November 2024 arXiv papers — page 71

Zhilin Du, Zhenyu Liu, Haozhen Li, Shilong Fan

The application of deep learning (DL)-based channel state information (CSI) feedback frameworks in massive multiple-input multiple-output (MIMO) systems has significantly improved reconstruction accuracy. However, the limited generalization of widely adopted autoencoder-based networks for CSI feedback challenges consistent performance under dynamic wireless

Yixiao Gao, Junyang Ge, Zhaofeng Gu, Lei Xu

Resonant metasurfaces driven by bound states in the continuum (BIC) offer an intriguing approach to engineer high-Q resonances. Merging multiple BICs in the momentum space could further enhance the Q-factor as well as its robustness to fabrication imperfections. Here, we report doubly-degenerate guided mode resonances (GMR) in a resonant metasurface, whose r

Aline Goeminne, Benjamin Monmege

We study multi-strategies in multiplayer reachability games played on finite graphs. A multi-strategy prescribes a set of possible actions, instead of a single action as usual strategies: it represents a set of all strategies that are consistent with it. We aim for profiles of multi-strategies (a multi-strategy per player), where each profile of consistent s

Mahmoud Raeisi, Hui Chen, Henk Wymeersch, Ertugrul Basar

This paper introduces a novel approach to efficient localization in next-generation communication systems through a base station (BS)-enabled passive beamforming utilizing beyond diagonal reconfigurable intelligent surfaces (BD-RISs). Unlike conventional diagonal RISs (D-RISs), which suffer from limited beamforming capability, a BD-RIS provides enhanced cont

David Hume

We quantify the topological expansion properties of bounded degree simplicial complexes in terms of a family of sublinear functions, in analogy with the separation profile of Benjamini-Schramm-Tim\'ar for classical expansion of bounded degree graphs. We prove that, like the separation profile, these new invariants are monotone under regular maps between comp

Laura Doval, Ran Eilat, Tianhao Liu, Yangfan Zhou

An analyst observes the frequency with which a decision maker (DM) takes actions, but not the frequency conditional on payoff-relevant states. We ask when the analyst can rationalize the DM's choices as if the DM first learns something about the state before acting. We provide a support-function characterization of the triples of utility functions, prior bel

Hyun-Chul Kim

This brief review is dedicated to the memory of Maxim V. Polyakov and his pioneering contributions to pentaquark physics. We focus on his seminal 1997 work with Diakonov and Petrov that predicted the $\Theta^+$ pentaquark, a breakthrough that initiated an intense period of research in hadron physics. The field faced a significant setback when the CLAS Collab

Nirmal Joshua Kapu, Raghav Karan

This article surveys convolution-based models including convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comparative training cost assessment, model

Weicai Ye, Xinyu Chen, Ruohao Zhan, Di Huang

This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild. Traditional frameworks, such as ParticleSfM~\cite{zhao2022particlesfm}, address this problem by sequentially computing the optical flow between adjacent frames to obtain point trajectories. They then

Rishabh Tripathi, Krishna K. Maurya, Pradeep Kumar, Bhaskar De

Calculation of the coherent nonlinear response of a system is essential to correctly interpret results from advanced techniques such as two-dimensional coherent spectroscopy (2DCS). Usually, even for the simplest systems, such calculations are either performed for low-intensity excitations where perturbative methods are valid and/or by assuming a simplified

Bernadett Kiss, Alexandra Buchmann, Daniel Renjewski, Alexander Badri-Spröwitz

Human walking efficiency relies on the elastic recoil of the Achilles tendon, facilitated by a "catapult mechanism" that stores energy during stance and releases it during push-off. The catapult release mechanism could include the passive flexion of the knee, as the main part of knee flexion was reported to happen passively after leading leg touch-down. This

Haoyi Wang, Xufang Chen, Yue Yang, Kewei Zhou

Electroencephalography (EEG) is essential in neuroscience and clinical practice, yet it suffers from physiological artifacts, particularly electromyography (EMG), which distort signals. We propose a deep learning model using pix2pixGAN to remove such noise and generate reliable EEG signals. Leveraging the EEGdenoiseNet dataset, we created synthetic datasets

Guanglu Sun, Jin Qiu, Lili Liang

Although great progress has been made in the research of unbiased scene graph generation, issues still hinder improving the predictive performance of both head and tail classes. An unbiased scene graph generation (TA-HDG) is proposed to address these issues. For modeling interactive and non-interactive relations, the Interactive Graph Construction is propose

