May 2024 arXiv papers — page 87

Yuki Itaya, Jun Tamura, Kenichi Hayashi, Kouji Yamamoto

Evaluating classifications is crucial in statistics and machine learning, as it influences decision-making across various fields, such as patient prognosis and therapy in critical conditions. The Matthews correlation coefficient (MCC) is recognized as a performance metric with high reliability, offering a balanced measurement even in the presence of class im

Matteo Bortoletto, Constantin Ruhdorfer, Adnen Abdessaied, Lei Shi

Recent work on dialogue-based collaborative plan acquisition (CPA) has suggested that Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. Although ToM was claimed to be important for effective collaboration, its real impact on this novel task remains under-explored. By representing pla

Hassan Alhuzali, Ashwag Alasmari, Hamad Alsaleh

Mental health disorders significantly impact people globally, regardless of background, education, or socioeconomic status. However, access to adequate care remains a challenge, particularly for underserved communities with limited resources. Text mining tools offer immense potential to support mental healthcare by assisting professionals in diagnosing and t

Anthony Scemama, Andreas Savin

The expectation value of the Hamiltonian using a model wave function is widely used to estimate the eigenvalues of electronic Hamiltonians. We explore here a modified formula for models based on long-range interaction. It scales differently the singlet and triplet component of the repulsion between electrons not present in the model (its short-range part). T

Hang Chen, Xinyu Yang, Jiaying Zhu, Wenya Wang

Large language models (LLMs) are widely recognized for their exceptional capacity to capture semantics meaning. Yet, there remains no established metric to quantify this capability. In this work, we introduce a quantitative metric, Information Emergence (IE), designed to measure LLMs' ability to extract semantics from input tokens. We formalize ``semantics''

Till Hielscher, Lukas Heuer, Frederik Wulle, Luigi Palmieri

Predictive planning is a key capability for robots to efficiently and safely navigate populated environments. Particularly in densely crowded scenes, with uncertain human motion predictions, predictive path planning, and control can become expensive to compute in real time due to the curse of dimensionality. With the goal of achieving pro-active and legible

Zhongwei Yu, Jingqing Ruan, Dengpeng Xing

Causal dynamics models (CDMs) have demonstrated significant potential in addressing various challenges in reinforcement learning. To learn CDMs, recent studies have performed causal discovery to capture the causal dependencies among environmental variables. However, the learning of CDMs is still confined to small-scale environments due to computational compl

Francesco Zanetta, Daniele Nerini, Matteo Buzzi, Henry Moss

Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical techniques to provide accurate and calibrated probabilistic predictions at a lower cost compared to numerical simulations. Wind represents a particularly challenging variable to model

Subhajit Kar, Ramkrishna Das, Blesson Mathew, Tapas Baug

We report the detection of high-frequency pulsations in WR\,135 from short cadence (10\,minutes) optical photometric and spectroscopic time series surveys. The harmonics up to $6^{th}$ order are detected from the integrated photometric flux variations while the comparatively weaker $8^{th}$ harmonic is detected from the strengths of the emission lines. We in

Peter Devine

Open source large language models (LLMs) have shown great improvements in recent times. However, many of these models are focused solely on popular spoken languages. We present a high quality dataset of more than 70k prompt-response pairs in 74 languages which consist of human generated prompts and synthetic responses. We use this dataset to train a state-of

Zeping Hao, David Loeffler

We construct a $p$-adic Rankin-Selberg $L$-function associated to the product of two families of modular forms, where the first is an ordinary (Hida) family, and the second an arbitrary universal-deformation family (without any ordinarity condition at $p$). This gives a function on a 4-dimensional base space - strictly larger than the ordinary eigenvariety,

Alessandra Canetta, Sergio Gonzalez-Munoz, Viet-Hung Nguyen, Khushboo Agarwal

Nanomechanical measurements of minimally twisted van der Waals materials remained elusive despite their fundamental importance for device realisation. Here, we use Ultrasonic Force Microscopy (UFM) to locally quantify the variation of out-of-plane Young's modulus in minimally twisted double bilayer graphene (TDBG). We reveal a softening of the Young's modulu

Xiangyu Zhang, Qiquan Zhang, Hexin Liu, Tianyi Xiao

Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of computations within the multi-head self-attention mechanism in Transformer, Selective State Space Models (i.e., Mamba) were proposed as an alternative. Mamba exhibited its effectiveness

Mario Bossler, Lars Chittka, Thorsten Schank

We present the first empirical evidence on the 22 percent increase in the German minimum wage, implemented in 2022, raising it from Euro 9.82 to 10.45 in July and to Euro 12 in October. Leveraging the German Earnings Survey, a large and novel data source comprising around 8 million employee-level observations reported by employers each month, we apply a diff

Hao Zhang, Fang Li, Samyak Rawlekar, Narendra Ahuja

Reconstructing dynamic articulated objects from a singular monocular video is challenging, requiring joint estimation of shape, motion, and camera parameters from limited views. Current methods typically demand extensive computational resources and training time, and require additional human annotations such as predefined parametric models, camera poses, and

R. Conte

A seventh order ordinary differential equation (ODE) arising by reduction of the Drinfeld-Sokolov hierarchyis shown to be identical to a similarity reduction of an equationin the hierarchy of Sawada-Kotera.We also exhibit its link with a particular F-VI,a fourth order ODE isolated by Cosgrove which is likely to define a higher order Painlev\'e function.

Fabio Leoni, John Russo, Francesco Sciortino, Taiki Yanagishima

In recent years, the possibility of algorithmically preparing ultra-stable glasses (UG), i.e., states that lie very deep in the potential energy landscape, has considerably expanded our understanding of the glassy state. In this work, we report on a new protocol for ultrastable glass preparation that iteratively modifies the particle diameters to reduce loca

Jiaxu Liu, Xiangyu Yin, Sihao Wu, Jianhong Wang

With the proliferation of red-teaming strategies for Large Language Models (LLMs), the deficiency in the literature about improving the safety and robustness of LLM defense strategies is becoming increasingly pronounced. This paper introduces the LLM-based \textbf{sentinel} model as a plug-and-play prefix module designed to reconstruct the input prompt with

Morgan Berkane, Camille Lévêque, Richard Taïeb, Jérémie Caillat

We consider the two-photon ionization of Hydrogen-like atoms. We find an approximate expression of the long-range phase based on an asymptotic expansion of the continuum eigenfunctions within the Wentzel-Kramers-Brillouin approximation. Combined with commonly used perturbative approaches, the resulting analytic formalism can treat, at the same time, the two-

Diganta Parai, Suman Kumar Panja

In this study, we investigate the thermodynamics of a relativistic ideal within the context of $\kappa$-deformed space-time and Rainbow gravity background. To achieve this, we construct a modified partition function by considering a deformed Hamiltonian and incorporating corrections based on the time-invariant phase-space volume. We explore the implications

Sajad Aghapour, Lars Andersson, Kjell Rosquist, Tomasz Smołka

In this paper, we study some of the properties of the $G \to 0$ limit of the Kerr-Newman solution of Einstein-Maxwell equations. Noting Carter's observation of the near equality between the $g = 2$ gyromagnetic ratio in the Kerr-Newman solution and that of the electron, we discuss additional such coincidences relating to the Kerr-Newman multipoles and proper

Shuai Liu, Boyang Li, Zhiyu Fang, Mingyue Cui

LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose a feature factorization activation map (FFAM) to generate

Waseem Akram, Sanjeev Saxena

Let P be a set of n weighted points, Q be a set of m unweighted points in the plane, and k a non-negative integer. We consider the problem of computing a subset $Q'\subseteq Q$ with size at most k such that the sum of the weights of the points of P dominated by at least one point in the set Q' is maximized. A point q in the plane dominates another point p if

Alessio Zaccone

A theory and mechanistic understanding of the thermal properties of solids under nanoscale confinement are currently missing. We develop a theoretical quantum confinement description of thin films which predicts a new physical law for the heat capacity. In particular, due to the suppression of vibrational modes caused by the thin film confinement, the vibrat

