October 2023 arXiv papers — page 62

Michal Hnatič, Matej Kecer, Tomáš Lučivjanský

Reaction-diffusion systems, which consist of the reacting particles subject to diffusion process, constitute one of the common examples of non-linear statistical systems. In low space dimensions $d \leq 2$ the usual description by means of kinetic rate equations is not sufficient and the effect of density fluctuations has to be properly taken into account. O

Marco Giordano, Silvano Cortesi, Michele Crabolu, Lavinia Pedrollo

This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and when it is actually being used. To achieve classification accuracy and power consumption preservation a newly released algo

Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves

Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best performing model and how it compares with others. To alleviate this issue, we introduce a unified benchmark for NL

Jiwon Choi, Seung Ki Baek

The random transverse-field Ising ferromagnet (RTFIF) is a highly disordered quantum system which contains randomness in the coupling strengths as well as in the transverse-field strengths. In one dimension, the critical properties are governed by an infinite-randomness fixed point (IRFP), and renormalization-group studies argue that the two-dimensional (2D)

Michael Skeide

Motivated by questions raised in the preprint [AL20] by Accardi and Lu (private communication), we examine criteria for when the product of two partial isometries between Hilbert spaces is again a partial isometry and we use this to define a new composition operation that always yields again a partial isometry. Then, we aim at promoting these results to (not

Emmanuel Franck, Victor Michel-Dansac, Laurent Navoret

This work concerns the enrichment of Discontinuous Galerkin (DG) bases, so that the resulting scheme provides a much better approximation of steady solutions to hyperbolic systems of balance laws. The basis enrichment leverages a prior - an approximation of the steady solution - which we propose to compute using a Physics-Informed Neural Network (PINN). To t

Zhiyuan Liu, Yaorui Shi, An Zhang, Enzhi Zhang

Masked graph modeling excels in the self-supervised representation learning of molecular graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three key components: (1) graph tokenizer, which breaks a molecular graph into smaller fragments (i.e., subgraphs) and converts them into tokens; (2) graph masking, which corrupts the grap

Yusuke Shibasaki

In this study, the non-equilibrium free energy corresponding to the curve generated by a modified stochastic Loewner evolution (SLE), which is driven by the Langevin equation, is theoretically investigated. Under certain conditions, we prove that the time derivative of the (generalized) free energy expressed by Kullback-Leibler divergence between the probabi

Subhojyoti Mukherjee, Ruihao Zhu, Branislav Kveton

Motivated by the importance of explainability in modern machine learning, we design bandit algorithms that are efficient and interpretable. A bandit algorithm is interpretable if it explores with the objective of reducing uncertainty in the unknown model parameter. To quantify the interpretability, we introduce a novel metric of model error, which compares t

Christopher T. Hill

Yukawa's old bilocal field theory, with modernization in the treatment of "relative time," can describe a relativistic bound state of chiral fermions. This connects to bosonized effective chiral Lagrangians and the Nambu-Jona-Lasinio (NJL) model, providing a description of the internal dynamics of the bound states. It features a static internal wave-function

Nastaran Dabiran, Brandon Robinson, Rimple Sandhu, Mohammad Khalil

Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct applicability is limited in models where observations possess nonlinear relationships with unknown parameters. Recently, a semi-analytical Bayesian framework known as nonlinear spa

Jaydip Sen, Arup Dasgupta, Partha Pratim Sengupta, Sayantani Roy Choudhury

This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the N

Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang

Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting. Recently, there has been a growing interest in transferring these reasoning abilities from LLMs to smaller models. However, achieving both the diversity and consistency in rationales presents a challenge. In this paper, we focus

Stephan Simonis, Nicolas Hafen, Julius Jeßberger, Davide Dapelo

In this series of studies, we establish homogenized lattice Boltzmann methods (HLBM) for simulating fluid flow through porous media. Our contributions in part I are twofold. First, we assemble the targeted partial differential equation system by formally unifying the governing equations for nonstationary fluid flow in porous media. A matrix of regularly arra

Evangelos Vlachos, Aryan Kaushik, Yonina C. Eldar, George C. Alexandropoulos

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures at both communication ends, which naturally leads to frequency selectivity. This problem is known as beam squint and may be

