March 2024 arXiv papers — page 90

Stephen C. Power

Every noncompact surface is shown to have a (3,6)-tight triangulation, and applications are given to the generic rigidity of countable bar-joint frameworks in R^3. In particular, every noncompact surface has a (3,6)-tight triangulation that is minimally 3-rigid. A simplification of Richards' proof of Ker\'ekj\'art\'o's classification of noncompact surfaces i

Alec Reed, Brendan Crowe, Doncey Albin, Lorin Achey

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. When entering space that was previously obstructed from view such as turning corners in hallways or entering new rooms, robots often pause to plan over the newly observed space. To address this we present SceneScene, a real

Andrew Katz, Mitchell Gerhardt, Michelle Soledad

Feedback is a critical aspect of improvement. Unfortunately, when there is a lot of feedback from multiple sources, it can be difficult to distill the information into actionable insights. Consider student evaluations of teaching (SETs), which are important sources of feedback for educators. They can give instructors insights into what worked during a semest

Zhiruo Wang, Zhoujun Cheng, Hao Zhu, Daniel Fried

Language models (LMs) are powerful yet mostly for text generation tasks. Tools have substantially enhanced their performance for tasks that require complex skills. However, many works adopt the term "tool" in different ways, raising the question: What is a tool anyway? Subsequently, where and how do tools help LMs? In this survey, we provide a unified defini

Irantzu Barrio, Javier Roca-Pardiñas, Cristobal Esteban, Maria Durban

The use of discretized variables in the development of prediction models is a common practice, in part because the decision-making process is more natural when it is based on rules created from segmented models. Although this practice is perhaps more common in medicine, it is extensible to any area of knowledge where a predictive model helps in decision-maki

Thomas W. Gries, Davide Regaldo, Hans Koebler, Titan Noor Hartono Putri

Solar cells based on inorganic perovskite CsPbI3 are promising candidates to resolve the challenge of operational stability in the field of perovskite photovoltaics. For stable operation, however, it is crucial to thoroughly understand the extractive and recombinative processes occurring at the interfaces of perovskite and the charge-selective layers. In thi

Sanghyun Hong, Nicholas Carlini, Alexey Kurakin

We present a certified defense to clean-label poisoning attacks under $\ell_2$-norm. These attacks work by injecting a small number of poisoning samples (e.g., 1%) that contain bounded adversarial perturbations into the training data to induce a targeted misclassification of a test-time input. Inspired by the adversarial robustness achieved by $randomized$ $

Banasree S. Mou, Xinshu Zhang, Li Xiang, Yuanyuan Xu

Co-based materials have recently been explored due to potential to realise complex bond-dependent anisotropic magnetism. Prominent examples include Na$_2$Co$_2$TeO$_6$, BaCo$_2$(AsO$_4$)$_2$, Na$_2$BaCo(PO$_4$)$_2$, and CoX$_2$ (X = Cl, Br, I). In order to provide insight into the magnetic interactions in these compounds, we make a comparative analysis of th

Junghun Chae, Roktaek Lim, Thomas L. P. Martin, Cheol-Min Ghim

The century-long Michaelis-Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time

S. G. Elgendi, A. Soleiman, Nabil L. Youssef

For a Finsler metric $F$, we introduce the notion of $F$-covariant coefficients $H_i$ of the geodesic spray of $F$ (Def. 3.1). We study some geometric consequences concerning the objects $H_i$. If the $F$-covariant coefficients $H_i$ are written in the form $H_i={\dot{\partial}}_iH$, for some smooth function $H$ on ${\mathcal T\hspace{-1pt}M}$, positively 3-

Jan Krejčí, Oliver Kost, Ondřej Straka, Jindřich Duník

A first-principle single-object model is proposed for pedestrian tracking. It is assumed that the extent of the moving object can be described via known statistics in 3D, such as pedestrian height. The proposed model thus need not constrain the object motion in 3D to a common ground plane, which is usual in 3D visual tracking applications. A nonlinear filter

Sandeep Kumar Chaluvadi, Shyni Punathum Chalil, Anupam Jana, Deepak Dagur

Metallic ferromagnetic transition metal dichalcogenides have emerged as important building blocks for scalable magnonics and memory applications. Downscaling such systems to the ultra-thin limit is critical to integrate them into technology. Here, we achieved layer-by-layer control over the transition metal dichalcogenide Cr$_{1.6}$Te$_{2}$ by using pulsed l

Alexander Hazeltine, Baiying Liu, Chi-Heng Lo, Freydoon Shahidi

In this paper we study the upper bound of wavefront sets of irreducible admissible representations of connected reductive groups defined over non-Archimedean local fields of characteristic zero. We formulate a new conjecture on the upper bound and show that it can be reduced to that of anti-discrete series representations, namely, those whose Aubert-Zelevins

Lorenzo Magnea, Calum Milloy, Chiara Signorile-Signorile, Paolo Torrielli

In the context of infrared subtraction algorithms beyond next-to-leading order, it becomes necessary to consider multiple infrared limits of scattering amplitudes, in which several particles become soft or collinear in a strongly-ordered sequence. We study these limits from the point of view of infrared factorisation, and we provide general definitions of st

Yang Li, Qiuyi Huang, Chong Zhong, Danjuan Yang

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (

Christopher J. Fewster, Daan W. Janssen, Leon Deryck Loveridge, Kasia Rejzner

We develop an operational framework, combining relativistic quantum measurement theory with quantum reference frames (QRFs), in which local measurements of a quantum field on a background with symmetries are performed relative to a QRF. This yields a joint algebra of quantum-field and reference-frame observables that is invariant under the natural action of

Ling-Xiao Dang, Ping Zhou, Lei Sun, Junjie Mao

G352.7$-$0.1 is a mixed-morphology (MM) supernova remnant (SNR) with multiple radio arcs and has a disputed supernova origin. We conducted a spatially resolved spectroscopic study of the remnant with XMM-Newton X-ray data to investigate its explosion mechanism and explain its morphology. The global X-ray spectra of the SNR can be adequately reproduced using

Adam L. Shaw, Daniel K. Mark, Joonhee Choi, Ran Finkelstein

Systems reaching thermal equilibrium are ubiquitous. For classical systems, this phenomenon is typically understood statistically through ergodicity in phase space, but translating this to quantum systems is a long-standing problem of interest. Recently a strong notion of quantum ergodicity has been proposed, namely that isolated, global quantum states unifo

Daniel K. Mark, Federica Surace, Andreas Elben, Adam L. Shaw

We report universal statistical properties displayed by ensembles of pure states that naturally emerge in quantum many-body systems. Specifically, two classes of state ensembles are considered: those formed by i) the temporal trajectory of a quantum state under unitary evolution or ii) the quantum states of small subsystems obtained by partial, local project

Jordi Font-Reverter, Alejandro Torres-Sánchez, Guillermo Vilanova, Marino Arroyo

In a prototypical mode of single-cell migration, retrograde cytoskeletal flow is mechanically coupled to the environment, propels the cell, and is sustained by an anterograde cytosolic flow of disassembled cytoskeletal components. Supracellular collectives also develop fountain-flows to migrate, but the opposing cellular streams interact with the environment

Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen

Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. In these applications, conditional diffusion models incorporate various conditional information, such as prompt input, to guide the sample generation towards desired properties. Despite

Botao Du, Ramya Suresh, Santiago López, Jeremy Cadiente

Transport measurements are fundamental for understanding condensed matter phenomena, from superconductivity to the fractional quantum Hall effect. Analogously, they can be powerful tools for probing synthetic quantum matter in quantum simulators. Here we demonstrate the measurement of in-situ particle current in a superconducting circuit lattice and apply it

Christian Schlauch, Christian Wirth, Nadja Klein

Prior parameter distributions provide an elegant way to represent prior expert and world knowledge for informed learning. Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data-efficiency. However, commonly used sampling-based approximations for probabilistic DL mo

Mahammad Sabir Ali, Deeptak Biswas, Amaresh Jaiswal, Hiranmaya Mishra

Within a 2+1 flavor Nambu\textendash Jona-Lasinio model, we calculate the curvature coefficients and check them against available lattice QCD estimations. With the observation that the flavor mixing due to the `t Hooft determinant term significantly affects the $\kappa_{2}^{S}$, we explore the effect of $\mu_{S}$ on the $T-\mu_{B}$ crossover lines. With the

Victor Dheur, Souhaib Ben Taieb

Generating calibrated and sharp neural network predictive distributions for regression problems is essential for optimal decision-making in many real-world applications. To address the miscalibration issue of neural networks, various methods have been proposed to improve calibration, including post-hoc methods that adjust predictions after training and regul

Vincent J. Straub, Youmna Hashem, Jonathan Bright, Satyam Bhagwanani

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic dec

Alkis Kalavasis, Ilias Zadik, Manolis Zampetakis

We study the fundamental problem of transfer learning where a learning algorithm collects data from some source distribution $P$ but needs to perform well with respect to a different target distribution $Q$. A standard change of measure argument implies that transfer learning happens when the density ratio $dQ/dP$ is bounded. Yet, prior thought-provoking wor

Mateo Anarella

We consider the pseudo-nearly K\"ahler $\mathrm{SL}(2,\mathbb{R})\times\mathrm{SL}(2,\mathbb{R})$ and we study its Lagrangian submanifolds. We provide examples of Lagrangian submanifolds which do not have an analogue in $\mathbb{S}^3\times\mathbb{S}^3$. We also provide an expression for the isometry group of $\mathrm{SL}(2,\mathbb{R})\times\mathrm{SL}(2,\mat

Siying Liu, Pier Luigi Dragotti

Deep neural networks for event-based video reconstruction often suffer from a lack of interpretability and have high memory demands. A lightweight network called CISTA-LSTC has recently been introduced showing that high-quality reconstruction can be achieved through the systematic design of its architecture. However, its modelling assumption that input signa

Baoyu Jing, Dawei Zhou, Kan Ren, Carl Yang

Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechanical damages and Internet outages. Imputing the missing values is crucial for analyzing time series. When recovering a specific data point, most existing methods consider all the in

Hang Wang, Zhi-Qi Cheng, Youtian Du, Lei Zhang

Video Action Counting (VAC) is crucial in analyzing sports, fitness, and everyday activities by quantifying repetitive actions in videos. However, traditional VAC methods have overlooked the complexity of action repetitions, such as interruptions and the variability in cycle duration. Our research addresses the shortfall by introducing a novel approach to VA

Mathieu Rita, Paul Michel, Rahma Chaabouni, Olivier Pietquin

Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agen

Shihang He

Motivated by the solution of the aspherical conjecture up to dimension 5 [CL20][Gro20], we want to study a relative version of the aspherical conjecture. We present a natural condition generalizing the model $X\times\mathbb{T}^k$ to the relative aspherical setting. Such model is closely related to submanifold obstruction of positive scalar curvature (PSC), a

Tengchuan Kou, Xiaohong Liu, Zicheng Zhang, Chunyi Li

With the rapid development of generative models, Artificial Intelligence-Generated Contents (AIGC) have exponentially increased in daily lives. Among them, Text-to-Video (T2V) generation has received widespread attention. Though many T2V models have been released for generating high perceptual quality videos, there is still lack of a method to evaluate the q

Jack Kolb, Karen M. Feigh

We investigate the real-time estimation of human situation awareness using observations from a robot teammate with limited visibility. In human factors and human-autonomy teaming, it is recognized that individuals navigate their environments using an internal mental simulation, or mental model. The mental model informs cognitive processes including situation

Max Welz

While there is a rich literature on robust methodologies for contamination in continuously distributed data, contamination in categorical data is largely overlooked. This is regrettable because many datasets are categorical and oftentimes suffer from contamination. Examples include inattentive responding and bot responses in questionnaires or zero-inflated c