P. Helander, R. J. J. Mackenbach

The available energy of a plasma is defined as the maximum amount by which the plasma energy can be lowered by volume-preserving rearrangements in phase space, a so-called Gardner re-stacking. A general expression is derived for the available energy of a nearly homogeneous plasma and is shown to be closely related to the Helmholtz free energy, which it can n

Giovanni Cemin, Marcel Cech, Erik Weiss, Stanislaw Soltan

World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channels, are implemented on these devices. To systematically infer those, we propose a neural-network algorithm app

Wen-Fong Ke, Johannes H. Meyer

We show that there exist zero-symmetric simple nearrings with identity which are not equiprime, solving a longstanding open problem.

Jarkko Kotaniemi, Niko Känsäkoski, Tapio Heikkilä

Automatic weeding technologies have attained a lot of attention lately, because of the harms and challenges weeds are causing for livestock farming, in addition to that weeds reduce yields. We are targeting automatic and mechanical Rumex weeding in open pasture fields using light weight mobile field robot technologies. We describe a mobile weeding robot with

Geng-Dong Zhou, Yi-Jie Wang, Wen-Xuan Wang, Xiao-Bo Lu

Motivated by recent experimental observations of displacement-field-tuned correlated insulators at integer and half-integer fillings in chirally twisted triple bilayer graphene (CTTBG), we study the single-particle and interacting physics of CTTBG. We find that there are two inequivalent stacking orders, {\it i.e.}, ABABBC and ABABAB, and both exhibit flat b

Jeung Rac Lee, June-Koo Kevin Rhee, Changjun Kim, Bo Hyun Choi

Adiabatic quantum annealers encounter scalability challenges due to exponentially fast diminishing energy gaps between ground and excited states with qubit-count increase. This introduces errors in identifying ground states compounded by a thermal noise. We propose a novel algorithmic scheme called statistical qubit freezing (SQF) that selectively fixes the

Antoine Alaguero, Nicolás Cuello, François Ménard, Simone Ceppi

V892 Tau is a young binary star surrounded by a circumbinary disc which show hints of interaction with the low-mass nearby star V892 Tau NE. The goal of this paper is to constrain the orbit of V892 Tau NE and to determine the resulting circumbinary disc dynamics. We present new ALMA observations of the V892 Tau circumbinary disc at a twice higher angular and

Maximilian Mueller, Matthias Hein

VisionTransformers have been shown to be powerful out-of-distribution detectors for ImageNet-scale settings when finetuned from publicly available checkpoints, often outperforming other model types on popular benchmarks. In this work, we investigate the impact of both the pretraining and finetuning scheme on the performance of ViTs on this task by analyzing

Shuo Wang, Xilin Feng, Jing-Zhi Fang, Jia-Peng Peng

The study of kagome materials has attracted much attention in the past few years due to the presence of many electron-electron interaction-driven phases in a single material. These include charge density waves, nematic phases, superconducting phases, and pair density waves. In this work, we report the discovery of intrinsic spin-polarized p-wave superconduct

Peiyu Liu, Ze-Feng Gao, Wayne Xin Zhao, Yipeng Ma

Key-value~(KV) caching is an important technique to accelerate the inference of large language models~(LLMs), but incurs significant memory overhead. To compress the size of KV cache, existing methods often compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. In this paper, we introduce \textbf{DecoQuant}

Mengwei Yang, Ismat Jarin, Baturalp Buyukates, Salman Avestimehr

Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. One key challenge in practical FL systems is data heterogeneity, particularly in handling clients with rare data, also referred to as Mavericks. These clients own one or more data classes exclusively, and the model performance becomes poor without thei

Haiquan Zhao, Yi Peng, Zian Cao

Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual ad-justment of parameters is excessive, greatly increasing the practical diff

Carlos Rafael Gonzalez Soffner, Manuele Leonelli

Australian Rules Football is a field invasion game where two teams attempt to score the highest points to win. Complex machine learning algorithms have been developed to predict match outcomes post-game, but their lack of interpretability hampers an understanding of the factors that affect a team's performance. Using data from the male competition of the Aus

