February 2024 arXiv papers — page 99

Timothy R. McIntosh, Teo Susnjak, Nalin Arachchilage, Tong Liu

The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their own LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using

Alice M. Aredes, Pablo L. Saldanha

Many quantum paradoxes based on a realistic view of weak values were discussed in the last decades. They lead to astonishing conclusions such as the measurement of a spin component of a spin-1/2 particle resulting in $100\hbar$, the separation of a photon from its polarization, and the possibility of having 3 particles in 2 boxes without any 2 particles bein

Julian Hintermayr, Bert Koopmans

Using the microscopic three temperature model, we simulate single-pulse all-optical switching (AOS) in alloys and bilayers consisting of Co and Gd. In particular, we investigate its dependence on the spin-flip probability of Gd $a_\mathrm{sf,Gd}$, a material parameter describing the strength of spin-phonon coupling. We do so to elucidate the mechanisms behin

Alberto Pozanco, Daniel Borrajo, Manuela Veloso

In many real-world planning applications, agents might be interested in finding plans whose actions have costs that are as uniform as possible. Such plans provide agents with a sense of stability and predictability, which are key features when humans are the agents executing plans suggested by planning tools. This paper adapts three uniformity metrics to aut

George Metcalfe, Simon Santschi

This paper provides answers to several open problems about equational theories of idempotent semifields. In particular, it is proved that (i) no equational theory of a non-trivial class of idempotent semifields has a finite basis; (ii) there are continuum-many equational theories of classes of idempotent semifields; and (iii) the equational theory of the cla

Matteo Milani, Ty Phou, Guillame Prevot, Laurence Ramos

We present a novel dynamic light scattering setup to probe, with time and space resolution, the microscopic dynamics of soft matter systems confined within millimeter-sized spherical drops. By using an ad-hoc optical layout, we tackle the challenges raised by refraction effects due to the unconventional shape of the samples. We first validate the setup by in

Álvaro Huertas-García, Alejandro Martín, Javier Huertas-Tato, David Camacho

Adversarial attacks represent a substantial challenge in Natural Language Processing (NLP). This study undertakes a systematic exploration of this challenge in two distinct phases: vulnerability evaluation and resilience enhancement of Transformer-based models under adversarial attacks. In the evaluation phase, we assess the susceptibility of three Transform

V. K. Jaiswal, F. Montorsi, F. Aleotti, F. Segatta

We study the ultrafast time evolution of cyclobutanone excited to singlet n-->Rydberg state through XMS-CASPT2 nonadiabatic surface-hopping simulations. These dynamics predict relaxation to ground-state with a timescale of 822 +/- 45 fs with minimal involvement of triplets. The major relaxation path to the ground-state involves a three-state degeneracy regio

Arman Isajanyan, Artur Shatveryan, David Kocharyan, Zhangyang Wang

Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. The recent progress of text-conditioned image synthesis has ushered in a collaborative era where AI empowers users to craft original visual artworks seeking community validation. Nevertheless, assessing

Zihao Wang, Shuyu Li, Tao Zhang, Qi Wang

The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to semantic gaps between Music Information Retrieval (MIR) algorithms and human understanding, discrepancies between professionals and the public, and low precision

Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance

Zs. Bognár, Á. Sódor

Context. Knowing the rotation rates and masses of white dwarf stars is an important step towards characterising the angular momentum transport mechanism in their progenitors, and coupling the cores of red giants to their envelopes. However, deriving these rotation rates is not an easy task. One can use the rotational broadening of spectral lines, but there i

Eugenia Boffo, Pietro Antonio Grassi, Ondrej Hulik, Ivo Sachs

We describe a family of twisted partition functions for the relativistic spinning particle models. For suitable choices of fugacities this computes a refined Euler characteristics that counts the dimension of the physical states for arbitrary picture and, furthermore, encodes the complete BV-spectrum of the effective space-time gauge theory originating from

