October 2023 arXiv papers — page 114

Zheng Zhang, Z. Y. Chen, Y. X. Zhao

Wigner's seminal work on the Poincar\'e group revealed one of the fundamental principles of quantum theory: symmetry groups are projectively represented. The condensed-matter counterparts of the Poincar\'e group could be the spacetime groups of periodically driven crystals or spacetime crystals featuring spacetime periodicity. In this study, we establish the

Jonas F. G. Santos

High control in the preparation and manipulation of states is an experimental and theoretical important task in many quantum protocols. Shortcuts to adiabaticity methods allow to obtain desirable states of a adiabatic dynamics but in short time scales. In this work, the problem of considering this technique for two-coupled bosonic modes is addressed. By usin

Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi, Amin Nikanjam

Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents. Despite recent developments in DRL technology, the main challenges that developers face in the development of DRL applications are still unknown. To fill this gap, in this paper, we conduct a

Shutong Ding, Jingya Wang, Yali Du, Ye Shi

Recent advances in constrained reinforcement learning (RL) have endowed reinforcement learning with certain safety guarantees. However, deploying existing constrained RL algorithms in continuous control tasks with general hard constraints remains challenging, particularly in those situations with non-convex hard constraints. Inspired by the generalized reduc

Chak Tou Leong, Yi Cheng, Jiashuo Wang, Jian Wang

Language model detoxification aims to minimize the risk of generating offensive or harmful content in pretrained language models (PLMs) for safer deployment. Existing methods can be roughly categorized as finetuning-based and decoding-based. However, the former is often resource-intensive, while the latter relies on additional components and potentially comp

Alice C. Quillen, Stephen Luniewski, Adam E. Rubinstein, Jeremy Couturier

We consider the possibility that aeolian (wind blown) processes occur on small, 1 to 100~km diameter, planetesimals when they were embedded in the protosolar nebula. Drag from a headwind within a protostellar disk is sufficiently large to loft cm and smaller sized particles off the surface of a 10 km diameter asteroid in the inner solar system (at a few AU),

Piergiorgio Ladisa, Serena Elisa Ponta, Nicola Ronzoni, Matias Martinez

Current software supply chains heavily rely on open-source packages hosted in public repositories. Given the popularity of ecosystems like npm and PyPI, malicious users started to spread malware by publishing open-source packages containing malicious code. Recent works apply machine learning techniques to detect malicious packages in the npm ecosystem. Howev

Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic

The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfolios with the increased complexity of the AAS task. In practice, probably the most common way to choose the alg

Samah Syed, Angel Deborah S

In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We add

Vignesh V Menon, Reza Farahani, Prajit T Rajendran, Samira Afzal

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimatio

Zipei Nie

We construct petal diagrams from simple braids. This approach allows us to confirm a conjecture proposed by Kim, No and Yoo, which states that the petal number of the nontrivial torus knot $T_{r,s}$ ($r<s$) is at most $2s-2\lfloor\frac{s}{r}\rfloor+1$. As a consequence, we deduce that the petal number of a nontrivial torus knot $T_{r,s}$ is equal to $2s-1$ i

Yang Hu, Xinhan Lin, Huizheng Wang, Zhen He

Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand for the computing power of the hardware. As the computing demand for AI continues to grow, the growth of hardware computing power has failed to keep up. This has become a significan

Patricio Guerrero, Simon Bellens, Ricardo Santander, Wim Dewulf

This work is concerned with fan- and cone-beam computed tomography with circular source trajectory, where the reconstruction inverse problem requires an accurate knowledge of source, detector and rotational axis relative positions and orientations. We address this additional inverse problem as a preceding step of the reconstruction process directly from the

Deepak Bhoriya, Biswarup Biswas, Harish Kumar, Praveen Chandrashekhar

This article proposes entropy stable discontinuous Galerkin schemes (DG) for two-fluid relativistic plasma flow equations. These equations couple the flow of relativistic fluids via electromagnetic quantities evolved using Maxwell's equations. The proposed schemes are based on the Gauss-Lobatto quadrature rule, which has the summation by parts (SBP) property

D. C. Alyuruk, M. Iskin

We analyze the two-body spectrum within the Hofstadter-Hubbard model on a square lattice through an exact variational ansatz and study the topological properties of its low-lying two-body bound-state branches. In particular we discuss how the Hofstadter-Hubbard butterfly of the two-body branches evolves as a function of onsite interactions and how to efficie

Hajime Ogane, Yoshito Ono, Yosuke Minowa, Shin Oya

Atmospheric turbulence profile plays an important role in designing and operating adaptive optics (AO) systems with multiple laser guide stars. To obtain representative free atmospheric profiles and resolved ground layer profiles for future AO systems at the Subaru telescope, we are conducting the SHARPEST (Shack-Hartmann Atmospheric tuRbulence Profiling Exp

Hulingxiao He, Wu Yuan, Yidian Huang, Shilong Zhao

In this work, we propose Branch-to-Trunk network (BTNet), a representation learning method for multi-resolution face recognition. It consists of a trunk network (TNet), namely a unified encoder, and multiple branch networks (BNets), namely resolution adapters. As per the input, a resolution-specific BNet is used and the output are implanted as feature maps i

Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge

Foundation models like CLIP are trained on hundreds of millions of samples and effortlessly generalize to new tasks and inputs. Out of the box, CLIP shows stellar zero-shot and few-shot capabilities on a wide range of out-of-distribution (OOD) benchmarks, which prior works attribute mainly to today's large and comprehensive training dataset (like LAION). How

Konstantinos Lazaros, Dimitris E. Koumadorakis, Panagiotis Vlamos, Aristidis G. Vrahatis

Graph Neural Networks (GNN) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes and cells. As both algorithmic and biomedical technologies have advanced significantly, we're entering a transformative phase of personalized medicine. While pioneering tools like Graph Attention Networks (GAT) and Graph Convo

Yi Ke Yun, Weisi Lin

Although recent efforts in image quality assessment (IQA) have achieved promising performance, there still exists a considerable gap compared to the human visual system (HVS). One significant disparity lies in humans' seamless transition between full reference (FR) and no reference (NR) tasks, whereas existing models are constrained to either FR or NR tasks.

Richard Mcnair, Oliver E. Jensen, Julien R. Landel

Successive drops of coloured ink mixed with surfactant are deposited onto a thin film of water to create marbling patterns in the Japanese art technique of Suminagashi. To understand the physics behind this and other applications where surfactant transports adsorbed passive matter at gas-liquid interfaces, we investigate the Lagrangian trajectories of materi

A. A. Grib, Yu. V. Pavlov

We study the question of conditions for the existence of negative-energy states of particles in the absence of external fields in inertial and noninertial frames of reference. We show that in the nonrelativistic case in noninertial reference frames, there always exist domains where the energy of particles is negative. We also show that in the relativistic ca

Shyam Sundar, P. V. Lopes, S. Salem-Sugui,, Z. -Z. Li

Vortex matter in layered high-$T_c$ superconductors, including iron-pnictides, undergo several thermodynamic phase transitions due to the complex interplay of pinning energy, thermal energy and elastic energy. Moreover, the presence of anisotropy makes their vortex physics even more intriguing. Here, we report a detailed vortex dynamics study, using dc magne

Tripti Kumari, Chakali Sai Charan, Ayan Das

This paper presents the system submitted by the team from IIT(ISM) Dhanbad in FIRE IRSE 2023 shared task 1 on the automatic usefulness prediction of code-comment pairs as well as the impact of Large Language Model(LLM) generated data on original base data towards an associated source code. We have developed a framework where we train a machine learning-based

M. S. Nadirbekov, O. A. Bozarov, S. N. Kudiratov

In the framework free triaxiality model branching ratio reduced E2-transitions probabilities are studied for the full region changes of the triaxiality parameter. The variables of quadrupole deformations $\beta_2$ and $\gamma$ are dynamic. The Davidson potential for $\beta_2$ and $\gamma$ variables was being used. In the free triaxiality model the energy lev

Ming Wei, Xin Wang, Longzhao Liu, Hongwei Zheng

Indirect reciprocity unveils how social cooperation is founded upon moral systems. Within the frame of dyadic games based on individual reputations, the "leading-eight" strategies distinguish themselves in promoting and sustaining cooperation. However, in the real-world societies, there are widespread interactions at the group level, where individuals need t

Hugo Waltsburger, Erwan Libessart, Chengfang Ren, Anthony Kolar

Much work has been dedicated to estimating and optimizing workloads in high-performance computing (HPC) and deep learning. However, researchers have typically relied on few metrics to assess the efficiency of those techniques. Most notably, the accuracy, the loss of the prediction, and the computational time with regard to GPUs or/and CPUs characteristics. I

Tianyu Xie, Cheng Zhang

Designing flexible probabilistic models over tree topologies is important for developing efficient phylogenetic inference methods. To do that, previous works often leverage the similarity of tree topologies via hand-engineered heuristic features which would require pre-sampled tree topologies and may suffer from limited approximation capability. In this pape

William Dawson, Louis Beal, Laura E. Ratcliff, Martina Stella

Literate programming - the bringing together of program code and natural language narratives - has become a ubiquitous approach in the realm of data science. This methodology is appealing as well for the domain of Density Functional Theory (DFT) calculations, particularly for interactively developing new methodologies and workflows. However, effective use of

Seiya Sasaoka, Naoki Koyama, Diego Dominguez, Yusuke Sakai

In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived from three-dimensional numerical simulations of waveforms, which are injected onto real noise data from the third observing run of both Advanced LIGO and Advanced Virgo. To gain in

Yixuan Zhang, Haonan Li

Large language models (LLMs) have showcased remarkable capabilities in understanding and generating language. However, their ability in comprehending ancient languages, particularly ancient Chinese, remains largely unexplored. To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending a

Mark Vincent Ty, Rowel Atienza

Explainable AI (XAI) is the study on how humans can be able to understand the cause of a model's prediction. In this work, the problem of interest is Scene Text Recognition (STR) Explainability, using XAI to understand the cause of an STR model's prediction. Recent XAI literatures on STR only provide a simple analysis and do not fully explore other XAI metho

Diederik Aerts, Massimiliano Sassoli de Bianchi

Quantum mechanics has maintained over the years the reputation of being "the most obscure theory." It works perfectly well, but nobody seems to know why. It has been argued that the difficulty in understanding quantum theory is our failed attempt to force onto it a wrong conceptual scheme, wanting at all costs to think about the objects of the theory as, pre

Fabrizio Catanese

We first state a condition ensuring that having a birational map onto the image is an open property for families of irreducible normal non uniruled varieties. We give then some criteria to ensure general birationality for a family of rational maps, via specializations. Among the applications is a new proof of a result obtained jointly with Luca Cesarano: tha

Dandan Wang, Daokuan Zhu, Zichong Ou, Jie Lu

This paper focuses on distributed constrained optimization over time-varying directed networks, where all agents cooperate to optimize the sum of their locally accessible objective functions subject to a coupled inequality constraint consisting of all their local constraint functions. To address this problem, we develop a buffering drift-plus-penalty algorit

Hui Hui Xie, Jian Li, Haozhao Liang

Muonic atom transitions have been measured for almost all stable nuclei to extract nuclear structure properties, including nuclear charge radii and quadrupole moment. To investigate the possibilities of extracting higher-order radial moments of nuclear charge density %what kind of information of nuclear charge distribution can be extracted precisely from muo

Pan Zhao, Yifan Cui

Recently, there has been a surge in methodological development for the difference-in-differences (DiD) approach to evaluate causal effects. Standard methods in the literature rely on the parallel trends assumption to identify the average treatment effect on the treated. However, the parallel trends assumption may be violated in the presence of unmeasured con

Xiaoxiao Hu, Haoran Lei

An expert seller chooses an experiment to influence a client's purchasing decision, but may manipulate the experiment result for personal gain. When credibility surpasses a critical threshold, the expert chooses a fully-revealing experiment and, if possible, manipulates the unfavorable result. In this case, a higher credibility strictly benefits the expert,

David Blanco-Mulero, Oriol Barbany, Gokhan Alcan, Adrià Colomé

Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation. By doing so, researchers can circumvent challenges such as sensing the deformation of the object in the realworld. In spite of the extensive use of simulations for this task, few works have evaluated the reality gap between deformable o

Marius Bozga, Lucas Bueri, Radu Iosif, Florian Zuleger

The treewidth boundedness problem for a logic asks for the existence of an upper bound on the treewidth of the models of a given formula in that logic. This problem is found to be undecidable for first order logic. We consider a generalization of Separation Logic over relational signatures, interpreted over standard relational structures, and describe an alg

Tanmoy Bera, Mithun Kumar Das, Anirban Mukhopadhyay

In this article, we examine the Poissonian pair correlation (PPC) statistic for higher-dimensional real sequences. Specifically, we demonstrate that for $d\geq 3$, almost all $(\alpha_1,\ldots,\alpha_d) \in \mathbb{R}^d$, the sequence $\big(\{x_n\alpha_1\},\dots,\{x_n\alpha_d\}\big)$ in $[0,1)^d$ has PPC conditionally on the additive energy bound of $(x_n).$

Martin C. E. Huber, Martin Mattmüller, Ernst Meyer, Friedrich-Karl Thielemann

On 22 September 2023 the inauguration of the "Stachelsch\"utzenhaus" at Basel's Petersplatz (St. Peter's Square) as an EPS Historic Site was hosted by the University of Basel. In the following article we will focus first on Daniel Bernoulli's career path, before discussing his major scientific achievements and finally adding some aspects of the inauguration

Gabriele Gionti S. J., Matteo Galaverni

A longstanding issue is the classical equivalence between the Jordan and the Einstein frames, which is considered just a field redefinition of the metric tensor and the scalar field. In this work, based on the previous result that the Hamiltonian transformations from the Jordan to the Einstein frame are not canonical on the extended phase space, we study the

Sören Aguirre Reid, Frank Kammer, Johannes Kunz, Timon Pellekoorne

SQL is a central component of any database course. Despite the small number of SQL commands, students struggle to practice the concepts. To overcome this challenge, we developed an intelligent tutoring system (ITS) to guide the learning process with a small effort by the lecturer. Other systems often give only basic feedback (correct or incorrect) or require

Nicolas Schwenke, Heinrich Söbke, Eckhard Kraft

The release of the large language model based chatbot ChatGPT in November 2022 has brought considerable attention to the subject of artificial intelligence, not only in the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment

Yan Guo, Zi-Xiang Yang, Zi-Qi Zeng, Chunling Ding

Hong-Ou-Mandel (HOM) interference of multi-mode frequency entangled states plays a crucial role in quantum metrology. However, as the number of modes increases, the HOM interference pattern becomes increasingly complex, making it challenging to comprehend intuitively. To overcome this problem, we present the theory and simulation of multi-mode-HOM interferen

K. Hagino, G. F. Bertsch

The decay of quantum complex systems through a potential barrier is often described with transition-state theory, also known as RRKM theory in chemistry. Here we derive the basic formula for transition-state theory based on a generic Hamiltonian as might be constructed in a configuration-interaction basis. Two reservoirs of random Hamiltonians from Gaussian

Md Rashad Al Hasan Rony, Christian Suess, Sinchana Ramakanth Bhat, Viju Sudhi

Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However, leveraging LLMs for domain-specific question answering suffers from severe limitations. The generated answer tends to hallucinate due to the training data collection time (when u

Gonçalo Paulo, Alberto Gubbiotti, Giovanni Di Muccio, Alberto Giacomello

Reliable and controllable switches are crucial in nanofluidics and iontronics. Ion channels in nature serve as a rich source of inspiration due to their intricate mechanisms modulated by stimuli like pressure, temperature, chemicals, and voltage. The artificial replication of the properties of these channels is challenging due to their complex chemistry, lim

Sen Zhang, Yongdi Dang, Xinran Li, Iqbal Naeem

The near-field radiative heat transfer (NFRHT) between one-dimensional metamaterials comprising phonon dielectric multilayers was experimented. Large sized (1cm x 1cm) near-field samples were fabricated using SiC, SiO2 and Ge layers at a certain gap distance, and the effect of layer stacking order and phonon resonance quality on the NFRHT was examined. The m

Yufei Huang, Siyuan Li, Jin Su, Lirong Wu

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g.

Yicheng Song, Shuyong Gao, Haozhe Xing, Yiting Cheng

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine

Ami Marowka

The adoption of heterogeneous computing systems based on diverse architectures to achieve exascale computing power has worsened the performance portability problem of scientific applications that were designed to run on these platforms. To cope with the challenges posed by supercomputing, new performance portability frameworks have been developed alongside a

Allan K. de Almeida, Antonio F. B. A. Prado, Daniele Mortari

This work shows that a class of astrodynamics problems subject to mission constraints can be efficiently solved using the Theory of Functional Connections (TFC) mathematical framework by a specific change of coordinates. In these problems, the constraints are initially written in non-linear and coupled mathematical forms using classical rectangular coordinat

Valentina Baccetti, Ruomin Zhu, Zdenka Kuncic, Francesco Caravelli

Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices. In this study, we explore the connection between ergodicity in memristive and nanowire networks, showing that the performance of reservoir devices improves when

Katherine Ge, Dayna Olson, Michel F. Sanner

Molecular docking is a structure-based computational drug design technique for predicting the interaction between a small molecule (ligand) and a macromolecule (receptor). Over the past three decades various docking software programs have been developed, mostly for drug-like molecules. With the recent interest in peptides as therapeutic molecules, several pe

Woojin Cho, Kookjin Lee, Donsub Rim, Noseong Park

In various engineering and applied science applications, repetitive numerical simulations of partial differential equations (PDEs) for varying input parameters are often required (e.g., aircraft shape optimization over many design parameters) and solvers are required to perform rapid execution. In this study, we suggest a path that potentially opens up a pos

Sören Aguirre Reid, Frank Kammer, Jonas-Ian Kuche, Pia-Doreen Ritzke

Spreadsheets are one of the most widely used tools for end users. As a result, spreadsheets such as Excel are now included in many curricula. However, digital solutions for assessing spreadsheet assignments are still scarce in the teaching context. Therefore, we have developed an Intelligent Tutoring System (ITS) to review students' Excel submissions and pro

Long Yuan, Qiya Hu

In this paper we propose a {\it discontinuous} plane wave neural network (DPWNN) method with $hp-$refinement for approximately solving Helmholtz equation and time-harmonic Maxwell equations. In this method, we define a quadratic functional as in the plane wave least square (PWLS) method with $h-$refinement and introduce new discretization sets spanned by ele

A. S. Ilin, A. O. Strugova, I. A. Cohn, V. V. Pavlovskiy

Superconductivity has been found in RuN films obtained by reactive magnetron sputtering. This is a novel member of the metal nitride superconductors family. The critical temperature of the superconducting transition varies depending on the substrate and ranges from 0.77 K to 1.29 K. The parameters of the crystal lattice of superconducting films have been det

Juan Zou, Shenghong Wu, Yizhang Xia, Weiwei Jiang

Neural network architecture search provides a solution to the automatic design of network structures. However, it is difficult to search the whole network architecture directly. Although using stacked cells to search neural network architectures is an effective way to reduce the complexity of searching, these methods do not able find the global optimal neura

Grady Robbins, Aaron M. Meisner, Adam C. Schneider, Adam J. Burgasser

Y dwarfs, the coolest known spectral class of brown dwarfs, overlap in mass and temperature with giant exoplanets, providing unique laboratories for studying low-temperature atmospheres. However, only a fraction of Y dwarf candidates have been spectroscopically confirmed. We present Keck/NIRES near-infrared spectroscopy of the nearby ($d \approx 6-8$ pc) bro