Sylvain Crovisier, Mikhail Lyubich, Enrique Pujals, Jonguk Yang

We carry out a detailed quantitative analysis on the geometry of invariant manifolds for smooth dissipative systems in dimension two. We begin by quantifying the regularity of any orbit (finite or infinite) in the phase space with a set of explicit inequalities. Then we relate this directly to the quasi-linearization of the local dynamics on regular neighbor

Yuxin Wang, Qi Liu, Linhui Chen, Xiewei Tan

In this paper, for any $\lambda \geq 1, R_\lambda^2$ is the Bana\'s-Fr\k{a}czek space. The exact value of $L_{\mathrm{YJ}}(\xi, \eta, X)$ for this space will be calculated. Specifically, $L_{\mathrm{YJ}}\left(\xi, \eta, R_\lambda^2\right)=1+\frac{2 \xi \eta}{\xi^2+\eta^2}\left(1-\frac{1}{\lambda^2}\right)$ is the result thereafter through meticilous computat

Julian Strohmayer, Rafael Sterzinger, Matthias Wödlinger, Martin Kampel

Cross-domain generalization is an open problem in WiFi-based sensing due to variations in environments, devices, and subjects, causing domain shifts in channel state information. To address this, we propose Domain-Adversarial Test-Time Adaptation (DATTA), a novel framework combining domain-adversarial training (DAT), test-time adaptation (TTA), and weight re

Darius Dramburg, Mads Hustad Sandøy

We investigate compatibility of gradings for an almost Koszul or Koszul algebra $R$ that is also the higher preprojective algebra $\Pi_{n+1}(A)$ of an $n$-hereditary algebra $A$. For an $n$-representation finite algebra $A$, we show that $A$ must be Koszul if $\Pi_{n+1}(A)$ can be endowed with an almost Koszul grading. For an acyclic basic $n$-representation

João Gonçalves

This paper presents CAALM-TC (Combining Autoregressive and Autoencoder Language Models for Text Classification), a novel method that enhances text classification by integrating autoregressive and autoencoder language models. Autoregressive large language models such as Open AI's GPT, Meta's Llama or Microsoft's Phi offer promising prospects for content analy

Ziyang Luo, Haoning Wu, Dongxu Li, Jing Ma

Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice questions in benchmarks such as VideoMME and LongVideoBench, which are prone to lack the depth needed to capture the complex demands of real-world users. To address t

Keyue Qiu, Yuxuan Song, Jie Yu, Hongbo Ma

Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this

Oriol Vilanova, Alberto Martinez-Serra, Marco P Monopoli, Giancarlo Franzese

Nanoparticles (NPs) in contact with biological fluid adsorb biomolecules into a corona. This corona comprises proteins that strongly bind to the NP (hard corona) and loosely bound proteins (soft corona) that dynamically exchange with the surrounding solution. While the kinetics of hard corona formation is relatively well understood, thanks to experiments and

Calvin Dani, Shiva Jahangiri, Thomas Hütter

This paper introduces a novel approach to schema inference as an on-demand function integrated directly within a DBMS, targeting NoSQL databases where schema flexibility can create challenges. Unlike previous methods relying on external frameworks like Apache Spark, our solution enables schema inference as a SQL function, allowing users to infer schemas nati

Matthieu Kowalski, Benoît Malézieux, Thomas Moreau, Audrey Repetti

In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, in a Plug-and-Play (PnP) fashion. In particular, we consider both analysis and synthesis Gaussian denoisers within a dictionary framework, obtained by unrolling dual-FB iterations o

Mohammad Farazmand

In wildfires, burning pieces of ember-firebrands-are carried downstream by wind. At the time of landing, these firebrands can start secondary fires far away from the main burning unit. This phenomenon is called spotting and the secondary fires are referred to as spot fires. Here, we first present numerical evidence that atmospheric traveling waves can increa

Masood Valipour, Gniewomir Sarbicki, Karolina Słowik, Anita Dąbrowska

This work discusses the problem of optimal excitation of a three-level atom of ladder-configuration by light in the two-photon state and coherent light carrying an average of two photons. The applied atom-light interaction model is based on the Wigner-Weisskopf approximation. We characterize the properties of the optimal two-photon state that excites an atom