Henry Liu

Wall-crossing formulas for various flavors of elliptic genus can be obtained using master spaces. We give a topological criterion which implies that such wall-crossing formulas are trivial. Applications are given for: GIT quotients, following Thaddeus; moduli of sheaves, following Mochizuki; Donaldson-Thomas and Vafa-Witten theory, following Joyce and Tanaka

Tomasz Drab

The lambda calculus since more than half a century is a model and foundation of functional programming languages. However, lambda expressions can be evaluated with different reduction strategies and thus, there is no fixed cost model nor one canonical implementation for all applications of the lambda calculus. This article is an introduction to a dissertatio

M. Molero, F. Matteucci, E. Spitoni, A. Rojas-Arriagada

The metallicity distribution function (MDF) of the Galactic bulge features a multi-peak shape, with a metal-poor peak at [Fe/H]=-0.3 dex and a metal-rich peak at [Fe/H]=+0.3 dex. This bimodality is also seen in [alpha/Fe] versus [Fe/H] ratios, indicating different stellar populations in the bulge. We aim to replicate the observed MDF by proposing a scenario

Ziqin Lin, Heng Li, Zinan Li, Huazhu Fu

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the

Krishnendu Chatterjee, David Lurie, Raimundo Saona, Bruno Ziliotto

We study a class of two-player zero-sum stochastic games known as \textit{blind stochastic games}, where players neither observe the state nor receive any information about it during the game. A central concept for analyzing long-duration stochastic games is the \textit{uniform value}. A game has a uniform value $v$ if for every $\varepsilon>0$, Player 1 (re

Stefano Forti, Jacopo Soldani, Antonio Brogi

The significant carbon footprint of the ICT sector calls for methodologies to contain carbon emissions of running software. This article proposes a novel framework for implementing, configuring and assessing carbon-aware interactive software services. First, we propose a methodology to implement carbon-aware services leveraging the Strategy design pattern to

Anna Bonnet, Felix Cheysson, Miguel Martinez Herrera, Maxime Sangnier

Classic estimation methods for Hawkes processes rely on the assumption that observed event times are indeed a realisation of a Hawkes process, without considering any potential perturbation of the model. However, in practice, observations are often altered by some noise, the form of which depends on the context. It is then required to model the alteration me

Huiqiang Xie, Zhijin Qin, Zhu Han, Khaled B. Letaief

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approa

Guangyao Lu, Yulin Liu

Fact-checking based on commercial LLMs has become mainstream. Although these methods offer high explainability, it falls short in accuracy compared to traditional fine-tuning approaches, and data security is also a significant concern. In this paper, we propose a self-instruction based fine-tuning approach for fact-checking that balances accuracy and explain

Laurent Desvillettes, Kim Dang Phung, Bao Quoc Tang

The trend to equilibrium for reaction-diffusion systems modelling chemical reaction networks is investigated, in the case when reaction processes happen on subsets of the domain. We prove the convergence to equilibrium by directly showing functional inequalities in terms of entropy method. Our approach allows us to deal with nonlinearities of arbitrary order

Yichi Zhang, Binbin Hu, Zhuo Chen, Lingbing Guo

Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enhance different downstream tasks, which is a key direction for KG management, maintenance, and applications. Ex

Yuan Fu, Zheng Zhang, Guangyang Zeng, Chun Liu

In this paper, we investigate the problem of estimating the 4-DOF (three-dimensional position and orientation) robot-robot relative frame transformation using odometers and distance measurements between robots. Firstly, we apply a two-step estimation method based on maximum likelihood estimation. Specifically, a good initial value is obtained through unconst

Nikolaos Chalmoukis, Giuseppe Lamberti

We give a capacitary type characterization of Carleson measures for a class of Hardy-Sobolev spaces (also known as weighted Dirichlet spaces) on the Siegel upper half-space, introduced by Arcozzi et al. This answers in part a question raised by the same authors.

