March 2025 arXiv papers — page 82

Ramis Sh. Khasianov

In this article, the new inequalities for the weighted sums of coefficients in the class of bounded functions in the disk are obtained. We develop the methods of I.R.~Kayumov and S.~Ponnusamy, using E.~Reich's theorem on the majorization of subordinate functions. The sharp estimates for the area of the image of the disk of radius $r$ under the action of the

Xufeng Cai, Jason M. Altschuler, Jelena Diakonikolas

In 1960, Osborne proposed a simple iterative algorithm for matrix balancing with outstanding numerical performance. Today, it is the default preconditioning procedure before eigenvalue computation and other linear algebra subroutines in mainstream software packages such as Python, Julia, MATLAB, EISPACK, LAPACK, and more. Despite its widespread usage, Osborn

Aritra Bhowmik, Fida Mohammad Thoker, Carlos Hinojosa, Bernard Ghanem

Masked modeling has emerged as a powerful self-supervised learning framework, but existing methods largely rely on random masking, disregarding the structural properties of different modalities. In this work, we introduce structured noise-based masking, a simple yet effective approach that naturally aligns with the spatial, temporal, and spectral characteris

Yingdong Ru, Lipeng Zhuang, Zhuo He, Florent P. Audonnet

This paper presents a rigorous evaluation of Real-to-Sim parameter estimation approaches for fabric manipulation in robotics. The study systematically assesses three state-of-the-art approaches, namely two differential pipelines and a data-driven approach. We also devise a novel physics-informed neural network approach for physics parameter estimation. These

Slobodan Radošević, Sonja Gombar, Milica Rutonjski, Petar Mali

We discuss the geometry behind classical Heisenberg model at the level suitable for third or fourth year students who did not have the opportunity to take a course on differential geometry. The arguments presented here rely solely on elementary algebraic concepts such as vectors, dual vectors and tensors, as well as Hamiltonian equations and Poisson brackets

Giovanni Adorni, Daniele Grosso

This paper explores the integration of AI tools, such as ChatGPT and GitHub Copilot, in the Software Architecture for Embedded Systems course. AI-supported workflows enabled students to rapidly prototype complex projects, emphasizing real-world applications like SLAM robotics. Results demon-started enhanced problem-solving, faster development, and more sophi

Summer Eldridge, Ivo David de Oliveira, Yogev Shpilman

We identify a new type of paradoxical behavior in dice, where the sum of independent rolls produces a deceptive sequence of dominance relations. We call these ``anti-inductive dice". Consider a game with two players and two non-identical dice. Each rolls their die $k$ times, adding the results, and the player with the highest sum wins. For each $k$, this ind

A. G. Kudryavtsev, N. N. Myagkov

New exact spatially localized stationary solutions against the background of a zonal flow are found for the (3+1)-dimensional nonlinear non-dissipative quasi-geostrophic potential vorticity equation, which describes Rossby waves and vortices in an exponential atmosphere. In total, three solutions are presented. The nonlinear boundary conditions with a flat b

Peiran Gu, Fuhao Duan, Wenhao Li, Bochen Xu

In recent years, the development of Large Language Models (LLMs) has made significant breakthroughs in the field of natural language processing and has gradually been applied to the field of humanities and social sciences research. LLMs have a wide range of application value in the field of humanities and social sciences because of its strong text understand

Lubomir Skvarenina, Stephen Simpson, Yashar Alizadeh, Martin Lavery

Mode mixing in optical fibers caused by mechanical bending induces perturbations that distort the spatial field profile of coherent beams as they propagate through few-mode or multimode fibers. The observed output from a bent fiber commonly appears as complex speckle, which is challenging to relate directly to the underlying deformation, particularly in cont

Zeqiang Lai, Yunfei Zhao, Zibo Zhao, Haolin Liu

3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vecset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles with high-speed generation. Challenges exist because of difficulties not only in accel

Ali Doğdu, Murad Kayacan

There are different views in the literature regarding economic growth and development. Growth and development hypotheses have been addressed from many different perspectives. It is aimed to examine the view of production with the Heavy Industry Initiative proposed by Prof. Dr. Necmettin Erbakan a crucial part of Material Development in T\"urkiye. In this con