Wenyi Zhang, Yue Chen, Rui Xie, Yunjian Xu

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power

Harry Emerson, Ryan McConville, Matthew Guy

Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising avenue for extending simulator capabilities; however, these algorithms are limited in that they do not necessarily learn physiologically correct glucose dynamics and can learn inco

Zihao Gu, Yiqing Lin, Kun Xu

This paper aims to solve a super-hedging problem along with insurance re-payment under running risk management constraints. The initial endowment for the super-heding problem is characterized by a class of mean reflected backward stochastic differential equation driven by a marked point process (MPP) and a Brownian motion. By Lipschitz assumptions on the gen

Li Feng, Zhongqun Cheng, Wei Wang, Zhiyuan Li

Using archival {\it Fermi}-LAT data with a time span of $\sim12$ years, we study the population of Millisecond Pulsars (MSPs) in Globular Clusters (GlCs) and investigate their dependence on cluster dynamical evolution in the Milky Way Galaxy. We show that the $\gamma$-ray luminosity ($L_{\gamma}$) and emissivity ($\epsilon_{\gamma}=L_{\gamma}/M$) are good in

Matteo Fiacchi

In this paper we study the hyperbolicity in the sense of Gromov of domains in $\mathbb{R}^d$ $(d\geq3)$ with respect to the minimal metric introduced by Forstneri\v{c} and Kalaj. In particular, we prove that every bounded strongly minimally convex domain is Gromov hyperbolic and its Gromov compactification is equivalent to its Euclindean closure. Moreover, w

Yecheng Yang, Pu Pang, Jiawen Wang, Quan Chen

The technologies of heterogeneous multi-core architectures, co-location, and virtualization can be used to reduce server power consumption and improve system utilization, which are three important technologies for data centers. This article explores the scheduling strategy of Emulator threads within virtual machine processes in a scenario of co-location of m

Yunlang Guo, Bo Wang, Xiangdong Li

Black widows (BWs) are a type of eclipsing millisecond pulsars (MSPs) with low companion masses ($\lesssim0.05\,\rm M_\odot$) and tight orbits ($<1\,$d). PSR J1953+1844 is a BW with the shortest orbital period ($\sim53$ minutes) ever discovered, which was recently detected by Five-hundred-meter Aperture Spherical radio Telescope. Its companion mass is $\sim0

Maarten De Boeck, Jozefien D'haeseleer, Morgan Rodgers

Cameron-Liebler sets of generators in polar spaces were introduced a few years ago as natural generalisations of the Cameron-Liebler sets of subspaces in projective spaces. In this article we present the first two constructions of non-trivial Cameron-Liebler sets of generators in polar spaces. Also regular m-ovoids of k-spaces are introduced as a generalizat

Antonia Diana

We consider a curve with boundary points free to move on a line in $\mathbb R^2$, which evolves by the $L^2$--gradient flow of the elastic energy, that is a linear combination of the Willmore and the length functional. For such planar evolution problem we study the short and long--time existence. Once we establish under which boundary conditions the PDE's sy

Nan He, Yutong Cui, David Wai Quan Chin, Thierry Darnige

We investigate experimentally the dissolution of an almost spherical butyramide particle during its sedimentation, in the low Reynolds high P\'eclet regime. The particle sediments in a quiescent aqueous solution, and its shape and position are measured simultaneously by a camera attached to a translation stage. The particle is tracked in real time, and the t

Benjamin Missaoui, Chongbin Yuan

In Computer Vision, self-supervised contrastive learning enforces similar representations between different views of the same image. The pre-training is most often performed on image classification datasets, like ImageNet, where images mainly contain a single class of objects. However, when dealing with complex scenes with multiple items, it becomes very unl

Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu

This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o

Daichi Igarashi, Jingzu Yee, Yuto Yokoyama, Hiroaki Kusuno

The control of the velocity of a high-speed laser-induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity is heavily influenced by the volumes of secondary cavitation bubbles generated through laser absorption. However, there has been a lack of investigation of the relationship between

Nimisha Ghosh, Daniele Santoni, Indrajit Saha, Giovanni Felici

Prediction of binding sites for transcription factors is important to understand how they regulate gene expression and how this regulation can be modulated for therapeutic purposes. Although in the past few years there are significant works addressing this issue, there is still space for improvement. In this regard, a transformer based capsule network viz. D