Zain Taufique, Muhammad Awais Bin Altaf, Antonio Miele, Pasi Liljeberg

Electroencephalography (EEG) recordings are analyzed using battery-powered wearable devices to monitor brain activities and neurological disorders. These applications require long and continuous processing to generate feasible results. However, wearable devices are constrained with limited energy and computation resources, owing to their small sizes for prac

Fredy A Orjuela, Jorge Enrique García-Farieta, Héctor J Hortúa, E Munévar

In recent years, science simulations have become popular among educators due to their educational usefulness, availability, and potential for increasing the students' knowledge on scientific topics. In this paper, we introduce the implementation of a user-friendly simulation based on Easy Java/JavaScript Simulations (EJS) to study the problem of damped oscil

Momir Adžemović, Predrag Tadić, Andrija Petrović, Mladen Nikolić

Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a train

Aldo Pratelli, Giorgio Saracco

We prove a lower bound for the Cheeger constant of a cylinder $\Omega\times (0,L)$, where $\Omega$ is an open and bounded set. As a consequence, we obtain existence of minimizers for the shape functional defined as the ratio between the first Dirichlet eigenvalue of the $p$-Laplacian and the $p$-th power of the Cheeger constant, within the class of bounded c

Wen-Yuan Ai, Jean Alexandre, Matthias Carosi, Bjorn Garbrecht

Assuming a toroidal space with finite volume, we derive analytically the full one-loop vacuum energy for a scalar field tunnelling between two degenerate vacua, taking into account discrete momentum. The Casimir energy is computed for an arbitrary number of dimensions using the Abel-Plana formula, while the one-loop instanton functional determinant is evalua

Michelle R. Greene, Benjamin J. Balas, Mark D. Lescroart, Paul R. MacNeilage

We introduce the Visual Experience Dataset (VEDB), a compilation of over 240 hours of egocentric video combined with gaze- and head-tracking data that offers an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 58 observers ranging from 6-49 years old. This paper outlines the data coll

Boumediene Abdellaoui, Giovanni Siclari, Ana Primo

In this paper we analyse the existence and non-existence of non-negative solutions to a non-local parabolic equation with a Hardy-Leray type potential. More precisely, we consider the problem $$ \begin{cases} (w_t-\Delta w)^s=\frac{\lambda}{|x|^{2s}} w+w^p +f, &\text{ in }\mathbb{R}^N\times (0,+\infty),\\ w(x,t)=0, &\text{ in }\mathbb{R}^N\times (-\infty,0],

Juan Calderón Bustillo, Adrian del Rio, Nicolas Sanchis-Gual, Koustav Chandra

Certain precessing black-hole mergers produce gravitational waves with net circular polarization, understood as an imbalance between right- and left-handed amplitudes. According to the Cosmological Principle, such emission must average to zero across all binary mergers in our Universe to preserve mirror-reflection symmetry at very large scales. We present a

Augustin Bouquillard, Florent Jacquemard

We revisit the problems of pitch spelling and tonality guessing with a new algorithm for their joint estimation from a MIDI file including information about the measure boundaries. Our algorithm does not only identify a global key but also local ones all along the analyzed piece. It uses Dynamic Programming techniques to search for an optimal spelling in ter

Luiz Morais, Georgia Panagiotidou, Sarah Hayes, Tatiana Losev

Data physicalizations have gained prominence across domains, but their environmental impact has been largely overlooked. This work addresses this gap by investigating the interplay between sustainability and physicalization practices. We conducted interviews with experts from diverse backgrounds, followed by a survey to gather insights into how they approach

Yao Zhu, Xiaopeng Yuan, Yulin Hu, Bo Ai

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a t

Itzhak Goldman

It has been suggested recently that the Hubble tension could be eliminated by a sharp, $\sim 10\%$ increase of the effective gravitational constant at $z \sim 0.01$. This would decrease the luminosities of type 1a supernovae in just the needed amount to explain the larger value of the Hubble parameter. In the present paper we call attention to a dramatic eff

Laurie Davies

The goal of this paper is to provide a theory linear regression based entirely on approximations. It will be argued that the standard linear regression model based theory whether frequentist or Bayesian has failed and that this failure is due to an 'assumed (revealed?) truth' (John Tukey) attitude to the models. This is reflected in the language of statistic

Laura. J. Hunt, Kevin. A. Pimbblet, David. M. Benoit

We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of galaxies by training an artificial neural network (ANN) to recognise the relationships between the equivalent widths of sp

Jorge Enrique García-Farieta, Alejandro Hurtado

Teaching magnetism is one of the most challenging topics at undergraduate level in programmes with scientific background. A basic course includes the description of the magnetic interaction along with empirical results such as the Biot-Savart law's. However, evaluating the magnetic field due to certain current carrying system at any point in space is not an

Antonio Cauchi, Armando Gutierrez Terradillos

We give a two-variable Rankin--Selberg integral for generic cusp forms on $\mathrm{PGL}_4$ and $\mathrm{PGU}_{2,2}$ which represents a product of exterior square $L$-functions. As a residue of our integral, we obtain an integral representation on $\mathrm{PGU}_{2,2}$ of the degree 5 $L$-function of $\mathrm{GSp}_4$ twisted by the quadratic character of $E/F$

Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten, Peter A. N. Bosman

Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently interpretable expressions. GP-GOMEA is a form of GP that has been found particularly effective at evolving expressions that are accurate yet of limited size and, thus, promote interpret

V. V. Peller

The survey is devoted to diverse applications of Besov classes in operator theory. It is illustrated how Besov classes are used to describe Hankel operators of Schatten--von Neumann classes; various applications of this description are considered. Next, we discuss the role of Besov classes in norm estimates of polynomials of power bounded operators on Hilber

Jean-Stefan Koskivirta

We first extend previous results of the author with T. Wedhorn and W. Goldring regarding the existence of $\mu$-ordinary Hasse invariants for Hodge-type Shimura varieties to other automorphic line bundles. We also determine exactly which line bundles admit nonzero sections on the stack of $G$-zips of Pink--Wedhorn--Ziegler. Then, we define and study the Cox

Rida T. Farouki, Marjeta Knez, Vito Vitrih, Emil Žagar

By interpreting planar polynomial curves as complex-valued functions of a real parameter, an inner product, norm, metric function, and the notion of orthogonality may be defined for such curves. This approach is applied to the complex pre-image polynomials that generate planar Pythagorean-hodograph (PH) curves, to facilitate the implementation of bounded mod

Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins

Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, approximations are typically needed, which typically do require tuning. We argue that this requirement for tuning c

B. Blanco, R. Palma, M. Hurtado, G. JimÉnez

Targeted therapeutic interventions utilizing low-inten\-sity ultrasound (LIUS) exhibit substantial potential for hindering the proliferation of cancer stem cells. This investigation introduces a multiscale model and computational framework to comprehensively explore the therapeutic LIUS on poroelastic tumor dynamics, thereby unraveling the intricacies of mec

Ting-Shuo Yo, Shih-Hao Su, Jung-Lien Chu, Chiao-Wei Chang

In this study, we propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorith

Maik Ender, Felix Hahn, Marc Fyrbiak, Amir Moradi

Fuzzing is a well-established technique in the software domain to uncover bugs and vulnerabilities. Yet, applications of fuzzing for security vulnerabilities in hardware systems are scarce, as principal reasons are requirements for design information access (HDL source code). Moreover, observation of internal hardware state during runtime is typically an ine

Quentin Gallouédec, Edward Beeching, Clément Romac, Emmanuel Dellandréa

The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research. The prevailing methodology in Reinforcement Learning (RL) typically limits models to a single task within a unimodal framework, a limitation that contrasts with the broader vision of a versatile, multi-domain model. In this pape

A. B. Aleksandrov, V. V. Peller

The paper studies the problem, for which continuous functions $f$ on the real line ${\Bbb R}$, the difference of the functions $f(B)-f(A)$ of self-adjoint operators $A$ and $B$ with trace class difference must also be of trace class. The main result of the paper shows that this happens if and only if the function $f$ is operator Lipschitz on a neighbourhood

Kelvin Kiprono, János Tóth

The differential equations of chemical kinetics are systems of nonlinear (polynomial) differential equations, therefore their solutions cannot usually be found in symbolic form. Here we offer a method to solve classes of kinetic differential equations based on the Cauchy--Riemann equations. It turns out that the method can be used to symbolically solve some

Marcel Lamott, Yves-Noel Weweler, Adrian Ulges, Faisal Shafait

Recent advances in training large language models (LLMs) using massive amounts of solely textual data lead to strong generalization across many domains and tasks, including document-specific tasks. Opposed to that there is a trend to train multi-modal transformer architectures tailored for document understanding that are designed specifically to fuse textual

Şeyma Çalışkan, Jannat Mushreq Kamil Alazzawi, Yahya Nasolo

We present chemical abundances of the very bright metal-poor star HD~1936 based on high-resolution and high SNR spectra from AUKR. We obtain the abundances of 29 atomic species with atomic numbers between 3 and 63. In this context, the derived lithium abundance of 1.01 is consistent with the thin Li plateau observed in lower red giant branch stars. The star

Benedikt Jahnel, Utkir Rozikov

We consider a version of the solid-on-solid model on the Cayley tree of order two in which vertices carry spins of value $0,1$ or $2$ and the pairwise interaction of neighboring vertices is given by their spin difference to the power $p>0$. We exhibit all translation-invariant splitting Gibbs measures (TISGMs) of the model and demonstrate the existence of up

Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic

When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. We propose a new framework to model this phenomenon, where the current environment depends on the deployed policy as well as its previous dynamics. This is a generalization of Performative RL (PRL) [Mandal et al., 2023]. Unlike PRL,

Maicon J. Karling, Daniele Durante, Marc G. Genton

The broad class of multivariate unified skew-normal (SUN) distributions has been recently shown to possess important conjugacy properties. When used as priors for the coefficients vector in probit, tobit, and multinomial probit models, these distributions yield posteriors that still belong to the SUN family. Although this result has led to important advancem

Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding

The powerful reasoning capabilities of large language models (LLMs) have brought revolutionary changes to many fields, but their performance in human behaviour generation has not yet been extensively explored. This gap likely emerges because the internal processes governing behavioral intentions cannot be solely explained by abstract reasoning. Instead, they

Andreas Emil Feldmann, Michael Lampis

In this paper we reassess the parameterized complexity and approximability of the well-studied Steiner Forest problem in several graph classes of bounded width. The problem takes an edge-weighted graph and pairs of vertices as input, and the aim is to find a minimum cost subgraph in which each given vertex pair lies in the same connected component. It is kno

Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng

Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a single model is trained on vast and diverse datasets spanning multiple domains -- a paradigm we term `All in One'. This methodology empowers LLMs with super generalization capabiliti

P. Podlaski

NA61/SHINE is a multipurpose fixed-target experiment at the CERN Super Proton Synchrotron. The main goals of the NA61/SHINE strong interaction program are to discover the critical point of strongly interacting matter as well as to study the properties of produced particles relevant for the study of the onset of deconfinement -- the transition between the sta

Chris Waltham, Andy Perrett, Rakshit Soni, Charles Fox

A key component of any robot is the interface between robotics middleware software and physical motors. New robots often use arbitrary, messy mixtures of closed and open motor drivers and error-prone physical mountings, wiring, and connectors to interface them. There is a need for a standardizing OSH component to abstract this complexity, as Arduino did for

Vincent Beck, César Lecoutre

If $\Delta$ and $\Gamma$ are two derivations of a commutative algebra $A$ such that $\Delta\Gamma-\Gamma\Delta=\Delta$ is locally nilpotent, one can endow $A$ with a new product $\ast$ whose filtered semiclassical limit is the Poisson structure $\Delta\wedge\Gamma$.In this article we first study theses (Poisson) algebras from an algebraic point of view, and