Guowei Yang, Zhanghuan Li, Sai Yang, Jiyuan Li

Altermagnetism, a kind of collinear magnetism that is characterized by a momentum-dependent band and spin splitting without net magnetization, has recently attracted considerable interest. Finding altermagnetic materials with large splitting near the Fermi level necessarily requires three-dimensional k-space mapping. While this is crucial for spintronic appl

Daniele Faenzi, Victor Do Valle Pretti

We construct Ulrich bundles on Veronese threefolds of arbitrary degree as generic deformations of symmetric squares of equivariant instanton bundles on the projective space, thus classifying the rank of Ulrich bundles on such varieties and proving a conjecture of Costa and Mir{\'o}-Roig.

Jan Steckel, Wouter Jansen, Nico Huebel

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated by an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse natu

Ioana Ciotir, Dan Goreac, Juan Li, Antoine Tonnoir

We aim at studying a novel mathematical model associated to a physical phenomenon of infiltration in an homogeneous porous medium. The particularities of our system are connected to the presence of a gravitational acceleration term proportional to the level of saturation, and of a Brownian multiplicative perturbation. Furthermore, the boundary conditions int

Zhuoli Tian, Yuyang Zhang, Jinsheng Wei, Meng Guo

Exploration of unknown scenes before human entry is essential for safety and efficiency in numerous scenarios, e.g., subterranean exploration, reconnaissance, search and rescue missions. Fleets of autonomous robots are particularly suitable for this task, via concurrent exploration, multi-sensory perception and autonomous navigation. Communication however am

Z. Was

One of purposes for High Energy accelerator experiments is confrontation of theory and measurements in ever new realms. Any new agreement extends theory applicability domain, any discrepancy hints to unexplained. That calls for better calculations or new, deeper theory. Often one has to search for small contributions over large Standard Model background. Mul

David Doat

This study proposes an analysis of the different types of ethical approaches involved in the ethics of AI, and situates their interests and limits. First, the author introduces to the contemporary need for and meaning of ethics. He distinguishes it from other registers of normativities and underlines its inadequacy to formalization. He then presents a cartog

Yiliang Sang, Ke Ma, Yang Ming, Jin Lian

The latest TypeII codebook selects partial strongest angular-delay ports for the feedback of downlink channel state information (CSI), whereas its performance is limited due to the deficiency of utilizing the correlations among the port coefficients. To tackle this issue, we propose a tailored autoencoder named TypeII-CsiNet to effectively integrate the Type

Thomas Dreyfus, Tanguy Rivoal

Fres\'an and Jossen have given a negative answer to a question of Siegel about the representability of every $E$-function as a polynomial with algebraic coefficients in $E$-functions of type ${}_pF_q[\underline{a};\underline{b};\gamma x^{q-p+1}]$ with $q\geq p\geq 0$, $\gamma \in \overline{\mathbb Q}$ and rational parameters $\underline{a}, \underline{b}$. I

Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot

We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-level, in only one round of communication between the agents and the server. Using the quantile-of-quantiles family of estimators and split conformal prediction, we introduce a collec

Alessandra Recordare, Guglielmo Cola, Tiziano Fagni, Maurizio Tesconi

In today's digital landscape, the proliferation of conspiracy theories within the disinformation ecosystem of online platforms represents a growing concern. This paper delves into the complexities of this phenomenon. We conducted a comprehensive analysis of two distinct X (formerly known as Twitter) datasets: one comprising users with conspiracy theorizing p

Yaxin Pang, Shenle Pan, Eric Ballot

Supply chain resilience analysis aims to identify the critical elements in the supply chain, measure its reliability, and analyze solutions for improving vulnerabilities. While extensive methods like stochastic approaches have been dominant, robust optimization-widely applied in robust planning under uncertainties without specific probability distributions-r

Zhiyuan Liu, An Zhang, Hao Fei, Enzhi Zhang

Language Models (LMs) excel in understanding textual descriptions of proteins, as evident in biomedical question-answering tasks. However, their capability falters with raw protein data, such as amino acid sequences, due to a deficit in pretraining on such data. Conversely, Protein Language Models (PLMs) can understand and convert protein data into high-qual

Dongha Chung, Jinwhan Kim

Over the last few decades, numerous LiDAR-inertial odometry (LIO) algorithms have been developed, demonstrating satisfactory performance across diverse environments. Most of these algorithms have predominantly been validated in open outdoor environments, however they often encounter challenges in confined indoor settings. In such indoor environments, reliabl

Erik Burman, Deepika Garg, Johnny Guzman

In this paper we continue the work on implicit-explicit (IMEX) time discretizations for the incompressible Oseen equations that we started in \cite{BGG23} (E. Burman, D. Garg, J. Guzm\`an, {\emph{Implicit-explicit time discretization for Oseen's equation at high Reynolds number with application to fractional step methods}}, SIAM J. Numer. Anal., 61, 2859--28

Yann Goetgheluck, Sarah Mernit, Julie Pereira

This article examines the integration of IT competitions, in particular Capture The Flag, into an information systems management course to fill skills gaps, particularly in the field of cybersecurity. An educational CTF team has been set up at IAE Paris-Est with the aim of developing students' skills. Workshops, challenges, and events have been organised to

E. V. Arbuzova, A. D. Dolgov, A. A. Nikitenko

The origin of the ultra high energy cosmic rays via annihilation of heavy stable, fermions "f", of the cosmological dark matter (DM) is studied. The particles in question are supposed to be created by the scalaron decays in $R^2$ modified gravity. Novel part of our approach is the assumption that the mass of these carriers of DM is slightly below than a half

Auguste Hebert, Paul Philippe

Let $G$ be a split Kac-Moody group over a local field. In their study of the Iwahori-Hecke algebra of $G$, A.Braverman, D. Kazhdan and M. Patnaik defined a partial order - called the affine Bruhat order - on the extended affine Weyl semi-group $W^+$ of $G$. In this paper, we study finiteness questions for covers and co-covers of $W^+$, generalizing results o

Heng-Hsi Li, Po-Yao Chang

We study the entanglement properties in non-equilibrium quantum systems with the SU(1, 1) structure. Through M\"obius transformation, we map the dynamics of these systems following a sudden quench or a periodic drive onto three distinct trajectories on the Poincar\'e disc, corresponding the heating, non-heating, and a phase boundary describing these non-equi

Yochay Jerby

Sections of the Hardy $Z$-function are given by $Z_N(t) := \sum_{k=1}^{N} \frac{cos(\theta(t)-ln(k) t) }{\sqrt{k}}$ for any $N \in \mathbb{N}$. Sections approximate the Hardy $Z$-function in two ways: (a) $2Z_{\widetilde{N}(t)}(t)$ is the Hardy-Littlewood approximate functional equation (AFE) approximation for $\widetilde{N}(t) = \left [ \sqrt{\frac{t}{2 \pi

Maelle Moranges, Marc Plantevit, Moustafa Bensafi

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data sc

Marcos Faundez, Moises Diaz, Miguel Angel Ferrer

This research introduces an innovative approach to explore the cognitive and biologically inspired underpinnings of feature vector splitting for analyzing the significance of different attributes in e-security biometric signature recognition applications. Departing from traditional methods of concatenating features into an extended set, we employ multiple sp

Marius Costandin

In this paper we study the Product Partition Problem (PPP), i.e. we are given a set of $n$ natural numbers represented on $m$ bits each and we are asked if a subset exists such that the product of the numbers in the subset equals the product of the numbers not in the subset. Our approach is to obtain the integer factorization of each number. This is the sube

Wen-Xiang Chen, Yao-Guang Zheng

To construct new Schwarzschild and Kerr-Newman metric solutions, we start from the Lagrangian in entropy and statistical mechanics, introducing $f(R)$ gravity theory and dark energy definitions. Through a series of calculations, we derive the corrected metric solutions under different forms of $f(R)$ gravity.

