November 2024 arXiv papers — page 112

Seulong Kim, Kihong Kim

Wave propagation in time-varying media has attracted significant attention for its innovative potential to control wave-matter interactions and to develop versatile active materials. While most research has focused on electromagnetic waves, studies on Dirac-type waves remain limited. In this work, we investigate temporal scattering in pseudospin-1/2 Dirac sy

Yi-Qiao Xu, Bing-Zhao Li

We present a new Clifford-valued linear canonical Stockwell transform aimed at providing efficient and focused representation of Clifford-valued functions in high-dimensional time-frequency analysis. This transform improves upon the windowed Fourier and wavelet transforms by incorporating angular, scalable, and localized windows, allowing for greater directi

Hongqing Wu^, Ruoyue Tang^, Yuxin Dong, Xinrui Ren

As crucial additives and intermediate, alcohols, ethers, and aldehydes play a significant role in the combustion process. However, the chemistry of NOXhydrocarbon interactions and the rate rules governing these interactions remain largely unexplored in this combustion system. To address this gap, this study provides a comprehensive investigation of H-atom ab

Seyed Saied Mirahmadi

The transformation formulas between two inertial frames for the minimum phase velocity and the phase velocity in an arbitrary direction are given. The derived transformation formula for the reciprocal of the phase velocity along the particle's direction of motion is a correction of the formula provided by J.J. Zhang and P.X. Wang in Wave Motion 50 (2013) 520

Fuma Hidaka, Yusuke Matsui

A learned multi-dimensional index is a data structure that efficiently answers multi-dimensional orthogonal queries by understanding the data distribution using machine learning models. One of the existing problems is that the search performance significantly decreases when the distribution of data stored in the data structure becomes skewed due to update op

Wenqi Ma, