduce SEDTalker, an emotion-aware framework for speech-driven 3D facial animation that leverages frame-level speech emotion diarization to achieve fine-grained expressive control. Unlike prior approaches that rely on utterance-level or manually specified emotion labels, our method predicts temporally dense emotion categories and intensities directly f

Iris Zheng, Guojun Tang, Alexander Doronin, Paul Teal

We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting, accurately modeling light-material interactions is essential for faithful appearance reproduction. However, existing 3DGS-ba

Jingyun Jia, Chandan Singh, Rich Caruana, Ben Lengerich

Identifying meaningful feature interactions is a central challenge in building accurate and interpretable models for tabular data. Generalized additive models (GAMs) have shown great success at modeling tabular data, but often rely on heuristic procedures to select interactions, potentially missing higher-order or context-dependent effects. To meet this chal

Bojan Kuzma, Srdjan Stefanović

We describe additive surjections on direct sum of matrix algebras that preserve singularity in one direction. As an application, we classify additive surjections on finite-dimensional $C^\ast$-algebras that preserve mutual strong Birkhoff-James orthogonality in one direction.

Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Chen Chen, Dongjie Wang

In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and procedure codes) is fundamental for downstream clinical prediction. However, ro bust concept representation learning is hindered by two key challenges: (i) clinically important cross-type dependencies (e.g., diagn

Omid Bateniparvar, Farzan Farahmand, Ranajay Ghosh

Overlapping fish-scale architectures are among nature's most distinctive surface adaptations, combining protection, contact regulation, hydrodynamics, optical and directional mechanical response within a thin textured integument. Here, we show that their biomimetic structural analogues can host deterministic chaos. Biomimetic scale substrates develop chaotic

Jiahao Shao, Anam Nawaz Khan, Christopher Brett, Tom Berg

Pathology reports serve as the definitive record for breast cancer staging, yet their unstructured format impedes large-scale data curation. While Large Language Models (LLMs) offer semantic reasoning, their deployment is often limited by high computational costs and hallucination risks. This study introduces a parameter-efficient, multi-task framework for a

Hongyi Jin, Bohan Hou, Guanjie Wang, Ruihang Lai

Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter-kernel parallelism. Recent megakernel techniques fuse multiple operators into a single persistent kernel to eliminate launch gaps and expose inter-kernel parallelism, but struggle to handle dynamic shapes and

CMS Collaboration

A search for a resonance heavier than 250 GeV decaying into four leptons via two intermediate bosons is presented. The search uses proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb$^{-1}$. Novel techniques are used to enhance the sensitivity to a collimated pair of dilepto

James Dallas, Thomas Lew, John Talbot, Jonathan DeCastro

Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high-dimensional systems. In this

Kai-Hsiang Wang

In this article, we show that for one-phase free boundary problems in noncollapsed limits of $n$-dimensional manifolds with two-sided Ricci curvature bounds, the Hausdorff dimension of the singular set of the free boundary can be bounded by $n-5$, which is sharp in this context.

Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu, Yan Gu

Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take ten

Xinan Zhang, Haolin Wang, Zhongyu Yang, Yi-Chang

Raveling, the loss of aggregates, is a major form of asphalt pavement surface distress, especially on highways. While research has shown that machine learning and deep learning-based methods yield promising results for raveling detection by classification on range images, their performance often degrades in large-scale deployments where more diverse inferenc

Anju Gopinath, Nikhil Krishnaswamy, Bruce Draper

Multimodal Large Language Models (MLLMs) struggle with tasks that require reasoning about 2D object orientation in images, as documented in prior work. Tong et al. and Nichols et al. hypothesize that these failures originate in the visual encoder, since commonly used encoders such as CLIP and SigLIP are trained for image-text semantic alignment rather than g

CMS Collaboration

A new technique is developed to identify dielectrons (e$^+$e$^-$) with Lorentz boost $\gamma_\mathrm{L}$ $\gt$ 20 that produce one single merged cluster in the electromagnetic calorimeter of the CMS detector. The identification uses two multivariate models: one for the case where both electron tracks are reconstructed, and another where only one of the track