Adrien Reingruber, Kitinan Pongsangangan, Fakher Assaad, Maksim Ulybyshev

The emergence of hydrodynamic behavior in electronic flow within clean, particle-hole-symmetric systems at half-filling is a non-trivial problem. Navier-Stokes (NS) equations describe the momentum flow, while experimental measurements typically capture the current flow profiles. However, in particle-hole-symmetric systems, electric current and momentum flow

Kai-Uwe Bux, Elisa Hartmann, José Pedro Quintanilha

In this paper we develop the homological version of $\Sigma$-theory for locally compact Hausdorff groups, leaving the homotopical version for another paper. Both versions are connected by a Hurewicz-like theorem. They can be thought of as directional versions of type $\mathrm{CP}_m$ and type $\mathrm{C}_m$, respectively. And classical $\Sigma$-theory is reco

Jean Dolbeault

Obtaining explicit stability estimates in classical functional inequalities like the Sobolev inequality has been an essentially open question for 30 years, after the celebrated but non-constructive result of G. Bianchi and H. Egnell in 1991. Recently, new methods have emerged which provide some clues on these fascinating questions. The goal of the course is

Héctor Ibarra-Medel, Castalia Alenka Negrete, Ivan Lacerna, Héctor Manuel Hernández-Toledo

We present a new iterative deblending method to separate the host galaxy (HG) and their Active Galactic Nuclei (AGN) emission with the use of Integral Field spectroscopic (IFS) data. The method decomposes the resolved HG emission from the unresolved AGN emission by modelling the two-dimensional surface brightness (SB) profile of the point-spread function (PS

Minal Suresh Patil, Gustav Ung, Mattias Nyberg

The paper studies how code generation by LLMs can be combined with formal verification to produce critical embedded software. The first contribution is a general framework, spec2code, in which LLMs are combined with different types of critics that produce feedback for iterative backprompting and fine-tuning. The second contribution presents a first feasibili

Leonardo Balocchi, Lorenzo Piro, Luca Biferale, Stefania Bonafoni

Identifying a gas source in turbulent environments presents a significant challenge for critical applications such as environmental monitoring and emergency response. This issue is addressed through an approach that combines distributed IoT smart sensors with an algorithm based on Bayesian inference and Monte Carlo sampling techniques. Employing a probabilis

Jiayi Zhu, Ling Liang, Lei Yang, Kim-Chuan Toh

Inexact proximal augmented Lagrangian methods (ipALMs) have been widely used for solving linearly constrained convex optimization problems, owing to their strong theoretical guarantees and excellent numerical performance. In practice, however, existing ipALMs typically employ Rockafellar-type absolute error criteria for solving the subproblems, which require

Jinlong Wei, Wei Wang, Guangying Lv, Jinqiao Duan

We introduce the Lebesgue--H\"{o}lder--Dini and Lebesgue--H\"{o}lder spaces $L^p(\mathbb{R};{\mathcal C}_{\vartheta,\varsigma}^{\alpha,\rho}({\mathbb R}^n))$ ($\vartheta\in \{l,b\}, \, \varsigma\in \{d,s,c,w\}$, $p\in (1,+\infty]$ and $\alpha\in [0,1)$), and then use a vector-valued Calder\'{o}n--Zygmund theorem to establish the maximal Lebesgue--H\"{o}lder-

Jing Yi Wang, Nicholas Sukiennik, Tong Li, Weikang Su

The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction. This survey provid

Nikita Denisov, Andrey Orekhov, Johan Verbeeck

The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies such as those found in commercial scanning electron microscopes (SEM).The lower acceleration voltage does however put co

Riya Mahesh, Rahul Vashisht, Chandrashekar Lakshminarayanan

Temporal causal analysis means understanding the underlying causes behind observed variables over time. Deep learning based methods such as transformers are increasingly used to capture temporal dynamics and causal relationships beyond mere correlations. Recent works suggest self-attention weights of transformers as a useful indicator of causal links. We lev

Aaditya V. Rangan, Wai-Shing Tang, Pilar Cossio, Kexin Zhang

Electron cryomicroscopy (cryo-EM) is a technique in structural biology used to reconstruct accurate volumetric maps of molecules. One step of the cryo-EM pipeline involves solving an inverse-problem. This inverse-problem, referred to as \textit{ab-initio} single-particle reconstruction, takes as input a collection of 2d-images -- each a projection of a molec

Yuxuan Chen, Yixin Han, Xiao Li

With the rapid development of large language models (LLM), robots are starting to enjoy the benefits of new interaction methods that large language models bring. Because edge computing fulfills the needs for rapid response, privacy, and network autonomy, we believe it facilitates the extensive deployment of large models for robot navigation across various in

Song Gao, Shu-Min Zhao, Shuang Di, Xing-Xing Dong

The CMS and ATLAS Collaborations have recently reported their findings based on the comprehensive run 2 dataset, detailing their searches for a light Higgs boson with a mass of approximately 95 GeV. We investigate the excesses observed in the $\gamma\gamma$ and $b{\bar b}$ data at approximately 95 GeV in the $U(1)_X$ extension of the minimal supersymmetric s

Peichao Wang, Jiabao Wang, Yao Chen, Rui Zhang

The data-driven method for infrared small target detection (IRSTD) has achieved promising results. However, due to the small scale of infrared small target datasets and the limited number of pixels occupied by the targets themselves, it is a challenging task for deep learning methods to directly learn from these samples. Utilizing human expert knowledge to a

Ahmad Abdelfattah, Willow Ahrens, Hartwig Anzt, Chris Armstrong

The standardization of an interface for dense linear algebra operations in the BLAS standard has enabled interoperability between different linear algebra libraries, thereby boosting the success of scientific computing, in particular in scientific HPC. Despite numerous efforts in the past, the community has not yet agreed on a standardization for sparse line

François Arleo, Guillaume Falmagne

The universal dependence of hadron suppression, $R_{\rm{AA}}(p_\perp)$, observed at large-$p_\perp$ in heavy ion collisions at RHIC and LHC allows for a systematic determination of the average parton energy loss $\langle \epsilon \rangle$ in quark-gluon plasma (QGP). A simple relation between $\langle \epsilon \rangle$ and the soft particle multiplicity allo

Martin T. Barlow

We consider models for inference which involve observers which may have multiple copies, such as in the Sleeping Beauty problem. We establish a framework for describing these problems on a probability space satisfying Kolmogorov's axioms, and this enables the main competing solutions to be compared precisely.

H. R. Strauss

Resistive wall tearing modes (RWTM) can cause major disruptions. A signature of RWTMs is that the rational surface is sufficiently close to the wall. For $(m,n) = (2,1)$ modes, at normalized minor radius $\rho = 0.75$, the value of $q$ is $q_{75} < 2.$ This is confirmed in simulations and theory and in a DIII-D locked mode disruption database. The $q_{75} <

Peng-Cheng Hang, Min-Jie Luo

For any $a\in\mathbb{C}$, the zeros of $\zeta(s)-a$, denoted by $\rho_a=\beta_a+i\gamma_a$, are called $a$-points of the Riemann zeta function $\zeta(s)$. In this paper, we reformulate some basic results about the $a$-points of $\zeta(s)$ shown by Garunk\v{s}tis and Steuding. We then deduce an asymptotic of the sum \[S_T(a,\delta)=\sum_{\tau<\gamma_a\leqslan

Jitendra Kethepalli, Manas Kulkarni, Anupam Kundu, Herbert Spohn

We study a class of random matrices arising from the Lax matrix structure of classical integrable systems, particularly the Calogero family of models. Our focus is the density of eigenvalues for these random matrices. The problem can be mapped to analyzing the density of eigenvalues for generalized versions of conventional random matrix ensembles, including

Giulio Loddi, Chiara Pugliese, Francesco Lettich, Fabio Pinelli

With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present a methodol

Corin C. Wagen, Jonathon E. Vandezande

In recent years, "composite" density-functional-theory-based methods comprising specially optimized combinations of functionals, basis sets, and empirical corrections have become widely used owing to their robustness and computational efficiency, but the bespoke nature of these methods makes them challenging to develop. Here, we report that the recently repo

Roman Stoklasa

Deployment of machine learning algorithms into real-world practice is still a difficult task. One of the challenges lies in the unpredictable variability of input data, which may differ significantly among individual users, institutions, scanners, etc. The input data variability can be decreased by using suitable data preprocessing with robust data harmoniza

Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal

Analog integrated circuit (IC) floorplanning is typically a manual process with the placement of components (devices and modules) planned by a layout engineer. This process is further complicated by the interdependence of floorplanning and routing steps, numerous electric and layout-dependent constraints, as well as the high level of customization expected i

