March 2024 arXiv papers — page 17

Andrii Kompanets, Remco Duits, Davide Leonetti, Nicky van den Berg

Safety-critical infrastructures, such as bridges, are periodically inspected to check for existing damage, such as fatigue cracks and corrosion, and to guarantee the safe use of the infrastructure. Visual inspection is the most frequent type of general inspection, despite the fact that its detection capability is rather limited, especially for fatigue cracks

Bobing Ye, Xuefeng Zhang

TianQin is a proposed space-based gravitational-wave observatory mission that critically relies on the stability of an equilateral-triangle constellation. Comprising three satellites in high Earth orbits of a $ 10^5 $ km radius, this constellation's geometric configuration is significantly affected by gravitational perturbations, primarily originating from t

Alireza Ganjdanesh, Shangqian Gao, Heng Huang

Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained devices. Yet, the majority of proposed ideas require a pretrained model before pruning, which is costly to secure. In this paper, we propose a novel structural pruning approach to jo

Irina Maliukov, Gera Weiss, Oded Margalit, Achiya Elyasaf

In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code

Ovidiu Popescu, Cristina Maria Pacurar

The aim of the current paper is to introduce a new class of contractive mappings, which are contracting (a feature of) triangles. We prove that maps contracting triangles are continuous and give the fixed point result for such mappings. We emphasize that our main theorem encompasses many functions, with significant applicability, for which the result holds,

Anna Abbatiello, Giovanna Andreucci, Emanuele Spadaro

The thin obstacle problem or $n$-dimensional Signorini problem is a classical variational problem arising in several applications, starting with its first introduction in elasticity theory. The vast literature concerns mostly quadratic energies, whereas only partial results have been proved in the nonlinear case. In this paper we consider the thin boundary o

Tim S. G. van Eck, Pieter Kleer, Johan S. H. van Leeuwaarden

Problem definition: Traditional monopoly pricing assumes sellers have full information about consumer valuations. We consider monopoly pricing under limited information, where a seller only knows the mean, variance and support of the valuation distribution. The objective is to maximize expected revenue by selecting the optimal fixed price. Methodology/result

Jay Gopalakrishnan, Jacob Grosek, Gabriel Pinochet-Soto, Pieter VandenBerge

An adaptive algorithm for computing eigenmodes and propagation constants of optical fibers is proposed. The algorithm is built using a dual-weighted residual error estimator. The residuals are based on the eigensystem for leaky hybrid modes obtained from Maxwell equations truncated to a finite domain after a transformation by a perfectly matched layer. The a

Zixi Wang, Yubo Huang, Yukai Zhang, Yifei Sheng

This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Medical robotics, particularly in vascular treatments, necessitates precise resource allocation and optimization due to the complex nature of robot and operator maintenance. Traditional heuristic methods,

Pascal Oswald

We study ancestral lineages of individuals of a stationary discrete-time branching annihilating random walk (BARW) on the $d$-dimensional lattice $\mathbb{Z}^d$. Each individual produces a Poissonian number of offspring with mean $\mu$ which then jump independently to a uniformly chosen site with a fixed distance $R$ of their parent. By interpreting the ance

J. Garnier, H. Haddar, H. Montanelli

We present an extension of the linear sampling method for solving the sound-soft inverse scattering problem in two dimensions with data generated by randomly distributed small scatterers. The theoretical justification of our novel sampling method is based on a rigorous asymptotic model, a modified Helmholtz--Kirchhoff identity, and our previous work on the l

Mark A. Stern

In this note we describe basic geometric properties of p-harmonic forms and p-coclosed forms and use them to reprove vanishing theorems of Pansu and new injectivity theorems for the Lp -cohomology of simply connected, pinched negatively curved manifolds. We also provide a partial resolution of a conjecture of Gromov on the vanishing of Lp -cohomology on symm

Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a detailed study of $H$-consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish $H$-consistency bounds. This generalization proves essential for analyzing $H$-consistency bounds specific to regression. Next, we prove a series of novel $H$-consistency bounds for surrogate loss functio

