April 2024 arXiv papers — page 121

Jiazhen Liu, Peihan Li, Yuwei Wu, Gaurav S. Sukhatme

Multi-robot target tracking finds extensive applications in different scenarios, such as environmental surveillance and wildfire management, which require the robustness of the practical deployment of multi-robot systems in uncertain and dangerous environments. Traditional approaches often focus on the performance of tracking accuracy with no modeling and as

Vigneshwaran Shankaran, Rajesh Sharma

Online social media has become increasingly popular in recent years due to its ease of access and ability to connect with others. One of social media's main draws is its anonymity, allowing users to share their thoughts and opinions without fear of judgment or retribution. This anonymity has also made social media prone to harmful content, which requires mod

Andrew Adiletta, M. Caner Tol, Kemal Derya, Berk Sunar

Since its inception, Rowhammer exploits have rapidly evolved into increasingly sophisticated threats compromising data integrity and the control flow integrity of victim processes. Nevertheless, it remains a challenge for an attacker to identify vulnerable targets (i.e., Rowhammer gadgets), understand the outcome of the attempted fault, and formulate an atta

Joel Kuperman, Alejandro Petrovich, Pedro Sánchez Terraf

The idempotent semigroups (bands) that give rise to partial orders by defining $a \leq b \iff a \cdot b = a$ are the "right-regular" bands (RRB), which are axiomatized by $x\cdot y \cdot x = y \cdot x$. In this work we consider the class of "associative posets", which comprises all partial orders underlying right-regular bands, and study to what extent the o

Sebastián Donoso, Andreas Koutsogiannis, Wenbo Sun

We establish sufficient and necessary conditions for the joint transitivity of linear iterates in a minimal topological dynamical system with commuting transformations. This result provides the first topological analogue of the classical Berend and Bergelson joint ergodicity criterion in measure-preserving systems.

Gholamreza Fardipour Raki, Mohsen Khakzad

SiPMs operate in Geiger mode, wherein photodiode cells are reverse-biased to the breakdown by even a single photon. Each cell is connected in series with a quenching resistor, which prevents cell damage and resets the cell after making a signal. All cells are arranged in parallel, making SiPMs and biasing circuits vulnerable to over-illumination, where the c

Carlos A. Silvera Batista, Kun Wang, Hannah Blake, Vivian Nwosu-Madueke

Diffusiophoretic motion induced by gradients of dissolved species has enabled the manipulation of colloids over large distances, spanning hundreds of microns. Nonetheless, studies have primarily focused on simple geometries that feature 1D gradients of solutes generated by reactions or selective dissolution. Thus, our understanding of 3D diffusiophoresis rem

D M A Meyer, P F Velazquez, M Pohl, K Egberts

Core-collapse supernova remnants are the nebular leftover of defunct massive stars which have died during a supernova explosion, mostly while undergoing the red supergiant phase of their evolution. The morphology and emission properties of those remnants are a function of the distribution of circumstellar material at the moment of the supernova, the intrisic

Mohsen Abdoli, Ramin G. Youvalari, Karam Naser, Kevin Reuzé

A quantitative analysis of post-VVC luma and chroma intra tools is presented, focusing on their statistical behaviors, in terms of block selection rate under different conditions. The aim is to provide insights to the standardization community, offering a clearer understanding of interactions between tools and assisting in the design of an optimal combinatio

Kaysiyavash Kaykavoosi, Nicklas Anttu, Mario Zapata-Herrera, Javad Ahmadi-Shokouh

Manipulating external stimuli is crucial for enhancing and controlling the optical response in chiral structures. This study introduces chiral structures composed of single, dimer, or trimer arrays of plasmonic and magneto-plasmonic trapezoidal and triangular nanoantennas. Through modeling, we demonstrate how these systems can (i) function as chiral metasurf

Annika Fürnsinn, Christian Ebenbauer, Bahman Gharesifard

In this paper, we develop a systematic method for constructing a generalized discrete-time control Lyapunov function for the flexible-step Model Predictive Control (MPC) scheme, recently introduced in [2], when restricted to the class of linear systems. Specifically, we show that a set of Linear Matrix Inequalities (LMIs) can be used for this purpose, demons

Zakari Denis, Giuseppe Carleo

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacting fermions or continuous-variable systems. However, accurate neural representations of the ground state

Simon Bolduc Beaudoin, Edouard Pinsolle, Bertrand Reulet

We report measurements of counting statistics, average and variance, of microwave photons of ill-defined frequency : bichromatic photons, i.e. photons involving two well separated frequencies, and "white" broadband photons. Our setup allows for the analysis of single photonic modes of arbitrary waveform over the 1-10 GHz frequency range. The photon statistic

Tim Büchner, Niklas Penzel, Orlando Guntinas-Lichius, Joachim Denzler

Facial expression-based human emotion recognition is a critical research area in psychology and medicine. State-of-the-art classification performance is only reached by end-to-end trained neural networks. Nevertheless, such black-box models lack transparency in their decision-making processes, prompting efforts to ascertain the rules that underlie classifier

Antoine Grimaldi, Amélie Gruel, Camille Besnainou, Jean-Nicolas Jérémie

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog repre

Ying Chen, Jiajing Xie, Yuxiang Lin, Yuhang Song

Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integra

Alvitta Ottley

The quest to develop intelligent visual analytics (VA) systems capable of collaborating and naturally interacting with humans presents a multifaceted and intriguing challenge. VA systems designed for collaboration must adeptly navigate a complex landscape filled with the subtleties and unpredictabilities that characterize human behavior. However, it is notew

Gabriel Arpino, Xiaoqi Liu, Julia Gontarek, Ramji Venkataramanan

We consider the problem of localizing change points in a generalized linear model (GLM), a model that covers many widely studied problems in statistical learning including linear, logistic, and rectified linear regression. We propose a novel and computationally efficient Approximate Message Passing (AMP) algorithm for estimating both the signals and the chan

Wuming Pan

This paper introduces the Token Space framework, a novel mathematical construct designed to enhance the interpretability and effectiveness of deep learning models through the application of category theory. By establishing a categorical structure at the Token level, we provide a new lens through which AI computations can be understood, emphasizing the relati

Weiyu Sun, Xinyu Zhang, Hao Lu, Yingcong Chen

Contrastive Learning (CL) has attracted enormous attention due to its remarkable capability in unsupervised representation learning. However, recent works have revealed the vulnerability of CL to backdoor attacks: the feature extractor could be misled to embed backdoored data close to an attack target class, thus fooling the downstream predictor to misclassi

Antal Jevicki, Debangshu Mukherjee, Junggi Yoon

We investigate the emergent factorization of Hilbert space in the low-energy description of matrix models, addressing key aspects of the black hole information paradox. We examine the collective description for the low-energy sector of $SU(N)$ matrix model, characterized by a factorized Hilbert space composed of a finite number of boxes and anti-boxes. This

Onur Ozkan

Konnektor is a connection protocol designed to solve the challenge of managing unique peers within distributed peer-to-peer networks. By prioritizing network integrity and efficiency, Konnektor offers a comprehensive solution that safeguards against the spread of duplicate peers while optimizing resource utilization. This paper provides a detailed explanatio

Przemysław Wrona, Maciej Grzenda, Marcin Luckner

Public transport systems are expected to reduce pollution and contribute to sustainable development. However, disruptions in public transport such as delays may negatively affect mobility choices. To quantify delays, aggregated data from vehicle locations systems are frequently used. However, delays observed at individual stops are caused inter alia by fluct

Elisabetta Masut

We re-interpret Goodwin's translation functors for a finite $W$-algebra $H_\ell$ as an action of a monoidal subcategory of $U(\mathfrak{g})$-mod on the category of finitely generated $H_\ell$-modules. This action is obtained by transporting the tensor product of $U(\mathfrak{g})$-modules through Skryabin's equivalence. We apply this interpretation to show th

Zhanyunxin Du, Yue-Xin Huang, Xiao Li

We theoretically investigate the bilinear current, scaling as $j\sim EB$, in two- and three-dimensional systems. Based on the extended semiclassical theory, we develop a unified theory including both longitudinal and transverse currents. We classify all contributions according to their different scaling relations with the relaxation time. We reveal the disti

Hao Wang, Jianqi Hu, YoonSeok Baek, Kohei Tsuchiyama

Artificial neural networks with internal dynamics exhibit remarkable capability in processing information. Reservoir computing (RC) is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency. Recently, a new RC paradigm known as next generation reservoir computing (NGRC) further im

Felipe A. Barros, Hugo N. Ulloa, Gabriel Aguayo, Arnold J. T. M. Mathijssen

Earth's aquatic environments are inherently stratified layered systems where interfaces between layers serve as ecological niches for microbial swimmers, forming colonies known as Active Carpet (AC). Previous theoretical studies have explored the hydrodynamic fluctuations exerted by ACs in semi-infinite fluid media, demonstrating their capability to enhance

Weiyu Sun, Xinyu Zhang, Hao Lu, Ying Chen

Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis. Existing rPPG methods utilize multiple datasets for training to enhance the generalizability of models. However, they oft

Carl Feghali, Malory Marin, Rémi Watrigant

We prove a number of results related to the computational complexity of recognizing well-covered graphs. Let $k$ and $s$ be positive integers and let $G$ be a graph. Then $G$ is said - $\mathbf{W_k}$ if for any $k$ pairwise disjoint independent vertex sets $A_1, \dots, A_k$ in $G$, there exist $k$ pairwise disjoint maximum independent sets $S_1, \dots,S_k$ i

S. H. Mejias, A. L. Cortajarena, R. Mincigrucci, C. Svetina

Understanding complex biological macromolecules, especially proteins, is vital for grasping their diverse chemical functions with direct impact in biology and pharmacology. While techniques like X-ray crystallography and cryo-electron microscopy have been valuable, they face limitations such as radiation damage and difficulties in crystallizing certain prote

Dayeon Ki, Marine Carpuat

Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality, derived from Multidimensional Quality Metric (MQM) annot

Shizun Wang, Songhua Liu, Zhenxiong Tan, Xinchao Wang

Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems f

E. Atza, N. Budko

This work demonstrates that applying a fixed-effect multiple linear regression (MLR) model to an overparameterized dataset is mathematically equivalent to fitting a hyper-curve parameterized by a single scalar. This reformulation shifts the focus from global coefficients to individual predictors, allowing each to be modeled as a function of a common paramete

Kateryna Goloviznina, Johann Fleischhaker, Tobias Binninger, Benjamin Rotenberg

Molecular dynamics simulations at a constant electric potential are an essential tool to study electrochemical processes, providing microscopic information on the structural, thermodynamic, and dynamical properties. Despite the numerous advances in the simulation of electrodes, they fail to accurately represent the electronic structure of materials such as g

Lei Chen, Xinghang Gao, Fei Chao, Xiang Chang

In the field of crowd counting research, many recent deep learning based methods have demonstrated robust capabilities for accurately estimating crowd sizes. However, the enhancement in their performance often arises from an increase in the complexity of the model structure. This paper discusses how to construct high-performance crowd counting models using o

Junyi Li, Zhilu Zhang, Wangmeng Zuo

Blind-spot networks (BSN) have been prevalent neural architectures in self-supervised image denoising (SSID). However, most existing BSNs are conducted with convolution layers. Although transformers have shown the potential to overcome the limitations of convolutions in many image restoration tasks, the attention mechanisms may violate the blind-spot require

Jennifer Schober, Igor Rogachevskii, Axel Brandenburg

At high energies, the dynamics of a plasma with charged fermions can be described in terms of chiral magnetohydrodynamics. Using direct numerical simulations, we demonstrate that chiral magnetic waves (CMWs) can produce a chiral asymmetry $\mu_5 = \mu_\mathrm{L} - \mu_\mathrm{R}$ from a spatially fluctuating (inhomogeneous) chemical potential $\mu = \mu_\mat

Yunhong Deng, Sihong Shao, Alex Mogilner, Mingtao Xia

In this paper, we develop a new adaptive hyperbolic-cross-space mapped Jacobi (AHMJ) method for solving multidimensional spatiotemporal integrodifferential equations in unbounded domains. By devising adaptive techniques for sparse mapped Jacobi spectral expansions defined in a hyperbolic cross space, our proposed AHMJ method can efficiently solve various spa

Riccardo Adami, Filippo Boni, Raffaele Carlone, Lorenzo Tentarelli

We discuss the problem of establishing the existence of the Ground States for the subcritical focusing Nonlinear Schr\"odinger energy on a domain made of a line and a plane intersecting at a point. The problem is physically motivated by the experimental realization of hybrid traps for Bose-Einstein Condensates, that are able to concentrate the system on stru

J. M. Carmona, J. L. Cortés, F. Rescic, M. A. Reyes

Very high-energy astrophysical gamma rays suffer a suppression of their flux along their propagation due to their interaction, through the $\gamma\gamma\to e^+e^-$ pair-production process, with the soft photon backgrounds present in the Universe. We examine the Universe's transparency to gamma rays within a Lorentz Invariance Violation (LIV) framework, focus

Eyal Heifetz, Nimrod Bratspiess, Anirban Guha, Leo Maas

Here we show how Feynman's simplified model for the Josephson junction, as a macroscopic two-state coupled quantum system, has a one-to-one correspondence with the stable dynamics of two interfacial Rossby waves in piecewise linear shear flows. The conservation of electric charge and energy of the superconducting electron gas layers become respectively equiv

Dominik Hamara, Mara Strungaru, Jamie Massey, Quentin Remy

An antiferromagnet emits spin currents when time-reversal symmetry is broken. This is typically achieved by applying an external magnetic field below and above the spin-flop transition or by optical pumping. In this work we apply optical pump-THz emission spectroscopy to study picosecond spin pumping from metallic FeRh as a function of temperature. Intriguin

Yekun Chai, Qingyi Liu, Shuohuan Wang, Yu Sun

Amidst the rapid advancements in generative language models, the investigation of how training data shapes the performance of GPT models is still emerging. This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models. Our approach not only traces the i

Aleksandar Botev, Soham De, Samuel L Smith, Anushan Fernando

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing

Luca Ballotta, Michal Yemini

We consider a multi-agent system where agents aim to achieve a consensus despite interactions with malicious agents that communicate misleading information. Physical channels supporting communication in cyberphysical systems offer attractive opportunities to detect malicious agents, nevertheless, trustworthiness indications coming from the channel are subjec

Jonas Eschmann, Dario Albani, Giuseppe Loianno

Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification

Juliette Faille, Quentin Brabant, Gwenole Lecorve, Lina M. Rojas-Barahona

We explore question generation in the context of knowledge-grounded dialogs focusing on explainability and evaluation. Inspired by previous work on planning-based summarisation, we present a model which instead of directly generating a question, sequentially predicts first a fact then a question. We evaluate our approach on 37k test dialogs adapted from the

Joao Encarnacao, Christian Siemes, Ilias Daras, Olivier Carraz

Mapping the Earth's gravity field from space offers valuable insights into climate change, hydro- and biosphere evolution, and seismic activity. Current satellite gravimetry missions have demonstrated the utility of gravity data in understanding global mass transport phenomena, climate dynamics, and geological processes. However, state-of-the-art measurement

Sylvain Carrozza

We provide a brief overview of tensor models and group field theories, focusing on their main common features. Both frameworks arose in the context of quantum gravity research, and can be understood as higher-dimensional generalizations of matrix models. We describe the common combinatorial structure underlying such models and review some of the key mathemat

Handi Deng, Yucheng Zhou, Jiaxuan Xiang, Liujie Gu

Foundation models have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we propose a method based on foundation models and zero training to

Antonio Pedro Ramos

We consider a variant of a problem first introduced by Hughes and Rudnick (2003) and generalized by Bernard (2015) concerning conditional bounds for small first zeros in a family of $L$-functions. Here we seek to estimate the size of the smallest intervals centered at a low-lying height for which we can guarantee the existence of a zero in a family of $L$-fu

Md Masruk Aulia, Nitol Saha, Md. Mostafizur Rahman

The pharmaceutical manufacturing faces critical challenges due to the global threat of counterfeit drugs. This paper proposes a new approach of protected QR codes to secure unique product information for safeguarding the pharmaceutical supply chain. The proposed solution integrates secure QR code generation and encrypted data transmission to establish a comp

Geng Chen, Faris A. El-Katri, Yanbo Hu, Yannan Shen

In this paper, we define the rarefaction and compression characters for the supersonic expanding wave of the compressible Euler equations with radial symmetry. Under this new definition, we show that solutions with rarefaction initial data will not form shock in finite time, i.e. exist global-in-time as classical solutions. On the other hand, singularity for

Alina Glaubitz, Feng Fu

In fighting infectious diseases posing a global health threat, ranging from influenza to Zika, non-pharmaceutical interventions (NPI), such as social distancing and face covering, remain mitigation measures public health can resort to. However, the success of NPI lies in sufficiently high levels of collective compliance, otherwise giving rise to waves of inf

Aleks Kissinger, John van de Wetering

This is the second in a series of "graphical grokking" papers in which we study how stabiliser codes can be understood using the ZX-calculus. In this paper we show that certain complex rules involving ZX-diagrams, called spider nest identities, can be captured succinctly using the scalable ZX-calculus, and all such identities can be proved inductively from a

Zhiliang Peng, Yicheng Wang, Zhengwu Yuan, Xingsheng Wang

This paper introduces an innovative parameter extraction method for BSIM-CMG compact models, seamlessly integrating curve feature extraction and machine learning techniques. This method offers a promising solution for bridging the division between TCAD and compact model, significantly contributing to the Design Technology Co-Optimization (DTCO) process. The

Giuseppe Canonaco, Leo Ardon, Alberto Pozanco, Daniel Borrajo

The use of Potential-Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, choosing the right potential function remains an open challenge. Additionally, RL techniques are usually constrained to use a finite horizon for computational limitations, which introdu

Pieter H. Harkema, Michael Iversen, Anne E. B. Nielsen

The eigenstate thermalization hypothesis describes how isolated many-body quantum systems reach thermal equilibrium. However, quantum many-body scars and Hilbert space fragmentation violate this hypothesis and cause nonthermal behavior. We demonstrate that Hilbert space fragmentation may arise from lattice geometry in a spin-1/2 model that conserves the numb

Yuichi Inoue, Kento Sasaki, Yuma Ochi, Kazuki Fujii

Vision Language Models (VLMs) have undergone a rapid evolution, giving rise to significant advancements in the realm of multimodal understanding tasks. However, the majority of these models are trained and evaluated on English-centric datasets, leaving a gap in the development and evaluation of VLMs for other languages, such as Japanese. This gap can be attr

David Manheim, Sammy Martin, Mark Bailey, Mikhail Samin

Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing Artificial Intelligence (AI) systems have therefore understandably gained momentum. However, we argue that creating auditing standar

Yu Teng, Miaomiao Zhang, Jie An

We present an algorithm for active learning of deterministic timed automata with multiple clocks. The algorithm is within the querying framework of Angluin's $L^*$ algorithm and follows the idea proposed in existing work on the active learning of deterministic one-clock timed automata. We introduce an equivalence relation over the reset-clocked language of a

Benjamin Lieberman, Salah-Eddine Dahbi, Andreas Crivellin, Finn Stevenson

To mitigate the model dependencies of searches for new narrow resonances at the Large Hadron Collider (LHC), semi-supervised Neural Networks (NNs) can be used. Unlike fully supervised classifiers these models introduce an additional look-elsewhere effect in the process of optimising thresholds on the response distribution. We perform a frequentist study to q