A. K. Srivastava, Sripan Mondal, Eric R. Priest, Sudheer K. Mishra

The Sun's outer atmosphere, the corona, is maintained at mega-Kelvin temperatures and fills the heliosphere with a supersonic outflowing wind. The dissipation of magnetic waves and direct electric currents are likely to be the most significant processes for heating the corona, but a lively debate exists on their relative roles. Here, we suggest that the two

Piotr Bargiela, Tong-Zhi Yang

In this work, we investigate the finite basis topologies of two-loop dimensionally regularized Feynman integrals in the `t Hooft-Veltman scheme in the Standard Model. We present a functionally distinct finite basis of Master Integrals which spans the whole transcendental space of all two-loop Feynman integrals with external momenta in four dimensions. We als

Jade Preston, William Basener

Unmixing is a fundamental process in hyperspectral image processing in which the materials present in a mixed pixel are determined based on the spectra of candidate materials and the pixel spectrum. Practical and general utility requires a large spectral library with sample measurements covering the full variation in each candidate material as well as a suff

Yash Vekaria, Aurelio Loris Canino, Jonathan Levitsky, Alex Ciechonski

Generative AI (GenAI) browser assistants integrate powerful capabilities of GenAI in web browsers to provide rich experiences such as question answering, content summarization, and agentic navigation. These assistants, available today as browser extensions, can not only track detailed browsing activity such as search and click data, but can also autonomously

Alex Viguerie, Elisa Iacomini

Due to the widespread availability of effective antiretroviral therapy (ART) regimens, average lifespans of persons with HIV (PWH) in the United States have increased significantly in recent decades. In turn, the demographic profile of PWH has shifted. Older persons comprise an ever-increasing percentage of PWH, with this percentage expected to further incre

Stephanie Chen

The Grothendieck classes of melonic graphs satisfy a recursive relation and may be written as polynomials in the class of the moduli space $\mathcal{M}_{0,4}$ with nonnegative integer coefficients, conjectured to be log-concave. In this article, we investigate log-concavity and ultra-log-concavity for the Grothendieck class of banana graphs and the three fam

M. Pioldi, G. De Simoni, A. Braggio, F. Giazotto

Owing to their sensitivity to temperature fluctuations, normal metal-insulator-superconductor (NIS) junctions are leveraged in various thermal devices. This study illustrates that two NISIN reservoirs can achieve a measurable negative differential thermal conductance (NDTC). This phenomenon is enabled by photon-mediated heat exchange, which is profoundly aff

Nicolas Valade, Jérémie Bec, Simon Thalabard

SQG describes the 2D active transport of a scalar field, such as temperature, which -- when properly rescaled -- shares the same physical dimension of length/time as the advecting velocity field. This duality has motivated analogies with 3D turbulence. In particular, the Kraichnan-Leith-Batchelor similarity theory predicts a Kolmogorov-type inertial range sc

Fabiola Fortuna, Gerardo Hernández-Tomé, Diego Portillo-Sánchez, Genaro Toledo

We study the four-body heavy baryon decay, including two leptons in the final state, of the form $B_A \to B_B P \ell_\alpha \ell_\beta$, which can be either a lepton number conserving (LNC) or a lepton number violating (LNV) process (where $B$, $P$ and $\ell$ are baryons, pseudoscalar mesons and leptons, respectively), including all kinematic allowed lepton

George Grispos, Logan Mears, Larry Loucks, William Mahoney

As technology increasingly integrates into farm settings, the food and agriculture sector has become vulnerable to cyberattacks. However, previous research has indicated that many farmers and food producers lack the cybersecurity education they require to identify and mitigate the growing number of threats and risks impacting the industry. This paper present

Haochen Zhang, Nader Zantout, Pujith Kachana, Ji Zhang

With the recent rise of large language models, vision-language models, and other general foundation models, there is growing potential for multimodal, multi-task robotics that can operate in diverse environments given natural language input. One such application is indoor navigation using natural language instructions. However, despite recent progress, this

Zhi-Bo Chen, Shao-Ming Fei

Quantification of quantum entanglement plays a crucial role in the study of quantum information tasks. We present analytical lower bounds for both concurrence and 2-concurrence based on the correlation matrices of bipartite quantum states. Compared with other related lower bounds, our approach provides a better estimation of the entanglement, particularly fo

Fan Huang, Wei Wang

Graph-based collaborative filtering has been established as a prominent approach in recommendation systems, leveraging the inherent graph topology of user-item interactions to model high-order connectivity patterns and enhance recommendation performance. Recent advances in Graph Contrastive Learning (GCL) have demonstrated promising potential to alleviate da

Inwoo Hwang, Bing Zhou, Young Min Kim, Jian Wang

Modeling human-scene interactions (HSI) is essential for understanding and simulating everyday human behaviors. Recent approaches utilizing generative modeling have made progress in this domain; however, they are limited in controllability and flexibility for real-world applications. To address these challenges, we propose reformulating the HSI modeling prob

Panqi Jia, Fabian Brand, Dequan Yu, Alexander Karabutov

Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-based image codecs, namely Joint Photographic Experts Group (JPEG) AI. The objective of JPEG AI is to enhance compression efficiency and provide a software and hardwarefriendly solut

Hanshuo Qiu, Jing Lian, Xiaoyuan Wang, Jizhao Liu

The rapid development of low-Earth orbit (LEO) satellite constellations and satellite communication systems has elevated the importance of secure video transmission, which is the key to applications such as remote sensing, disaster relief, and secure information exchange. In this context, three serious issues arise concerning real-time encryption of videos o

Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can, Christelle Schneuwly Diaz

Alzheimer's disease (AD) affects 50 million people worldwide and is projected to overwhelm 152 million by 2050. AD is characterized by cognitive decline due partly to disruptions in metabolic brain connectivity. Thus, early and accurate detection of metabolic brain network impairments is crucial for AD management. Chief to identifying such impairments is FDG

Martin Bichler, Davide Legacci, Panayotis Mertikopoulos, Matthias Oberlechner

Understanding the convergence landscape of multi-agent learning is a fundamental problem of great practical relevance in many applications of artificial intelligence and machine learning. While it is known that learning dynamics converge to Nash equilibrium in potential games, the behavior of dynamics in many important classes of games that do not admit a po

Sharon Peled, Yosef E. Maruvka, Moti Freiman

Whole Slide Images (WSIs) are high-resolution digital scans widely used in medical diagnostics. WSI classification is typically approached using Multiple Instance Learning (MIL), where the slide is partitioned into tiles treated as interconnected instances. While attention-based MIL methods aim to identify the most informative tiles, they often fail to fully

Mark Freyhof, George Grispos, Santosh K. Pitla, William Mahoney

As various technologies are integrated and implemented into the food and agricultural industry, it is increasingly important for stakeholders throughout the sector to identify and reduce cybersecurity vulnerabilities and risks associated with these technologies. However, numerous industry and government reports suggest that many farmers and agricultural equi

Zhaochong An, Guolei Sun, Yun Liu, Runjia Li

Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods enhance prototypes via interacting with support or query features but remain limited by sparse knowledge from few-shot samples. Meanwhile, 3D vision-language models (3D VLMs), gener

Aniruddha Chakraborty, Suvodip Mukherjee

The lensing of Gravitational Waves (GWs) due to intervening matter distribution in the universe can lead to chromatic and achromatic signatures in the wave-optics and geometrical-optics limit respectively. This makes it difficult to model for the unknown mass distribution of the lens and hence requires a model-independent lensing detection technique from GW

Rafael Frongillo, Ian Kash, Mary Monroe

Peer prediction mechanisms are typically proposed and analyzed under the assumption that the report and signal spaces are identical. In practice, however, agents often observe richer information which they then map to a coarser report space. Motivated by this discrepancy between theory and practice, we initiate the study of peer prediction mechanisms with si

Karsten Müller, Andreas Wanke, Yves Burkhardt, Herbert De Gersem

In this paper, various skewing configurations for a permanent magnet synchronous machine are evaluated by comparing torque ripple amplitudes and tooth forces. Since high-frequency pure tones emitted by an electrical machine significantly impact a vehicle's noise, vibration, and harshness (NVH) behavior, it is crucial to analyze radial forces. These forces ar

Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan Adams

With the advent of automatic vectorization tools (e.g., JAX's $\texttt{vmap}$), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain code. Whilst convenient, for various MCMC algorithms this results in a synchronization problem -- loosely speaking, at each iteration all chains running in parallel must wait until

Shuqi Lu, Haowei Lin, Lin Yao, Zhifeng Gao

3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D spatial patterns, current approaches remain fragmented, with models narrowly specialized for specific domains and unable to generalize across tasks or scales. We propose Uni-3DAR,

Sang-Wook Cheong, Fei-Ting Huang

Kinetomagnetism refers to magnetization induced by (electric) current, encompassing longitudinal or transverse effects and even- or odd-order phenomena. The essential prerequisite for kinetomagnetism is the breaking of PT (=Parity times Time reversal) symmetry. Altermagnets, characterized by full spin compensation and broken PT symmetry, are associated with

Yuqing He, Pierre-Paul De Breuck, Hongming Weng, Matteo Giantomassi

A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2%

Tian Yi Lim, Boyang Sun, Marc Pollefeys, Hermann Blum

(Visual) Simultaneous Localization and Mapping (SLAM) remains a fundamental challenge in enabling autonomous systems to navigate and understand large-scale environments. Traditional SLAM approaches struggle to balance efficiency and accuracy, particularly in large-scale settings where extensive computational resources are required for scene reconstruction an

Ömer K. Büyükuslu, Fabrice Yang, Dierk Raabe, Moritz to Baben

Direct reduction of iron using hydrogen-rich gas is rapidly emerging as a key strategy for green steel production. This process involves complex, multiscale phenomena, encompassing solid-state phase transformations and gas transport through pores, that must be accurately represented for predictive industrial implementation. Here, we present a thermodynamical

A. Neronov, O. Kalashev, D. V. Semikoz, D. Savchenko

Recent detection of very-high-energy neutrino emission from Seyfert type active galactic nuclei (AGN) provides a new insight into the physics of the AGN central engines. We notice that if high-energy protons responsible for neutrino emission are accelerated close to the surface of the accretion disk, the neutrino flux may have no unambiguously identifiable e

David Tamayo

We perform a comprehensive thermodynamic analysis of three sign-switching dark energy models in a flat FLRW cosmology: graduated dark energy (gDE), sign-switching cosmological constant ($\Lambda_s$), and smoothed sign-switching cosmological constant ($\Lambda_t$). We systematically derive key cosmological thermodynamic quantities -- horizon temperature, hori

João Borges S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich, Antonio E. Cinà

The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Current evaluation methods predominantly measure robustness through the lens of standard generalization, relying on task performance measures like accuracy. This approach lacks a theore

Min-xin Huang, Sheldon Katz, Albrecht Klemm, Xin Wang

We relate the counting of refined BPS numbers on compact elliptically fibred Calabi-Yau threefolds $X$ to Wilson loop expectations values in the gauge theories that emerge in various rigid local limits of the 5d supergravity theory defined by M-theory compactification on $X$. In these local limits $X_*$ the volumes of curves in certain classes go to infinity

N. Canale, M. Romagnoni, A. Sytov, F. Alharthi

It is known that the alignment of an high-energy e- beam with specific crystal directions leads to a significant increase of the coherent radiation emission. This enhancement can be exploited to create an intense photon source. An elective application is an innovative positron source design for future lepton colliders. Such scheme takes advantage of lattice

Chenxu Hao, Fenglin Huang, Aoteng Xia

We study the total variation (TV) distance between the laws of the 2D Ising/FK-Ising model in a box of side-length $N$ with and without an i.i.d.\ Gaussian external field with variance $\epsilon^2$. Letting the external field strength $\epsilon = \epsilon(N)$ depend on the size of the box, we derive a phase transition for each model depending on the order of

Samik Basu, Ramesh Kasilingam, Ankur Sarkar

In this paper, we compute the concordance inertia group of the product $M \times \mathbb{S}^k$, where $M$ is a simply connected, closed, smooth 6-manifold, for $1 \leq k \leq 10$, using known low-dimensional computations of the stable homotopy groups of spheres. Specifically, for $M = \mathbb{C}P^3$, we determine the inertia group of $\mathbb{C}P^3 \times \m

Ziang Li, Hongguang Zhang, Juan Wang, Meihui Chen

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs nece

Rong Zhang, Yong-Qiang Wang

In this paper, we construct a static spherical symmetric Bardeen-Proca star (BPS) model, which consists of the electromagnetic field and Proca field minimally coupled with gravity. The introduction of the Proca field disrupts the formation of event horizons, ensuring that these solutions are globally regular throughout the spacetime. We obtain families of BP

Dounia Hammou, Yancheng Cai, Pavan Madhusudanarao, Christos G. Bampis

Image and video quality metrics, such as SSIM, LPIPS, and VMAF, aim to predict perceived visual quality and are often assumed to reflect principles of human vision. However, relatively few metrics explicitly incorporate models of human perception, with most relying on hand-crafted formulas or data-driven training to approximate perceptual alignment. In this

Ayberk Acar, Mariana Smith, Lidia Al-Zogbi, Tanner Watts

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally invasive surgeries in tight spaces but additional processing is

Steffen Herbold, Christoph Knieke, Andreas Rausch, Christian Schindler

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoni

Youssef Aiache, Asghar Ullah, Özgür E. Müstecaplıoğlu, Abderrahim El Allati

Accurately characterizing the properties of structured reservoirs is a key challenge in quantum systems and is of great importance for advances in quantum metrology and sensing. In this work, we employ a two-level system (qubit) as a probe, which is coupled to a structured reservoir consisting of an ancilla qubit and a Markovian environment modeled as a ther

Zijian Li, Jingjing Fu, Lei Song, Jiang Bian

Visual reasoning is crucial for multimodal large language models (MLLMs) to address complex chart queries, yet high-quality rationale data remains scarce. Existing methods leveraged (M)LLMs for data generation, but direct prompting often yields limited precision and diversity. In this paper, we propose \textit{Chain of Functions (CoF)}, a novel programmatic

Jianmin Chen, Weikang Weng

We construct a family of $2$-tilting bundles on a Geigle-Lenzing projective space of type $(2,2,p,q)$ via the action of iterated $2$-APR mutations. As an application, we give some non-examples for an open question raised by Herschend, Iyama, Minamoto and Oppermann in the paper "Representation theory of Geigle-Lenzing complete intersections".

Aamir H. Dar, Neeraj Kumar Sharma

In non-stationary signal processing, prior work has incorporated the quadratic-phase Fourier transform (QPFT) into the ambiguity function (AF) and Wigner distribution (WD) to enhance their performance. This paper introduces an advanced Wigner distribution and ambiguity function in the quadratic-phase Fourier transform domain (AWDQ/AAFQ), extending classical

Keda Tao, Haoxuan You, Yang Sui, Can Qin

Video large language models (VideoLLMs) have demonstrated the capability to process longer video inputs and enable complex reasoning and analysis. However, due to the thousands of visual tokens from the video frames, the key-value (KV) cache can significantly increase memory requirements, becoming a bottleneck for inference speed and memory usage. KV cache q

Jean Tapie, Philipp del Hougne

We prototype a PCB-realized tunable load network whose ports serve as additional "virtual" VNA ports in a "Virtual VNA" measurement setup. The latter enables the estimation of a many-port antenna array's scattering matrix with a few-port VNA, without any reconnections. We experimentally validate the approach for various eight-element antenna arrays in an ane

Richard Haburcak, Montserrat Teixidor i Bigas

We investigate limit linear series on chains of elliptic curves, giving a simple proof of a conjecture of Farkas stating the existence of curves with a theta-characteristic with a given number of sections for the expected range of genera. Using the additional structure afforded by considering limit linear series on chains of elliptic curves, we find examples

Markus Karmann, Peng-Tao Jiang, Bo Li, Onay Urfalioglu

We present Markov Map Nearest Neighbor V2 (M2N2V2), a novel and simple, yet effective approach which leverages depth guidance and attention maps for unsupervised and training-free point-prompt-based interactive segmentation. Following recent trends in supervised multimodal approaches, we carefully integrate depth as an additional modality to create novel dep

Antonios Valamontes, Emmanuel Markoulakis, Ioannis Adamopoulos

The detection of exceptionally high-energy {\gamma}-photons (up to 18 TeV) from GRB 221009A by the LHAASO Collaboration challenges conventional physics. Photon-axion-like particle (ALP) oscillations have been proposed to explain this anomaly, but they rely on specific parameter tuning. We present an alternative explanation involving superluminal dark photons

Zhaowei Liu, Xin Guo, Zhi Yang, Fangqi Lou

In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their application to finance remains challenging, due to fragmented data sources, intransparent reasoning processes, and weak transferability to business applications. In response, we intr

Dawood Wasif, Terrence J. Moore, Jin-Hee Cho

Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model training without centralized data aggregation. However, FL frameworks that protect privacy often sacrifice fairness and reliability. Differential privacy can reduce data leakage, but it may also obscure sensitive attributes n

Nikolas Adaloglou, Johannes Hauber

We use almost toric fibrations and the symplectic rational blow-up to determine when certain Lagrangian pinwheels, which we call liminal, embed in symplectic rational and ruled surfaces. The case of $L_{2,1}$-pinwheels, namely Lagrangian $\mathbb{R}P^2$'s, answers a question of Kronheimer in the negative, exhibiting a symplectic non-spin $4$-manifold that do

Ruoao Yang, Xingang Jin, Ya Wang, Minghe Zhao

We present a fully stabilized 1-GHz Yb-fiber laser frequency comb built on silica substrates, utilizing "optical cubes" to house all optical components, ensuring long-term stability and practical operation. Both the femtosecond laser and f-to-2f interferometer are constructed to silica bricks, with a compact footprint of 290 mm * 250 mm, and a total weight o

Atharv Singh Patlan, Peiyao Sheng, S. Ashwin Hebbar, Prateek Mittal

AI agents integrated with Web3 offer autonomy and openness but raise security concerns as they interact with financial protocols and immutable smart contracts. This paper investigates the vulnerabilities of AI agents within blockchain-based financial ecosystems when exposed to adversarial threats in real-world scenarios. We introduce the concept of context m

Max Gutbrod, David Rauber, Danilo Weber Nunes, Christoph Palm

The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for

Aghil Alaee, Jiusen Liu

In this paper, we consider compact graphical manifolds with boundary over (locally) hyperbolic static space. We prove the stability of the positive mass theorem with respect to the Federer--Fleming flat distance for the static quasi-local Brown-York energy of the outer boundary of compact (locally) hyperbolic graphical manifolds.

Srimoy Bhattacharya, Benjamin Lieberman, Mukesh Kumar, Andreas Crivellin

The Higgs boson discovery at the Large Hadron Collider (LHC) at CERN confirmed the existence of the last missing particle of the Standard Model (SM). The existence of new fundamental constituents of matter beyond the SM is of great importance for our understanding of Nature. In this context, indirect (non-resonant) indications for new scalar bosons were foun

Hamid Hamidani, Yuri Sato, Kazumi Kashiyama, Masaomi Tanaka

The recent Einstein Probe (EP) event EP240414a exhibits several unusual observational features. Its prompt and afterglow emissions place it between long gamma-ray bursts (LGRBs) and low-luminosity GRBs (LLGRBs). The event is followed by a fast optical transient (AT 2024gsa), initially exhibiting a thermal-like spectrum but later evolving into an unusually re

Hamid Hamidani, Kunihito Ioka, Kazumi Kashiyama, Masaomi Tanaka

Recent observations indicate that stripped-envelope core-collapse supernovae are often surrounded by dense circumstellar material (CSM). Motivated by this, we develop an analytic model to systematically study the dynamics of long gamma-ray burst (LGRB) jet propagation in various CSM environments. We derive a general expression for the jet head velocity ($\be

Shantonu Mukherjee, Sayantan Sharma, Hridis K. Pal

Chiral anomaly is a key feature of Lorentz-invariant quantum field theories: in presence of parallel external electric and magnetic fields, the number of massless Weyl fermions of a given chirality is not conserved. In condensed matter, emergent chiral fermions in Weyl semimetals exhibit the same anomaly, directly tied to the topological charge of the Weyl n

Bartosz Prokop, Jimmy Billen, Nikita Frolov, Lendert Gelens

We introduce CLINE (Computational Learning and Identification of Nullclines), a neural network-based method that uncovers the hidden structure of nullclines from oscillatory time series data. Unlike traditional approaches aiming at direct prediction of system dynamics, CLINE identifies static geometric features of the phase space that encode the (non)linear

Shih-Yu Chang

The Double Operator Integral (DOI) framework provides a powerful tool for analyzing perturbations and interactions between self-adjoint operators in functional analysis and spectral theory. However, most existing DOI formulations rely on self-adjointness (Hermitian) or unitary assumptions, limiting their applicability to non-Hermitian settings. Motivated by

Caifeng Liu, Wanwan Zhang

Recently, Kiselev and Sarsam proposed the following nonlocal transport equation as a one-dimensional analogue of the 2D incompressible porous media (IPM) equation \begin{eqnarray*} \partial_t\rho+u\partial_x\rho= 0,~u=gH_a\rho, \end{eqnarray*} where the transform $H_a$ is defined by \begin{eqnarray*} H_af(x)=\frac{1}{\pi}P.V.\int\limits_{\mathbb{R}}\frac{a^2

I. Labadie-García, J. Garrido, L. Verdes-Montenegro, M. Á. Mendoza

Next-generation telescopes will bring groundbreaking discoveries but they will also present new technological challenges. The Square Kilometre Array Observatory (SKAO) will be one of the most demanding scientific infrastructures, with a projected data output of 700 PB per year to be distributed to a network of SKA Regional Centres. Current tools are not full

Astrid Holm Filtenborg Kitchen, Mikkel Sebastian Lundsgaard Brøndt, Marie Saugstrup Jensen, Troels Pedersen

We propose a distributed joint localization and tracking algorithm using a message passing framework, for multiple-input multiple-output radars. We employ the mean field approach to derive an iterative algorithm. The obtained algorithm features a small communication overhead that scales linearly with the number of radars in the system. The proposed algorithm

Shamisa Shoja, Daniel Arnström, Daniel Axehill

In model predictive control (MPC) for hybrid systems, solving optimization problems efficiently and with guarantees on worst-case computational complexity is critical to satisfy the real-time constraints in these applications. These optimization problems often take the form of mixed-integer linear programs (MILPs) or mixed-integer quadratic programs (MIQPs)

Chenpeng Feng

We study the geometry of the moduli space of planes in a general cubic 5-fold and its deformation. We show that this moduli space is a smooth projective surface whose canonical bundle is ample. We also show that the variation of degree 1 Hodge structures of a particular family of such surfaces is maximal. The main technical input is the hyper-Kahler geometry

Chakradhar Sahoo, Yann in 't Veld, Alfred J. H. Jones, Zhihao Jiang

The electronic band gap of a two-dimensional semiconductor within a device architecture is sensitive to variations in screening properties of adjacent materials in the device and to gate-controlled doping. Here, we employ micro-focused angle resolved photoemission spectroscopy to separate band gap renormalization effects stemming from environmental screening

Dawood Wasif, Dian Chen, Sindhuja Madabushi, Nithin Alluru

Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unified large-scale empirical study of privacy-fairness-utility trade-offs in FL, advancing toward responsible AI deployment. Specifically, we s

Elizabeth Gasparim

This text is contribution 77 to the ZAG Handbook of Modern Algebraic Geometry, edited by I. Cheltsov and J. Martinez-Garcia, and summarises the Short Communication I presented at the Geometry and Topology Session of the International Congress of Mathematicians which took place at the University of Copenhagen in 2022.

Andrea Braides, Fabrizio Caragiulo

We consider large spin systems with short-range ferromagnetic interactions and long-range antiferromagnetic interactions subjected to periodic boundary conditions which have been proved by Giuliani, Lebowitz and Lieb to have minimizers that tend to alternate groups of $1$ and $-1$ of the same length $h^\star$. We consider states with energy of the same order

Yuhao Zhao, Xiande Zhang

We study the generalized Tur\'an problem regarding cliques with restricted intersections, which highlights the motivation from extremal set theory. Let $L=\{\ell_1,\dots,\ell_s\}\subset [0,r-1]$ be a fixed integer set with $|L|\notin \{1,r\}$ and $\ell_1<\dots<\ell_s$, and let $\Psi_r(n,L)$ denote the maximum number of $r$-cliques in an $n$-vertex graph whos

S. Simpson, U. Dey, R. J. Sjökvist, J. Wright

We discover a rare structural manifestation of the Goldstone paradigm in a hexagonal polytype of the archetypal ferroelectric BaTiO3. First-principles calculations confirm the Goldstone character of the order parameter, and high-resolution diffraction measurements link this to a quasi-continuous domain texture in the vicinity of the low-temperature phase tra

Jeremy C. -H. Wang, Ming Hou, David Dunwoody, Marko Ilievski

This paper examines how trust is formed, maintained, or diminished over time in the context of human-autonomy teaming with an optionally piloted aircraft. Whereas traditional factor-based trust models offer a static representation of human confidence in technology, here we discuss how variations in the underlying factors lead to variations in trust, trust th

Ana I. C. Pereda

This study pioneers the application of the Gai-Kapadia framework, originally developed for interbank contagion, to global equity markets. It offers a novel approach to assess systemic risk and default cascades. Using a 20-asset network (13 Brazilian and 7 developed market assets) from 2015 to 2025, we construct exposure-based networks from price co-movements

Daniel Peterseim, Jonas Püschel, Tatjana Stykel

This paper presents a novel Riemannian conjugate gradient method for the Kohn-Sham energy minimization problem in density functional theory (DFT), with a focus on non-metallic crystal systems. We introduce an energy-adaptive metric that preconditions the Kohn-Sham model, significantly enhancing optimization efficiency. Additionally, a carefully designed shif

Hadi Amini, Md Jueal Mia, Yasaman Saadati, Ahmed Imteaj

Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as text. LMs have a wide range of applications in natural language processing (NLP) tasks, including autocomplete and machine translation. Although larger datasets typically enhance LM p

Wei Wei, Yuling Xiang, Qiang Hou, Yue Sun

Since the discovery of high-temperature superconductivity in cuprates, understanding the unconventional pairing mechanism has remained one of the most significant challenges. The upper critical field ($H_{\rm{c2}}$) is an essential parameter for obtaining information on the pair-breaking mechanism, coherence length $\xi$, and pairing symmetry, all of which a

Laurine Martinien, Gaspard Duchêne, François Ménard, Ryo Tazaki

The James Webb Space Telescope now enables the spectral study of ices with unprecedented sensitivity and angular resolution. Water ice plays a crucial role in the growth of grains and in planetary formation but its spatial distribution in protoplanetary disks is poorly constrained. To help the interpretation of future observations, we study here for the firs

Teresa Klatzer, Savvas Melidonis, Marcelo Pereyra, Konstantinos C. Zygalakis

This paper studies plug-and-play (PnP) Langevin sampling strategies for Bayesian inference in low-photon Poisson imaging problems, a challenging class of problems with significant applications in astronomy, medicine, and biology. PnP Langevin sampling offers a powerful framework for Bayesian image restoration, enabling accurate point estimation as well as ad

William A. Bevidas, Joseph M. Colosimo, Abraham D. Falcone, Timothy Emeigh

Hybrid CMOS detectors (HCDs) have several excellent features as high-performance X-ray detectors, including rapid readout, deep-depletion silicon for high quantum efficiency, radiation hardness, and low power. Random telegraph noise (RTN) is a type of noise that can reduce the performance of HCDs and other CMOS sensors. After finding and quantifying RTN in t

Stefana-Lucia Anita

This paper concerns a Mean Field Game (MFG) system related to a Nash type equilibrium for dynamical games associated to large populations. One shows that the MFG system may be viewed as the Euler-Lagrange system for an optimal control problem related to a Fokker-Planck equation with control in the drift. One derives the existence of a weak solution to the MF

Yuta Yahagi, Kiichi Obuchi, Fumihiko Kosaka, Kota Matsui

Simulation-to-Real (Sim2Real) transfer learning, the machine learning technique that efficiently solves a real-world task by leveraging knowledge from computational data, has received increasing attention in materials science as a promising solution to the scarcity of experimental data. We proposed an efficient transfer learning scheme from first-principles

Quy-Anh Dang, Chris Ngo

Enhancing the reasoning capabilities of large language models (LLMs) typically relies on massive computational resources and extensive datasets, limiting accessibility for resource-constrained settings. Our study investigates the potential of reinforcement learning (RL) to improve reasoning in small LLMs, focusing on a 1.5-billion-parameter model, DeepSeek-R

Yu Cao, Zengqun Zhao, Ioannis Patras, Shaogang Gong

Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. In our analysis, we identify three distinct phases in the diffusion generative process: Profiling, Mutation, and Refinemen

Qi-Cheng Wu, Yan-Hui Zhou, Tong Liu, Yi-Hao Kang

Enhancing the sensitivity of quantum sensing near an exceptional point represents a significant phenomenon in non-Hermitian (NH) systems. However, the application of this property in time-modulated NH systems remains largely unexplored. In this work, we propose two theoretical schemes to achieve enhanced quantum sensing in time-modulated NH systems by levera