Seetharam Killivalavan, Durairaj Thenmozhi

This paper presents a novel approach to enhance the performance of binary code comment quality classification models through the application of Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a dataset comprising 1239 newly generated code-comment pairs, extracted from various GitHub repositories and open-source projects, has been label

Yuanyuan Chen, Dandan Fan, Huiqiu Lin

The Brouwer's toughness conjecture states that every $d$-regular connected graph always has $t(G)>\frac{d}{\lambda}-1$ where $\lambda$ is the second largest absolute eigenvalue of the adjacency matrix. In 1988, Enomoto introduced a variation of toughness $\tau(G)$ of a graph $G$. By incorporating the variation of toughness and spectral conditions, we provide

Jiajun Lu, Wei Huang, Hao Zhang

SSP distribution is an important parameter for underwater positioning, navigation and timing (PNT) because it affects the propagation mode of underwater acoustic signals. To accurate predict future sound speed distribution, we propose a hierarchical long short--term memory (H--LSTM) neural network for future sound speed prediction, which explore the distribu

Zhenyi Zheng, Tao Zeng, Tieyang Zhao, Shu Shi

Electrical control of the non-trivial topology in Weyl antiferromagnet is of great interests to develop next-generation spintronic devices. Recent works suggest that spin Hall effect can switch the topological antiferromagnetic order. However, the switching efficiency remains relatively low. Here, we demonstrate effective manipulation of antiferromagnetic or

Haikang Deng, Colin Raffel

While large language models have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute. In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text that has

Zirui Wan, Saeid Sanei

This paper introduces a crowd modeling and motion control approach that employs diffusion adaptation within an adaptive network. In the network, nodes collaboratively address specific estimation problems while simultaneously moving as agents governed by certain motion control mechanisms. Our research delves into the behaviors of agents when they encounter sp

Bruce W. Lee, Hyunsoo Cho, Kang Min Yoo

In this work, we (1) introduce Curriculum Instruction Tuning, (2) explore the potential advantages of employing diverse curriculum strategies, and (3) delineate a synthetic instruction-response generation framework that complements our theoretical approach. Distinct from the existing instruction tuning dataset, our generation pipeline is systematically struc

Houcai Guo, Dingqi Ye, Lorenzo Bruzzone

Spatiotemporal fusion aims to improve both the spatial and temporal resolution of remote sensing images, thus facilitating time-series analysis at a fine spatial scale. However, there are several important issues that limit the application of current spatiotemporal fusion methods. First, most spatiotemporal fusion methods are based on pixel-level computation

Yuxin Wang, Xiannian Hu, Quan Gan, Xuanjing Huang

Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each node that are later combined by a simple decoder to make predictions. While extremely efficient at inference time, model expressiveness is limited such that isomorphic nodes contri

Emre Işık, Jennifer L. van Saders, Ansgar Reiners, Travis S. Metcalfe

Magnetic activity is a ubiquitous feature of stars with convective outer layers, with implications from stellar evolution to planetary atmospheres. Investigating the mechanisms responsible for the observed stellar activity signals from days to billions of years is important in deepening our understanding of the spatial configurations and temporal patterns of

Arijit Manna, Sabyasachi Pal

Phosphorus (P) is one of the important elements for the formation of life and plays a crucial role in several biochemical processes. Recent spectral line surveys have confirmed the existence of P-bearing molecules, especially PN and PO, in the star-formation regions, but their formation mechanisms are poorly understood. The P-bearing molecule phosphorus nitr

Janchiv Shinebayar, Ravdandorj Togoo, Tseepeldorj Baatar, Baatar Otgongerel

The temperature characteristics of carbon spectator fragments formed in carbon collisions with carbon nuclei at a primary momentum of 4.2 GeV/c per nucleon were presented and discussed on corrected experimental data. As well as studied the multiplicities formed by the spectator protons, deuterons, and tritons in the inelastic nucleus-nucleus interactions. We

Shuyang Jiang, Jun Zhang, Jiangtao Feng, Lin Zheng

Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency and growing efficacy. However, its efficiency is still bottlenecked by quadratic complexity in sequence lengths, which is prohibitive for scaling to long sequence generation and few

Jianguo Chen, Jinlong Lei, Hongsheng Qi, Yiguang Hong

This work studies the parameter identification problem of a generalized non-cooperative game, where each player's cost function is influenced by an observable signal and some unknown parameters. We consider the scenario where equilibrium of the game at some observable signals can be observed with noises, whereas our goal is to identify the unknown parameters

S M Mostaq Hossain, Shampa Banik, Trapa Banik, Ashfak Md Shibli

Connected and autonomous vehicles, also known as CAVs, are a general trend in the evolution of the automotive industry that can be utilized to make transportation safer, improve the number of mobility options available, user costs will go down and new jobs will be created. However, as our society grows more automated and networked, criminal actors will have

Gayatri Das, S. Saha Ray

This article is concerned with solving the time fractional Vakhnenko Parkes equation using the reproducing kernels. Reproducing kernel theory, the normal basis, some important Hilbert spaces, homogenization of constraints, and the orthogonalization process are the main tools of this technique. The main advantage of reproducing kernel method is it is truly me

Aman Sinha, Priyanshu Raj Mall, Dwaipayan Roy

The overwhelming volume of data generated and indexed by search engines poses a significant challenge in retrieving documents from the index efficiently and effectively. Even with a well-crafted query, several relevant documents often get buried among a multitude of competing documents, resulting in reduced accessibility or `findability' of the desired docum

DongAo Ma, Jiaxuan Pang, Michael B. Gotway, Jianming Liang

Deep learning nowadays offers expert-level and sometimes even super-expert-level performance, but achieving such performance demands massive annotated data for training (e.g., Google's proprietary CXR Foundation Model (CXR-FM) was trained on 821,544 labeled and mostly private chest X-rays (CXRs)). Numerous datasets are publicly available in medical imaging b

Jihong Park, Seung-Woo Ko, Jinho Choi, Seong-Lyun Kim

The forthcoming 6G systems are expected to address a wide range of non-stationary tasks. This poses challenges to traditional medium access control (MAC) protocols that are static and predefined. In response, data-driven MAC protocols have recently emerged, offering ability to tailor their signaling messages for specific tasks. This article presents a novel

Hongfu Liu, Hengguan Huang, Ye Wang

Acoustic foundation models, fine-tuned for Automatic Speech Recognition (ASR), suffer from performance degradation in wild acoustic test settings when deployed in real-world scenarios. Stabilizing online Test-Time Adaptation (TTA) under these conditions remains an open and unexplored question. Existing wild vision TTA methods often fail to handle speech data

Natarajan Meghanathan

We propose a principal component analysis (PCA)-based approach to quantify (the node dissimilarity index, NDI) the extent of dissimilarity among nodes in a network with respect to values incurred for a suite of node-level metrics (like centrality metrics). We subject the dataset (n nodes and their values incurred for four commonly studied centrality metrics:

Jiayi Ji, Haowei Wang, Changli Wu, Yiwei Ma

The rising importance of 3D understanding, pivotal in computer vision, autonomous driving, and robotics, is evident. However, a prevailing trend, which straightforwardly resorted to transferring 2D alignment strategies to the 3D domain, encounters three distinct challenges: (1) Information Degradation: This arises from the alignment of 3D data with mere sing

Zachary Lamb, Zachary I. Bell, Matthew Longmire, Jared Paquet

Recent literature in the field of machine learning (ML) control has shown promising theoretical results for a Deep Neural Network (DNN) based Nonlinear Adaptive Controller (DNAC) capable of achieving trajectory tracking for nonlinear systems. Expanding on this work, this paper applies DNAC to the Attitude Control System (ACS) of a quadrotor and shows improve

Jivnesh Sandhan, Yaswanth Narsupalli, Sreevatsa Muppirala, Sriram Krishnan

Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound's components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identific

Junichiro Matsuda

Connectedness and bipartiteness are basic properties of classical graphs, and the purpose of this paper is to investigate the case of quantum graphs. We introduce the notion of connectedness and bipartiteness of quantum graphs in terms of graph homomorphisms. This paper shows that regular tracial quantum graphs have the same algebraic characterization of con

Konstantinos Gaitanas

Let $\mathcal{N}[k]$ be the multiset containing the $\binom{n-1}{k}$ products of $k$-subsets of $\{1,\ldots, n-1\}$. We show that if $n\geq (2c+3)^2$, then \begin{gather*}\left((-1)^c+\sum_{M\in \mathcal{N}[n-1-c]}M\right)\cdot(c+1)\equiv 0\pmod{n},\end{gather*} if and only if $n=(c+1)p$, where $p$ is prime. This provides a combinatorial extension of Wilson'

Hang Shao, Bei Liu, Bo Xiao, Ke Zeng

Various Large Language Models~(LLMs) from the Generative Pretrained Transformer(GPT) family have achieved outstanding performances in a wide range of text generation tasks. However, the enormous model sizes have hindered their practical use in real-world applications due to high inference latency. Therefore, improving the efficiencies of LLMs through quantiz

Haigang Li, Longjuan Xu

In this paper, we establish the estimates for the gradient and the second-order partial derivatives for the Stokes flow in the presence of two closely located strictly convex inclusions in dimension three. Moreover, the blow-up rate of the gradient is showed to be optimal by a pointwise upper bound and a lower bound in the narrowest region. We also show the

Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon

We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering

T. A. Leatham, D. M. Paganin, K. S. Morgan

X-ray diffusive dark-field imaging, which allows spatially unresolved microstructure to be mapped across a sample, is an increasingly popular tool in an array of settings. Here, we present a new algorithm for phase and dark-field computed tomography based on the x-ray Fokker-Planck equation. Needing only a coherent x-ray source, sample, and detector, our pro

Jihun Han, Yoonsang Lee, Anne Gelb

We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps. The complex nature of image data and the lack of temporal information pose significant challenges in capturing the unique evolving patterns. Our proposed method focuses on estimating the intermediary stages of image evolution, allowing for

Takeshi Saga, Hiroki Tanaka, Satoshi Nakamura

[See full abstract in the pdf] Formal Thought Disorder (FTD), which is a group of symptoms in cognition that affects language and thought, can be observed through language. FTD is seen across such developmental or psychiatric disorders as Autism Spectrum Disorder (ASD) or Schizophrenia, and its related Schizotypal Personality Disorder (SPD). This paper colle

Catherine Xinrui Yu, Jiaqi Gu, Zhaomeng Chen, Zihuai He

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary sta

Jiezhou Chen, Guankun Wang, Weixiang Liu, Xiaopin Zhong

Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning. However, scene complexity and large-scale variation in the real world make accurate detection more difficult. Therefore, we present a modified Yolov8 which improves head detection performa

Gilyoung Cheong, Yifeng Huang

We prove new statistical results about the distribution of the cokernel of a random integral matrix with a concentrated residue. Given a prime $p$ and a positive integer $n$, consider a random $n \times n$ matrix $X_n$ over the ring $\mathbb{Z}_p$ of $p$-adic integers whose entries are independent. Previously, Wood showed that regardless of the distribution

Guilherme Delfino, Yizhi You

In this work, we introduce an anyon condensation web that interconnects a broad class of 2D fracton gauge theories with multipolar conservation laws at a microscopic level. We find that condensation of anyons triggers the emergence of additional spatially modulated symmetries, which has the general effect of increasing the number of super-selection anyon sec

Siyuan Song, Jiyuan Chen, Jianbei Liu, Yong Liu

Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconst

Jiecheng Lu, Xu Han, Shihao Yang

Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships. As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate LTSF Transformers to model series-wise