Edouard Pauwels

Minimizing a smooth function f on a closed subset C leads to different notions of stationarity: Fr{\'e}chet stationarity, which carries a strong variational meaning, and criticality, which is defined through a closure process and involves the notion of limiting, or Mordukovitch, subdifferential. The latter is an optimality condition which may loose the varia

Mengran Zhu, Yulu Gong, Yafei Xiang, Hanyi Yu

Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions. This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection, comparing their advantages with traditional methods. GANs, a type of Artificial Neural Network (ANN), have shown

Yuchen Ding

Let $\mathcal{P}$ be the set of primes and $\pi(x)$ the number of primes not exceeding $x$. Let also $P^+(n)$ be the largest prime factor of $n$ with convention $P^+(1)=1$ and $$ T_c(x)=\#\left\{p\le x:p\in \mathcal{P},P^+(p-1)\ge p^c\right\}. $$ Motivated by a 2017 conjecture of Chen and Chen, we show that for any $8/9\le c<1$ $$ \limsup_{x\rightarrow\infty

Chiara Garavelli, Alessandra Aldieri, Marco Palanca, Enrico Dall'Ara

The incidence of vertebral fragility fracture is increased by the presence of preexisting pathologies such as metastatic disease. Computational tools could support the fracture prediction and consequently the decision of the best medical treatment. Anyway, validation is required to use these tools in clinical practice. To address this necessity, in this stud

Andreas Reinhart

Let $d\geq 2$ be a squarefree integer, let $\omega\in\{\sqrt{d},\frac{1+\sqrt{d}}{2}\}$ be such that $\mathbb{Z}[\omega]$ is the ring of algebraic integers of the real quadratic number field $\mathbb{Q}(\sqrt{d})$, let $\varepsilon>1$ be the fundamental unit of $\mathbb{Z}[\omega]$ and let $x$ and $y$ be the unique nonnegative integers with $\varepsilon=x+y\

Ingrid Beltita, Jordy Timo van Velthoven

For an exponential Lie group $G$ and an irreducible unitary representation $(\pi,\mathcal{H}_{\pi})$ of $G$, we consider the natural action defined by $\pi$ on the projective space of $\mathcal{H}_{\pi}$, and show that the stabilisers of this action coincide with the projective kernel of $\pi$. Using this, we prove that, if $G/\mathrm{pker}(\pi)$ is unimodul

Shuangle Li, Bingkai Lin, Yuwei Liu

In this paper we present a new gap-creating randomized self-reduction for parameterized Maximum Likelihood Decoding problem over $\mathbb{F}_p$ ($k$-MLD$_p$). The reduction takes a $k$-MLD$_p$ instance with $k\cdot n$ vectors as input, runs in time $f(k)n^{O(1)}$ for some computable function $f$, outputs a $(3/2-\varepsilon)$-Gap-$k'$-MLD$_p$ instance for an

Fryderyk Falniowski, Panayotis Mertikopoulos

We consider three distinct discrete-time models of learning and evolution in games: a biological model based on intra-species selective pressure, the dynamics induced by pairwise proportional imitation, and the exponential / multiplicative weights (EW) algorithm for online learning. Even though these models share the same continuous-time limit - the replicat

Alexis Garcia

We classify meromorphic affine connections on compact complex surfaces with algebraic dimension one, extending the work of Inoue,Kobayashi and Ochiai (1981) in the holomorphic case. The motivation is to investigate possible extension of the principle of uniformization of complex compact curves by means of geometric structures, in higher dimensions and allowi

Malcolm J. Coe, Jamie A. Kennea, Itumeleng M. Monageng, Lee J. Townsend

SMC X-2 exhibits X-ray outburst behaviour that makes it one of the most luminous X-ray sources in the Small Magellanic Cloud. In the last decade it has undergone two such massive outbursts - in 2015 and 2022. The first outburst is well reported in the literature, but the 2022 event has yet to be fully described and discussed. That is the goal of this paper.

Jean-Marie Lemercier, Julius Richter, Simon Welker, Eloi Moliner

With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect transmission pipeline. To address this proble

Yulu Gong, Mengran Zhu, Shuning Huo, Yafei Xiang

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is an important part of a complete and effective defense system. How to effectively detect unknown threats is one of the c

Yuri A. Fadeyev

Pulsation period evolution during the helium-shell flash in the Mira variable R Hya is investigated using consistent stellar evolution and non-linear stellar pulsation computations. The initial and time-dependent inner boundary conditions for the equations of radiation hydrodynamics describing non-linear stellar oscillations were determined using a grid of T

Abror Khudoyberdiyev, Bakhtiyor Yusupov

In this work, we introduce the notion of local and $2$-local $\delta$-derivations and describe local and $2$-local $\frac{1}{2}$-derivation of finite-dimensional solvable Lie algebras with filiform, Heisenberg, and abelian nilradicals. Moreover, we describe the local $\frac{1}{2}$-derivation of oscillator Lie algebras, Schr{\"o}dinger algebras, and Lie algeb

Antonio Celentano, Carlo Nitsch, Cristina Trombetti

In this paper, we prove a Serrin-type result for an elliptic system of equations, overdetermined with both Dirichlet and a generalized Neumann conditions. With this tool, we characterize the critical shapes under volume constraint of some domain functionals.

Angelos Zavras, Dimitrios Michail, Begüm Demir, Ioannis Papoutsis

Deep Learning (DL) is undergoing a paradigm shift with the emergence of foundation models. In this work, we focus on Contrastive Language-Image Pre-training (CLIP), a Vision-Language foundation model that achieves high accuracy across various image classification tasks and often rivals fully supervised baselines, despite not being explicitly trained for thos

S. N. Artekha, A. V. Belyan

The crucial part of electromagnetic phenomena in many atmospheric processes is verified by systematized data. The multilayered charged system of clouds represents some dynamically equilibrium structure kept by the ionic and polarization forces. The estimates of acting forces are presented and it is demonstrated the necessity to take into account the plasma-l

Lourenço Beirão da Veiga, Daniele A. Di Pietro, Kirubell B. Haile

We analyze a Discontinuous Galerkin method for a problem with linear advection-reaction and $p$-type diffusion, with Sobolev indices $p\in (1, \infty)$. The discretization of the diffusion term is based on the full gradient including jump liftings and interior-penalty stabilization while, for the advective contribution, we consider a strengthened version of

Kévin Guillon, Romane Hélie, Philippe Helluy

We propose a new stability analysis of the Vectorial Lattice-Boltzmann Method (VLBM). The VLBM is a variant of the LBM with extended stability features: it allows to handle compressible flows with shock waves, while the LBM is limited to low-Mach number regime. The stability analysis is based on the Legendre transform theory. We also propose a new tool: the

Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin

The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address

Jorge Enrique García-Farieta, Alejandro Hurtado

Earth's magnetosphere, beyond protecting the ozone layer, is a natural phenomena which allows to study the interaction between charged particles from solar activity and electromagnetic fields. In this paper we studied trajectories of charged particles interacting with a constant dipole magnetic field as first approach of the Earth's magnetosphere using diffe

Dhruv Kudale, Badri Vishal Kasuba, Venkatapathy Subramanian, Parag Chaudhuri

Several recent deep learning (DL) based techniques perform considerably well on image-based multilingual text detection. However, their performance relies heavily on the availability and quality of training data. There are numerous types of page-level document images consisting of information in several modalities, languages, fonts, and layouts. This makes t

Zexin Fang, Bin Han, Hans D. Schotten

This paper presents a trustworthy framework for achieving accurate cooperative localization in multiple unmanned aerial vehicle (UAV) systems. The The Cramer-Rao Lower Bound (CRLB) for the three-dimensional (3D) cooperative localization network is derived, with particular attention given to practical scenarios involving non-uniform spatial distribution of an