Xiaomin Guo, Fading Lin, Jiehong Lin, Zhijie Song

Quantum systems are particularly suited for generating true randomness due to their inherent unpredictability, which can be justified on physical principles. However, practical implementations of Quantum RNGs (QRNGs) are always subject to noise, or uncontrollable influences, diminishing the quality of raw randomness produced. This necessitates post-processin

Prasanta K. Nayak, Mayank Narang, P. Manoj, D. K. Ojha

The paper demonstrates the spectroscopic and photometric capabilities of the Ultra-Violet Imaging Telescope (UVIT) to study T-Tauri stars (TTSs). We present the first UVIT/Far-UV (FUV) spectrum of a TTS, TW Hya. Based on C~{\sc iv} line luminosity, we estimated accretion luminosity (0.12$\pm$0.03 $L_\odot$) and mass accretion rate (2.4$\pm$0.6 $\times$ $10^{

Bryan C. Runck, Bobby Schulz, Jeff Bishop, Nathan Carlson

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality

Diego Rossit, Jonathan Bard

An efficient Municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and livability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system,

Giorgia Adorni, Alberto Piatti, Engin Bumbacher, Lucio Negrini

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the characteristics of computational thinking problems (CTPs) - activities requiring CT to be solved - and how the

Yaxu Xie, Alain Pagani, Didier Stricker

Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of many downstream tasks such as scene graph aided point cloud registration, mosaicking, overlap checking, and robot navigation. In this work, we treat 3D scene graph alignment as a p

T. Dodo, M. K. Cheoun, J. H. Choi, J. Y. Choi

JSNS$^2$ (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\bar{\nu}_{\mu} \rightarrow \bar{\nu}_e$ appearance oscillations using neutrinos with muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD

Tongyan Hua, Lin Wang

Implicit neural representation (INR), in combination with geometric rendering, has recently been employed in real-time dense RGB-D SLAM. Despite active research endeavors being made, there lacks a unified protocol for fair evaluation, impeding the evolution of this area. In this work, we establish, to our knowledge, the first open-source benchmark framework

Eilind Karlsson, Claudia I. Scheimbauer, Tashi Walde

We provide a toolbox of extension, gluing, and assembly techniques for factorization algebras. Using these tools, we fill various gaps in the literature on factorization algebras on stratified manifolds, the main one being that constructible factorization algebras form a sheaf of symmetric monoidal $\infty$-categories. Additionally, we explain how to assembl

Merve Bodur, Timothy C. Y. Chan, Ian Yihang Zhu

Adaptive robust optimization problems have received significant attention in recent years, but remain notoriously difficult to solve when recourse decisions are discrete in nature. In this paper, we propose new reformulation techniques for adaptive robust binary optimization (ARBO) problems with objective uncertainty. Without loss of generality, we focus on

Jacob Varey, Jessica D. Ruprecht, Michael Tierney, Ryan Sullenberger

The Space Domain Awareness (SDA) community routinely tracks satellites in orbit by fitting an orbital state to observations made by the Space Surveillance Network (SSN). In order to fit such orbits, an accurate model of the forces that are acting on the satellite is required. Over the past several decades, high-quality, physics-based models have been develop

Yunwen Yin, Liang Yan

In this paper, we consider a deep learning approach to the limited aperture inverse obstacle scattering problem. It is well known that traditional deep learning relies solely on data, which may limit its performance for the inverse problem when only indirect observation data and a physical model are available. A fundamental question arises in light of these

S. Forstner, H. Choi, G. I. Harris, A. Sawadsky

Despite extensive experimental efforts over the past two decades, the quest for Majorana fermions in superconductors remains inconclusive. We propose an experimental method that can conclusively confirm, or rule out, the existence of these quasiparticles: Firstly, we shift focus from superconductors, whose very topological nature is disputed, to the unambigu

Sz. Kálmán, A. Derekas, Sz. Csizmadia, A. Pál

Ultra-hot Jupiters (UHJs) orbiting pulsating A/F stars represent an important subset of the exoplanetary demographic, as they are excellent candidates for the study of exoplanetary atmospheres, as well as being astrophysical laboratories for the investigation of planet-to-star interactions. We analyse the \texttt{TESS} (Transiting Exoplanet Survey Satellite)

Mingze Sun, Chao Xu, Xinyu Jiang, Yang Liu

In this paper, we introduce an innovative task focused on human communication, aiming to generate 3D holistic human motions for both speakers and listeners. Central to our approach is the incorporation of factorization to decouple audio features and the combination of textual semantic information, thereby facilitating the creation of more realistic and coord

Jianwei Cui, Wenhang Shi, Honglin Tao, Wei Lu

As the ubiquity of deep learning in various machine learning applications has amplified, a proliferation of neural network models has been trained and shared on public model repositories. In the context of a targeted machine learning assignment, utilizing an apt source model as a starting point typically outperforms the strategy of training from scratch, par

Taejin Park

This paper introduces a Large Language Model (LLM)-based multi-agent framework designed to enhance anomaly detection within financial market data, tackling the longstanding challenge of manually verifying system-generated anomaly alerts. The framework harnesses a collaborative network of AI agents, each specialised in distinct functions including data conver

Shyam Popat

In this paper, we extend the notion of stochastic kinetic solutions introduced in arXiv:2108.08858 to establish the well-posedness of stochastic kinetic solutions of generalized Dean-Kawasaki equations with correlated noise on bounded, $C^2$-domains with Dirichlet boundary conditions. The results apply to a wide class of non-negative boundary data, which is

Kostiantyn Tolmachov

For the group GL(n), we construct an action of the equivariant derived category of coherent sheaves on the Grothendieck-Springer resolution on a certain subcategory of a finite monodromic Hecke category. We use this to construct a partial categorification of the projection from the extened affine to the finite Hecke algebra of GL(n). As a crucial intermediat

George Kourmoulakis, Sotiris Psilodimitrakopoulos, George Miltos Maragkakis, Leonidas Mouchliadis

Two-dimensional (2D) graphene and graphene-related materials (GRMs) show great promise for future electronic devices. Nevertheless, GRMs result distinct properties under the influence of the substrate that serves as support through uneven compression/ elongation of GRMs surface atoms. Strain in GRM monolayers is the most common feature that alters the intera

Zehao Jin, Andrea V. Macciò, Nicholas Faucher, Mario Pasquato

Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches for evaluating galaxy simulations is either through scaling relations based on a few key physical galaxy prope

A. A. Sinchenko, P. D. Grigoriev, A. V. Frolov, A. P. Orlov

Magnetic quantum oscillations (MQO) of Hall coefficient are measured in rare-earth tritelluride TmTe$_{3}$ and shown to be much stronger and persist to higher temperature than the Shubnikov oscillations. It is general for MQO in strongly anisotropic metals, and the combined measurements of Hall and diagonal magnetoresistance provide useful informations about

Teodor V. Marinov, Alekh Agarwal, Mircea Trofin

This work studies a Reinforcement Learning (RL) problem in which we are given a set of trajectories collected with K baseline policies. Each of these policies can be quite suboptimal in isolation, and have strong performance in complementary parts of the state space. The goal is to learn a policy which performs as well as the best combination of baselines on

Simon Idoko, Basant Sharma, Arun Kumar Singh

Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variation

Jean Martins, Igor Almeida, Ricardo Souza, Silvia Lins

As mobile networks embrace the 5G era, the interest in adopting Reinforcement Learning (RL) algorithms to handle challenges in ultra-low-latency and high throughput scenarios increases. Simultaneously, the advent of packetized fronthaul networks imposes demanding requirements that traditional congestion control mechanisms cannot accomplish, highlighting the

Chongkai Gao, Zhengrong Xue, Shuying Deng, Tianhai Liang

We present RiEMann, an end-to-end near Real-time SE(3)-Equivariant Robot Manipulation imitation learning framework from scene point cloud input. Compared to previous methods that rely on descriptor field matching, RiEMann directly predicts the target poses of objects for manipulation without any object segmentation. RiEMann learns a manipulation task from sc

Fergal Stapleton, Brendan Cody-Kenny, Edgar Galván

Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neuroevolution. However, neuroevolution is an inherently resource-intensive process, with certain studies reporti

Bhojraj Singh Jayas, Vinod Kumar Bhardwaj

We have explored a transitioning cosmic model, depicting late-time accelerated expansion in $f(R,T^{\phi})$ theory of gravity for an isotropic and homogeneous universe, where the trace of energy-momentum tensor $T^{\phi}$ is the function of the self-interacting scalar field $\phi$. We have proposed an explicit solution to the derived model by utilizing a sca

Xusheng Zhu, Qingqing Wu, Wen Chen

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transmitter-enabled spatial modulation (SM) multiple-input multiple-output (MIMO) system. In the transmission phase, a column-wise activation strategy is implemented for the TRIS panel, where the specific column elements are activated per time slot. Concurrently, the rec

Salem Said, Franziskus Steinert, Cyrus Mostajeran

The present work develops certain analytical tools required to construct and compute invariant kernels on the space of complex covariance matrices. The main result is the $\mathrm{L}^1$--Godement theorem, which states that any invariant kernel, which is (in a certain natural sense) also integrable, can be computed by taking the inverse spherical transform of

Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang

Personalized generation paradigms empower designers to customize visual intellectual properties with the help of textual descriptions by tuning or adapting pre-trained text-to-image models on a few images. Recent works explore approaches for concurrently customizing both content and detailed visual style appearance. However, these existing approaches often g

Jukka-Pekka Humaloja, Nikolaos Bekiaris-Liberis

We establish that stabilization of a class of linear, hyperbolic partial differential equations (PDEs) with a large (nevertheless finite) number of components, can be achieved via employment of a backstepping-based control law, which is constructed for stabilization of a continuum version (i.e., as the number of components tends to infinity) of the PDE syste

Ksh. Newton Singh, G. R. P. Teruel, S. K. Maurya, Tanmoy Chowdhury

We present an exhaustive study of wormhole configurations in $\kappa(\mathcal{R},\mathcal{T})$ gravity with linear and non-linear functions. The model assumed Morrison-Thorne spacetime where the redshift and shape functions linked with the matter contain and geometry of the spacetime through non-covariant conservation equation of the stress-energy tensor. Th

Eri Onami, Shuhei Kurita, Taiki Miyanishi, Taro Watanabe

Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society. This is known as a quite challenging task because it requires not only text understanding but also understanding of figures a

Gukyeong Bang

In this paper, we study the submodularity of the covolume function in global function fields. The submodular property is often needed in the study of homogeneous dynamics, especially to define a Margulis function. We proved that the covolume function is submodular when the class group of the global function field is trivial.

Christian T Preuss

The implementation of a new final-state parton-shower algorithm in the Pythia event generator is described. The shower algorithm, dubbed Apollo, combines central aspects of the Vincia antenna shower with the global transverse-recoil scheme of the Alaric framework in order to achieve formal consistency with next-to-leading logarithmic (NLL) resummation. The s

Peijin Zhang, Diana E. Morosan, Pietro Zucca, Sanna Normo

Context. Observations of solar type II radio bursts provide a unique opportunity to analyze the non-thermal electrons accelerated by coronal shocks and also to diagnose the plasma density distribution in the corona. However, there are very rare high-frequency resolution interferometric observations for type II radio bursts that are capable of tracking these