Juraj Krsnik, Dino Novko, Osor Slaven Barišić

We investigate the dynamical effects of electron-phonon coupling (EPC) on the superconducting properties of two-dimensional (2D) systems, calculating the Eliashberg function in terms of dynamically renormalized phonons. By studying different approximations for the phonon self-energy, we identify the important role of charge fluctuations in shaping the superc

Holger Dette, Carina Graw

Stochastic Gradient Descent (SGD) is a widely used tool in machine learning. In the context of Differential Privacy (DP), SGD has been well studied in the last years in which the focus is mainly on convergence rates and privacy guarantees. While in the non private case, uncertainty quantification (UQ) for SGD by bootstrap has been addressed by several author

Alexander Chamolly, Sébastien Michelin, Eric Lauga

Bubble-propelled catalytic colloids stand out as a uniquely efficient design for artificial controllable micromachines, but so far lack a general theoretical framework that explains the physics of their propulsion. Here we develop a combined diffusive and hydrodynamic theory of bubble growth near a spherical catalytic colloid, that allows us to explain the u

G. Pantelis

RA is a software package that couples machine learning with formal reasoning in an attempt to find the laws that generate the empirical data that it has been given access to. A brief outline of RA in its initial stage of development is presented. Particular emphasis is given to current design strategies that aim to endow RA with the ability to construct its

Yucheng Cai, Si Chen, Yuxuan Wu, Yi Huang

Recently, increasing research interests have focused on retrieval augmented generation (RAG) to mitigate hallucination for large language models (LLMs). Following this trend, we launch the FutureDial-RAG challenge at SLT 2024, which aims at promoting the study of RAG for dialog systems. The challenge builds upon the MobileCS2 dataset, a real-life customer se

Anichur Rahman, Dipanjali Kundu, Tanoy Debnath, Muaz Rahman

Currently, Blockchain (BC), Artificial Intelligence (AI), and smart Industrial Internet of Things (IIoT) are not only leading promising technologies in the world, but also these technologies facilitate the current society to develop the standard of living and make it easier for users. However, these technologies have been applied in various domains for diffe

Nektarios Vlahakis

A novel method for finding the eigenvalues of a Sturm-Liouville problem is developed. Following the minimalist approach the problem is transformed to a single first-order differential equation with appropriate boundary conditions. Although the resulting equation is nonlinear, its form allows to find the general solution by adding a second part to a particula

Shahar Simon, Hennadii Yerzhakov, Sajilesh K. P., Atzmon Vakahi

Metallic transition metal dichalcogenides (TMDs), consisting of H-NbSe$_2$, H-NbS$_2$, H-TaSe$_2$ and H-TaS$_2$, remain superconducting down to a thickness of a single layer. In these materials, thickness affects a variety of properties, including Ising protection, two-band superconductivity, and the critical temperature $T_C$, which decreases for the Nb-bas

Norbert Magyar, Jaye Verniero, Adam Szabo, Jiyuan Zhang

In-situ measurements of the solar wind, a turbulent and anisotropic plasma flow originating at the Sun, are mostly carried out by single spacecraft, resulting in one-dimensional time series. The conversion of these measurements to the spatial frame of the plasma is a great challenge, but required for direct comparison of the measurements with MHD turbulence

Qianni Cao, Chen Shen

With high penetrations of renewable energy and power electronics converters, less predictable operating conditions and strong uncertainties in under-frequency events pose challenges for emergency frequency control (EFC). On the other hand, the fast adjustability of converter-based sources presents opportunities to reduce economic losses from traditional load

Chiara Bellotti

We will provide an explicit log-free zero-density estimate for $\zeta(s)$ of the form $N(\sigma,T)\le AT^{B(1-\sigma)}$. In particular, this estimate becomes the sharpest known explicit zero-density estimate uniformly for $\sigma\in[\alpha_0,1]$, with $0.985\le \alpha_0\le 0.9927$ and $3\cdot 10^{12}<T\le \exp(6.7\cdot 10^{12})$.

Tatiana S. Kostiuchenko, Alexander V. Shapeev, Ivan S. Novikov

Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized density functional theory (DFT) served as the primary models for describing interatomic interactions in atomistic simulations

Yuyu Jia, Qing Zhou, Wei Huang, Junyu Gao

Few-shot learning aims to generalize the recognizer from seen categories to an entirely novel scenario. With only a few support samples, several advanced methods initially introduce class names as prior knowledge for identifying novel classes. However, obstacles still impede achieving a comprehensive understanding of how to harness the mutual advantages of v

Indu Bala, Dikshit Chauhan, Lewis Mitchell

This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO, significantly boosting the adaptability of swarm-based optimization algorithms. The article further presents archive-based self-ada

Bufang Yang, Siyang Jiang, Lilin Xu, Kaiwei Liu

Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn cons

Matthew Craigie, Peter L. Taylor, Yuan-Sen Ting, Carolina Cuesta-Lazaro

Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simulation used to model the noise properties of the galaxy distribution. In a recent paper, we introduce an unsupervised learning approach which offers an alternative method that avoids

Weijia Liu, Bo Miao, Jiuxin Cao, Xuelin Zhu

Current methods for Video Moment Retrieval (VMR) struggle to align complex situations involving specific environmental details, character descriptions, and action narratives. To tackle this issue, we propose a Large Language Model-guided Moment Retrieval (LMR) approach that employs the extensive knowledge of Large Language Models (LLMs) to improve video cont

Alexandr Malijevský, Jiří Janek

We study the condensation of fluids confined by a pair of non-parallel plates of finite height $H$. We show that such a system experiences two types of condensation, termed single- and double-pinning, which can be characterized by one (single-pinning) or two (double-pinning) edge contact angles describing the shape of menisci pinned at the system edges. For

Yi Cheng, Ziwei Xu, Dongyun Lin, Harry Cheng

For visual content generation, discrepancies between user intentions and the generated content have been a longstanding problem. This discrepancy arises from two main factors. First, user intentions are inherently complex, with subtle details not fully captured by input prompts. The absence of such details makes it challenging for generative models to accura

F. Hamano, T. Fukui

The notion of the flow introduced by Kitaev is a manifestly topological formulation of the winding number on a real lattice. First, we show in this paper that the flow is quite useful for practical numerical computations for systems without translational invariance. Second, we extend it to three dimensions. Namely, we derive a formula of the flow on a three-

Aaron Ngai, Katrin Dulitz, Sebastian Hartweg, Janine Franz

We present a method for the reconstruction of ion kinetic energy distributions from ion time-of-flight mass spectra through ion trajectory simulations. In particular, this method is applicable to complicated spectrometer geometries with largely anisotropic ion collection efficiencies. A calibration procedure using a single ion mass peak allows the accurate d

Yuhua Zhu

In this paper, we study policy evaluation in continuous-time reinforcement learning (RL), where the state follows an unknown stochastic differential equation (SDE), but only discrete-time data are available. We first highlight that the discrete-time Bellman equation (BE) is not always a reliable approximation to the true value function because it ignores the

Gabriel R. Bengochea, Gabriel Leon, Alejandro Perez

A Planck scale inflationary era -- in a quantum gravity theory predicting discreteness of quantum geometry at the fundamental scale -- produces the scale invariant spectrum of inhomogeneities with very small tensor-to-scalar ratio of perturbations and a hot big bang leading to a natural dark matter genesis scenario. Here we evoke the possibility that some of

Hiba Maryam, Ling Fu, Jiajun Song, Tajrian ABM Shafayet

The development of Urdu scene text detection, recognition, and Visual Question Answering (VQA) technologies is crucial for advancing accessibility, information retrieval, and linguistic diversity in digital content, facilitating better understanding and interaction with Urdu-language visual data. This initiative seeks to bridge the gap between textual and vi

Dongjie Yang, XiaoDong Han, Yan Gao, Yao Hu

Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time applications like chatbots. To accelerate inference, we store computed keys and values (KV cache) in the GPU memory. Existing methods study the KV cache compression to reduce memory by

Haocong Rao, Minlin Zeng, Xuejiao Zhao, Chunyan Miao

Recent years have witnessed an increasing global population affected by neurodegenerative diseases (NDs), which traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial in