December 2024 arXiv papers — page 34

Feng-Zhi Chen, Qiaoyi Wen, Fanrong Xu

This work examines the $h\to Z\gamma$ and $h\to\gamma\gamma$ decays in the flavor-gauged two Higgs doublet model (FG2HDM), which augments the Standard Model (SM) with an additional scalar doublet, a singlet, and a $U(1)'$ flavor gauge symmetry. Beyond the SM spectrum, FG2HDM predicts five additional physical scalars and a new neutral gauge boson, $Z'$. We de

El Mahdi Khribch, Pierre Alquier

Recent advances in statistical learning theory have revealed profound connections between mutual information (MI) bounds, PAC-Bayesian theory, and Bayesian nonparametrics. This work introduces a novel mutual information bound for statistical models. The derived bound has wide-ranging applications in statistical inference. It yields improved contraction rates

On Kim

The Muon $g-2$ experiment at Fermilab aims to measure the muon anomalous magnetic moment with an unprecedented precision of 140 parts per billion (ppb). Data collection concluded in June 2023, and analysis of the largest dataset (2021-2023) is underway. Previous publications based on data from 2018-2020 established the experimental foundation. This document

Derong Xu, Xinhang Li, Ziheng Zhang, Zhenxi Lin

Large Language Models (LLMs) demonstrate remarkable capabilities, yet struggle with hallucination and outdated knowledge when tasked with complex knowledge reasoning, resulting in factually incorrect outputs. Previous studies have attempted to mitigate it by retrieving factual knowledge from large-scale knowledge graphs (KGs) to assist LLMs in logical reason

Jayashish Das, Arnab Kundu

We explore and analyze bulk geometric aspects corresponding to a driven two-dimensional holographic CFT, where the drive Hamiltonian is constructed from the $sl^{(q)}(2,\mathbb{R})$ generators. In particular, we demonstrate that starting with a thermal initial state, the evolution of the event horizon is characterized by distinct geometric transformations in

Xinyu Yang, Yu Sun, Xinyang Chen, Ying Zhang

Spatial-temporal data collected across different geographic locations often suffer from missing values, posing challenges to data analysis. Existing methods primarily leverage fixed spatial graphs to impute missing values, which implicitly assume that the spatial relationship is roughly the same for all features across different locations. However, they may

Christodoulos Peltekis, Giorgos Dimitrakopoulos

Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized GCN hardware accelerators. In this work, we address a key architectural challenge for GCN accelerators: how to detect errors in GCN computations arising from random hardware faults

Eduardo Willwock Lussi, Rafael de Santiago, Eduardo Inacio Duzzioni

Quantum computing has garnered attention for its potential to solve complex computational problems with considerable speedup. Despite notable advancements in the field, achieving meaningful scalability and noise control in quantum hardware remains challenging. Incoherent errors caused by decoherence restrict the total computation time, making it very short.

Eftychios Kaimakkamis, Hervé Partouche, Karunava Sil, Nicolaos Toumbas

We consider the classical minisuperspace model describing a closed, homogeneous and isotropic Universe, with a positive cosmological constant. Upon canonical quantization, the infinite number of possible operator orderings in the quantum Hamiltonian leads to distinct Wheeler-DeWitt equations for the wavefunction $\psi$ of the Universe. Similarly, ambiguity a

Umut Cihan, Vahid Haratian, Arda İçöz, Mert Kaan Gül

Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs). The effects of such tools in the industry are

Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas

We study language generation in the limit - introduced by Kleinberg and Mullainathan [KM24] - building on classical works of Gold [Gol67] and Angluin [Ang79]. [KM24]'s main result is an algorithm for generating from any countable language collection in the limit. While their algorithm eventually generates unseen strings from the target language $K$, it sacri

Ilias Mitrai, Prodromos Daoutidis

Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we dis

Kevin Loo, Qing-Rui Wang

We use the pullback trivialization technique to systematically construct gapped interfaces and anomalous boundaries for fermionic symmetry-protected topological (FSPT) states by extending their symmetry group $G_f = \mathbb{Z}_2^f \times_{\omega_2} G$ to larger groups. These FSPT states may involve decoration layers of both Majorana chains and complex fermio

Sheng-Juan Jiang, Bai-Qing Li, Guang-Zhi Xu, Kui-Yong Liu

In this paper, we calculate the toponium spectrum in the potential model with the screened effects. Coulombic part is dominant for toponium, and the coefficient of the Coulomb potential is chosen from lattice QCD calculations at an infinite quark mass in accord with the ultrasoft scale choice. We also derive the relative E1 transitions, decays, and productio

Fei-Chi Zhang, Jin-Han Xie, Xiaojing Zheng

We develop a multi-range fractional (MRF) model to capture the turbulent spectrum consisting of multiple self-similar ranges impacted by multiple effects. The MRF model is validated using long-term observational atmospheric surface layer data from Qingtu lake with extreme Reynolds numbers up to Re$_\tau\sim O(10^6)$. The spectral exponent in each range and t

Yang Shen, Xiu-Shen Wei, Yifan Sun, Yuxin Song

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we explore the idea that CV adopts discrete and ter

Mohammed Hamdan, Abderrahmane Rahiche, Mohamed Cheriet

Despite significant advances in deep learning, current Handwritten Text Recognition (HTR) systems struggle with the inherent complexity of historical documents, including diverse writing styles, degraded text quality, and computational efficiency requirements across multiple languages and time periods. This paper introduces HTR-JAND (HTR-JAND: Handwritten Te

Yushi Hamaguchi, Dai Taguchi

In this paper, we study functional type weak approximation of weak solutions of stochastic functional differential equations by means of the Euler--Maruyama scheme. Under mild assumptions on the coefficients, we provide a quantitative error estimate for the weak approximation in terms of the L\'evy--Prokhorov metric of probability laws on the path space. The

Karishma Thakrar

Graph Retrieval-Augmented Generation (GRAG or Graph RAG) architectures aim to enhance language understanding and generation by leveraging external knowledge. However, effectively capturing and integrating the rich semantic information present in textual and structured data remains a challenge. To address this, a novel GRAG framework, Dynamic Graph Retrieval-

Hadar Ben-Efraim, Susan B. Davidson, Amit Somech

Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This paper develops a measure of an element's contribution to a set of asso

Romain Murenzi, Aidan Zlotak, Jean Pierre Gazeau

Covariant integral quantization is implemented for systems whose phase space is $Z_{d} \times Z_{d}$, i.e., for systems moving on the discrete periodic set $Z_d= \{0,1,\dotsc d-1$ mod$ d\}$. The symmetry group of this phase space is the periodic discrete version of the Weyl-Heisenberg group, namely the central extension of the abelian group $Z_d \times Z_d$.

Ziyu Li, Minyu Lu, Tianyu Wang

In late 1990's Tsujii proved monotonicity of topological entropy of real quadratic family $f_c(x)=x^2+c$ on parameter $c$ by proving an inequality concerning orbital information of the critical point. In this paper, we consider a weak analog of such inequality for the general family $f_{c,r}(x)=|x|^r+c$ with rational $r>1$, by following an algebraic approach

Pradeep Mantha, Florian J. Kiwit, Nishant Saurabh, Shantenu Jha

As quantum hardware advances, integrating quantum processing units (QPUs) into HPC environments and managing diverse infrastructure and software stacks becomes increasingly essential. Pilot-Quantum addresses these challenges as a middleware designed to provide unified application-level management of resources and workloads across hybrid quantum-classical env

Omer Ekmekcioglu, Nursen Aydin, Juergen Branke

Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-making processes in various fields such as economics, engineering, and machine learning. This paper focuses on bilevel optimization where both upper-level and lower-level functions a

Hau-Wen Huang

Assume that $\mathbb F$ is an algebraically closed field and fix a nonzero scalar $q\in \mathbb F$ with $q^4\not=1$. The universal Askey--Wilson algebra $\triangle_q$ is a unital associative algebra over $\mathbb F$ defined by generators and relations. The generators are $A,B,C$ and the relations assert that each of \begin{gather*} A+\frac{qBC-q^{-1}CB}{q^2-

David Sobrín-Hidalgo, Ángel Manuel Guerrero-Higueras, Vicente Matellán-Olivera

Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Aut

Andrew J. Blumberg, Mathieu Carrière, Jun Hou Fung, Michael A. Mandell

We introduce a new algorithm for finding robust circular coordinates on data that is expected to exhibit recurrence, such as that which appears in neuronal recordings of C. elegans. Techniques exist to create circular coordinates on a simplicial complex from a dimension 1 cohomology class, and these can be applied to the Rips complex of a dataset when it has

Simon Kohaut, Nikolas Hohmann, Sebastian Brulin, Benedict Flade

Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the development of such systems has been impeded by the complexity of legal restrictions and physical constraints. While airspaces are often tightly

Tianzhe Xiao, Yichen Li, Yining Qi, Haozhao Wang

Recent studies have shown that Federated learning (FL) is vulnerable to Gradient Inversion Attacks (GIA), which can recover private training data from shared gradients. However, existing methods are designed for dense, continuous data such as images or vectorized texts, and cannot be directly applied to sparse and discrete graph data. This paper first explor

Dana A Abdullah, Hewir A. Khidir, Ismail Y. Maolood, Aso K. Ameen

In today's digital age, information systems (IS) are indispensable tools for organizations of all sizes. The quality of these systems, encompassing system, information, and service dimensions, significantly impacts organizational performance. This study investigates the intricate relationships between these three quality dimensions and their collective influ

Hao Pang, Zhenpo Wang, Guoqiang Li

Deep reinforcement learning (DRL) shows promising potential for autonomous driving decision-making. However, DRL demands extensive computational resources to achieve a qualified policy in complex driving scenarios due to its low learning efficiency. Moreover, leveraging expert guidance from human to enhance DRL performance incurs prohibitively high labor cos

Igor Bandos, Patrick Meessen, Tomás Ortín

We find the defining equations for a Killing vector and its superpartner (called the generalized Killing spinor) and use them to construct the generalized Komar superform of minimal N=2, d=4 supergravity using the superspace formulation. The superspace procedure presented here can be used for the construction of generalized Komar forms in more general superg

T. Dodo, M. K. Cheoun, J. H. Choi, J. Y. Choi

JSNS$^2$ (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment searching for sterile neutrinos through the observation of $\bar{\nu}_{\mu} \rightarrow \bar{\nu}_e$ appearance oscillations, using neutrinos produced by muon decay-at-rest. A key aspect of the experiment involves accurately understanding the neutrino flux and the

V. A. Vassiliev

We study the space of codimension two subalgebras in $C^\infty(S^1, {\mathbb R})$ defined by pairs of conditions $f(\varphi)=f(\psi)$, $\varphi \neq \psi \in S^1$, or by their limits. We compute the mod 2 cohomology ring of this space, and also the Stiefel--Whitney classes of the tautological vector bundle on it.

Kunal Bhatnagar, Sagana Chattanathan, Angela Dang, Bhargav Eranki

In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory

A. G. Pazukhin, I. I. Zinchenko, E. A. Trofimova

Massive stars play an important role in the Universe. Unlike low-mass stars, the formation of these objects located at great distances is still unclear. It is expected to be governed by some combination of self-gravity, turbulence, and magnetic fields. In this work, we aim to study the chemical and physical conditions of dense clumps at different evolutionar

Owais Ishtiaq Siddiqui, Nouhaila Innan, Alberto Marchisio, Mohamed Bennai

Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical implementation faces challenges, including limited quantum hardware and the complexity of integrating quantum algorithms with classical systems. One critical challenge is handling im

James E. Gallagher, Edward J. Oughton

This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordi

Naoto Shiraishi, Hal Tasaki

We study the $S=\frac{1}{2}$ quantum spin system on the $d$-dimensional hypercubic lattice with $d\ge2$ with uniform nearest-neighbor interaction of the XY or XYZ type and arbitrary uniform magnetic field. By extending the method recently developed for quantum spin chains, we prove that the model possesses no local conserved quantities except for the trivial

Yufei Zhang, Ziyang Chen, Bin Luo, Hong Guo

High-precision time transfer over a long haul of fiber plays a significant role in many fields. The core method, namely cascading relay nodes for the compensation of signal attenuation and dispersion, is however insufficient to deal with crucial point-to-point transfer scenarios, such as harsh environments with extremely deficient infrastructure and emergenc

Rıza Özçelik, Francesca Grisoni

"How to evaluate the de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple question has no clear answer. The absence of standardized guidelines challenges both the benchmarking of generative approaches and the selection of molecules for prospective studies. I

Wenjia Jing, Jack Xin, Yifeng Yu

Using formal renormalization theory, Yakhot derived in ([32], 1988) an $O\left(\frac{A}{\sqrt{\log A}}\right)$ growth law of the turbulent flame speed with respect to large flow intensity $A$ based on the inviscid G-equation. Although this growth law is widely cited in combustion literature, there has been no rigorous mathematical discussion to date about it

Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville

In the past years, many signal processing operations have been successfully adapted to the graph setting. One elegant and effective approach is to exploit the eigendecomposition of a graph shift operator (GSO), such as the adjacency or Laplacian operator, to define a graph Fourier transform when projecting graph signals on the corresponding basis. However, t

Mamady Delamou, Ahmed Naeem, Huseyin Arslan, El Mehdi Amhoud

Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number o

Adam LaClair, Matthew Mastroeni, Jason McCullough, Irena Peeva

The graded M\"{o}bius algebra of a matroid is a commutative graded algebra which encodes the combinatorics of the lattice of flats of the matroid. As a special subalgebra of the augmented Chow ring of the matroid, it plays an important role in the recent proof of the Dowling-Wilson Top Heavy Conjecture. Recently, Mastroeni and McCullough proved that the Chow

Qian Lei, Chi Seng Pun, Jingxiang Tang

This paper studies dynamic mean-variance (MV) asset allocation problems in general incomplete markets. Besides of the conventional MV objective on portfolio's terminal wealth, our framework can accommodate running MV objectives with general (non-exponential) discounting factors while in general, any time-dependent preferences. We attempt the problem with a g

Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu

We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-3.2 model fine-tuned with LoRA show consistent neuron-level

Angela Cao, Faye Holt, Jonas Chan, Stephanie Richter

With the goal of supporting scalable lexical semantic annotation, analysis, and theorizing, we conduct a comprehensive evaluation of different methods for generating event descriptions under both syntactic constraints -- e.g. desired clause structure -- and semantic constraints -- e.g. desired verb sense. We compare three different methods -- (i) manual gene

Sara Papi, Peter Polak, Ondřej Bojar, Dominik Macháček

Simultaneous speech-to-text translation (SimulST) translates source-language speech into target-language text concurrently with the speaker's speech, ensuring low latency for better user comprehension. Despite its intended application to unbounded speech, most research has focused on human pre-segmented speech, simplifying the task and overlooking significan

Xiaofan Zhou, Suotang Jia, Jian-Song Pan

An important aspect in categorizing topological phases is whether the system is spinless or spinful, given that these classes exhibit distinct symmetry algebras, leading to disparate topological classifications. By utilizing the projective presentation strategy, the topological phases of spinless (or spinful) systems can be emulated using spinful (or spinles

Orlando Luongo, Marco Muccino

Model-independent bounds on the Hubble constant $H_0$ are important to shed light on cosmological tensions. We work out a model-independent analysis based on the sum rule, which is applied to late- and early-time data catalogs to determine $H_0$. Through the model-independent B\'ezier interpolation of the observational Hubble data (OHD) and assuming a flat u

Ramachandran Anantharaman, Alexandre Mauroy

In this work, we develop a method to identify continuous-time nonlinear networked dynamics via the Koopman operator framework. The proposed technique consists of two steps: the first step identifies the neighbors of each node, and the second step identifies the local dynamics at each node from a predefined set of dictionary functions. The technique can be us

Alexei Bazavov, Claude W. Bernard, David A. Clarke, Christine Davies

We present results for the dominant light-quark connected contribution to the long-distance window (LD) of the hadronic vacuum polarization contribution (HVP) to the muon $g-2$ from lattice quantum chromodynamics (QCD). Specifically, with a new determination of the lattice scale on MILC's physical-mass HISQ ensembles, using the $\Omega^-$ baryon mass, we obt

Simon Metayer, Sofian Teber

We review the field-theoretic renormalization-group approach to critical properties of flat polymerized membranes. We start with a presentation of the flexural effective model that is entirely expressed in terms of a transverse (flexural) field with non-local interactions. We then provide a detailed account of the full three-loop computations of the renormal

Gnaneswar Villuri, Alex Doboli

This report characterized the suitability of existing datasets for devising new Machine Learning models, decision making methods, and analysis algorithms to improve Collaborative Problem Solving and then enumerated requirements for future datasets to be devised. Problem solving was assumed to be performed in teams of about three, four members, which talked t

Kalam Khadka, Abu Barkat Ullah, Wanli Ma, Elisa Martinez Marroquin

Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link between principles of persuasion and social engineering attacks, particularly in phishing email attacks, is an important

Shahar Katz, Liran Ringel, Yaniv Romano, Lior Wolf

Modern Language Models (LMs) owe much of their success to masked causal attention, the backbone of Generative Pre-Trained Transformer (GPT) models. Although GPTs can process the entire user prompt at once, the causal masking is applied to all input tokens step-by-step, mimicking the generation process. This imposes an unnecessary constraint during the initia

Mark Whitmeyer

I conduct Rabin's (2000) calibration exercise in the subjective expected utility realm. I show that the rejection of some risky bet by a risk-averse agent only implies the rejection of more extreme and less desirable bets and nothing more.

Kalam Khadka

This study extends the research of Ferreira and Teles (2019), who synthesized works by Cialdini (2007), Gragg (2003), and Stajano and Wilson (2011) to propose a unique list of persuasion principles in social engineering. While Ferreira and Teles focused on email subject lines, this research analyzed entire email contents to identify principles of human persu

Xiaolin Wen, Tai D. Nguyen, Lun Zhang, Jun Sun

Smart contracts are the fundamental components of blockchain technology. They are programs to determine cryptocurrency transactions, and are irreversible once deployed, making it crucial for cryptocurrency investors to understand the cryptocurrency transaction behaviors of smart contracts comprehensively. However, it is a challenging (if not impossible) task

L. D. Estes, A. Wussah, M. Asipunu, M. Gathigi

African agriculture is undergoing rapid transformation. Annual maps of crop fields are key to understanding the nature of this transformation, but such maps are currently lacking and must be developed using advanced machine learning models trained on high resolution remote sensing imagery. To enable the development of such models, we delineated field boundar

Changpeng Lin, Samuel Poncé, Francesco Macheda, Francesco Mauri

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending rigidity tensors of crystalline solids, leveraging interatomic force constants and long-wavelength perturbation theory.

Zhihao Yan, Zhengxiao Li, Lujun Zhu, Xin Lin

The development of magnetic heterostructures with strong spin-orbit torques (SOTs), low impedance, strong perpendicular magnetic anisotropy (PMA), and good integration compatibility at the same time is central for high-performance spintronic memory and computing applications. Here, we report the development of the symmetry-broken spin-orbit superlattice [Pt/

Kaizhe Chen, Shiping Liu

In this paper, we derive new sharp diameter bounds for distance regular graphs, which better answer a problem raised by Neumaier and Penji\' c in many cases. Our proof is built upon a relation between the diameter and long-scale Ollivier Ricci curvature of a graph, which can be considered as an improvement of the discrete Bonnet-Myers theorem. Our method fur

Yannick Heiser, Farzaneh Pourahmadi, Jalal Kazempour

We develop a bidding strategy for a hybrid power plant combining co-located wind turbines and an electrolyzer, constructing a price-quantity bidding curve for the day-ahead electricity market while optimally scheduling hydrogen production. Without risk management, single imbalance pricing leads to an all-or-nothing trading strategy, which we term 'betting'.

Manuel de León, Jaime Bajo

In this paper we show how almost cosymplectic structures are a natural framework to study thermodynamical systems. Indeed, we are able to obtain the same evolution equations obtained previously by Gay-Balmaz and Yoshimura (see Entropy, 21(8):39, 2019) using variational arguments. The proposed geometric description allows us to apply geometrical tools to disc

Philippe Naveau, Johan Segers

When passing from the univariate to the multivariate setting, modelling extremes becomes much more intricate. In this introductory exposition, classical multivariate extreme value theory is presented from the point of view of multivariate excesses over high thresholds as modelled by the family of multivariate generalized Pareto distributions. The formulation

Kirandeep Kaur

In this work, we study the effect of noise-induced coherence on the performance analysis of a degenerate four-level quantum heat engine with particular focus on the universal nature of efficiency, which refers to the appearance of the first two universal terms $\eta_C/2$ and $\eta_C^2/8$ in series expansion of the efficiency at maximum power under a left-rig

Nikhil Manoj, G. D. Veerappa Gowda, Sudarshan Kumar K

Non-local systems of conservation laws play a crucial role in modeling flow mechanisms across various scenarios. The well-posedness of such problems is typically established by demonstrating the convergence of robust first-order schemes. However, achieving more accurate solutions necessitates the development of higher-order schemes. In this article, we prese

Zijin Li, Xinghong Pan

In the paper, we consider the solvability of the two-dimensional Navier-Stokes equations in an exterior unit disk. On the boundary of the disk, the tangential velocity is subject to the perturbation of a rotation, and the normal velocity is subject to the perturbation of an interior sources or sinks. At infinity, the flow stays at rest. We will construct a s

Oscar Jarrin, Geremy Loachamin

We introduce a general coupled system of parabolic equations with quadratic nonlinear terms and diffusion terms defined by fractional powers of the Laplacian operator. We develop a method to establish the rigorous convergence of the fractional diffusion case to the classical diffusion case in the strong topology of Sobolev spaces, with explicit convergence r

Monika Dziubelski, Joanna M Zajac

Single photons are resources needed for developing quantum networks QN. They distribute quantum information services across commercial optical fiber links and are key ingredient in developing quantum repeaters architectures. Currently, the most robust quantum light sources are Quantum Dots made of III-V materials. They emit highly indistinguishable photons o

Bahia Hadj Ali, Ania Adil, Fazia Bedouhene

In this research, we apply the observer approach introduced by Djennoune et al. \cite{djennoune2019modulating} to estimate water levels in a coupled tanks system. Central to this approach is the use of a remarkable modulating function-based transformation $T_n $, which employs a time/output-dependent coordinate transformation. This transformation converts th

Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still

Zhan Wang, Heng-Jia Zhang, Kun Jiang, Fu-Chun Zhang

The bilayer structure of recently discovered high-temperature superconducting nickelates La$_3$Ni$_2$O$_7$ provides a new platform for investigating correlation and superconductivity. Starting from a bilayer Hubbard model, we show that there is a molecular Mott insulator limit formed by the bonding band owing to Hubbard interaction $U$ and large interlayer c

Afonso S. Bandeira, Kevin Lucca, Petar Nizić-Nikolac, Ramon van Handel

Matrix concentration inequalities and their recently discovered sharp counterparts provide powerful tools to bound the spectrum of random matrices whose entries are linear functions of independent random variables. However, in many applications in theoretical computer science and in other areas one encounters more general random matrix models, called matrix

Jonathan Bevan, Martin Kružík, Jan Valdman

Let $\mathbb{D}(u)$ be the Dirichlet energy of a map $u$ belonging to the Sobolev space $H^1_{u_0}(\Omega;\mathbb{R}^2)$ and let $A$ be a subclass of $H^1_{u_0}(\Omega;\mathbb{R}^2)$ whose members are subject to the constraint $\det \nabla u = g$ a.e. for a given $g$, together with some boundary data $u_0$. We develop a technique that, when applicable, enabl

KyeongRo Kim, Shuhei Maruyama

The existence of quasimorphisms on groups of homeomorphisms of manifolds has been extensively studied under various regularity conditions, such as smooth, volume-preserving, and symplectic. However, in this context, nothing is known about groups of `area'-preserving diffeomorphisms on non-orientable manifolds. In this paper, we initiate the study of groups o

Ferdinand Jacobé de Naurois

We give an example of a sequence of positive harmonic functions on $\mathbb{Z}^d$, $d\geq 2$, that converges pointwise to a non-harmonic function.

Tengfei He, Xiao Zhou

Social segregation in cities, spanning racial, residential, and income dimensions, is becoming more diverse and severe. As urban spaces and social relations grow more complex, residents in metropolitan areas experience varying levels of social segregation. If left unaddressed, this could lead to increased crime rates, heightened social tensions, and other se

Mingjun Liu, Yijia Zhang, Yun Wang, Rui Xue

Blazars are often observed to flare across multiple wavelengths. Orphan flares from blazars have been only detected a few times, providing an opportunity to understand the structure of the jet in the accreting system. We report a remarkable orphan X-ray flare from a blazar candidate EP240709a, detected by Einstein Probe (EP) in July 2024. The multi-band spec

Shah Faisal

The original proof of the Gromov's non-squeezing theorem [Gro85] is based on pseudo-holomorphic curves. The central ingredient is the compactness of the moduli space of pseudo-holomorphic spheres in the symplectic manifold $(\mathbb{CP}^1\times T^{2n-2}, \omega_{\mathrm{FS}}\oplus \omega_{\mathrm{std}})$ representing the homology class $[\mathbb{CP}^1\times\

Qikun Tian, Qi Yang, An Huang, Bo Peng

Anisotropic heat transfer offers promising solutions to the efficient heat dissipation in the realm of electronic device thermal management. However, the fundamental origin of the anisotropy of thermal transport remains mysterious. In this paper, by combining frequency domain thermoreflectance (FDTR) technique and first-principles-based multiscale simulation

Honggu Kang, Seohyeon Cha, Joonhyuk Kang

Federated learning (FL) is a distributed training paradigm that enables collaborative learning across clients without sharing local data, thereby preserving privacy. However, the increasing scale and complexity of modern deep models often exceed the computational or memory capabilities of edge devices. Furthermore, clients may be constrained to use heterogen

Xiaojiao Guo, Yihang Dong, Xuhang Chen, Weiwen Chen

Underwater Image Restoration (UIR) remains a challenging task in computer vision due to the complex degradation of images in underwater environments. While recent approaches have leveraged various deep learning techniques, including Transformers and complex, parameter-heavy models to achieve significant improvements in restoration effects, we demonstrate tha

Zefan Du, Yanni Li, Zijian Mo, Wenqi Wei

Quantum computing offers unparalleled computational capabilities but faces significant challenges, including limited qubit counts, diverse hardware topologies, and dynamic noise/error rates, which hinder scalability and reliability. Distributed quantum computing, particularly chip-to-chip connections, has emerged as a solution by interconnecting multiple pro

Richard Evan Schwartz

Let $X=SL_3(\R)/SO(3)$. Let $\cal DFR$ be the space of discrete faithful representations of the modular group into ${\rm Isom\/}(X)$ which map the order $2$ generator to an isometry with a unique fixed point. I prove many things about the component $\cal B$ of $\cal DFR$ known as the Barbot component: It is homeomorphic to $\R^2 \times [0,\infty)$. The bound

Juliane Graf, Janine Splettstoesser, Juliette Monsel

The Mpemba effect has initially been noticed in macroscopic systems -- namely that hot water can freeze faster than cold water -- but recently its extension to open quantum systems has attracted significant attention. This phenomenon can be explained in the context of nonequilibrium thermodynamics of Markovian systems, relying on the amplitudes of different

Yongsheng Li, Ke Lan, Hui Cao, Yao-Hua Chen

Burn efficiency is a key for commercial feasibility of fusion power station for inertial fusion energy, while burn efficiency is usually lower than 30% in the central ignition scheme of inertial confinement fusion (ICF). A recent conceptual design for a 10 MJ laser driver [Z. Sui and K. Lan et al., Matter Radiat. Extremes 9, 043002 (2024)] provides a new roo

Chao Li, Olga Petruchik, Elizaveta Grishanina, Sergey Kovalchuk

This paper presents a Multi-Agent Norm Perception and Induction Learning Model aimed at facilitating the integration of autonomous agent systems into distributed healthcare environments through dynamic interaction processes. The nature of the medical norm system and its sharing channels necessitates distinct approaches for Multi-Agent Systems to learn two ty

Zhixuan Xie, Jianjun Chen, Guoliang Li, Shuai Wang

Occlusion is a key factor leading to detection failures. This paper proposes a motion-assisted detection (MAD) method that actively plans an executable path, for the robot to observe the target at a new viewpoint with potentially reduced occlusion. In contrast to existing MAD approaches that may fail in cluttered environments, the proposed framework is robus

Adam Onus, Nina Otter, Renata Turkes

The Persistent Homology Transform (PHT) was introduced in the field of Topological Data Analysis about 10 years ago, and has since been proven to be a very powerful descriptor of Euclidean shapes. The PHT consists of scanning a shape from all possible directions $v\in S^{n-1}$ and then computing the persistent homology of sublevel set filtrations of the resp

Li Pan, Qian Liang, Chang-An Yang, Yu Huang

We investigate the chiral dynamics of locally interacting bosons in a two-leg flux ladder, where on-site interactions, despite being fully isotropic, counterintuitively reverse the flux-induced chiral transport of density distribution. For a Bose-Einstein condensate (in the mean-field regime), this reversal arises from an interactiondriven dynamical band-occ

Tatiana Zemskova, Dmitry Yudin

A 3D scene graph represents a compact scene model by capturing both the objects present and the semantic relationships between them, making it a promising structure for robotic applications. To effectively interact with users, an embodied intelligent agent should be able to answer a wide range of natural language queries about the surrounding 3D environment.

Lucas Pahl, Carlos Pimienta

We prove the 2-player, generic extensive-form case of the conjecture of Govindan and Wilson (1997a,b) and Hauk and Hurkens (2002) stating that an equilibrium component is essential in every equivalent game if and only if the index of the component is nonzero. This provides an index-theoretic characterization of the concept of hyperstable components of equili

Alice Doimo, Giorgio Nicoletti, Davide Bernardi, Prajwal Padmanabha

Spatial metapopulation models are fundamental to theoretical ecology, enabling to study how landscape structure influences global species dynamics. Traditional models, including recent generalizations, often rely on the deterministic limit of stochastic processes, assuming large population sizes. However, stochasticity - arising from dispersal events and pop

Ke Lan, Xiumei Qiao, Yongsheng Li, Xiaohui Zhao

The National Ignition Facility successfully achieved target gain 2.4 thus marginally entering into burn stage.Meanwhile, a recent conceptual design on 10 MJ laser driver [Matter Radiat. Extremes 9, 043002 (2024)] provides a new room for exploring novel target designs and interesting phenomena in a burning plasma after ignition. In this paper, we propose an a

Georges Derache, Mounira Msahli, Aurelien Botbol, Fabien Romain

The importance of the development of IoT and LoRaWAN in military applications has been widely established. Since security is one of its important challenges, in this paper we study two attacks scenarios: replay and sniff attacks on military LoRaWAN network. The aim is to highlight cybersecurity threats that must be taken into consideration when using such te

C. C. Zhao, L. S. Wang, W. Xia, Q. W. Yin

The V-based kagome superconductors $A$V$_3$Sb$_5$ ($A$ = K, Rb, and Cs) host charge density wave (CDW) and a topological nontrivial band structure, thereby provide a great platform to study the interplay of superconductivity (SC), CDW, frustration, and topology. Here, we report ultralow-temperature thermal conductivity measurements on CsV$_3$Sb$_5$ and Ta-do

Gaël Van der Lee, Anatole Lécuyer, Maxence Naud, Reinhold Scherer

Vection, the visual illusion of self-motion, provides a strong marker of the VR user experience and plays an important role in both presence and cybersickness. Traditional measurements have been conducted using questionnaires, which exhibit inherent limitations due to their subjective nature and preventing real-time adjustments. Detecting vection in real tim