October 2023 arXiv papers — page 47

David Heurtel-Depeiges, Blakesley Burkhart, Ruben Ohana, Bruno Régaldo-Saint Blancard

In cosmology, the quest for primordial $B$-modes in cosmic microwave background (CMB) observations has highlighted the critical need for a refined model of the Galactic dust foreground. We investigate diffusion-based modeling of the dust foreground and its interest for component separation. Under the assumption of a Gaussian CMB with known cosmology (or cova

Peiwen Tan

This research underscores the efficacy of Fourier topological optimization in refining MRI imagery, thereby bolstering the classification precision of Alzheimer's Disease through convolutional neural networks. Recognizing that MRI scans are indispensable for neurological assessments, but frequently grapple with issues like blurriness and contrast irregularit

Yuliang Xu, Timothy D Johnson, Mary Heitzeg, Jian Kang

Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis with neuroimaging data which involves high dimensionality, complex spatial correlations, sparse activation patterns and relatively low signal-to-noise ratio. To address these issues,

Amanda Goodrick, Hiroki Sayama

We propose a method of analyzing multivariate time series data that investigates lead-lag relationships among economic indicators during the COVID-19 era with a weighted directed network of lagged variables. The analysis includes a stock index, average unemployment, and several variables that are used to calculate inflation. Three complex networks are create

Hongren Wang

This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditional machine learning and deep neural networks for predictions. However, both approaches have their limitations. Traditional machine learning o

Yuichi Masubuchi

Fujiyabu et al. have experimentally reported that mixing of 3-arm star prepolymers into 4-arm analog improves the toughness of the resultant polymer networks compared to the base network composed of 4-arm star polymers only. For the mechanism of this phenomenon, this study conducted phantom chain simulations for polymer networks composed of mixtures of star

Xiaoyu Cheng

A decision-maker faces uncertainty governed by a data-generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non-identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be \emph{object

Hanna Matusik, Chao Liu, Daniela Rus

Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedom requires a complex multi-step manual process, which significantly increases their cost. We present a design of a multi-material 15 DoF robotic hand with five fingers including an

Xiao Lin, Deming Wang, Guangliang Zhou, Chengju Liu

Estimating the 6D object pose is an essential task in many applications. Due to the lack of depth information, existing RGB-based methods are sensitive to occlusion and illumination changes. How to extract and utilize the geometry features in depth information is crucial to achieve accurate predictions. To this end, we propose TransPose, a novel 6D pose fram

Yi-Chen Chang, Canasai Kruengkrai, Junichi Yamagishi

This paper introduces the Cross-lingual Fact Extraction and VERification (XFEVER) dataset designed for benchmarking the fact verification models across different languages. We constructed it by translating the claim and evidence texts of the Fact Extraction and VERification (FEVER) dataset into six languages. The training and development sets were translated

Shiyu Shen, Bin Pan, Tianyang Shi, Tao Li

Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domains. Recently, Bayesian Neural Networks have achieved promising results in domain invariant learning, but most works concentrate on aligning features distributions rather than parameter distribu

Bishal Shrestha, Griwan Khakurel, Kritika Simkhada, Badri Adhikari

Reading dirty license plates accurately in moving vehicles is challenging for automatic license plate recognition systems. Moreover, license plates are often intentionally tampered with a malicious intent to avoid police apprehension. Usually, such groups and individuals know how to fool the existing recognition systems by making minor unnoticeable plate cha

Xiaoqi Zhang, Jie Fu, Sheng Hua, Han Liang

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading; first, we find that information heterogeneity and its induce

Hirokazu Fujiwara, Yuki Itoya, Masaharu Kobayashi, Cédric Bareille

HfO$_2$-based ferroelectrics are one of the most actively developed functional materials for memory devices. However, in HfO$_2$-based ferroelectric devices, dielectric breakdown is a main failure mechanism during repeated polarization switching. Elucidation of the breakdown process may broaden the scope of applications for the ferroelectric HfO$_2$. Here, w

Luyang Fang, Gyeong-Geon Lee, Xiaoming Zhai

Machine learning-based automatic scoring faces challenges with unbalanced student responses across scoring categories. To address this, we introduce a novel text data augmentation framework leveraging GPT-4, a generative large language model, specifically tailored for unbalanced datasets in automatic scoring. Our experimental dataset comprised student writte

Hao Li, Xiangxiong Zhang

We construct a monotone continuous $Q^1$ finite element method on the uniform mesh for the anisotropic diffusion problem with a diagonally dominant diffusion coefficient matrix. The monotonicity implies the discrete maximum principle. Convergence of the new scheme is rigorously proven. On quadrilateral meshes, the matrix coefficient conditions translate into

Jianping Yao, Son N. Tran, Saurabh Garg, Samantha Sawyer

Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful

Kazushi Aoyama

In two-dimensional superconductors with a Rashba-type spin-orbit coupling, it is known that an in-plane magnetic field can induce a helical superconducting (SC) state with a phase modulation $e^{i {\bf q}\cdot {\bf r}}$. Here, we theoretically investigate the stability of a stripe order, a weight-biased superposition state composed of $+{\bf q}$ and $-{\bf q

Jixiang Hong, Quan Tu, Changyu Chen, Xing Gao

Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical concern. Reinforcement learning from human feedback (RLHF) with algorithms like PPO is a prevalent approach for alignment but is often complex, unstable, and resource-intensive. Rec

Mansi Sakarvadia, Arham Khan, Aswathy Ajith, Daniel Grzenda

Transformer-based Large Language Models (LLMs) are the state-of-the-art for natural language tasks. Recent work has attempted to decode, by reverse engineering the role of linear layers, the internal mechanisms by which LLMs arrive at their final predictions for text completion tasks. Yet little is known about the specific role of attention heads in producin

Cristina España-Bonet

Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of new

Y. Lee, P. Aleynikov, P. C. de Vries, H. -T. Kim

Dreicer generation is one of the main mechanisms of runaway electrons generation, in particular during tokamak startup. In fully ionized plasma it is described as a diffusive flow from the Maxwellian core into high energies under the effect of the electric field. In this work we demonstrate a critical role of the non-differential nature of inelastic collisio

Mohammadreza Mahmoudi

This study investigates the influence of monetary policy and monetary policy uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from July 2010 to August 2023, and employing the Markov Switching Means VAR (MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized into two distinct regimes: 1) regime 1 with low volatili

Fan Yang, Xiaofei Wang

Using deep learning methods to detect students' classroom behavior automatically is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available spatio-temporal datasets on student behavior, as well as the high cost of manually labeling such datasets, pose significant challenges for

Ricardo Monge Neria, Muhammad Zeeshan, Aman Kapoor, Tae Kyong John Kim

Chemical separations are costly in terms of energy, time, and money. Separation methods are optimized with inefficient trial-and-error approaches that lack insight into the molecular dynamics that lead to the success or failure of a separation and, hence, ways to improve the process. We perform super-resolution imaging of fluorescent analytes in four differe

Wenquan Liu, Zhibo Niu, Wei Cheng, Xin Li

The Jarzynski equality (JE), which connects the equilibrium free energy with non-equilibrium work statistics, plays a crucial role in quantum thermodynamics. Although practical quantum systems are usually multi-level systems, most tests of the JE were executed in two-level systems. A rigorous test of the JE by directly measuring the work distribution of a ph

Raoul Andriulli, Shaun Andrews, Nabil Souhair, Mirko Magarotto

A fully kinetic 2D axisymmetric Particle-in-Cell (PIC) model is used to examine the effects of background facility pressure on the plasma transport and propulsive efficiency of magnetic nozzles. Simulations are performed for a low-power (150 W class) cathode-less radio-frequency (RF) plasma thruster, operating with xenon, between background pressures up to 1

Jiexin Wang, Liuwen Cao, Xitong Luo, Zhiping Zhou

Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. To effectively mitigate this concern, this paper present

Eunice Jun, Edward Misback, Jeffrey Heer, René Just

Statistical models should accurately reflect analysts' domain knowledge about variables and their relationships. While recent tools let analysts express these assumptions and use them to produce a resulting statistical model, it remains unclear what analysts want to express and how externalization impacts statistical model quality. This paper addresses these

Ting-Rui Chiang, Dani Yogatama

We analyze the masked language modeling pretraining objective function from the perspective of the distributional hypothesis. We investigate whether better sample efficiency and the better generalization capability of models pretrained with masked language modeling can be attributed to the semantic similarity encoded in the pretraining data's distributional

Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang

This paper proposes new methodologies for conducting practical differentially private (DP) estimation and inference in high-dimensional linear regression. We first introduce a DP Bayesian Information Criterion (DP-BIC) for selecting the unknown sparsity parameter in differentially private sparse linear regression (DP-SLR), eliminating the need for prior know

Shahab Ramezanpour

Systems operating at exceptional points (EPs) are highly responsive to small perturbations, making them suitable for sensing applications. Although this feature impedes the system working exactly at an EP due to imperfections arising during the fabrication process. We propose a fast self-tuning scheme based on Kerr nonlinearity in a coupled dielectric resona

M. De Sanctis

The QCD effective charge extracted from the experimental data is used to construct the vector interaction of a Dirac relativistic model for the charmonium spectrum. The process required to fit the spectrum is discussed and the relationship with a previous study of the vector interaction is analyzed.

A. Arellano Ferro, Z. Prudil, M. A. Yepez, I. Bustos Fierro

We present a comprehensive analysis of the variable stars projected on the field of the Galactic bulge globular cluster NGC 6522, offering valuable insights into their characteristics. Using proper motions from Gaia DR3, we aim to distinguish between field stars and true cluster members. For an accurate color-magnitude diagram of the member stars, we produce

Yuan Li, Li Liu, Penggang Chen, Youmin Zhang

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity cha

Christopher Maxey, Jaehoon Choi, Hyungtae Lee, Dinesh Manocha

Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models is prevalent to create synthetic data to augment the learning in the ground-based imaging domain. However, se

Haoli Yin, Jiayao Li, Eva Schiller, Luke McDermott

Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning. Current models, although promising, reveal scalability limitations with increasing modalities as they rely heavily on late fusion, which postpones the integration of specific modality insights. Addressing this, we intro

Jinlu Li, Li Cheng, Lishan Liu, Linsen Xie

Let $H$ be a real Hilbert space and $C$ a nonempty closed and convex subset of $H$. Let $P_C: H\rightarrow C$ denote the (standard) metric projection operator. In this paper, we study the G\^ateaux directional differentiability of $P_C$ and investigate some of its properties. The G\^ateaux directionally derivatives of $P_C$ are precisely given for the follow

Yonghao Wu, Zheng Li, Jie M. Zhang, Yong Liu

With the growing interest on Large Language Models (LLMs) for fault localization and program repair, ensuring the integrity and generalizability of the LLM-based methods becomes paramount. The code in existing widely-adopted benchmarks for these tasks was written before the the bloom of LLMs and may be included in the training data of existing popular LLMs,

Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian J. Ratliff

We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model where any learner can sample an entry $(i,j)$ of the input matrix $A\in[-1,1]^{n\times m}$ and observe $A_{i,j}+\eta$ where $\eta$ is a zero-mean 1-sub-Gaussian noise. The aim of th

Dhruv Kumar, Vipul Raheja, Alice Kaiser-Schatzlein, Robyn Perry

We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes. The user can interact with the system through instructions or dictation, and the system generates a well-formatted and coherent document. We describe the system architecture a

Piero A. P. Molinari, Paola C. M. Delgado, Rodrigo F. Pinheiro, Nelson Pinto-Neto

A very simple non-singular inflationary model is presented where the unique matter content is a radiation fluid. The model slowly contracts from a very large, almost empty and flat spacetime and realizes a bounce. It is then launched to a quasi-de Sitter inflationary expansion with more than sixty e-folds, which smoothly changes to the usual classical, decel

Ramaseshan Kannan

This note introduces an unsupervised learning algorithm to debug errors in finite element (FE) simulation models and details how it was productionised. The algorithm clusters degrees of freedom in the FE model using numerical properties of the adjacency of its stiffness matrix. The algorithm has been deployed as a tool called `Model Stability Analysis' tool

Amir Hossein Kargaran, Ayyoob Imani, François Yvon, Hinrich Schütze

Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages. However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously evaluated and reliable and (iii) efficient and easy to use. Here, we publish GlotLID-M, an LID model that

Duván Cardona, Julio Delgado, Vishvesh Kumar, Michael Ruzhansky

We establish the $L^p$-$L^q$-boundedness of subelliptic pseudo-differential operators on a compact Lie group $G$. Effectively, we deal with the $L^p$-$L^q$-bounds for operators in the sub-Riemmanian setting because the subelliptic classes are associated to a H\"ormander sub-Laplacian. The Riemannian case associated with the Laplacian is also included as a sp

Isaac K. Martin, Andrew G. Moore, John T. Daly, Jess J. Meyer

Ising machines are a form of quantum-inspired processing-in-memory computer which has shown great promise for overcoming the limitations of traditional computing paradigms while operating at a fraction of the energy use. The process of designing Ising machines is known as the reverse Ising problem. Unfortunately, this problem is in general computationally in

Zhiyuan Geng, Arnab Roy, ArghirZarnescu

In this paper we study a mathematical model describing the movement of a colloidal particle in a fixed, bounded three dimensional container filled with a nematic liquid crystal fluid. The motion of the fluid is governed by the Beris-Edwards model for nematohydrodynamics equations, which couples the incompressible Navier-Stokes equations with a parabolic syst

Katsuyuki Bando

We construct a new affine Grassmannian which connects an equal characteristic affine Grassmannian and Zhu's Witt vector affine Grassmannian. As a result, we deduce the mixed characteristic version of the Bezrukavnikov-Finkelberg's derived Satake equivalence. By the same argument, we also obtain the mixed characteristic version of the Bezrukavnikov's equivale

Mattia Bruno, Leonardo Giusti, Matteo Saccardi

Hadronic spectral densities play a pivotal role in particle physics, a prime example being the R-ratio defined from electron-positron scattering into hadrons. To predict them from first principles using Lattice QCD, we face a numerically ill-posed inverse problem, due to the Euclidean signature adopted in practical simulations. Here we present a recent numer

Yonchanok Khaokaew, Kaixin Ji, Thuc Hanh Nguyen, Hiruni Kegalle

This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models (LLMs). The primary objective is to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions. This innovative approach in

Afiya Ayman, Ayan Mukhopadhyay, Aron Laszka

When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models. However, the advantage of MTL depends on various factors, such as the similarity of the tasks, the sizes of the datasets, and so on; in fact, some tasks might not benefit from MTL

Raymond Li, Gabriel Murray, Giuseppe Carenini

In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tuning (PEFT) setting. In our approach, parallel adapter modules encoding different linguistic structures are combined using a novel Mixture-of-Linguistic-Experts architecture, where Gu

Vladimir Norkin, Anton Kozyriev

Shor's r-algorithm (Shor, Zhurbenko (1971), Shor (1979)) with space stretching in the direction of difference of two adjacent subgradients is a competitive method of nonsmooth optimization. However, the original r-algorithm is designed to minimize convex ravine functions without constraints. The standard technique for solving constraint problems with this al

Takashi Taguchi

Tendon and ligament injuries are debilitating conditions across species. Poor regenerative capacities of these tissues limit restoration of original functions. The first study of this dissertation evaluated the effect of cellular administration on tendon/ligament injuries in horses using meta-analysis. The findings led to the second study that engineered imp

Sruthi Rachamalla, Henry Hexmoor

Cooperative driving (or Platooning) focuses on improving the safety and efficiency by connecting two or more vehicles on a road by vehicular communication protocols. The leader is crucial as it manages the platoon, establishes communication between cars, and perform platoon maneuvers. In this paper, we proposed a driver incentive model which encourages plato

Jianxiao Yang, Martijn J. Schuemie, Marc A. Suchard

The Cox proportional hazards model stands as a widely-used semi-parametric approach for survival analysis in medical research and many other fields. Numerous extensions of the Cox model have further expanded its versatility. Statistical computing challenges arise, however, when applying many of these extensions with the increasing complexity and volume of mo

Kieran Ricardo, Kenneth Duru, David Lee

We present a novel discontinuous Galerkin finite element method for numerical simulations of the rotating thermal shallow water equations in complex geometries using curvilinear meshes, with arbitrary accuracy. We derive an entropy functional which is convex, and which must be preserved in order to preserve model stability at the discrete level. The numerica

Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian J. Ratliff

We study the query complexity of finding the set of all Nash equilibria $\mathcal X_\star \times \mathcal Y_\star$ in two-player zero-sum matrix games. Fearnley and Savani (2016) showed that for any randomized algorithm, there exists an $n \times n$ input matrix where it needs to query $\Omega(n^2)$ entries in expectation to compute a single Nash equilibrium

Tatsuya Horiguchi, Mikiya Masuda, Takashi Sato

The solution of Shareshian-Wachs conjecture by Brosnan-Chow and Guay-Paquet tied the graded chromatic symmetric functions on indifference graphs (or unit interval graphs) and the cohomology of regular semisimple Hessenberg varieties with the dot action. A similar result holds between unicellular LLT polynomials and twins of regular semisimple Hessenberg vari

Cuong Manh Hoang, Byeongkeun Kang

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. Therefore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for seg

Yu. M. Sinyukov, V. M. Shapoval, M. D. Adzhymambetov

The analysis of the spatiotemporal picture of particle radiation in relativistic heavy-ion collisions in terms of correlation femtoscopy scales, emission and source functions allows one to probe the character of evolution of the system created in the collision. Realistic models, like the integrated hydrokinetic model (iHKM), used in the present work, are abl

Alberto Ohashi, Francesco Russo

In this article, we present the exact expression of the $L^2$-norm of the forward stochastic integral driven by the multi-dimensional fractional Brownian motion with parameter $\frac{1}{2} < H < 1$. The class of integrands only requires rather weak integrability conditions compatible w.r.t. a random finite measure whose density is expressed as a second-order

Dhruvit Patel, Alexander Wikner

A common technique to reduce model bias in time-series forecasting is to use an ensemble of predictive models and pool their output into an ensemble forecast. In cases where each predictive model has different biases, however, it is not always clear exactly how each model forecast should be weighed during this pooling. We propose a method for pooling that pe

Thomas A. Trainor

Nuclear modification factors (NMFs) applied to A-B collision systems consist of $p_t$ spectrum ratios rescaled by an estimated number of nucleon-nucleon binary collisions. Interest in NMFs is motivated by possible modification (suppression?) of jet production in more-central A-B collisions conjectured to arise from a deconfined quark-gluon plasma or QGP. Int

Yang Xiao, Jun-Rong Zhang, Sheng-Yu Wang, Weijie Hua

X-ray photoelectron spectroscopy (XPS) is an important characterization tool in the pursuit of controllable fluorination of two-dimensional hexagonal boron nitride ($h$-BN). However, there is a lack of clear spectral interpretation and seemingly conflicting measurements exist. To discern the structure-spectroscopy relation, we performed a comprehensive first

Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael C. Mozer

Deep-learning models can extract a rich assortment of features from data. Which features a model uses depends not only on \emph{predictivity} -- how reliably a feature indicates training-set labels -- but also on \emph{availability} -- how easily the feature can be extracted from inputs. The literature on shortcut learning has noted examples in which models

A. Mitrašinović, B. Vukotić, M. Micic, M. M. Ćirković

Recent studies of Galactic evolution revealed that the dynamics of the stellar component might be one of the key factors when considering galactic habitability. We run an N-body simulation model of the Milky Way, which we evolve for 10 Gyr, to study the secular evolution of stellar orbits and the resulting galactic habitability-related properties, i.e., the

Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli

Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We introduce the first set of web-scale Time-Continual (TiC) benc

Susanna Rücker, Alan Akbik

The CoNLL-03 corpus is arguably the most well-known and utilized benchmark dataset for named entity recognition (NER). However, prior works found significant numbers of annotation errors, incompleteness, and inconsistencies in the data. This poses challenges to objectively comparing NER approaches and analyzing their errors, as current state-of-the-art model

Xinglong Chang, Katharina Dost, Gillian Dobbie, Jörg Wicker

The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors are tied to either specific data types, models, or attacks, and therefore have limited applicability in real-world scenarios. This paper presents a novel fully-agnostic framework, DI

Xu-Fan Hu, Zhen-Yi Cai, Jun-Xian Wang

The variability of active galactic nuclei (AGNs) is ubiquitous but has not yet been understood. Measuring the optical variation properties of AGNs, such as variation timescale and amplitude, and then correlating them with their fundamental physical parameters, have long served as a critical way of exploring the origin of AGN variability and the associated ph

Katie Bezdjian, Mathieu Francoeur

Pyromark 2500 is a widely used coating for concentrated solar power central receiver systems due to its high absorptivity, ease in application, and relatively low cost. Pyromark's performance is quantified by its figure of merit (FOM), which relates the coating's heat losses to its solar-to-thermal conversion efficiency. After long-term exposure to high temp

Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

Real-world data is complex and often consists of objects that can be decomposed into multiple entities (e.g. images into pixels, graphs into interconnected nodes). Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise be

J. W. Burby, M. H. Updike

We present a formalism for importing techniques from dynamical systems theory in the study of three-dimensional magnetohydrodynamic (MHD) equilibria. By treating toroidal angle as time, we reformulate the equilibrium equations as hydrodynamic equations on the unit disc. They satisfy a variational principle and comprise a Lie-Poisson Hamiltonian system. We us

Jing Li, Yoonyoung Kim, David Jewitt

Long-period comet C/2020 S3 (Erasmus) reached perihelion at 0.398 au on UT 2020 December 12.67, making it a bright, near-Sun object. Images taken between mid-November and December 2020 using the HI-1 camera and COR2 coronagraph onboard STEREO-A, as well as the LASCO/C3 coronagraph onboard SoHO, show significant variations in the plasma tail position angles.

Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng

Large language models (LLMs) have recently transformed both the academic and industrial landscapes due to their remarkable capacity to understand, analyze, and generate texts based on their vast knowledge and reasoning ability. Nevertheless, one major drawback of LLMs is their substantial computational cost for pre-training due to their unprecedented amounts

Mattia Spandri, Roberto Ferrara, Christian Deppe, Moritz Wiese

We consider the problem of information-theoretic secrecy in identification schemes rather than transmission schemes. In identification, large identities are encoded into small challenges sent with the sole goal of allowing at the receiver reliable verification of whether the challenge could have been generated by a (possibly different) identity of his choice

Fatemeh P. A. Beik, Michele Benzi

We propose an augmented Lagrangian-based preconditioner to accelerate the convergence of Krylov subspace methods applied to linear systems of equations with a block three-by-three structure such as those arising from mixed finite element discretizations of the coupled Stokes-Darcy flow problem. We analyze the spectrum of the preconditioned matrix and we show

Svetlana Kotochigova, Qingze Guan, Eite Tiesinga, Vito Scarola

Molecules have vibrational, rotational, spin-orbit and hyperfine degrees of freedom or quantum states, each of which responds in a unique fashion to external electromagnetic radiation. The control over superpositions of these quantum states is key to coherent manipulation of molecules. For example, the better the coherence time the longer quantum simulations

Adrian Perez Dieguez, Margarita Amor Lopez

GPU-embedded systems have gained popularity across various domains due to their efficient power consumption. However, in order to meet the demands of real-time or time-consuming applications running on these systems, it is crucial for them to be tuned to exhibit high performance. This paper addresses the issue by developing and comparing two tuning methodolo

Saptati Datta, Rachael Shudde, Valen E. Johnson

We describe Bayes factors based on z, t, $\chi^2$, and F statistics when non-local moment prior distributions are used to define alternative hypotheses. The non-local alternative prior distributions are centered on standardized effects. The prior densities include a dispersion parameter that can be used to model prior precision and the variation of effect si

Rudraksh Kapil, Seyed Mojtaba Marvasti-Zadeh, Nadir Erbilgin, Nilanjan Ray

Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature of forest canopy and the presence of diverse environmental variations, e.g., overlapping canopies, occlusions, and varying lighting conditions. Additionally, the lack of data for training robust models adds another limitation in effecti

Pedro Fernández

The goal of this paper is to clarify some differences between the critical Lebesgue space $L^3$ and the critical weak Lebesgue space $L^{3,\infty}$, when these are considered in the hypothesis of classical statements for the 3D homogeneous incompressible Navier-Stokes equations.

Atefeh Rezaei, Ata Khalili, Falko Dressler

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOM

Jon Alvarez Justo, Alexandru Ghita, Daniel Kovac, Joseph L. Garrett

Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference. The use of Deep Learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications. In this work, we train and test 20 models for multi-class segmentation in hyperspectral imagery, selected fo

M. Andrecut

We discuss a boosting approach for the Ridge Regression (RR) method, with applications to the Extreme Learning Machine (ELM), and we show that the proposed method significantly improves the classification performance and robustness of ELMs.

David M T Kuo

In this comprehensive study, we undertake a thorough theoretical examination of the electronic subband structures within cove-edged zigzag graphene nanoribbons (CZGNRs) using the tight-binding model. These unique nanostructures arise from the systematic removal of carbon atoms along the zigzag edges of conventional zigzag graphene nanoribbons (ZGNRs). Notabl

Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum, Els Goetghebeur

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model i

Ivan Pyltsyn

Game semantics allows us to look at basic logical concepts from another side. This approach to logic has a long history, there are plenty of different types of games: provability games, semantic games, etc. And there is an interesting type of provability games called Mezhirov's game proposed by Iliya Mezhirov for intuitionistic logic of propositions and Grze

Amanda A. Sickafoose, Stephen E. Levine, Amanda S. Bosh, Michael J. Person

A stellar occultation of Gaia DR3 2646598228351156352 by the Centaur (2060) Chiron was observed from the South African Astronomical Observatory on 2018 November 28 UT. Here we present a positive detection of material surrounding Chiron from the 74-in telescope for this event. Additionally, a global atmosphere is ruled out at the tens of mircobar level for se

Narender Khatri

Inertial effects should be considered for micro- and nano-swimmers moving in a low-density medium confined by irregular structures that create entropic barriers, where viscous effects are no longer paramount. Here, we present a separation mechanism of self-propelled particles in a two-dimensional asymmetric channel, which leads to the drift of particles of d

Lan Luo, Chengchun Shi, Jitao Wang, Zhenke Wu

Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there are multivariate and conditionally dependent mediators, and when the variables are observed over multiple time points. The problem is challenging, because the effect of a mediator i

Omar Lakkis, Alexandros Skouras, Vanessa Styles

We study a model for lithium (Li) electrodeposition on Li-metal electrodes that leads to dendritic pattern formation. The model comprises of a system of three coupled PDEs, taking the form of an Allen--Cahn equation, a Nernst--Planck equation and a Poisson equation. We prove existence of a weak solution and stability results for this system and present numer

Walden Marshall, Bassam Bamieh, Emily Jensen

The optimal controller design problem for systems equipped with sensors that measure only relative, rather than absolute, quantities is considered. This relative measurement structure is formulated as a design constraint; it is demonstrated that the resulting constrained controller design problem can be written as a convex program. Certain additional network

Alicja Jokiel-Rokita, Sylwester Piątek

Classical inequality curves and inequality measures are defined for distributions with finite mean value. Moreover, their empirical counterparts are not resistant to outliers. For these reasons, quantile versions of known inequality curves such as the Lorenz, Bonferroni, Zenga and $D$ curves, and quantile versions of inequality measures such as the Gini, Bon

Siyuan Wang, Andrey Polyakov, Gang Zheng, Xubin Ping

The invariant ellipsoid method is aimed at minimization of the smallest invariant and attractive set of a linear control system operating under bounded external disturbances. This paper extends this technique to a class of the so-called generalized homogeneous system by defining the $\dn-$homogeneous invariant/attractive ellipsoid. The generalized homogeneou

R. M. de Oliveira, M. S. Rocha

We perform a single molecule force spectroscopy study to characterize the role of the size (molecular weight) and concentration of depleting agents on DNA condensation and on the stabilization of the double-helix structure, showing that important features such as the threshold concentration for DNA condensation, the force in which the melting plateau occurs

Adithya Pratapa, Kevin Small, Markus Dreyer

Generating concise summaries of news events is a challenging natural language processing task. While journalists often curate timelines to highlight key sub-events, newcomers to a news event face challenges in catching up on its historical context. In this paper, we address this need by introducing the task of background news summarization, which complements

Monan Ma, Kamil L. Ekinci

We study the electrothermal actuation of nanomechanical motion using a combination of numerical simulations and analytical solutions. The nanoelectrothermal actuator structure is a u-shaped gold nanoresistor that is patterned on the anchor of a doubly-clamped nanomechanical beam or a microcantilever resonator. This design has been used in recent experiments

Jean Tapie, Hugo Prod'homme, Mohammadreza F. Imani, Philipp del Hougne

Wireless networks-on-chip (WNoCs) are an enticing complementary interconnect technology for multi-core chips but face severe resource constraints. Being limited to simple on-off-keying modulation, the reverberant nature of the chip enclosure imposes limits on allowed modulation speeds in sight of inter-symbol interference, casting doubts on the competitivene