Owen Henkel, Hannah Horne-Robinson, Nessie Kozhakhmetova, Amanda Lee

This study evaluates the impact of Rori, an AI powered conversational math tutor accessible via WhatsApp, on the math performance of approximately 1,000 students in grades 3-9 across 11 schools in Ghana. Each school was assigned to a treatment group or control group; the students in the control group continued their regular math instruction, while students i

Tatsuya Hiraoka, Naoaki Okazaki

Do pretrained language models have knowledge regarding the surface information of tokens? We examined the surface information stored in word or subword embeddings acquired by pretrained language models from the perspectives of token length, substrings, and token constitution. Additionally, we evaluated the ability of models to generate knowledge regarding to

Tongliang Yao, Zi Xu

In this paper, we propose a Minimax Trust Region (MINIMAX-TR) algorithm and a Minimax Trust Region Algorithm with Contractions and Expansions(MINIMAX-TRACE) algorithm for solving nonconvex-strongly concave minimax problems. Both algorithms can find an $(\epsilon, \sqrt{\epsilon})$-second order stationary point(SSP) within $\mathcal{O}(\epsilon^{-1.5})$ itera

Xavier Gonze, Samare Rostami, Christian Tantardini

Density functional perturbation theory is a well-established method to study responses of molecules and solids, especially responses to atomic displacements or to different perturbing fields (electric, magnetic). Like for density functional theory, the treatment of metals is delicate, due to the Fermi-Dirac statistics and electronic bands crossing the Fermi

Stefano Milani, Ioannis Chatzigiannakis, Domenico Garlisi, Matteo Di Fraia

LoRaWAN is a wireless technology that enables high-density deployments of IoT devices. Designed for Low Power Wide Area Networks (LPWAN), LoRaWAN employs large cells to service a potentially extremely high number of devices. The technology enforces a centralized architecture, directing all data generated by the devices to a single network server for data pro

Xiaojin Xiong, Ziyan Zeng, Minyu Feng, Attila Szolnoki

While actors in a population can interact with anyone else freely, social relations significantly influence our inclination towards particular individuals. The consequence of such interactions, however, may also form the intensity of our relations established earlier. These dynamical processes are captured via a coevolutionary model staged in multiplex netwo

Lukas Weissinger, Simon Hubmer, Ronny Ramlau, Henning Uwe Voss

Cardiac pulsations in the human brain have received recent interest due to their possible role in the pathogenesis of neurodegenerative diseases. Further interest stems from their possible application as an endogenous signal source that can be utilized for brain imaging in general. The (pulse-)wave describing the blood flow velocity along an intracranial art

Matthew J. Holland

In this work, we consider the notion of "criterion collapse," in which optimization of one metric implies optimality in another, with a particular focus on conditions for collapse into error probability minimizers under a wide variety of learning criteria, ranging from DRO and OCE risks (CVaR, tilted ERM) to non-monotonic criteria underlying recent ascent-de

Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An

Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination. To eliminate hallucinations, existing methods manually annotate paired responses with and without hallucinations,

Diederick Vermetten, Carola Doerr, Hao Wang, Anna V. Kononova

The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community. One particular criticism that is raised towards many new algorithms is their focus on metaphors used to present the method, rather than emphasizing their potential algorithmic contributions.

Katharina Kloppenborg, Mad Price Ball, Steven Jonas, Gary Isaac Wolf

Personal science is the practice of addressing personally relevant health questions through self-research. Implementing personal science can be challenging, due to the need to develop and adopt research protocols, tools, and methods. While online communities can provide valuable peer support, tools for systematically accessing community knowledge are lacking

Franc Forstneric, Finnur Larusson

In this paper we investigate Oka-1 manifolds and Oka-1 maps, a class of complex manifolds and holomorphic maps recently introduced by Alarc\'on and Forstneri\v{c}. Oka-1 manifolds are characterised by the property that holomorphic maps from any open Riemann surface to the manifold satisfy the Runge approximation and Weierstrass interpolation conditions, whil

Hyewon Han, Naveen Kumar

In this work, we propose a novel cross-talk rejection framework for a multi-channel multi-talker setup for a live multiparty interactive show. Our far-field audio setup is required to be hands-free during live interaction and comprises four adjacent talkers with directional microphones in the same space. Such setups often introduce heavy cross-talk between c

Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi

Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations. The problem is well-known to be intractable for most application domains, except in notable cases such as the tabular setting or for linear dynamical systems with gaussian noise. In this work, we propose a new class of filters

Sakib Anwar Rieyan, Md. Raisul Kabir News, A. B. M. Muntasir Rahman, Sadia Afrin Khan

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a cen

I-Te Lu, Michael Ruggenthaler, Nicolas Tancogne-Dejean, Simone Latini

Quantum-electrodynamical density-functional theory (QEDFT) provides a promising avenue for exploring complex light-matter interactions in optical cavities for real materials. Similar to conventional density-functional theory, the Kohn-Sham formulation of QEDFT needs approximations for the generally unknown exchange-correlation functional. In addition to the

Chih-Tang Liao, Andrew Lemus, Ali Gürbüz, Alan C. H. Tsang

Microorganisms and synthetic microswimmers often encounter complex environments consisting of networks of obstacles embedded into viscous fluids. Such settings include biological media, such as mucus with filamentous networks, as well as environmental scenarios, including wet soil and aquifers. A fundamental question in studying their locomotion is how the i

S. Shrivastava, A. Biswas, S. Chakrabarty, G. Dash

Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computation of weight updates for in-memory computing. Charge Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs. However, prior work has shown that the weight updates

Chiara Garavelli, Alessandra Aldieri, Marco Palanca, Luca Patruno

Vertebral fractures prediction in clinics lacks of accuracy. The most used scores have limitations in distinguishing between subjects at risk or not. Finite element (FE) models generated from computed tomography (CT) of these patients may improve the predictive capability. Many models have already been proposed but the most of them considered the single vert

Martin Bustos

I provide a model of rational inattention with heterogeneity and prove it is observationally equivalent to a state-dependent stochastic choice model subject to attention costs. I demonstrate that additive separability of unobservable heterogeneity, together with an independence assumption, suffice for the empirical model to admit a representative agent. Usin

Yoichi Miyata, Takayuki Shiohama, Toshihiro Abe

Sine-skewed circular distributions are identifiable and have easily-computable trigonometric moments and a simple random number generation algorithm, whereas they are known to have relatively low levels of asymmetry. This study proposes a new family of circular distributions that can be skewed more significantly than that of existing models. It is shown that

Alvin Grissom, Ryan F. Lei, Matt Gusdorff, Jeova Farias Sales Rocha Neto

Generative adversarial networks (GANs) generate photorealistic faces that are often indistinguishable by humans from real faces. While biases in machine learning models are often assumed to be due to biases in training data, we find pathological internal color and luminance biases in the discriminator of a pre-trained StyleGAN3-r model that are not explicabl

Stefan Velja, Jannis Krumland, Caterina Cocchi

Mechanical deformations, either spontaneously occurring during sample preparation or purposely induced in their nanoscale manipulation, drastically affect the electronic and optical properties of transition metal dichalcogenide monolayers. In this first-principles work based on density-functional theory, we shed light on the interplay among strain, curvature

Hansol Jung, Hyunwoo Seo, Chiehyeon Lim

Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Although traditional deep-learning-based models and modern transformer-based models in previous studies capture unidirectional and bidirectional patterns within user-item interactions, the importance of temporal contexts, such as indiv

T. Ravinet, C. Reylé, N. Lagarde, A. Burgasser

Context: The local census of very low-mass stars and brown dwarfs is crucial to improving our understanding of the stellar-substellar transition and their formation history. These objects, known as ultra-cool dwarfs (UCDs), are essential targets for searches of potentially habitable planets. However, their detection poses a challenge because of their low lum

Josué Pagán, José L. Risco-Martín, José M. Moya, José L. Ayala

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This pa