Ji Liu, Zifeng Zhang, Mingjie Lu, Hongyang Wei

Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, which highly depend on the training dataset and remain fixed during inference. We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane

Zhiqi Chen, Runhan Li, Yingxi Bai, Ning Mao

Recent advances in manipulation of orbital angular momentum (OAM) within the paradigm of orbitronics present a promising avenue for the design of future electronic devices. In this context, the recently observed orbital Hall effect (OHE) occupies a special place. Here, focusing on both the second-order topological and quantum anomalous Hall insulators in two

Phoebe Hollowbread-Smith, Riccardo W. Maffucci

We classify and construct all line graphs that are $3$-polytopes (planar and $3$-connected). Apart from a few special cases, they are all obtained starting from the medial graphs of cubic (i.e., $3$-regular) $3$-polytopes, by applying two types of graph transformations. This is similar to the generation of other subclasses of $3$-polytopes.

Yiling Chen, Jessie Finocchiaro

Analyses of voting algorithms often overlook informational externalities shaping individual votes. For example, pre-polling information often skews voters towards candidates who may not be their top choice, but who they believe would be a worthwhile recipient of their vote. In this work, we aim to understand the role of external information in voting outcome

Lanpei Li, Elia Piccoli, Andrea Cossu, Davide Bacciu

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucia

Martin Kalck, Carlo Klapproth, Nebojsa Pavic

We show that odd-dimensional projective varieties with tilting objects and only ADE-hypersurface singularities are nodal, i.e. they only have $A_1$-singularities. This is a very special case of more general obstructions to the existence of semiorthogonal decompositions for projective Gorenstein varieties. More precisely, for many isolated hypersurface singul

Angelo Di Cataldo, Hamed Eivazi, Giuseppe Aiello, Dario Patti

This paper presents the design of an 800 V 11 kVA three-level three-phase active neutral point clamped inverter, utilizing 650 V gallium nitride enhancement-mode high-electron-mobility transistors, to evaluate its feasibility in electric traction systems. The modular approach of the presented power converter design is detailed discussed and the different pri

Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin

Trained models are often composed with post-hoc transforms such as temperature scaling (TS), ensembling and stochastic weight averaging (SWA) to improve performance, robustness, uncertainty estimation, etc. However, such transforms are typically applied only after the base models have already been finalized by standard means. In this paper, we challenge this

Stefan Bott, Horacio Saggion, Nelson Peréz Rojas, Martin Solis Salazar

Automatic lexical simplification is a task to substitute lexical items that may be unfamiliar and difficult to understand with easier and more common words. This paper presents the description and analysis of two novel datasets for lexical simplification in Spanish and Catalan. This dataset represents the first of its kind in Catalan and a substantial additi

Xiaoyutao Luo

We prove that the 3D Euler and Navier-Stokes equations are strongly illposed in supercritical Sobolev spaces. In the inviscid case, for any $0 < s < \frac{5}{2} $, we construct a $C^\infty_c$ initial velocity field with arbitrarily small $H^{s}$ norm for which the unique local-in-time smooth solution of the 3D Euler equation develops large $\dot{H}^{s}$ norm

A. S. Voloshina, A. D. Lavrukhina, M. V. Pruzhinskaya, K. L. Malanchev

Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ two approaches: a modification of a traditional method of parametric fit search and a machine learning algorit

Jérôme Crassous, Samuel Poincloux, Audrey Steinberger

Knitted fabrics are metamaterials with remarkable mechanical properties, such as extreme deformability and multiple history-dependent rest shapes. This letter shows that those properties may stem from a continuous set of metastable states for a mechanically relaxed fabric, evidenced through experiments, numerical simulations and analytical developments. Thos

Yingrui Zhuang, Lin Cheng, Ning Qi, Mads R. Almassalkhi

Scenario reduction (SR) aims to identify a small yet representative scenario set to depict the underlying uncertainty, which is critical to scenario-based stochastic optimization (SBSO) of power systems. Existing SR techniques commonly aim to achieve statistical approximation to the original scenario set. However, SR and SBSO are commonly considered as two d

Timothée Crin-Barat, Shuichi Kawashima, Jiang Xu

We investigate the Navier-Stokes-Cattaneo-Christov (NSC) system in $\mathbb{R}^d$ ($d\geq3$), a model of heat-conductive compressible flows serving as a finite speed of propagation approximation of the Navier-Stokes-Fourier (NSF) system. Due to the presence of Oldroyd's upper-convected derivatives, the system (NSC) exhibits a \textit{lack of hyperbolicity} w

Adam Ginsburg, John Bally, Ashley T. Barnes, Cara Battersby

A compact source, G0.02467-0.0727, was detected in ALMA \threemm observations in continuum and very broad line emission. The continuum emission has a spectral index $\alpha\approx3.3$, suggesting that the emission is from dust. The line emission is detected in several transitions of CS, SO, and SO$_2$ and exhibits a line width FWHM $\approx160$ \kms. The lin

Mayura Manawadu, Udaya Wijenayake

Traffic signs are important in communicating information to drivers. Thus, comprehension of traffic signs is essential for road safety and ignorance may result in road accidents. Traffic sign detection has been a research spotlight over the past few decades. Real-time and accurate detections are the preliminaries of robust traffic sign detection system which

Yue Hu, Alex Lazarian

Synchrotron observation serves as a tool for studying magnetic fields in the interstellar medium and intracluster medium, yet its ability to unveil three-dimensional (3D) magnetic fields, meaning probing the field'splane-of-the-sky (POS) orientation, inclination angle relative to the line of sight, and magnetization from one observational data, remains large

Yongxin Li, Zhongshuo Lin, Yifan Wang, Hehu Xie

Based on tensor neural network, we propose an interpolation method for high dimensional non-tensor-product-type functions. This interpolation scheme is designed by using the tensor neural network based machine learning method. This means that we use a tensor neural network to approximate high dimensional functions which has no tensor product structure. In so

G. Pierrou, C. Valero-De La Flor, G. Hug

In this paper, a novel Energy Management System (EMS) algorithm to achieve optimal Electric Vehicle (EV) charging scheduling at the parking lots of electric railway stations is proposed. The proposed approach uncovers the potential of leveraging EV charging flexibility to prevent overloading in the combined EV charging and railway operation along with renewa

Ivan Cheltsov, Oliver Li, Sione Ma'u, Antoine Pinardin

We study Fano threefolds that can be obtained by blowing up the three-dimensional projective space along a smooth curve of degree six and genus three. We produce many new K-stable examples of such threefolds, and we describe all finite groups that can act faithfully on them.

Simone Cantori, Andrea Mari, David Vitali, Sebastiano Pilati

We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expec

Pascal Koiran

We give a new, constructive uniqueness theorem for tensor decomposition. It applies to order 3 tensors of format $n \times n \times p$ and can prove uniqueness of decomposition for generic tensors up to rank $r=4n/3$ as soon as $p \geq 4$. One major advantage over Kruskal's uniqueness theorem is that our theorem has an algorithmic proof, and the resulting al

Alexander J. White, Galen T. Craven, Vidushi Sharma, Lee A. Collins

Predicting the charged particle transport properties of warm dense matter / hot dense plasma mixtures is a challenge for analytical models. High accuracy ab initio methods are more computationally expensive, but can provide critical insight by explicitly simulating mixtures. In this work, we investigate the transport properties and optical response of warm d

V. Valenzuela-Villaseca, J. M. Molina, D. B. Schaeffer, S. Malko

We present results from X-ray imaging of high-aspect-ratio magnetic reconnection experiments driven at the National Ignition Facility. Two parallel, self-magnetized, elongated laser-driven plumes are produced by tiling 40 laser beams. A magnetic reconnection layer is formed by the collision of the plumes. A gated X-ray framing pinhole camera with micro-chann

David Hochberg, Isabel Herreros

The Glansdorff and Prigogine General Evolution Criterion (GEC) is an inequality that holds for macroscopic physical systems obeying local equilibrium and that are constrained under timeindependent boundary conditions. The latter, however, may prove overly restrictive for many applications involving fluid flow in physics, chemistry and biology. We therefore a

Danae Antunez Vazquez, Laura Pilozzi, Eugenio DelRe, Claudio Conti

Terahertz imaging provides valuable insights into the composition and structure of objects or materials, with applications spanning security screening, medical imaging, materials science, and cultural heritage preservation. Despite its widespread utility, traditional terahertz imaging is limited in spatial resolution to approximately 1 mm according to Abbe's

Hongyu Wang, Ying Li, Ronghong Huang, Xianghang Mi

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing

Xiaofeng Xiang, Yijun Tong, Aaron Gehrke, Scott Dunham

CdTe and its alloy CdTeSe are widely used in optoelectronic devices, such as radiation detectors and solar cells, due to their superior electrical properties. However, the formation of defects and defect complexes in these materials can significantly affect their performance. As a result, understanding the defect formation and recombination processes in CdTe

Ali Imran, Vivek Shankar Varadharajan, Rafael Gomes Braga, Yann Bouteiller

Artistic performances involving robotic systems present unique technical challenges akin to those encountered in other field deployments. In this paper, we delve into the orchestration of robotic artistic performances, focusing on the complexities inherent in communication protocols and localization methods. Through our case studies and experimental insights

Shaocong Long, Qianyu Zhou, Xiangtai Li, Xuequan Lu

Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic complexities issues. Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global

Rupali Jindal, Rachana Yogi, Alok Shukla

The objective of our research is to investigate the electrocatalytic properties of novel metal-free quantum dots (QDs) composed of the recently discovered 2D material penta-CN2, with the aim of replacing costly and scarce catalysts such as Pt and Pd. Employing a first-principles density functional theory (DFT) based approach, the geometries of the three pent

Stephen Bothwell, Abigail Swenor, David Chiang

This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two methods for doing so on the basis of th

Clément Le Priol, Joy M. Monteiro, Freddy Bouchet

Computing the return times of extreme events and assessing the impact of climate change on such return times is fundamental to extreme event attribution studies. However, the rarity of such events in the observational record makes this task a challenging one, even more so for "record-shattering" events that have not been previously observed at all. While cli

Meng Yu, Te Cui, Haoyang Lu, Yufeng Yue

Image dehazing poses significant challenges in environmental perception. Recent research mainly focus on deep learning-based methods with single modality, while they may result in severe information loss especially in dense-haze scenarios. The infrared image exhibits robustness to the haze, however, existing methods have primarily treated the infrared modali

Serio Agriesti, Claudio Roncoli, Bat-hen Nahmias-Biran

This paper proposes an iterative methodology to integrate large-scale behavioral activity-based models with dynamic traffic assignment models. The main novelty of the proposed approach is the decoupling of the two parts, allowing the ex-post integration of any existing model as long as certain assumptions are satisfied. A measure of error is defined to chara

Yansheng Li, Kun Li, Yongjun Zhang, Linlin Wang

Scene graph generation (SGG) aims to understand the visual objects and their semantic relationships from one given image. Until now, lots of SGG datasets with the eyelevel view are released but the SGG dataset with the overhead view is scarcely studied. By contrast to the object occlusion problem in the eyelevel view, which impedes the SGG, the overhead view

Kunyan Wang, Yichun Dai, Bin Wang, Xu Tan

For segmented telescopes, achieving fine co-focus adjustment is essential for realizing co-phase adjustment and maintenance, which involves adjusting the millimeter-scale piston between segments to fall within the capture range of the co-phase detection system. CGST proposes using a SHWFS for piston detection during the co-focus adjustment stage. However, th

Samuel Plesnik, Maria Violaris

We compare how the impossibility of a universal work extractor from coherence arises from different approaches to quantum thermodynamics: an explicit protocol accounting for all relevant quantum resources, and axiomatic, information-theoretic constraints imposed by constructor theory. We first explain how the impossibility of a universal work extractor from