Denis Hoornaert, Cole Strickler, Manos Athanassoulis, Marco Caccamo

The shift to data-intensive processing from the cloud to the edge has introduced new challenges and expectations for the next generation of intelligent computing systems. As the memory wall continues to grow, modern systems can only meet these performance expectations by displaying data access patterns that exhibit ideal layouts in memory and ideal spatiotem

Zhaoyang Wang, Qianhui Wu, Xuchao Zhang, Chaoyun Zhang

Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptati

Arnab Paul Choudhury, Nihal Patel

Skill training is crucial for enabling dignified livelihood opportunities. In India, various schemes and initiatives aim to provide skill training in different domains, with ICT and digital technologies playing a vital role. However, there is limited research on understanding on-ground capacities \& constraints and the use of digital tools in these programs.

Samrendra Roy, Souvik Chakraborty, Syed Bahauddin Alam

Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a critical liability for safety-critical digital twin deployments. We present a synergistic defense that combines active learning-based data generation with an input denoising architecture. The active learning compo

Akshit Gupta, Joris Timmermans, Filip Biljecki, Remko Uijlenhoet

High-resolution data in spatial and temporal contexts is imperative for developing climate resilient cities. Current datasets for monitoring urban parameters are developed primarily using manual inspections, embedded-sensing, remote sensing, or standard street-view imagery (RGB). These methods and datasets are often constrained respectively by poor scalabili

Adnan Aijaz

Sixth-generation (6G) mobile networks are expected to operate for multiple decades, supporting mission-critical and globally federated digital services. This long operational horizon coincides with rapid advances in quantum computing that threaten the cryptographic foundations of contemporary mobile systems. While post-quantum cryptography is widely recogniz

Julio César Jaramillo Quiceno

We introduce a family of metric-deformed Heisenberg algebras $M_1$ and $M_2$, where the commutation relations are expressed directly in terms of the components of a diagonal Lorentzian metric. We show that these algebras unify several known $q$-deformed Heisenberg algebras, including the $q$-$\hbar$ algebra, the new $q$-Heisenberg algebra, and the $q$-genera

Eun Woo Im, Dhruv Madhwal, Vivek Gupta

Vision-Language Models demonstrate remarkable capabilities but often struggle with compositional reasoning, exhibiting vulnerabilities regarding word order and attribute binding. This limitation arises from a scarcity of informative samples needed to differentiate subtle semantic variations during contrastive pretraining. Although hard negative mining offers

Goutam Das, Takashi Tanaka

This paper extends path integral control (PIC) to partially observed systems by formulating the problem in Gaussian belief space. PIC relies on the diffusion being proportional to the control channel -- the so-called matching condition -- to linearize the Hamilton-Jacobi-Bellman equation via the Cole-Hopf transform; we show that this condition fails in infin

Gabriel Santana, Jemirson Ramirez

Traditional risk measures in finance, predominantly based on the second moment of return distributions or tail risk heuristics (VaR/CVaR), fail to account for the intrinsic geometric structure of market dynamics. This paper introduces a rigorous mathematical framework utilizing Topological Data Analysis (TDA) to quantify risk as the structural instability of

Aaron Agulnick, Toby Busick-Warner

The question of determining a signal from its higher-order autocorrelation data is of practical interest in fields as varied as X-ray crystallography, image processing, and satellite communications. At the heart of the issue is how much of this autocorrelation data one truly needs. We prove two new upper bounds on the order of data needed to determine a sign

Sreejani Chatterjee, Venkatesh Mullur, Abhinav Gandhi, Berk Calli

We present a novel visual servoing framework for controlling a robotic manipulator in configuration space using only natural visual features. To train our data-driven keypoint detector, we attach ArUco markers along the robot body, use their centers as keypoint labels, and apply image inpainting to remove the markers and reconstruct the occluded regions. Thi

Andrii Vakhnovskyi

The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements exist for agricultural operations and no formal threat model has been published for Controlled Environment Agriculture (CEA) systems. This paper presents the first comprehensive threat model for IoT-enabled CEA, app

Vutichart Buranasiri, James M. Murphy

An unsupervised framework for hyperspectral image (HSI) clustering is proposed that incorporates masked deep representation learning with diffusion-based clustering, extending the Spatially-Regularized Superpixel-based Diffusion Learning ($S^2DL$) algorithm. Initially, a denoised latent representation of the original HSI is learned via an unsupervised masked

Stefan Fischer

In-body molecular nanonetworks promise early abnormality detection close to the source of biochemical events, but their communication capabilities are severely constrained by slow diffusion-based signaling and unstable alarm traffic. We study whether simple embedded DNA-based inference at the nanonode can improve alarm transmission to an external gateway. We

Alexandre Linhares

We present a formal verification of Wolstenholme's theorem -- $\binom{2p}{p} \equiv 2 \pmod{p^3}$ for prime $p \geq 5$ -- in Lean~4 with Mathlib. The proof proceeds by expanding the shifted factorial product $\prod_{k=1}^{p-1}(p+k)$ to second order in $p$, identifying the quadratic coefficient as the second elementary symmetric product, and showing its d

Salma Abdel Magid, Grace Guo, Esin Tureci, Amaya Dharmasiri

Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have become crucial components of text-to-image (T2I) generation systems where they are used at various stages for dataset filtering, as evaluation metrics, as a supervisory signal during op

Gerasimos Chatzoudis, Konstantinos D. Polyzos, Zhuowei Li, Difei Gu

Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoencoders (SAEs) have been used to extract human-interpretable features, they operate on individual layers and fail to capture the cross-layer computational structure of Transformers, as well as the relative sign

David Bowman

We study regularity properties for solutions to the nakedly degenerate elliptic equation $a_{ij}\partial_{ij}u =0$, where the coefficients satisfy $I \ge a_{ij}(x) \ge \lambda(x) I$ and the only assumption is that $\lambda^{-1} \in L^p$. We prove an improvement of oscillation and a Liouville theorem for $p>d-1$, and a Harnack inequality for $p$ sufficiently

Yimeng Qiu, Qiwei Han

This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets. We construct a structural multi-agent market model calibrated using high-frequency microstructural moments. AI agents are modeled through a two-layer decision arc

Frederic Heihoff, Michael Winkler

For the Keller-Segel system \[ \left\{\, \begin{aligned} u_t &= \Delta u - \nabla \cdot ( u \nabla v ), \\ v_t &= \Delta v - v + u \end{aligned} \right. \tag{$\star$} \] posed in a planar domain $\Omega$ with Neumann boundary conditions, the existence of classical solutions blowing up at some finite time $T$ has long been established. In fact, it has been sh

S. Afanasiev, G. Agakishiev, A. Aleksandrov, E. Aleksandrov

The BM@N experiment (Baryonic Matter at the Nuclotron) is the first fixed-target experiment at the JINR NICA accelerator complex. In this work, data on the interactions of a carbon-ion beam with kinetic energies of 4.0A~GeV and 4.5A~GeV with C, Al, Cu, and Pb targets are used to measure transverse momentum spectra and rapidity distributions of $\Lambda$ hype

Sujan Ghimire, Parsa Mirfasihi, Muhtasim Alam Chowdhury, Veeramani Pugazhenthi

The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted stages of the supply chain, raising concerns about reverse engineering, piracy, tampering, and overbuilding. Hardware netlist obfuscation is a promising countermeasure, but automating the generation of functiona

Binh Nguyen, Nam T. Nguyen, Truong X. Nghiem

This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and stability properties. We propose a novel neural-network-based port-Hamiltonian modeling technique that relaxes the convexity constraint commonly imposed by neural network-based Hamiltonian approximations, thereby

Elisa Maria Alessi, Robert Jedicke

The population of natural objects in a 1:1 mean motion resonance with Earth are known as Earth's co-orbitals. Main belt objects can dynamically evolve into Earth co-orbitals but taxonomic studies of some of them have suggested that they are more likely to be lunar material. While it has long been known that lunar ejecta can achieve Earth co-orbital status, i

Xulin Fan, Vishal Sunder, Samuel Thomas, Mark Hasegawa-Johnson

Recent advances in speech-aware language models have coupled strong acoustic encoders with large language models, enabling systems that move beyond transcription to produce richer outputs. Among these, word-level timestamp prediction is critical for applications such as captioning, media search, and multimodal synchronization, yet it is often handled by exte