Jiaxu Wang, Qiang Zhang, Jingkai Sun, Jiahang Cao

An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment representation directly influences the achievement of the learning task. Previous vision-based RL typically uses explicit or implicit ways to represent environments, such as images, points

Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu

To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt50 as the strong feature extractor, initializing it with pretrained weight

Katrin Rajamäe-Soosaar, Anastasija Nikiforova

Estonia has a global reputation of a digital state or e-country. However, despite the success in digital governance, the country has faced challenges in the realm of Open Government Data (OGD) area, with significant advancements in its OGD ecosystem, as reflected in various open data rankings from 2020 and onwards, in the recent years being recognized among

Farnik Nikakhtar, Ravi K. Sheth, Nikhil Padmanabhan, Bruno Lévy

We demonstrate the effectiveness of one of the many multi-tracer analyses enabled by Optimal Transport (OT) reconstruction. Leveraging a semi-discrete OT algorithm, we determine the displacements between initial and observed positions of biased tracers and the remaining matter field. With only redshift-space distorted final positions of biased tracers and a

Benedikt T. Arnold, Johannes Theissen-Lipp, Diego Collarana, Christoph Lange

Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the qu

Philip Thomas, Leonardo Ruscio, Olivier Morin, Gerhard Rempe

Entanglement has evolved from an enigmatic concept of quantum physics to a key ingredient of quantum technology. It explains correlations between measurement outcomes that contradict classical physics, and has been widely explored with small sets of individual qubits. Multi-partite entangled states build up in gate-based quantum-computing protocols, and $\un

Andreas Fring, Takano Taira, Bethan Turner

We compare a relativistic and a nonrelativistic version of Ostrogradsky's method for higher-time derivative theories extended to scalar field theories and consider as an alternative a multi-field variant. We apply the schemes to space-time rotated modified Korteweg-de Vries systems and, exploiting their integrability, to Hamiltonian systems built from space-

Bernardo Fichera, Aude Billard

Dynamical Systems (DS) are an effective and powerful means of shaping high-level policies for robotics control. They provide robust and reactive control while ensuring the stability of the driving vector field. The increasing complexity of real-world scenarios necessitates DS with a higher degree of non-linearity, along with the ability to adapt to potential

Gargya Gokhale, Bert Claessens, Chris Develder

With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of sustainable energy sources. Besides traditional sources, the residential sector is another major and largely untapped source of flexibility, driven by the increased adoption of sola

KM3NeT Collaboration

The measurement of the flux of muons produced in cosmic ray air showers is essential for the study of primary cosmic rays. Such measurements are important in extensive air shower detectors to assess the energy spectrum and the chemical composition of the cosmic ray flux, complementary to the information provided by fluorescence detectors. Detailed simulation

Yongkang Huo, Thomas Chaffey, Rodolphe Sepulchre

The paper is a follow-up of the recently introduced kernel-based framework to identify nonlinear input-output systems regularized by desirable input-output incremental properties. Assuming that the system has fading memory, we propose to learn the functional that maps the past input to the present output rather than the operator mapping input trajectories to

Wen Ji, Shenglin Liu, Ke Han, Tao Liu

This study explores the potential of using ride-hailing vehicles (RVs) for integrated passenger and freight transport based on shared mobility. In this crowd-sourced mode, ride-hailing platforms can profit from parcel delivery services, and logistics companies can reduce operational costs by utilizing the capacities of RVs. The Share-a-Ride problem with ride

Alexei Entin

For a fixed prime power $q$ and natural number $d$ we consider a random polynomial $$f=x^n+a_{n-1}(t)x^{n-1}+\ldots+a_1(t)x+a_0(t)\in\mathbb F_q[t][x]$$ with $a_i$ drawn uniformly and independently at random from the set of all polynomials in $\mathbb F_q[t]$ of degree $\le d$. We show that with probability tending to 1 as $n\to\infty$ the Galois group $G_f$

Jun Yu, Zhihong Wei, Zhongpeng Cai, Gongpeng Zhao

Facial Expression Recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the upcoming 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, The limited size of the FER da

Tom Gur, Jack O'Connor, Nicholas Spooner

We construct perfect zero-knowledge probabilistically checkable proofs (PZK-PCPs) for every language in #P. This is the first construction of a PZK-PCP for any language outside BPP. Furthermore, unlike previous constructions of (statistical) zero-knowledge PCPs, our construction simultaneously achieves non-adaptivity and zero knowledge against arbitrary (ada

Alexander Levine, Peter Stone, Amy Zhang

In real-world control settings, the observation space is often unnecessarily high-dimensional and subject to time-correlated noise. However, the controllable dynamics of the system are often far simpler than the dynamics of the raw observations. It is therefore desirable to learn an encoder to map the observation space to a simpler space of control-relevant

Ángel Javier Alonso, Michael Kerber, Primoz Skraba

Multiparameter persistence modules can be uniquely decomposed into indecomposable summands. Among these indecomposables, intervals stand out for their simplicity, making them preferable for their ease of interpretation in practical applications and their computational efficiency. Empirical observations indicate that modules that decompose into only intervals

Patricia Pauli, Dennis Gramlich, Frank Allgöwer

From the perspective of control theory, convolutional layers (of neural networks) are 2-D (or N-D) linear time-invariant dynamical systems. The usual representation of convolutional layers by the convolution kernel corresponds to the representation of a dynamical system by its impulse response. However, many analysis tools from control theory, e.g., involvin

Stanley Snelson, Eduardo V. Teixeira

We consider a generalization of the Bernoulli free boundary problem where the underlying differential operator is a nonlocal, non-translation-invariant elliptic operator of order $2s\in (0,2)$. Because of the lack of translation invariance, the Caffarelli-Silvestre extension is unavailable, and we must work with the nonlocal problem directly instead of trans

Kanchan Poudel, Lisasha Poudel, Prabin Raj Shakya, Atit Poudel

Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs), which are often performed screening camps or primary/community health centers by nurses instead of the preferred but unavailable expert Gynecologist. To address the highly subjective n

M. Kerem Aydin, Qi Guo, Emma Alexander

Hyperspectral cameras face challenging spatial-spectral resolution trade-offs and are more affected by shot noise than RGB photos taken over the same total exposure time. Here, we present a colorization algorithm to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. We demonstrate that our algorithm generalizes

Hamza Kheddar, Yassine Himeur, Abbes Amira, Rachik Soualah

Nowadays, there has been a growing trend in the field of high-energy physics (HEP), in both its experimental and phenomenological studies, to incorporate machine learning (ML) and its specialized branch, deep learning (DL). This review paper provides a thorough illustration of these applications using different ML and DL approaches. The first part of the pap

Cristian Villalobos, María Luisa Cordero, Eric Clément, Rodrigo Soto

The properties of an active fluid, for example, a bacterial bath or a collection of microtubules and molecular motors, can be accessed through the dynamics of passive particle probes. Here, in the perspective of analyzing experimental situations of confinement in droplets, we consider the kinematics of a negatively buoyant probe particle in an active fluid,

Touraj Soleymani, John S. Baras, Siyi Wang, Sandra Hirche

In this chapter, we study the consistency of the value of information$\unicode{x2014}$a semantic metric that claims to determine the right piece of information in networked control systems tasks$\unicode{x2014}$in a lossy and delayed communication regime. Our analysis begins with a focus on state estimation, and subsequently extends to feedback control. To t

Constantin Dalyac, Lucas Leclerc, Louis Vignoli, Mehdi Djellabi

Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native e

Patrick C. Chaumet, Pierre Bon, Guillaume Maire, Anne Sentenac

This article presents a thorough comparison of themain QPM techniques, focusing on their accuracy in terms of measurement precision and trueness. We focus on 8 techniques, namely digital holographic microscopy (DHM), cross-grating wavefront microscopy (CGM), which is based on QLSI (quadriwave lateral shearing interferometry), diffraction phase microscopy (DP

Runhui Huang, Kaixin Cai, Jianhua Han, Xiaodan Liang

Despite the success of generating high-quality images given any text prompts by diffusion-based generative models, prior works directly generate the entire images, but cannot provide object-wise manipulation capability. To support wider real applications like professional graphic design and digital artistry, images are frequently created and manipulated in m

Adrian Pacheco-Pozo, Michał Balcerek, Agnieszka Wyłomańska, Krzysztof Burnecki

The Langevin equation is a common tool to model diffusion at a single-particle level. In non-homogeneous environments, such as aqueous two-phase systems or biological condensates with different diffusion coefficients in different phases, the solution to a Langevin equation is not unique unless the interpretation of stochastic integrals involved is selected.

Touraj Soleymani, John S. Baras, Sandra Hirche, Karl H. Johansson

In this chapter, we present our recent invention, i.e., the notion of the value of information$\unicode{x2014}$a semantic metric that is fundamental for networked control systems tasks. We begin our analysis by formulating a causal tradeoff between the packet rate and the regulation cost, with an encoder and a decoder as two distributed decision makers, and

Yining Wu, Shengyu Duan, Gaole Sai, Chenhong Cao

Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely incre

Touraj Soleymani, John S. Baras, Karl H. Johansson

In this chapter, we investigate the value of information as a more comprehensive instrument than the age of information for optimally shaping the information flow in a networked control system. In particular, we quantify the value of information based on the variation in a value function, and discuss the structural properties of this metric. Through our anal

Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal

In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significant challenge for the global convergence of policy gradient methods. This requirement is particularly problematic due to t

Thomas Galvani, Ali K. Hamze, Laura Caputo, Onurcan Kaya

We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about $100$ atoms in the periodic cell) sample sizes to explore the emergence of mid-gap states and its correlation with structural features. Next, by

Manuele Maistrello, Romain Maccary, Cristiano Guidorzi, Lorenzo Amati

Long gamma-ray burst (GRB) prompt emission shows a correlation between the intrinsic peak energy, $E_{\mathrm{p,i}}$, of the time-average $\nu F_{\nu}$ spectrum and the isotropic-equivalent peak gamma-ray luminosity, $L_{{\rm p,iso}}$, as well as the total released energy, $E_{\rm iso}$. The same correlation is found within individual bursts, when time-resol

Santiago D. Barrionuevo, Federico Fioravanti, Jorge M. Nuñez, David Muñeton Arboleda

The layer stacking morphology in nanocarbons is paramount for achieving new properties and outperforming applications. Here, we demonstrate that graphene quantum dots (GQDs) retain crystallinity and a stacking structure from CVD graphene grown on Ni foam. Our results reveal that GQD subdomains comprise a few-layer graphene structure in the AB -- AB and ABC -

Olivier Kraif

In this paper, we present an adaptive bitextual alignment system called AIlign. This aligner relies on sentence embeddings to extract reliable anchor points that can guide the alignment path, even for texts whose parallelism is fragmentary and not strictly monotonic. In an experiment on several datasets, we show that AIlign achieves results equivalent to the

Rudra Pratap Deb Nath, Tithi Rani Das, Tonmoy Chandro Das, S. M. Shafkat Raihan

In Bangladesh, agriculture is a crucial driver for addressing Sustainable Development Goal 1 (No Poverty) and 2 (Zero Hunger), playing a fundamental role in the economy and people's livelihoods. To enhance the sustainability and resilience of the agriculture industry through data-driven insights, the Bangladesh Bureau of Statistics and other organizations co

Noé De Santo, Aurèle Barrière, Clément Pit-Claudel

We present an executable, proven-safe, faithful, and future-proof Coq mechanization of JavaScript regular expression (regex) matching, as specified by the latest published edition of ECMA-262 section 22.2. This is, to our knowledge, the first time that an industrial-strength regex language has been faithfully mechanized in an interactive theorem prover. We h

Hareesh Chundayil, Vinay P. Majety, Armin Scrinzi

We present a new implementation of the hybrid antisymmetrized Coupled Channels (haCC) method in the framework of the tRecX [A. Scrinzi, Comp. Phys. Comm., 270:108146, 2022.]. The method represents atomic and molecular multi-electron functions by combining CI functions, Gaussian molecular orbitals, and a numerical single-electron basis. It is suitable for des

Kerstin Schmitz, Aleksandra Zimmermann

We show existence and pathwise uniqueness of probabilistically strong solutions to a pseudomonotone stochastic evolution problem on a bounded domain $D\subseteq\mathbb{R}^d$, $d\in\mathbb{N}$, with homogeneous Dirichlet boundary conditions and random initial data $u_0\in L^2(\Omega;L^2(D))$. The main novelty is the presence of a merely H\"older continuous mu

Joshua A. Kable, Vivian Miranda

We assess the consistency of cosmological models that alter the size of the sound horizon at last scattering to resolve the Hubble tension with data from ACT + Planck CMB lensing, Big Bang Nucleosynthesis, and supernova data from Pantheon or Pantheon+. We use early dark energy (EDE) as an example model but conclude that the results apply to other similar mod

Federico Nudo

The Crouzeix--Raviart finite element method is widely recognized in the field of finite element analysis due to its nonconforming nature. The main goal of this paper is to present a general strategy for enhancing the Crouzeix--Raviart finite element using quadratic polynomial functions and three additional general degrees of freedom. To achieve this, we pres

Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy, Damien Querlioz

Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to input data but self-supervised learning requires even more computational and memory resources than superv

Shengchao Yan, Lukas König, Wolfram Burgard

Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we p

Chen YAN

We adopt an optimal-control framework for addressing the undiscounted infinite-horizon discrete-time restless $N$-armed bandit problem. Unlike most studies that rely on constructing policies based on the relaxed single-armed Markov Decision Process (MDP), we propose relaxing the entire bandit MDP as an optimal-control problem through the certainty equivalenc

Pascal Prass, Fabian R. Lux, Emil Prodan, Duco van Straten

Understanding the interaction of real- and reciprocal space topology in skyrmion crystals is an open problem. We approach it from the viewpoint of $C^\ast$-algebras and calculate all admissible Chern numbers of a strongly coupled tight-binding skyrmion system on a triangular lattice as a function of Fermi energy and texture parameters. Our analysis reveals t

B. Yagmur Koca, Ozgur B. Akan

Concurrent with advancements in molecular communication (MC), bacterial communication is emerging as a key area of interest. Given the frequent use of bacteria in various MC models, it is essential to have a thorough grasp of their intrinsic communication, signaling, and engineering techniques. Although it is crucial to have a strong understanding of the com

Daniel Bartl, Ariel Neufeld, Kyunghyun Park

We examine nonlinear Kolmogorov partial differential equations (PDEs). Here the nonlinear part of the PDE comes from its Hamiltonian where one maximizes over all possible drift and diffusion coefficients which fall within a $\varepsilon$-neighborhood of pre-specified baseline coefficients. Our goal is to quantify and compute how sensitive those PDEs are to s

Sibi Catley-Chandar, Richard Shaw, Gregory Slabaugh, Eduardo Perez-Pellitero

Recent advances in neural rendering have enabled highly photorealistic 3D scene reconstruction and novel view synthesis. Despite this progress, current state-of-the-art methods struggle to reconstruct high frequency detail, due to factors such as a low-frequency bias of radiance fields and inaccurate camera calibration. One approach to mitigate this issue is

François Gelis, Sigtryggur Hauksson

Motivated by isotropization of QCD matter in the initial stages of heavy-ion collisions, we consider a system of scalar fields that undergoes a boost invariant longitudinal expansion. We use the framework of the two-particle irreducible (2PI) effective action, which is close to the underlying quantum field theory, and resum self-energy corrections up to thre

Gargya Gokhale, Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder

Demand-side flexibility is gaining importance as a crucial element in the energy transition process. Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) source of energy flexibility. However, unlocking this flexibility requires developing a control framework that (1) easily scales across different

Ashot V. Kakosyan, Lev B. Klebanov

The main result of the paper is the following. Let a non-degenerate distribution have finite moments $\mu_k$ of all orders $k=0,1,2,\ldots$. Then the sequence $\{\mu_k/k!, \; k=0,1,2,\ldots\}$ either contains infinitely many different terms or at most three. In the latter case, this sequence has the form $\{1,a,1-b,a,1-b,a,1-b, \ldots\}$ and corresponds to a

Kevin Xu, Yeganeh Kordi, Tanay Nayak, Adi Asija

Can advanced multi-modal models effectively tackle complex web-based tasks? Such tasks are often found on crowdsourcing platforms, where crowdworkers engage in challenging micro-tasks within web-based environments. Building on this idea, we present TurkingBench, a benchmark consisting of tasks presented as web pages with textual instructions and multi-modal

Korbinian Randl, John Pavlopoulos, Aron Henriksson, Tony Lindgren

Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be applied to automatically detect such risks. We publish a dataset of 7,546 short texts describing public food recall announcements. Each text is manually labeled, on two granularity le

Miriam Wanner, Seth Ebner, Zhengping Jiang, Mark Dredze

As generated text becomes more commonplace, it is increasingly important to evaluate how well-supported such text is by external knowledge sources. Many approaches for evaluating textual support rely on some method for decomposing text into its individual subclaims which are scored against a trusted reference. We investigate how various methods of claim deco

M. Scalora, K. Hallman, S. Mukhopadhyay, S. Pruett

By uncovering novel aspects of second harmonic generation in aluminum we show that there are unusual and remarkable consequences of resonant absorption, namely an unexpectedly critical role that bound electrons play for light-matter interactions across the optical spectrum, suggesting that a different basic approach is required to fully explain the physics o

Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk

Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computation

Beatrice Popescu Braileanu, Rony Keppens

In this paper we study the role of radiative cooling in a two-fluid model consisting of coupled neutrals and charged particles. We first analyze the linearized two-fluid equations where we include radiative losses in the energy equation for the charged particles. In a 1D geometry for parallel propagation and in the limiting cases of weak and strong coupling,

LI Yang, WU Ruizheng, LI Jiyong, CHEN Ying-cong

Learning surfaces from neural radiance field (NeRF) became a rising topic in Multi-View Stereo (MVS). Recent Signed Distance Function (SDF)-based methods demonstrated their ability to reconstruct accurate 3D shapes of Lambertian scenes. However, their results on reflective scenes are unsatisfactory due to the entanglement of specular radiance and complicated

Abraham George, Selam Gano, Pranav Katragadda, Amir Barati Farimani

Tactile information is a critical tool for dexterous manipulation. As humans, we rely heavily on tactile information to understand objects in our environments and how to interact with them. We use touch not only to perform manipulation tasks but also to learn how to perform these tasks. Therefore, to create robotic agents that can learn to complete manipulat

Ofelia Bonesini, Antoine Jacquier, Aitor Muguruza

One the one hand, rough volatility has been shown to provide a consistent framework to capture the properties of stock price dynamics both under the historical measure and for pricing purposes. On the other hand, market price of volatility risk is a well-studied object in Financial Economics, and empirical estimates show it to be stochastic rather than deter

Peter J Barclay, Ashkan Sami

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such bias, making investigation difficult. Here, we examine bias in LLMss via back-translation, using the DeepL tran

Ning Xi, Yuan Gao, Chengchen Li, Shuang Liang

Low-dimensional quantum magnets, particularly those with strong spin frustration, are characterized by their notable spin fluctuations. Nuclear magnetic resonance (NMR) serves as a sensitive probe of low-energy fluctuations that offers valuable insight into rich magnetic phases and emergent phenomena in quantum magnets. Although experimentally accessible, th

Guangming Huang, Yingya Li, Shoaib Jameel, Yunfei Long

Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP