July 2023 arXiv papers — page 18

Dirk A. Lorenz, Maximilian Winkler

The Bregman-Kaczmarz method is an iterative method which can solve strongly convex problems with linear constraints and uses only one or a selected number of rows of the system matrix in each iteration, thereby making it amenable for large-scale systems. To speed up convergence, we investigate acceleration by heavy ball momentum in the so-called dual update.

Kaitao Meng, Qingqing Wu, Wen Chen, Deshi Li

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cro

Dimitri Korsch, Paul Bodesheim, Gunnar Brehm, Joachim Denzler

Automatic camera-assisted monitoring of insects for abundance estimations is crucial to understand and counteract ongoing insect decline. In this paper, we present two datasets of nocturnal insects, especially moths as a subset of Lepidoptera, photographed in Central Europe. One of the datasets, the EU-Moths dataset, was captured manually by citizen scientis

Jiang Li, Xiaoping Wang, Yingjian Liu, Zhigang Zeng

Multimodal emotion recognition in conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a Cross-modal Fusion Network with Emotion-Shift Awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information in these modalities, r

Paolo Bianchini, Alessandra Mastrobuono-Battisti

The study of the chemistry of the stellar populations in Globular Clusters (GCs) is a fundamental task to unveil their formation in the high-redshift universe and to reconstruct the build up of our Galaxy. Using metallicity estimates from BP/RP low-resolution Gaia DR3 spectra, a recent work presented the surprising detection of two stellar populations with d

Francisco Fuentes, Raimon Casanova, Sergi Alcaide, Jaume Abella

Safety-critical domains, such as automotive, space, and robotics, are adopting increasingly powerful multicores with abundant hardware shared resources for higher performance and efficiency. However, mutual interference due to parallel operation within the SoC must be properly validated. Recently, the SafeTI traffic injector has been released and integrated

Jing Tang, Yuangang Deng

Enhancing and tailoring light-matter interactions offer remarkable nonlinear resources with wide-ranging applications in various scientific disciplines. In this study, we investigate the construction of strong and deterministic tripartite `beamsplitter' (`squeeze') interactions by utilizing cavity-enhanced nonlinear anti-Stokes (Stokes) scattering within the

Yanqi Dai, Nanyi Fei, Zhiwu Lu

In multi-task learning (MTL), gradient balancing has recently attracted more research interest than loss balancing since it often leads to better performance. However, loss balancing is much more efficient than gradient balancing, and thus it is still worth further exploration in MTL. Note that prior studies typically ignore that there exist varying improvab

Peter Naylor, Diego Di Carlo, Arianna Traviglia, Makoto Yamada

Identifying changes in a pair of 3D aerial LiDAR point clouds, obtained during two distinct time periods over the same geographic region presents a significant challenge due to the disparities in spatial coverage and the presence of noise in the acquisition system. The most commonly used approaches to detecting changes in point clouds are based on supervised

Dimitri Korsch, Paul Bodesheim, Joachim Denzler

Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations. However, automatic recognition systems are rarely applied so far, and experts evaluate the generated data masses manually. Especially the support of deep learning methods for visual monitoring is not yet established in biodiversity research, compared

Norbert Dragon

We analyze relativistic quantum scattering in the Schr\"odinger picture. The suggestive requirement of translational invariance and conservation of the four-momentum, that the interacting Hamiltonian commute with the four-momentum $P$ of free particles, is shown to imply the absence of interactions. The relaxed requirement, that the interacting Hamiltonian $

Arash Hajikhani, Carolyn Cole

This paper examines the comparative effectiveness of a specialized compiled language model and a general-purpose model like OpenAI's GPT-3.5 in detecting SDGs within text data. It presents a critical review of Large Language Models (LLMs), addressing challenges related to bias and sensitivity. The necessity of specialized training for precise, unbiased analy

Conor Hassan, Robert Salomone, Kerrie Mengersen

This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of privacy-sensitive data. Additionally, we highlight the advantages of using deep generative models over other met

Maxime Dalery, Genevieve Dusson, Virginie Ehrlacher, Alexei Lozinski

The aim of this article is to propose a new reduced-order modelling approach for parametric eigenvalue problems arising in electronic structure calculations. Namely, we develop nonlinear reduced basis techniques for the approximation of parametric eigenvalue problems inspired from quantum chemistry applications. More precisely, we consider here a one-dimensi

Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash

Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be computationally expensive. To reduce costs, Multi-fidelity HPO (MF-HPO) leverages intermediate accuracy levels in the learning process and discards low-performing models early on. We compared various representative MF-HPO methods against a simple baseline on class

Wei Jiang, Jiayu Yang, Yongqi Zhai, Feng Gao

The latent representation in learned image compression encompasses channel-wise, local spatial, and global spatial correlations, which are essential for the entropy model to capture for conditional entropy minimization. Efficiently capturing these contexts within a single entropy model, especially in high-resolution image coding, presents a challenge due to

C. M. Wang, Z. Z. Du, Hai-Zhou Lu, X. C. Xie

Recently, the planar Hall effect has attracted tremendous interest. In particular, an in-plane magnetization can induce an anomalous planar Hall effect with a $2\pi/3$ period for hexagon-warped energy bands. This effect is similar to the anomalous Hall effect resulting from an out-of-plane magnetization. However, this anomalous planar Hall effect is absent i

Anirban Karan, Víctor Miralles, Antonio Pich

An updated global fit on the parameter-space of the Aligned Two-Higgs-Doublet model has been performed with the help of the open-source package HEPfit, assuming the Standard-Model Higgs to be the lightest scalar. No new sources of CP violation, other than the phase in the CKM matrix of the Standard Model, have been considered. A similar global fit was previo

Toshihiro Taen, Andhika Kiswandhi, Toshihito Osada

Materials with the mesoscopic scales have provided an excellent platform for quantum-mechanical studies. Among them, the periodic oscillations of the electrical resistivity against the direct and the inverse of the magnetic fields, such as the Aharonov-Bohm effect and the Shubnikov-de Haas effect, manifest the interference of the wavefunction relevant to the

G. Alagappan, C. E. Png

In this article, we demonstrate dense resonant peaks in the transmission spectra of a rectangular waveguide inscribed with a stretched moir\'e pattern. We investigated an array of silicon waveguides with sinusoidally modulated cladding of varying depth of modulation. The investigation reveals a critical depth of modulation that splits the geometries into wea

Amalendu Krishna, Subhadip Majumder

We establish a ramified class field theory for smooth projective curves over local fields. As key steps in the proof, we obtain new results in the class field theory for 2-dimensional local fields of positive characteristic, and prove a duality theorem for the logarithmic Hodge-Witt cohomology on affine curves over local fields.

Edmundo J. Huertas, Alberto Lastra, Víctor Soto-Larrosa

The aim of the work is to construct new polynomial systems, which are solutions to certain functional equations which generalize the second-order differential equations satisfied by the so called classical orthogonal polynomial families of Jacobi, Laguerre, Hermite and Bessel. These functional equations can be chosen to be of different type: fractional diffe

G. Alagappan, F. J. Garcia-Vidal, C. E. Png

In this study, we constructed a Fabry-Perot cavity with nanostructured, thin resonant metasurfaces as meta-mirrors. We developed a temporal coupled-mode theory and provided an accurate generalization of Fabry-Perot resonance and analytically derived the transmission characteristics. The presence of metasurface mirrors introduces a substantial group delay, ca

Zhizhen Zhang, Xiaohui Xie, Mengyu Yang, Ye Tian

Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising. Despite recent efforts to leverage the content of social media posts to improve prediction accuracy, many existing models fail to fully exploit the multiple dependencies betw

Johanna Bräunig, Christian Schüßler, Vanessa Wirth, Marc Stamminger

With the increasing popularity of human-computer interaction applications, there is also growing interest in generating sufficiently large and diverse data sets for automatic radar-based recognition of hand poses and gestures. Radar simulations are a vital approach to generating training data (e.g., for machine learning). Therefore, this work applies a ray t

Xindi Wang, Yufei Wang, Can Xu, Xiubo Geng

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowledge from the given prompts. In this paper, to make progress t

Rita Costa, Bruno Martins, Sérgio Viana, Luisa Coheur

State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract

Kai Liu, Sheng Jin, Zhihang Fu, Ze Chen

Without manually annotated identities, unsupervised multi-object trackers are inferior to learning reliable feature embeddings. It causes the similarity-based inter-frame association stage also be error-prone, where an uncertainty problem arises. The frame-by-frame accumulated uncertainty prevents trackers from learning the consistent feature embedding again

Herbert Jaeger, Beatriz Noheda, Wilfred G. van der Wiel

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to systematically engineer computing systems that are based on unconventional physical effects, we need guidance from a formal theory that is different from the symbolic-algorithmic th

Ashis Kundu, Yani Chen, Xiaolong Yang, Fanchen Meng

Recent theoretical and experimental research suggests that $\theta$-TaN is a semimetal with high thermal conductivity ($\kappa$), primarily due to the contribution of phonons ($\kappa_\texttt{ph}$). By using first-principles calculations, we show a non-monotonic pressure dependence of the $\kappa$ of $\theta$-TaN. $\kappa_\texttt{ph}$ first increases until i

Guilherme Catumba, Alberto Ramos, Bryan Zaldivar

Monte Carlo methods represent a cornerstone of computer science. They allow to sample high dimensional distribution functions in an efficient way. In this paper we consider the extension of Automatic Differentiation (AD) techniques to Monte Carlo process, addressing the problem of obtaining derivatives (and in general, the Taylor series) of expectation value

Thomas Gehrmann, Petr Jakubčík, Cesare Carlo Mella, Nikolaos Syrrakos

We compute the planar three-loop Quantum Chromodynamics (QCD) corrections to the helicity amplitudes involving a vector boson $V=Z,W^\pm,\gamma^*$, two quarks and a gluon. These amplitudes are relevant to vector-boson-plus-jet production at hadron colliders and other precision QCD observables. The planar corrections encompass the leading colour factors $N^3$

Brandon K. Sai, Jonas Gram, Thomas Bauernhansl

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing,

Jiale Linghu, Hao Dong, Weifeng Gao, Yufeng Nie

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertaint

Nick James, Max Menzies

In this paper we contrast the dynamics of the 2022 Ukraine invasion financial crisis with notable financial crises of the 21st century - the dot-com bubble, global financial crisis and COVID-19. We study the similarity in market dynamics and associated implications for equity investors between various financial market crises and we introduce new mathematical

Sen Yan, Shaoshu Zhu, Jaime B. Fernandez, Eric Arazo Sánchez

Pollution in urban areas can have significant adverse effects on the health and well-being of citizens, with traffic-related air pollution being a major concern in many cities. Pollutants emitted by vehicles, such as nitrogen oxides, carbon monoxide, and particulate matter, can cause respiratory and cardiovascular problems, particularly for vulnerable road u

Li Zhang, Huan Zhao, Yue Li, Bowen Pang

This paper describes the FlySpeech speaker diarization system submitted to the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech \textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We develop an end-to-end audio-visual speaker diarization~(AVSD) system, which consists of a lip encoder, a speaker encoder, and an audio-visual

N. Fang, Y. R. Chang, S. Fujii, D. Yamashita

The development of van der Waals heterostructures has introduced unconventional phenomena that emerge at atomically precise interfaces. For example, interlayer excitons in two-dimensional transition metal dichalcogenides show intriguing optical properties at low temperatures. Here we report on room-temperature observation of interface excitons in mixed-dimen

Jose M. Alvarez, Antonio Mastropietro, Salvatore Ruggieri

We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting $k$ candidates. Today, it is common for the ISO to be the product of an information access system, such as an online platform or a database query. The ISO has been largely overlooked

James Howie, Hamish Short

We generalise a result of D. J. Collins on intersections of conjugates of Magnus subgroups of one-relator groups to the context of one-relator products of locally indicable groups.

Nirmit Joshi, Gal Vardi, Nathan Srebro

Understanding how overparameterized neural networks generalize despite perfect interpolation of noisy training data is a fundamental question. Mallinar et. al. 2022 noted that neural networks seem to often exhibit ``tempered overfitting'', wherein the population risk does not converge to the Bayes optimal error, but neither does it approach infinity, yieldin

SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo

The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and beaks for birds, is a key in the task. However, this is particularly challenging when training data is limited. To address this, we propose Task Discrepancy Maximization (TDM), a task

Sören Kleine, Katharina Müller

Let $p$ be a rational prime, and let $X$ be a connected finite graph. In this article we study voltage covers $X_\infty$ of $X$ attached to a voltage assignment ${\alpha}$ which takes values in some uniform $p$-adic Lie group $G$. We formulate and prove an Iwasawa main conjecture for the projective limit of the Picard groups $\text{Pic}(X_n)$ of the intermed

Roxanne Turcotte, Stef Verpoest, Megha Venugopal

The IceCube Neutrino Observatory at the geographic South Pole is, with its surface and in-ice detectors, used for both neutrino and cosmic-ray physics. The surface array, named IceTop, consists of ice-Cherenkov tanks grouped in 81 pairs spanning a 1 km$^2$ area. An enhancement of the surface array, composed of elevated scintillation panels and radio antennas

Fengyu Zhao, Wen Chen, Ziwei Liu, Jun Li

In this letter, we investigate the discrete phase shift design of the intelligent reflecting surface (IRS) in a time division duplexing (TDD) multi-user multiple input multiple output (MIMO) system.We modify the design of deep reinforcement learning (DRL) scheme so that we can maximizing the average downlink data transmission rate free from the sub-channel c

Josu Diego, D. Subires, A. H. Said, D. A. Chaney

In order to characterize in detail the charge density wave (CDW) transition of 1$T$-VSe$_2$, its electronic structure and lattice dynamics are comprehensively studied by means of x-ray diffraction, angle resolved photoemission (ARPES), diffuse and inelastic x-ray scattering (IXS), and state-of-the-art first principles density functional theory calculations.

Alexis Boudon, Philippe Brax, Patrick Valageas

Black Holes (BH) traversing a dark matter cloud made out of a self-interacting scalar soliton are slowed down by two complementary effects. At low subsonic speeds, the BH accretes dark matter and this is the only source of dragging along its motion, if we neglect the backreaction of the cloud self-gravity. The situation changes at larger supersonic speeds wh

Yu. A. Markov, M. A. Markova, N. Yu. Markov

Hamiltonian theory for collective longitudinally polarized gluon excitations (plasmons) interacting with classical high-energy test color-charged particle propagating through a high-temperature gluon plasma is developed. A generalization of the Lie-Poisson bracket to the case of a continuous medium involving bosonic normal field variable $a^{\hspace{0.03cm}a

Xiaolong Qin, Vo Duc Thinh, Jen-Chih Yao

The notions and certain fundamental characteristics of the proximal and limiting normal cones with respect to a set are first presented in this paper. We present the ideas of the limiting coderivative and subdifferential with respect to a set of multifunctions and singleton mappings, respectively, based on these normal cones. The necessary and sufficient con

Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang

This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep le

Martina Karl

We search for additional neutrino emission from the direction of IceCube's highest energy public alert events. We take the arrival direction of 122 events with a high probability of being of astrophysical origin and look for steady and transient emission. We investigate 11 years of reprocessed and recalibrated archival IceCube data. For the steady scenario,

Tao Zhang

The representation and the cohomology theory of associative 2-algebras are developed. We study the deformations and abelian extensions of associative 2-algebras in details.

A. Zazunov, J. Rech, T. Jonckheere, B. Grémaud

We study charge transport in voltage-biased single-channel junctions involving helical superconductors with finite Cooper pair momentum. For a Josephson junction, the equilibrium current-phase relation shows a superconducting diode effect: the critical current depends on the propagation direction. We formulate a scattering theory for voltage-biased Josephson

Anda Chen, Zhigang Li, Yougang Wang, Yan Gong

We propose a new method for measuring the spatial density distribution of the stellar halo of the Milky Way. Our method is based on a pairwise statistic of the distribution of stars on the sky, the angular two-point correlation function (ATPCF). The ATPCF utilizes two dimensional data of stars only and is therefore immune to the large uncertainties in the de

N. T. Bagraev, S. A. Kukushkin, A. V. Osipov, V. V. Romanov

The article presents the results of measurement and analysis of the field dependences of the static magnetic susceptibility of thin epitaxial silicon carbide films grown on the (110) surface of single-crystal silicon by the method of the coordinated substitution of atoms. In weak magnetic fields, the occurrence of two quantum effects at room temperature was

Clément Ferise, Philipp del Hougne, Matthieu Davy

We present and experimentally verify a matrix approach for determining how to optimally sculpt an input wavefront both in space and time for any desired wave-control functionality, irrespective of the complexity of the wave scattering. We leverage a singular value decomposition of the transport matrix that fully captures how both the spatial and temporal deg

Wenxi Zhai, Xiangdong Zhang

In this paper, we present exact spherically symmetric Gauss-Bonnet black hole solutions surrounded by a cloud of strings fluid with cosmological constant in $D>4$ dimensions. Both charged and uncharged cases are considered. We focus on the de Sitter solutions in the main text and leave the Anti-de Sitter solutions in the appendix. We analyze the features of

Toni Mancini, Igor Melatti, Enrico Tronci

Cyber-Physical Systems (CPSs), comprising both software and physical components, arise in many industry-relevant domains and are often mission- or safety-critical. System-Level Verification (SLV) of CPSs aims at certifying that given (e.g., safety or liveness) specifications are met, or at estimating the value of some KPIs, when the system runs in its operat

Jiachen An, Yadong Xue, Zhoujian Cao, Xiaokai He

Since the first detection of gravitational waves, they have been used to investigate various fundamental problems, including the variation of physical constants. Regarding the gravitational constant, previous works focused on the effect of the gravitational constant variation on the gravitational wave generation. In this paper, we investigate the effect of t

Daniel Barath, Dmytro Mishkin, Luca Cavalli, Paul-Edouard Sarlin

We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal solvers. AffineGlue selects potential matches from one-to-many correspondences to estimate minimal models. Guided matching is then used to find matches consistent with the model,

Ting-Wei Chao, Hung-Hsun Hans Yu

A joint of a set of lines $\mathcal{L}$ in $\mathbb{F}^d$ is a point that is contained in $d$ lines with linearly independent directions. The joints problem asks for the maximum number of joints that are formed by $L$ lines. Guth and Katz showed that the number of joints is at most $O(L^{3/2})$ in $\mathbb{R}^3$ using polynomial method. This upper bound is m

Ting-Wei Chao, Hung-Hsun Hans Yu

In this paper, we investigate several extremal combinatorics problems that ask for the maximum number of copies of a fixed subgraph given the number of edges. We call problems of this type Kruskal--Katona-type problems. Most of the problems that will be discussed in this paper are related to the joints problem. There are two main results in this paper. First

Alejandro Rodríguez-Arias, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Noelia Sánchez-Marroño

Compartmental epidemiological models categorize individuals based on their disease status, such as the SEIRD model (Susceptible-Exposed-Infected-Recovered-Dead). These models determine the parameters that influence the magnitude of an outbreak, such as contagion and recovery rates. However, they don't account for individual characteristics or population acti

Petr Baroň, Michael H. Seymour, Andrzej Siódmok

In this paper, we present a new proposal on how to measure quark/gluon jet properties at the LHC. The measurement strategy takes advantage of the fact that the LHC has collected data at different energies. Measurements at two or more energies can be combined to yield distributions of any jet property separated into quark and gluon jet samples on a statistica

Junhyun Lee, Bumsoo Kim, Minji Jeon, Jaewoo Kang

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction).

Sanjana Kolar, Rohit Kumar

This research investigates the effectiveness of ChatGPT, an AI language model by OpenAI, in translating English into Hindi, Telugu, and Kannada languages, aimed at assisting tourists in India's linguistically diverse environment. To measure the translation quality, a test set of 50 questions from diverse fields such as general knowledge, food, and travel was

Ava Ghadimi, Marcos Santander

Neutron stars with very strong magnetic fields are known as magnetars. There are multiple theories that predict magnetars may be able to emit high-energy (HE) neutrinos through hadronic processes by accelerating cosmic rays to high energies. A subclass of magnetars known as soft gamma-ray repeaters (SGRs) can produce giant flares that can result in the produ

Huan Wu, Huan-Feng Duan, Wallace W. L. Lai, Kun Zhu

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to

Hisako Ito, Takatomo Mihana, Ryoichi Horisaki, Makoto Naruse

With the end of Moore's Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of light, such as high bandwidth and multiplicity, and the various synchronization phenomena that emerge in the realm of laser physics. These factors come into play as computer performanc

Jessie Thwaites, Justin Vandenbroucke

Despite being one of the longest known classes of astrophysical transients, novae continue to present modern surprises. The Fermi-LAT discovered that many if not all novae are GeV gamma ray sources, even though theoretical models had not even considered them as a possible source class. More recently, MAGIC and H.E.S.S. detected TeV gamma rays from a nova. Mo

B. Eslam Panah

Considering a three-dimensional $C$-metric and adding energy-dependent to this spacetime, we first create a three-dimensional energy-dependent $C$-metric. Then, we extract accelerating BTZ black hole solutions in gravity's rainbow. Besides, we show that (A)dS black holes cover by an event horizon that depends on all the parameters of this theory. Using the d

Daoguang Zan, Bei Chen, Yongshun Gong, Junzhi Cao

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, genera

Prottoy Das

Jets are collimated sprays of particles produced from the fragmentation and hadronization of hard-scattered partons in high energy hadronic and nuclear collisions. Jet properties are sensitive to details of parton showering processes and are expected to be modified in the presence of a dense partonic medium. Measurement of intra-jet properties in p--Pb colli

Masih Haseli, Jorge Cortés

This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling general (not necessarily control-affine) discrete-time nonlinear control systems with the aim of providing a solid theoretical foundation for the use of Koopman-based methods in systems with inputs. We demonstrate that the concept of KCF captures the behavior of non

Chan Hsu, Wei-Chun Huang, Jun-Ting Wu, Chih-Yuan Li

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care

Pablo Navarro Moreno, Felipe J. Llanes-Estrada, Eva Lope-Oter

We identify combinations of observables for rotating neutron stars that can one day bear on the question of whether there can be first order phase transitions in the neutron matter therein. We employ the Hartle-Thorne theory for stationary, rotating neutron stars at conventional angular velocities (in the pulsar and millisecond pulsar ranges) and extract thr

Matteo Serafino, Zhenkun Zhou, Jose S. Andrade,, Alexandre Bovet

The ongoing debate surrounding the impact of the Internet Research Agency s (IRA) social media campaign during the 2016 U.S. presidential election has largely overshadowed the involvement of other actors. Our analysis brings to light a substantial group of suspended Twitter users, outnumbering the IRA user group by a factor of 60, who align with the ideologi

Yang Chen, Haoran Wang

We determine the set of quaternionic Serre weights for generic two-dimensional mod $p$ representation of $\operatorname{Gal}(\overline{\mathbb Q}_p / K)$, where $K$ is a finite unramified extension of $\mathbb{Q}_p$.

Nicole Robinson, Brendan Tidd, Dylan Campbell, Dana Kulić

Robotic vision for human-robot interaction and collaboration is a critical process for robots to collect and interpret detailed information related to human actions, goals, and preferences, enabling robots to provide more useful services to people. This survey and systematic review presents a comprehensive analysis on robotic vision in human-robot interactio

Yuxiang Lu, Shalayiding Sirejiding, Yue Ding, Chunlin Wang

Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performance and model parameters is an important and difficult problem. In this paper, we introduce a simple and lightweight task-conditional model called Prompt Guided Transformer (PGT) to

Bitya Neuhof, Yuval Benjamini

Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to the models' predictions. The interpretation of the importance values provided by these methods is frequently based on the relative order of the features (their ranking) rather than t

Gabriel Bihain

Neutron stars are identified as pulsars, X-ray binary components, central objects of supernovae remnants, or isolated thermally emitting sources, and at distances beyond 120 pc. A population extrapolation suggests 10$^3$ objects within that boundary. Potentially, neutron stars could continuously emit gravitational waves at sensitivity reach of present instru

G. M. A. Almeida, R. F. Dutra, A. M. C. Souza, M. L. Lyra

We show that a qubit transfer protocol can be realized through a flat band hosted by a disordered $XX$ spin-1/2 diamond chain. In the absence of disorder, the transmission becomes impossible due to the compact localized states forming the flat band. When off-diagonal disorder is considered, the degeneracy of the band is preserved but the associated states ar

G. Bargiacchi, M. G. Dainotti, S. Capozziello

The longstanding search for the cosmological model that best describes the Universe has been made more intriguing since the recent discovery of the Hubble constant, $H_{0}$, tension observed between the value of $H_{0}$ from the Cosmic Microwave Background and from type Ia supernovae (SNe Ia). Hence, the commonly trusted flat $\Lambda$CDM model is under inve

Sankha S. Basu, Sayantan Roy

Paraconsistency is commonly defined and/or characterized as the failure of a principle of explosion. The various standard forms of explosion involve one or more logical operators or connectives, among which the negation operator is the most frequent and primary. In this article, we start by asking whether a negation operator is essential for describing explo

Ying Wang, Guoce Xin, Yingrui Zhang

Building upon the foundational work of Thomas and Williams on the modular sweep map, Garsia and Xin have developed a straightforward algorithm for the inversion of the sweep map on rational $(m,n)$-Dyck paths, where $(m,n)$ represents coprime pairs of integers. Our research reveals that their innovative approach readily generalizes to encompass a broader spe

Yafan Yang, Jingyu Wan, Jingfa Li, Guangsi Zhao

The understanding of geochemical interactions between H2 and geofluids is of great importance for underground H2 storage but requires further study. We report the first investigation on the three-phase fluid mixture containing H2, H2O, and n-C10H22. Molecular dynamics simulation and PC-SAFT density gradient theory are employed to estimate the interfacial pro

Botan Wang, Yi Wang, Dong Han, Zhigang Xiao

The impact parameter characterizes the centrality in nucleus-nucleus collision geometry. The determination of impact parameters in real experiments is usually based on the reconstructed particle attributes or the derived event-level observables. For the scheduled Cooler-storage-ring External-target Experiment (CEE), the low beam energy reduces correlation be

Jakub Janarek, Nicolas Cherroret, Dominique Delande

We extend the Berezinskii diagrammatic technique to one-dimensional disordered spin systems, in which time-reversal invariance is broken due to a spin-orbit coupling term inducing left-right asymmetric scattering. We then use this formalism to theoretically describe the dynamics of the quantum boomerang effect, a recently discovered manifestation of Anderson

Hai Jiang, Haipeng Li, Songchen Han, Haoqiang Fan

In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network. In the generation phase, given an unlabeled image pair, we utilize the pre-estimated dominant plane masks and homography of the pair, along with another sampled

Lino Benedetto, Clotilde Fermanian Kammerer, Véronique Fischer

In this paper, we introduce Wick's quantization on groups and discuss its links with Kohn-Nirenberg's. By quantization, we mean an operation that associates an operator to a symbol. The notion of symbols for both quantizations is based on representation theory via the group Fourier transform and the Plancherel theorem. As an application, we give a simple pro

Friedemann Klass, Alessandro Gabbana, Andreas Bartel

We introduce a non-reflecting boundary condition for the simulation of thermal flows with the lattice Boltzmann Method (LBM). We base the derivation on the locally one-dimensional inviscid analysis, and define target macroscopic values at the boundary aiming at minimizing the effect of reflections of outgoing waves on the bulk dynamics. The resulting macrosc

Marco Biroli, Hernán Larralde, Satya N. Majumdar, Grégory Schehr

Even though strongly correlated systems are abundant, only a few exceptional cases admit analytical solutions. In this paper we present a large class of solvable systems with strong correlations.. We consider a set of $N$ independent and identically distributed (i.i.d) random variables $\{X_1,\, X_2,\ldots, X_N\}$ whose common distribution has a parameter $Y

Philip Kennerberg, Ernst Wit

The insight that causal parameters are particularly suitable for out-of-sample prediction has sparked a lot development of causal-like predictors. However, the connection with strict causal targets, has limited the development with good risk minimization properties, but without a direct causal interpretation. In this manuscript we derive the optimal out-of-s

Sreetama Goswami

Active Galactic Nuclei (AGN) are powerful astronomical objects with very high luminosities. Theoretical arguments suggest that these objects are capable of accelerating particles to energies of 10$^{20}$ eV. In environments with matter or photon targets, cosmic-ray interactions transpire leading to the production of pionic gamma rays and neutrinos. Since the

Tom Szwagier, Xavier Pennec

Principal component analysis is a ubiquitous tool in exploratory data analysis. It is widely used by applied scientists for visualization and interpretability purposes. We raise an important issue (the curse of isotropy) about the interpretation of principal components with close eigenvalues. They may indeed suffer from an important rotational variability, w

Divya Rawat, Akash Garg, Mariano Méndez

We probe the spectropolarimetric properties of the black-hole binary source 4U 1630$-$47 in the steep power law state. We detect a significant polarization fraction of $\sim$7 % at a polarization angle of $\sim$21 $^\circ$. The $2-12$ keV NICER spectrum can be fitted with a combination of a thermal and a Comptonization component, the latter characterized by

Ginestra Bianconi, Sergey N. Dorogovtsev

Hypergraphs are higher-order networks that capture the interactions between two or more nodes. Hypergraphs can always be represented by factor graphs, i.e. bipartite networks between nodes and factor nodes (representing groups of nodes). Despite this universal representation, here we reveal that $k$-core percolation on hypergraphs can be significantly distin

Masashi Okada, Mayumi Komatsu, Ryo Okumura, Tadahiro Taniguchi

Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a novel stiffness learning method to satisfy both task performanc

Yifei Xin, Yuexian Zou

Most existing audio-text retrieval (ATR) methods focus on constructing contrastive pairs between whole audio clips and complete caption sentences, while ignoring fine-grained cross-modal relationships, e.g., short segments and phrases or frames and words. In this paper, we introduce a hierarchical cross-modal interaction (HCI) method for ATR by simultaneousl