M. D. Danarianto, I. Prasetyo, A. Suroso, B. E. Gunara

The advancement of astronomical observations opens the possibility of testing our current understanding of gravitational theory in the strong-field regime and probing any deviation from general relativity. We explore to what extent compact stars predicted by non-minimal derivative coupling (NMDC) gravity theory agree with observed data. We investigate white

Marcin Hoffmann, Paweł Kryszkiewicz

The 6G Massive Multiple-Input Multiple-Output (MMIMO) networks can follow the so-called User-Centric Cell-Free (UCCF) architecture, where a single user is served by multiple Access Points (APs) coordinated by the Central Processing Unit (CPU). In this paper, we propose how O-RAN functionalities, i.e., rApp-xApp pair, can be used for energy-efficient Serving

Johannes Müller, Semih Çaycı, Guido Montúfar

Kakade's natural policy gradient method has been studied extensively in recent years, showing linear convergence with and without regularization. We study another natural gradient method based on the Fisher information matrix of the state-action distributions which has received little attention from the theoretical side. Here, the state-action distributions

Niankun Yu, Zheng Zheng, Chao-Wei Tsai, Pei Zuo

The atomic-to-molecular gas conversion is a critical step in the baryon cycle of galaxies, which sets the initial conditions for subsequent star formation and influences the multi-phase interstellar medium. We compiled a sample of 94 nearby galaxies with observations of multi-phase gas contents by utilizing public H I, CO, and optical IFU data from the MaNGA

Zhengyuan Shi, Tiebing Tang, Jiaying Zhu, Sadaf Khan

The Circuit Satisfiability (CSAT) problem, a variant of the Boolean Satisfiability (SAT) problem, plays a critical role in integrated circuit design and verification. However, existing SAT solvers, optimized for Conjunctive Normal Form (CNF), often struggle with the intrinsic complexity of circuit structures when directly applied to CSAT instances. To addres

Pranava K. Sivakumar, Mostafa T. Ahari, Jae-Keun Kim, Yufeng Wu

Superconducting diode effects have recently attracted much attention for their potential applications in superconducting logic circuits. Several mechanisms such as magneto-chiral effects, finite momentum Cooper pairing, asymmetric edge currents have been proposed to give rise to a supercurrent diode effect in different materials. In this work, we establish t

Qi Gou, Cam-Tu Nguyen

Large Language Models (LLMs) have become increasingly popular due to their ability to process and generate natural language. However, as they are trained on massive datasets of text, LLMs can inherit harmful biases and produce outputs that are not aligned with human values. This paper studies two main approaches to LLM alignment: Reinforcement Learning with

Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp, Anne Roefs

In the evolving field of psychopathology, the accurate assessment and forecasting of data derived from Ecological Momentary Assessment (EMA) is crucial. EMA offers contextually-rich psychopathological measurements over time, that practically lead to Multivariate Time Series (MTS) data. Thus, many challenges arise in analysis from the temporal complexities in

Mamadou Dia, Ghazaleh Khodabandelou, Alice Othmani

Post-traumatic stress disorder (PTSD) is a mental disorder that can be developed after witnessing or experiencing extremely traumatic events. PTSD can affect anyone, regardless of ethnicity, or culture. An estimated one in every eleven people will experience PTSD during their lifetime. The Clinician-Administered PTSD Scale (CAPS) and the PTSD Check List for

Kin-ya Oda, Naoya Ogawa

We point out that the Gaussian wave-packet formalism can serve as a concrete realization of the joint measurement of position and momentum, which is an essential element in understanding Heisenberg's original philosophy of the uncertainty principle, in line with the universal framework of error, disturbance, and their uncertainty relations developed by Lee a

Muzi Chen, Yuhang Wang, Boyao Wu, Difang Huang

The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial

Binyuan Huang, Yuqing Wen, Yucheng Zhao, Yaosi Hu

Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data for autonomous driving applications and present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving

Bastian Dittrich, Daniel Wachsmuth

We consider constraints on the measure of the support for integrable functions on arbitrary measure spaces. It is shown that this non-convex and discontinuous constraint can be equivalently reformulated by the difference of two convex and continuous functions, namely the $L^1$-norm and the so-called largest-$K$-norm. The largest-$K$-norm is studied and its c

Rie Helene Hernandez, Qiurong Song, Yubo Kou, Xinning Gui

Tracking is inherent in and central to the gig economy. Platforms track gig workers' performance through metrics such as acceptance rate and punctuality, while gig workers themselves engage in self-tracking. Although prior research has extensively examined how gig platforms track workers through metrics -- with some studies briefly acknowledging the phenomen

Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee

Generating human motion from text has been dominated by denoising motion models either through diffusion or generative masking process. However, these models face great limitations in usability by requiring prior knowledge of the motion length. Conversely, autoregressive motion models address this limitation by adaptively predicting motion endpoints, at the

Thea Hood, Florian Debras, Claire Moutou, Baptiste Klein

Measuring the abundances of C- and O-bearing species in exoplanet atmospheres enables us to constrain the C/O ratio, that contains indications about the planet formation history. With a wavelength coverage going from 0.95 to 2.5 microns, the high-resolution (R$\sim$70 000) spectropolarimeter SPIRou can detect spectral lines of major bearers of C and O in exo

Haidong Xin, Fang Wu, Zhitong Zhou

We study the prediction and classification of Wordle solution words. After cleaning the public results log, we fit an ARIMA model to forecast the daily volume of reported outcomes through March 1, 2023. For each solution word, we compute three interpretable attributes: usage frequency (FREQ), word information entropy (WIE), and the number of repeated letters

Song Wang, Yiliang Zhou, Ziqiang Han, Cui Tao

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical

Timo Eckhardt, David Pym

We develop a proof-theoretic semantics -- in particular, a base-extension semantics -- for multi-agent S5 modal logic (and hence also for the usual unindexed S5). Following the inferentialist interpretation of logic, this gives us a semantics in which validity is based on proof rather than truth. In base-extension semantics, the validity of formulae is gener

Han Xiao, Qi-Bo Zeng

We study the one-dimensional non-Hermitian lattices with staggered onsite modulations and nonreciprocal hopping up to the next-nearest-neighboring (NNN) sites. Due to the NNN nonreciprocity, the non-Hermitian skin effect (NHSE) in the system under open boundary conditions (OBC) can be energy dependent, and there will be NHSE edges in the eigenenergy spectrum

Nadia Benlakhouy, Abderrahim El Mouhafid, Ahmed Jellal

The electronic transport properties of two junctions (BGB, GBG) made of borophene (B) and graphene (G) are investigated. Using the transfer matrix method with Chebyshev polynomials, we have studied single and multiple barriers in a superlattice configuration. We showed that a single barrier exhibits remarkable tilted transport properties, with perfect transm

Kyotaro Tokoro, Kazutoshi Akita, Norimichi Ukita

While burst LR images are useful for improving the SR image quality compared with a single LR image, prior SR networks accepting the burst LR images are trained in a deterministic manner, which is known to produce a blurry SR image. In addition, it is difficult to perfectly align the burst LR images, making the SR image more blurry. Since such blurry images

Min Tang, Chi Pang, Christian N. Saggau, Haiyun Dong

Topological boundary states localize at interfaces whenever the interface implies a change of the associated topological invariant encoded in the geometric phase. The generically present dynamic phase, however, which is energy and time dependent, has been known to be non-universal, and hence not to intertwine with any topological geometric phase. Using the e

Hermínio T. Honório, Maartje Houben, Kevin Bisdom, Arjan van der Linden

Renewable hydrogen storage in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. Such operational conditions raise concerns about the mechanical stability of salt caverns. Choosing an appropriate constitutive model for salt mechanics is an important step in investigating this issue,

Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew, Alexandre Hutton

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22

Jonathan Kamp, Lisa Beinborn, Antske Fokkens

Post-hoc explanation methods are an important tool for increasing model transparency for users. Unfortunately, the currently used methods for attributing token importance often yield diverging patterns. In this work, we study potential sources of disagreement across methods from a linguistic perspective. We find that different methods systematically select d

Aleksandra Sorokovikova, Michael Becker, Ivan P. Yamshchikov

This paper investigates the communication styles and structures of Twitter (X) communities within the vaccination context. While mainstream research primarily focuses on the echo-chamber phenomenon, wherein certain ideas are reinforced and participants are isolated from opposing opinions, this study reveals the presence of diverse communication styles across

Sangyun Lee, Duk Y. Kim, Priscila F. S. Rosa, Eric D. Bauer

The thermal conductivity of heavy-fermion superconductor CeCoIn$_5$ was measured with a magnetic field rotating in the tetragonal a-b plane, with the heat current in the anti-nodal direction, $J$ || [100]. We observe a sharp resonance in thermal conductivity for the magnetic field at an angle $\theta$ $\sim$ 12$^{\circ}$, measured from the heat current direc

Sana Ahmadi, Pierre Bellec, Tristan Glatard

Brain encoding with neuroimaging data is an established analysis aimed at predicting human brain activity directly from complex stimuli features such as movie frames. Typically, these features are the latent space representation from an artificial neural network, and the stimuli are image, audio, or text inputs. Ridge regression is a popular prediction model

T. Q. Thelen, D. A. Rehn, C. J. Fontes, C. E. Starrett

In a dense plasma environment, the energy levels of an ion shift relative to the isolated ion values. This shift is reflected in the optical spectrum of the plasma and can be measured in, for example, emission experiments. In this work, we use a recently developed method of modeling electronic states in warm dense matter to predict these level energies. In t

Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, Dimitris Fotakis

There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recommender systems, and HR automation, much work on fairness remains to be done. Two complications arise: first, the protected attribute may not be available in many applications. Secon

Michael F. Zimmer

This paper begins with a dynamical model that was obtained by applying a machine learning technique (FJet) to time-series data; this dynamical model is then analyzed with Lie symmetry techniques to obtain constants of motion. This analysis is performed on both the conserved and non-conserved cases of the 1D and 2D harmonic oscillators. For the 1D oscillator,

Xinyu Zhan, Lixin Yang, Yifei Zhao, Kangrui Mao

We present OAKINK2, a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation, OAKINK2 introduces three level of abstraction to organize the manipulation tasks: Affordance, Primitive Task, and Complex Task. OAKINK2 features on an object-centric perspective for d

Tomáš Brůna, Lars Gabriel, Katharina J. Hoff

Annotating the structure of protein-coding genes represents a major challenge in the analysis of eukaryotic genomes. This task sets the groundwork for subsequent genomic studies aimed at understanding the functions of individual genes. BRAKER and Galba are two fully automated and containerized pipelines designed to perform accurate genome annotation. BRAKER

Baris Imre, Elina Thibeau-Sutre, Jorieke Reimer, Kuan Kho

Chronic subdural hematoma (cSDH) is a common neurological condition characterized by the accumulation of blood between the brain and the dura mater. This accumulation of blood can exert pressure on the brain, potentially leading to fatal outcomes. Treatment options for cSDH are limited to invasive surgery or non-invasive management. Traditionally, the midlin

Yuhong He, Yongqi Zhang, Shizhu He, Jun Wan

Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. Previous works typically employ a sequence-to-sequence framework to generate medical responses by modeling dialogue context as sequential text with annotated medical entities. While these methods have been successful in generating fluent responses, they

Bin Wu, Ying Wang, Zewen Wang

In this paper, we study discrete Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation. As applications of these discrete Carleman estimates, we apply them to study two inverse problems for the spatial semi-discrete stochastic parabolic equations, including a discrete inverse random source problem and a di

Hongwei Ren, Jiadong Zhu, Yue Zhou, Haotian FU

Event cameras exhibit remarkable attributes such as high dynamic range, asynchronicity, and low latency, making them highly suitable for vision tasks that involve high-speed motion in challenging lighting conditions. These cameras implicitly capture movement and depth information in events, making them appealing sensors for Camera Pose Relocalization (CPR) t

Katrin Halbig, Alexander Hoen, Ambros Gleixner, Jakob Witzig

Semi-continuous decision variables arise naturally in many real-world applications. They are defined to take either value zero or any value within a specified range, and occur mainly to prevent small nonzero values in the solution. One particular challenge that can come with semi-continuous variables in practical models is that their upper bound may be large

Thomas Cai, Kyle Hambrook

We compute the exact Fourier dimension of the set of $\Psi$-well-approximable $m \times n$ matrices (and the set of $\Psi$-well-approximable numbers) in the homogeneous and inhomogeneous cases for any approximation function $\Psi$ satisfying $\sum_{q \in \mathbb{Z}^n} \Psi(q)^m < \infty$.

Zirui Chen, Zhaoyang Zhang, Zhaohui Yang, Chongwen Huang

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple

Guilherme Fiusa, Gabriel T. Landi

Collision models describe the sequential interactions of a system with independent ancillas. Motivated by recent advances in neutral atom arrays, in this Letter we investigate a model where the ancillas are governed by a classical controller that allows them to queue up while they wait for their turn to interact with the system. The ancillas can undergo indi

Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Mubarak Shah

Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining consistent object segmentation due to temporal context variability and the presence of other visually similar objects. We propose an end-to-end R-VOS paradigm that explicitly models temporal instance consistency alongside the referring segmentation. Specifically, we introduce

Aly H. Abdeldaim, Hlynur Gretarsson, Sarah J. Day, M. Duc Le

Magnetic materials are composed of the simple building blocks of magnetic moments on a crystal lattice that interact via magnetic exchange. Yet from this simplicity emerges a remarkable diversity of magnetic states. Some reveal the deep quantum mechanical origins of magnetism, for example, quantum spin liquid (QSL) states in which magnetic moments remain dis

Fredy Reusser

Examining the effect of different encoding techniques on entity and context embeddings, the goal of this work is to challenge commonly used Ordinal encoding for tabular learning. Applying different preprocessing methods and network architectures over several datasets resulted in a benchmark on how the encoders influence the learning outcome of the networks.

Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger

We introduce a theoretical framework for the rational approximation of optical response functions in resonant photonic systems. The framework is based on the AAA algorithm and further allows to solve the underlying nonlinear eigenproblems and to efficiently model sensitivities. An adaptive sampling strategy exploits the predominance of resonances in the phys

James Langley

The main result establishes an estimate for the growth of a real meromorphic function $f$ on the unit disc $\Delta$ such that: (i) at least one of $f$ and $1/f$ has finitely many poles and non-real zeros in $\Delta$; (ii)~$f^{(k)}$ has finitely many non-real zeros in $\Delta$, for some $k \geq 2$.

Nitish Kumar, Hershita Shukla, P. Rajalakhsmi

Today's major concern in traffic management systems includes time-efficient emergency transports. The awareness of environment and vehicle information is necessary for the emergency vehicles as well as the surrounding commercial vehicles that might be driven by inexperienced drivers to act accordingly if they both interact. The information exchange should be

Venkatesan Guruswami, Rishi Saket

In recent years the framework of learning from label proportions (LLP) has been gaining importance in machine learning. In this setting, the training examples are aggregated into subsets or bags and only the average label per bag is available for learning an example-level predictor. This generalizes traditional PAC learning which is the special case of unit-

Jingwei Ge, Pengbo Wang, Cheng Chang, Yi Zhang

Sampling critical testing scenarios is an essential step in intelligence testing for Automated Vehicles (AVs). However, due to the lack of prior knowledge on the distribution of critical scenarios in sampling space, we can hardly efficiently find the critical scenarios or accurately evaluate the intelligence of AVs. To solve this problem, we formulate the te