Bing Zhou, Kai Zhao, Yongduan Song, Zhen Chen

In this paper, we investigate the problem of unified prescribed performance tracking for a class of non-square strict-feedback nonlinear systems under relaxed controllability conditions. By using a skillful matrix decomposition and introducing some feasible auxiliary matrices, a more generalized controllability condition than the current state of the art is

Umamaheswaran Raman Kumar, Abdur Razzaq Fayjie, Jurgen Hannaert, Patrick Vandewalle

Large-scale 2D datasets have been instrumental in advancing machine learning; however, progress in 3D vision tasks has been relatively slow. This disparity is largely due to the limited availability of 3D benchmarking datasets. In particular, creating real-world point cloud datasets for indoor scene semantic segmentation presents considerable challenges, inc

I. Lovrekovic

We study holography of the 3d Chern-Simons theory as a gauge theory of $so(3,2)$, $sl_4$ and $sl_5$ algebras. For the near horizon boundary conditions we comment solutions from several projectors from Chern-Simons to the metric formulation. These solutions are generalized BTZ solutions for our theories. We also study the classification according to $so(3,2)$

Falk Schupp, Josef Pochodzalla, Michael Bölting, Martin Christiansen

We propose a new method to study the evolution of the neutron periphery between different isotopes by considering antiproton--nucleus interactions close to the production threshold of $\Lambda \overline{\Lambda }$ and $\Sigma^-\overline{\Lambda }$ pairs. At low energies, $\Lambda \overline{\Lambda }$ pairs are produced in $\overline{\text{p}} +\text{p}$ coll

Benedikt Mader, Christian Eichenmüller, Gaston Pugliese, Dennis Eckhardt

Lockdown Mode, introduced in 2022 as an optional security hardening setting for Apple&#39;s operating systems, aims to protect users from &#34;some of the most sophisticated digital threats&#34;. We present the first academic analysis of Lockdown Mode based on a three-month autoethnographic study of its everyday use. Our findings show that Lockdown Mode does

Alexander Tolmachev

Determining the maximal density $m_1(\mathbb{R}^2)$ of planar sets without unit distances is a fundamental problem in combinatorial geometry. This paper investigates lower bounds for this quantity. We introduce a novel approach to estimating $m_1(\mathbb{R}^2)$ by reformulating the problem as a Maximal Independent Set (MIS) problem on graphs constructed from

B. Fuhrmeister, P. C. Schneider, Th. Sperling, K. France

Understanding the interstellar and potentially circumstellar extinction in the sight lines of classical T Tauri stars is an important ingredient for constructing reliable spectral energy distributions, which catalyze protoplanetary disk chemistry, for example. Therefore, some attempts of measuring $A_{V}$ toward individual stars have been made using partly d

Samir Bedrouni

A holomorphic pre-foliation $\mathscr{F}=\mathcal{C}\boxtimes\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$ is the data of a reduced complex projective curve $\mathcal{C}$ of $\mathbb{P}^{2}_{\mathbb{C}}$ and a holomorphic foliation $\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$. When the foliation $\mathcal{F}$ is convex (resp. reduced convex) and the curve

Francisco M. Fernández

We apply the well known Rayleigh-Ritz method (RRM) to the projection of a Hamiltonian operator chosen recently for the extension of the Rayleigh-Ritz variational principle to ensemble states. By means of a toy model we show that the RRM eigenvalues approach to those of the projected Hamiltonian from below in most cases but a few ones. We also discuss the eff

Gaurav Vaghasiya, Shiva Jahangiri

Group-by-aggregate (GBA) queries are integral to data analysis, allowing users to group data by specific attributes and apply aggregate functions such as sum, average, and count. Database Management Systems (DBMSs) typically execute GBA queries using either sort- or hash-based methods, each with unique advantages and trade-offs. Sort-based approaches are eff

Zhibo Chu, Zichong Wang, Qitao Qin

Large Language Models (LLMs) exhibit impressive problem-solving skills across many tasks, but they still underperform compared to humans in various downstream applications, such as text-to-SQL. On the BIRD benchmark leaderboard, human performance achieves an accuracy of 92.96\%, whereas the top-performing method reaches only 72.39\%. Notably, these state-of-

Ziyi Wang, Yanbo Wang, Xumin Yu, Jie Zhou

Existing methodologies in open vocabulary 3D semantic segmentation primarily concentrate on establishing a unified feature space encompassing 3D, 2D, and textual modalities. Nevertheless, traditional techniques such as global feature alignment or vision-language model distillation tend to impose only approximate correspondence, struggling notably with deline

Amit Kumar, Kaushal Sharma

A rapidly spinning, millisecond magnetar is widely considered one of the most plausible power sources for gamma-ray burst-associated supernovae (GRB-SNe). Recent studies have demonstrated that the magnetar model can effectively explain the bolometric light curves of most GRB-SNe. In this work, we investigate the bolometric light curves of 13 GRB-SNe, focusin

Alexander Wietfeld, Pit Hofmann, Jonas Fuchtmann, Pengjie Zhou

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the fu

Zhen Hao, Ning Jiang, Liu Liu

In this paper, we develop and implement an efficient asymptotic-preserving (AP) scheme to solve the gas mixture of Boltzmann equations under the disparate mass scaling relevant to the so-called "epochal relaxation" phenomenon. The disparity in molecular masses, ranging across several orders of magnitude, leads to significant challenges in both the evaluation

Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife

This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalabi

Christian Hamster, Peter van Heijster, Eric Siero

We investigate (in)stabilities of periodic patterns under stochastic forcing in reaction-diffusion equations exhibiting a so-called Busse balloon. Specifically, we used a one-dimensional Klausmeier model for dryland vegetation patterns. Using numerical methods, we can accurately describe the transient dynamics of the stochastic solutions and compare several

Xu Zou

Recently, generative pre-trained models have made significant strides, particularly highlighted by the release of ChatGPT and GPT-4, which exhibit superior cross-domain capabilities. However, these models still face challenges on constrained writing tasks like poem generation under open-domain titles. In response to this challenge, we introduce Block Inverse

Claudio Serpico, Salvatore Perna, Massimiliano d'Aquino

This paper presents a quantum field theoretical formalism for studying magnons in finite nanostructures with arbitrary shapes and spatially nonuniform ground states. It extends the classical micromagnetic formalism by introducing a micromagnetic Hamiltonian quantum operator, which incorporates exchange, Dzyaloshinsky-Moriya, anisotropy, magnetostatic, and Ze

Pius Privatus, Umananda Dev Goswami

This study aims to investigate whether the environment and the nuclear activity of a particular galaxy influence the ageing and quenching at the transition stage of the galaxy evolution using the volume-limited sample constructed from the twelve release of the Sloan Digital Sky Survey. To this end, the galaxies were classified into isolated and non-isolated

Tiago Roux Oliveira, Miroslav Krstić, Tamer Başar

The development of extremum seeking (ES) has progressed, over the past hundred years, from static maps, to finite-dimensional dynamic systems, to networks of static and dynamic agents. Extensions from ODE dynamics to maps and agents that incorporate delays or even partial differential equations (PDEs) is the next natural step in that progression through asce

Weslem Liberato Silva, Rafael Moreira de Souza

Let $Y \to E \stackrel{p}{\to} B$ be a fibration and let $f: E \to E$ be a fiber map over $B$. In this work, we study the geometric and algebraic Reidemeister classes of the iterates of $f$ and introduce a Nielsen-type periodic number over $B$, denoted by $N_B P_n(f)$. When $B$ is a point, then $N_B P_n(f)$ coincides with the classical Nielsen periodic numbe

Edgar J. Patiño, L. Rios E., N. G. Kelkar, Daniel Lopez

Based on experimental data, we propose a model to evaluate the energy dissipated during quantum tunneling processes in solid-state junctions. This model incorporates a nonlinear friction force expressed in the general form f(x)={\gamma} v(x)^{\alpha}, where {\gamma} is the frictional coefficient, which is fitted to data. We study this by applying voltages ju

Martin Lopez-Corredoira

An analysis of a historical compilation of Hubble-Lema\^itre constant values ($H_0$: 163 data points measured between 1976 and 2019) assuming the standard cosmological model gives a $\chi^2$ value of the dispersion with respect to the weighted average of 580, much larger than the number of points, which has an associated probability that is very low. This me

Shrihari Gopalakrishna, Rakesh Tibrewala

In the theory with a Majorana fermion ($X$) coupled to quark-like fermions ($Q$) via a dimension-six four-fermion vector-vector interaction, we have computed in an earlier work the baryon asymmetry generated in the decay and scattering processes of the $X$ with $Q$. In this work we consider such processes in the expanding early Universe, set up the Boltzmann

Rajini Makam, Dhatri Shankari T M, Sharanya Patil, Suresh Sundram

Underwater environments pose significant challenges due to the selective absorption and scattering of light by water, which affects image clarity, contrast, and color fidelity. To overcome these, we introduce OceanLens, a method that models underwater image physics-encompassing both backscatter and attenuation-using neural networks. Our model incorporates ad

Thomas S Ball, Ben Balmford, Andrew Balmford, Daniele Rinaldo

Understanding the relationship between a populations probability of extinction and its carrying capacity frames conservation status assessments and guides efforts to understand and mitigate the ongoing biodiversity crisis. Despite this, our understanding of the mathematical form of this relationship remains limited. We conducted ~5 billion population viabili

Nikhil Mishra

This work addresses a trust-based enhancement to the Multipath Ad hoc On-Demand Distance Vector (AOMDV) routing protocol. While AODV and its multipath variant AOMDV have been fundamental in mobile ad hoc networks, they lack mechanisms to account for node reliability. A probabilistic link-trust model is proposed that incorporates factors such as past behavior

Zichen Wen, Dadi Guo, Huishuai Zhang

As large language models (LLMs) rapidly advance and integrate into daily life, the privacy risks they pose are attracting increasing attention. We focus on a specific privacy risk where LLMs may help identify the authorship of anonymous texts, which challenges the effectiveness of anonymity in real-world systems such as anonymous peer review systems. To inve

Yu-Chao Hsu, Tai-Yu Li, Kuan-Cheng Chen

The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture comp

Jiawei Hu, Hong Jia, Mahbub Hassan, Lina Yao

We propose LightLLM, a model that fine tunes pre-trained large language models (LLMs) for light-based sensing tasks. It integrates a sensor data encoder to extract key features, a contextual prompt to provide environmental information, and a fusion layer to combine these inputs into a unified representation. This combined input is then processed by the pre-t

Hang Zhou, Xiaoxu Zheng, Yunhe Wang, Michael Bi Mi

Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE). Due to the limitation of power and memory in low-resource devices, efficient RNN models are urgently required for real-world applications. In this paper, we propose an effici

Ana M. Barbancho, Lorenzo J. Tardon, Isabel Barbancho

In this paper, a system to build music in an intuitive and accessible way, with Lego bricks, is presented. The system makes use of the new powerful and cheap possibilities that technology offers for making old things in a new way. The Raspberry Pi is used to control the system and run the necessary algorithms, customized Lego bricks are used for building mel

Murray Shanahan, Beth Singler

Contemporary conversational AI systems based on large language models (LLMs) can engage users on a wide variety of topics, including philosophy, spirituality, and religion. Suitably prompted, LLMs can be coaxed into discussing such existentially significant matters as their own putative consciousness and the role of artificial intelligence in the fate of the

Nicolás Villarroel-Sepúlveda, Felipe A. Asenjo, Pablo S. Moya

Context. Magnetic batteries are potential sources that may drive the generation of a seed magnetic field, even if this field is initially zero. These batteries can be the result of non-aligned thermodynamic gradients in a plasma, as well as of special and general relativistic effects. So far, magnetic batteries have only been studied in ideal magnetized flui

Tianshu Jiang, Chenyu Zhang, Ruo-Yang Zhang, Yingjuan Yu

The hybrid skin-topological effect (HSTE) has recently been proposed as a mechanism where topological edge states collapse into corner states under the influence of the non-Hermitian skin effect (NHSE). However, directly observing this effect is challenging due to the complex frequencies of eigenmodes. In this study, we experimentally observe HSTE corner sta

Cheng Zhang, Tobias Kappé, David E. Narváez, Nico Naus

Guarded Kleene Algebra with Tests (GKAT) provides a sound and complete framework to reason about trace equivalence between simple imperative programs. However, there are still several notable limitations. First, GKAT is completely agnostic with respect to the meaning of primitives, to keep equivalence decidable. Second, GKAT excludes non-local control flow s

Ziyue Chen, Qi Zhang

The entropy regularization is inspired by information entropy from machine learning and the ideas of exploration and exploitation in reinforcement learning, which appears in the control problem to design an approximating algorithm for the optimal control. This paper is concerned with the optimal exploratory control for backward stochastic system, generated b

Lucas Michel, Pierre Mathonet, Naïm Zénaïdi

We consider cylindrical algebraic decompositions (CADs) as a tool for representing semi-algebraic subsets of $\mathbb{R}^n$. In this framework, a CAD $\mathscr{C}$ is adapted to a given set $S$ if $S$ is a union of cells of $\mathscr{C}$. Different algorithms computing an adapted CAD may produce different outputs, usually with redundant cell divisions. In th

Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardon, Isabel Barbancho

The human brain receives stimuli in multiple ways; among them, audio constitutes an important source of relevant stimuli for the brain regarding communication, amusement, warning, etc. In this context, the aim of this manuscript is to advance in the classification of brain responses to music of diverse genres and to sounds of different nature: speech and mus

Peter Hayman

We present a simple derivation of a point-source boundary condition for the Schwarzschild solution that relates the Schwarzschild radius to the mass of its source without appealing to the Newtonian limit. Interpretation of the Schwarzschild radius in terms of the mass of a point-like source traditionally means resorting to distant asymptotics and the safety

Matt Luckcuck, Mengwei Xu

This EPTCS volume contains the papers from the Sixth International Workshop on Formal Methods for Autonomous Systems (FMAS 2024), which was held between the 11th and 13th of November 2024. FMAS 2024 was co-located with 19th International Conference on integrated Formal Methods (iFM'24), hosted by the University of Manchester in the United Kingdom, in the Uni

Santiago Barbieri, Andrew Clarke

In this paper we consider the coin billiard introduced by M. Bialy. It is a modification of the classical billiard, obtained as the return map of a nonsmooth geodesic flow on a cylinder that has homeomorphic copies of a classical billiard on the top and on the bottom (a coin). The return dynamics is described by a map $T$ of the annulus $\mathbb A = \mathbb

Aldin Dželo, Amer Mešanović, Mirsad Cosovic

The development of more complex inverter-based resources (IBRs) control is becoming essential as a result of the growing share of renewable energy sources in power systems. Given the diverse range of control schemes, grid operators are typically provided with black-box models of IBRs from various equipment manufacturers. As such, they are integrated into sim

David Otero, Javier Parapar, Álvaro Barreiro

Offline evaluation of search systems depends on test collections. These benchmarks provide the researchers with a corpus of documents, topics and relevance judgements indicating which documents are relevant for each topic. While test collections are an integral part of Information Retrieval (IR) research, their creation involves significant efforts in manual

Evžen Wybitul, Evan Ryan Gunter, Mikhail Seleznyov, David Lindner

Using vision-language models (VLMs) as reward models in reinforcement learning holds promise for reducing costs and improving safety. So far, VLM reward models have only been used for goal-oriented tasks, where the agent must reach a particular final outcome. We explore VLMs' potential to supervise tasks that cannot be scored by the final state alone. To thi

Tsuyoshi Miyatsu, Myung-Ki Cheoun, Kyungsik Kim, Koichi Saito

The accurate measurement of neutron skin thickness of $^{208}$Pb by the PREX Collaboration suggests a large value of the nuclear symmetry energy slope parameter, $L$, whereas the smaller $L$ is preferred to account for the small neutron-star radii from NICER observations. To resolve this discrepancy between nuclear experiments and astrophysical observations,

Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita, Sushant Gautam

This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications. To address these issues, we propose and implement a fully integrated system that replaces conventional AFE m

Zhen-Yi Cai

Leveraging the photometric data of the Sloan Digital Sky Survey and the Galaxy Evolution Explorer (GALEX), we construct mean/median spectral energy distributions (SEDs) for unique bright quasars in redshift bins of 0.2 and up to $z \simeq 3$, after taking the GALEX non-detection into account. Further correcting for the absorption of the intergalactic medium,

Ofir Cohen, Gil Ari Agmon, Asaf Shabtai, Rami Puzis

The popularity of large language models (LLMs) continues to grow, and LLM-based assistants have become ubiquitous. Information security awareness (ISA) is an important yet underexplored area of LLM safety. ISA encompasses LLMs' security knowledge, which has been explored in the past, as well as their attitudes and behaviors, which are crucial to LLMs' abilit