Yvann Gaudillot-Estrada, Walter D. van Suijlekom

We consider Gromov-Hausdorff convergence of state spaces for spectral truncations of a compact metric group $G$. We work in the context of order-unit spaces and consider orthogonal projections $P_\Lambda$ in $L^2(G)$ corresponding to finite subsets of irreducible representations $\Lambda \subseteq \widehat G$. We then prove that the sequence of truncated sta

Maren Pielka, Svetlana Schmidt, Rafet Sifa

We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules. Our vision is to provide a condensed corpus of prototypical contradictions, allowing for in-depth linguistic analysis as well as efficient language model fine-tuning. To this end, we instruct the

Zan Tang, Tian Chen, Xing Tang, Xiangdong Zhang

The robust operation of quantum entanglement states are crucial for applications in quantum information, computing, and communications1-3. However, it has always been a great challenge to complete such a task because of decoherence and disorder. Here, we propose theoretically and demonstrate experimentally an effective scheme to realize robust operation of q

Niki Baccile, Ingo Hoffmann, Vincent Chaleix

Bending rigidity, k, is classically measured for lipid membranes to characterize their nanoscale mechanical properties as a function of composition. Widely employed as a comparative tool, it helps understanding the relationship between the lipid's molecular structure and the elastic properties of its corresponding bilayer. Widely measured for phospholipid me

Roy Kapon, Guy Tevet, Daniel Cohen-Or, Amit H. Bermano

We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As such, MAS opens opportunities to exciting and diverse fields of motion previously under-explored as 3D data is scarce and hard to collect. MAS works by simultaneously denoising multip

Zihao Gu, Yiqing Lin, Kun Xu

In this study, we investigate the well-posedness of exponential growth backward stochastic differential equations (BSDEs) driven by a marked point process (MPP) under unbounded terminal conditions. Our analysis utilizes a fixed-point argument, the $\theta$-method, and an approximation procedure. Additionally, we establish the solvability of mean-reflected ex

Niki Baccile, Alexandre Poirier, Patrick Le Griel, Petra Pernot

Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with a strong potential for commercialization due to their biological origin and mildness in contact with the skin and the environment compared to classical surfactants. However, their association properties in water are still poorly understood, they cannot be predicted and their beha

Mathijs J. Mol, Barbara Belfi, Zsuzsa Bakk

The growing demand for data scientists in the global labor market and the Netherlands has led to a rise in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skillsets of data scientists. This study aims to address this issue by employing Correlated Topic Mod

Alex Buna-Marginean, Vincent Cheval, Mahsa Shirmohammadi, James Worrell

We introduce the class of P-finite automata. These are a generalisation of weighted automata, in which the weights of transitions can depend polynomially on the length of the input word. P-finite automata can also be viewed as simple tail-recursive programs in which the arguments of recursive calls can non-linearly refer to a variable that counts the number

Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan

The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is cruc

Yu Pan, Jingwang Diao, Jing-Zhao Qi, Jin Li

The determination of the spatial geometry of the universe plays an important role in modern cosmology. Any deviation from the cosmic curvature $\Omega_K=0$ would have a profound impact on the primordial inflation paradigm and fundamental physics. In this paper, we carry out a systematic study of the prospect of measuring cosmic curvature with the inspiral si

Bastien Chassagnol, Grégory Nuel, Etienne Becht

Although bulk transcriptomic analyses have significantly contributed to an enhanced comprehension of multifaceted diseases, their exploration capacity is impeded by the heterogeneous compositions of biological samples. Indeed, by averaging expression of multiple cell types, RNA-Seq analysis is oblivious to variations in cellular changes, hindering the identi

Eléa Thibault Greugny, François Fages, Ovidiu Radulescu, Peter Szmolyan

The skin microbiome plays an important role in the maintenance of a healthy skin. It is an ecosystem, composed of several species, competing for resources and interacting with the skin cells. Imbalance in the cutaneous microbiome, also called dysbiosis, has been correlated with several skin conditions, including acne and atopic dermatitis. Generally, dysbios

Mathieu Hemery, François Fages

The Turing completeness of continuous Chemical Reaction Networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at most two reactants and mass action law kinetics. In this paper, we introduce a more stringent notion of robust onli

Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann, Anton Hinel

Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classification: real-world time series data often exhibit irregularities such as multi-modality, skewness and outlier

Hector Suzanne, Emmanuel Chailloux

An automated resource analysis technique is introduced, targeting a Call-By-Push-Value abstract machine, with memory prediction as a practical goal. The machine has a polymorphic and linear type system enhanced with a first-order logical fragment, which encodes both low-level operational semantics of resource manipulations and high-level synthesis of algorit

Ruixiang Zhang, Chang Xu, Fang Xu, Wen Yang

This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller sizes and larger quantities per image, increasing the difficulty of manual annotation. Meanwhile, the advanced SSOD technique can train superior detectors by leveraging limited labeled

Yann Balland, Luc Absil, Franck Pereira dos Santos

We report on the realization of a quantum sensor based on trapped atom interferometry in an optical lattice for the measurement of atom-surface interactions, with sub-micrometer-level control of the mean atom-surface separation distance. The force sensor reaches a short-term sensitivity of 3.4 x 10 --28 N at 1 s and a long-term stability of 4 qN (4 x 10 --30

Euclid Collaboration, Stéphane Paltani, J. Coupon, W. G. Hartley

The technique of photometric redshifts has become essential for the exploitation of multi-band extragalactic surveys. While the requirements on photo-zs for the study of galaxy evolution mostly pertain to the precision and to the fraction of outliers, the most stringent requirement in their use in cosmology is on the accuracy, with a level of bias at the sub

E. Nagel, S. Czesla, A. Kaminski, M. Zechmeister

Light from celestial objects interacts with the molecules of the Earth's atmosphere, resulting in the production of telluric absorption lines in ground-based spectral data. Correcting for these lines, which strongly affect red and infrared wavelengths, is often needed in a wide variety of scientific applications. Here, we present the template division tellur

Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu

Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery

Ted Pilcher

This paper presents a novel approach to solving the Flying Sidekick Travelling Salesman Problem (FSTSP) using a state-of-the-art self-adaptive genetic algorithm. The Flying Sidekick Travelling Salesman Problem is a combinatorial optimisation problem that extends the Travelling Salesman Problem (TSP) by introducing the use of drones. In FSTSP, the objective i

Christian Faßbender, Tim Bürchner, Philipp Kopp, Ernst Rank

Immersed boundary methods simplify mesh generation by embedding the domain of interest into an extended domain that is easy to mesh, introducing the challenge of dealing with cells that intersect the domain boundary. Combined with explicit time integration schemes, the finite cell method introduces a lower bound for the critical time step size. Explicit tran

Jean-Marc Bardet, Yves Gael Tchabo Mbienkeu

The purpose of this paper is to study the convergence of the quasi-maximum likelihood (QML) estimator for long memory linear processes. We first establish a correspondence between the long-memory linear process representation and the long-memory AR$(\infty)$ process representation. We then establish the almost sure consistency and asymptotic normality of the

Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, Laurent Heutte

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classifica

Sen Yang, Xin Li, Lidong Bing, Wai Lam

Our physical world is constantly evolving over time, rendering challenges for pre-trained language models to understand and reason over the temporal contexts of texts. Existing work focuses on strengthening the direct association between a piece of text and its time-stamp. However, the knowledge-time association is usually insufficient for the downstream tas

Andrei C. Coman, Gianni Barlacchi, Adrià de Gispert

Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both efficiency and effectiveness. In this paper, we study the State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and Fusion-in-Decoder (FiD) reader pipeline, and show that it s

Shwetha Salimath, Francesca Bugiotti, Frederic Magoules

Metal forging is used to manufacture dies. We require the best set of input parameters for the process to be efficient. Currently, we predict the best parameters using the finite element method by generating simulations for the different initial conditions, which is a time-consuming process. In this paper, introduce a hybrid approach that helps in processing

Antonio Liguori, Rebecca Barten, Filippo Baruffaldi, Anna Bergamaschi

Experiments at synchrotron radiation sources and X-ray Free-Electron Lasers in the soft X-ray energy range ($250$eV--$2$keV) stand to benefit from the adaptation of the hybrid silicon detector technology for low energy photons. Inverse Low Gain Avalanche Diode (iLGAD) sensors provide an internal gain, enhancing the signal-to-noise ratio and allowing single p

Pietro Balatti, Idil Ozdamar, Doganay Sirintuna, Luca Fortini

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot's end-effector. These component

Silvano Cortesi, Marc Dreher, Michele Magno

Indoor Positioning System (IPS) is a crucial technology that enables medical staff and hospital managements to accurately locate and track persons or assets inside the medical buildings. Among other technologies, Bluetooth Low Energy (BLE) can be exploited for achieving an energy-efficient and low-cost solution. This work presents the design and implementati

Gonzalo Martínez, Javier Conde, Elena Merino-Gómez, Beatriz Bermúdez-Margaretto

Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama, Mistral, and GPT. While most LLM evaluation benchmarks focus on specific tasks or domain-specific knowledge, they often neglect the fundamental linguistic aspects of language understanding and

Binyu Zhao, Wei Zhang, Zhaonian Zou

Collaborative perception enables agents to share complementary perceptual information with nearby agents. This would improve the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. Most existing approaches mainly focus on single modality (especially LiDAR), and not fully exploit the superiority of multi-

Binrui. shen, Qiang. niu, Shengxin. zhu

Graph matching aims to find correspondences between two graphs. It is a fundamental task in pattern recognition. The classical spectral matching algorithm has time complexity $\mathcal{O}(n^4)$ and space complexity $\mathcal{O}(n^4)$, where $n$ is the number of nodes. Such a complexity limits the applicability to large-scale graph matching tasks. This paper

Zihao Yan, Fubao Su, Mingyang Wang, Ruizhen Hu

We introduce an active 3D reconstruction method which integrates visual perception, robot-object interaction, and 3D scanning to recover both the exterior and interior, i.e., unexposed, geometries of a target 3D object. Unlike other works in active vision which focus on optimizing camera viewpoints to better investigate the environment, the primary feature o

Gaia Collaboration, P. David, F. Mignard, D. Hestroffer

Context. We report the exploitation of a sample of epoch astrometry for 157 000 asteroids, the same object in the Gaia Data Release 3, extended over the time coverage planned for the Gaia DR4, which is not expected before the end of 2025. This data set covers more than one full orbital period for the vast majority of these asteroids. The orbital solutions ar

Yousra Gati, Vladimir Petrov Kostov, Mohamed Chaouki Tarchi

We consider real univariate degree $d$ real-rooted polynomials with non-vanishing coefficients. Descartes' rule of signs implies that such a polynomial has $\tilde{c}$ positive and $\tilde{p}$ negative roots counted with multiplicity, where $\tilde{c}$ and $\tilde{p}$ are the numbers of sign changes and sign preservations in the sequence of its coefficients,

Justin Larouze, Etienne Martin, Pierre Calmon

This communication reports on a study carried out in the context of the collaborative FOEHN project (Human and Organizational Factors in Non-Destructive Evaluation) supported by the French National Research Agency. The motivation of this project comes from the observation that human and Organizational factors (HOF) are not sufficiently considered by the NDT

Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al., (2022). While it provides a comprehensive response without botheri

Omnia Mahmoud, Théo Ladune, Matthieu Gendrin

Modeling 3D scenes by volumetric feature grids is one of the promising directions of neural approximations to improve Neural Radiance Fields (NeRF). Instant-NGP (INGP) introduced multi-resolution hash encoding from a lookup table of trainable feature grids which enabled learning high-quality neural graphics primitives in a matter of seconds. However, this im

Weimin Jiang, Juan Li, Qingmeng Wei

The purpose of this paper is to investigate general mean-field backward stochastic differential equations (MFBSDEs) in multi-dimension with diagonally quadratic generators $f(\omega,t,y,z,\mu)$, that is, the coefficients depend not only on the solution processes $(Y,Z)$, but also on their law $\mathbb{P}_{(Y,Z)}$, as well as have a diagonally quadratic growt

Chongxiao Zhao, Qi Ou, Joonho Lee, Wenjie Dou

We apply a stochastic resolution of identity approximation (sRI) to the CC2 method for excitation energy calculations. A set of stochastic orbitals are employed to decouple the crucial 4-index electron repulsion integrals and optimize the contraction steps in CC2 response theory. The CC2 response for excitations builds upon sRI-CC2 ground-state calculations,

Lucas Grativol Ribeiro, Mathieu Leonardon, Guillaume Muller, Virginie Fresse

Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a cloud computing server. In many use-cases, communication cost is a major challenge in FL due to its natural intensive ne

Amit Bracha, Thomas Dagès, Ron Kimmel

While dealing with matching shapes to their parts, we often apply a tool known as functional maps. The idea is to translate the shape matching problem into "convenient" spaces by which matching is performed algebraically by solving a least squares problem. Here, we argue that such formulations, though popular in this field, introduce errors in the estimated

Charles K. Assaad, Emilie Devijver, Eric Gaussier, Gregor Gössler

We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abstractions of the true causal graph. We consider here two abstractions: the extended summary causal graph, which conflates all lagged causal relations but distinguishes between lagg

Jyotirmoy Mukherjee

We consider an antisymmetric gauge field in the Minkowski space of $d$-dimension and decompose it in terms of the antisymmetric tensor harmonics and fix the gauge. The Gauss law implies that the normal component of the field strength on the spherical entangling surface will label the superselection sectors. From the two-point function of the field strength o

Jiguang Li, Andrei Derevianko, D. S. Elliott

We explore the feasibility of extracting electroweak observables from a measurement of atomic parity violation in hydrogen. Our proposed quantum-control scheme focuses on the $2s-3s$ or $2s-4s$ transitions in hydrogen. This work is motivated by the recently observed anomaly in the W-boson mass, which may substantially modify the Standard Model value of the p

Jia Shen, Yifei Wu

In this paper, we consider the $L_x^2$-scattering of defocusing mass sub-critical nonlinear Schr\"odinger equations with low weighted initial condition. It is known that the scattering holds with $\mathcal{F} H^1$-data, while the continuity of inverse wave operator breaks down with $L^2$-data. Moreover, for large $\mathcal{F} H^s$-data with $s<1$, there only

Yihan Cao, Shuyi Chen, Ryan Liu, Zhiruo Wang

A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms. In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2)

Matthew T. Eiles, Frederic Hummel

We consider non-adiabatic coupling in the "trilobite"-like long-range Rydberg molecules created by perturbing degenerate high-$\ell$ Rydberg states with a ground-state atom. Due to the flexibility granted by the high Rydberg level density, the avoided crossings between relevant potential energy curves can become extremely narrow, leading to highly singular n

Anna M. Maddux, Maryam Kamgarpour

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent's action needs to belong to a feasible set, but the feasible set is a priori unknown. For example, in constrained multi-agent reinforcement learning, the constraints on the agents' pol

Hassan S. Shavarani, Anoop Sarkar

Entity linking is a prominent thread of research focused on structured data creation by linking spans of text to an ontology or knowledge source. We revisit the use of structured prediction for entity linking which classifies each individual input token as an entity, and aggregates the token predictions. Our system, called SpEL (Structured prediction for Ent

Xingchao Jian, Feng Ji, Wee Peng Tay

In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences. The note also contains errata of the previous version of the note.

Anne-Caroline Genix, Julian Oberdisse

Small-angle scattering is a commonly used tool to analyze the dispersion of nanoparticles in all kinds of matrices. Besides some obvious cases, the associated structure factor is often complex and cannot be reduced to a simple interparticle interaction, like excluded volume only. In recent experiments, we have encountered a surprising absence of structure fa

Silvano Cortesi, Christian Vogt, Elio Reinschmidt, Michele Magno

The rapid growth of the Internet of Things paradigm is pushing the need to connect billions of batteryoperated devices to the internet and among them. To address this need, the introduction of energy-efficient wireless mesh networks based on Bluetooth provides an effective solution. This paper proposes a testbed setup to accurately evaluate and compare the s

Anne-Caroline Genix, Vera Bocharova, Bobby Carroll, Philippe Dieudonné-George

Fundamental understanding of macroscopic properties of polymer nanocomposites (PNCs) remains difficult due to the complex interplay of microscopic dynamics and structure, namely interfacial layer relaxations and three-dimensional nanoparticle arrangements. The effect of surface modification by alkyl methoxysilanes at different grafting densities has been stu

Thierry Klein, Agnès Lagnoux, P Petit

Let $W$ be a nonnegative random variable with expectation $1$. For all $r \geqslant 2$, we consider the total mass $Z_r^\infty$ of the associated Mandelbrot multiplicative cascade in the $r$-ary tree. For all $n \geqslant 1$, we also consider the total mass $Z_r^n$ of the measure at height $n$ in the $r$-ary tree. Liu, Rio, Rouault \cite{lrr,liu2000limit,Rou

Georgi Vodev

We consider the anisotropic interior transmission problem with one complex-valued refraction index. Under the condition that all geodesics reach the boundary in a finite time, we obtain large regions near the real axis free of transmission eigenvalues.

Lulu Zhao

The CLEAR Space Weather Center of Excellence (CLEAR center) is a five year project that is funded by the NASA Space Weather Center of Excellence program. The CLEAR center will build a comprehensive prediction framework for solar energetic particles (SEPs) focusing on the timely and accurate prediction of low radiation periods (``all clear forecast") and the

Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer

Human gaze data offer cognitive information that reflects natural language comprehension. Indeed, augmenting language models with human scanpaths has proven beneficial for a range of NLP tasks, including language understanding. However, the applicability of this approach is hampered because the abundance of text corpora is contrasted by a scarcity of gaze da

Timo Sämann, Horst-Michael Groß

Ensuring safety in automated driving is a major challenge for the automotive industry. Special attention is paid to artificial intelligence, in particular to Deep Neural Networks (DNNs), which is considered a key technology in the realization of highly automated driving. DNNs learn from training data, which means that they only achieve good accuracy within t

M. Belovs, R. Livanovics, A. Cebers

We derive equations of motion for paramagnetic and ferromagnetic particles fully accounting for gyromagnetic effects. Considering the Einstein-de Haas effect for an ellipsoidal paramagnetic particle we find that starting from a quiescent non-magnetized state, after the field is switched on a rotation along the short axis is established. This is confirmed by

Jiayi Xu, Yoshihiro Kuroda, Shunsuke Yoshimoto, Osamu Oshiro

In recent years, thermal display has been studied intensively in order to represent a more realistic tactile quality of the object. Since human feels the temperature of the air without touching other objects, it is necessary to present thermal sensation in a non-contact manner. Studies on non-contact heat display have been explored; however, few studies have

Xinyu Huang, Yi-Jie Huang, Youcai Zhang, Weiwei Tian

In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e.g., CLIP) primarily utilize global text supervision paired with images, leading to sub-optimal performance in recognizing multiple individual semantic tags. In contrast, RAM++ sea

Jiayi Xu, Shoichi Hasegawa, Kiyoshi Kiyokawa, Naoto Ienaga

Thermal sensation is crucial to enhancing our comprehension of the world and enhancing our ability to interact with it. Therefore, the development of thermal sensation presentation technologies holds significant potential, providing a novel method of interaction. Traditional technologies often leave residual heat in the system or the skin, affecting subseque

Abhinav Pomalapally, Bassel El Mabsout, Renato Mansuco

In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As a result, a range of parameter schedules have been designed to leverage the capability of adjusting hyper-parameters during training to enh

Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu

Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper understanding. The first type of dataset bias is \emph{Unbalanced Matching} bias, where the

Hao Guo, Collin Meese, Wanxin Li, Chien-Chung Shen

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level

Vivien Lefranc, Soha Baydoun, Camille Gandiolle, Eva Héripré

Fretting wear is a surface degradation process caused by oscillatory motion and contact slipping. During gross slip, high local stresses and plastic deformation in the surface and subsurface can lead to the creation of a nanosized grained structure called Tribologically Transformed Structure (TTS). The current paper studies the formation of TTS in an alpha-b

Mohamed Houssein Mohamed, Luc Belloni, Daniel Borgis, Francesca Ingrosso

Environmental concerns are driving the search for greener yet efficient solvents. Supercritical CO2 (scCO2) is a promising candidate due to its non-toxicity and the potential for reusing CO2 emissions. It also offers a versatile range of properties that can be finely tuned by pressure adjustments. This adaptability is exploited in chemical industry processes

Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic

Prefetching is a crucial technique employed in traditional databases to enhance interactivity, particularly in the context of data exploitation. Data exploration is a query processing paradigm in which users search for insights buried in the data, often not knowing what exactly they are looking for. Data exploratory tools deal with multiple challenges such a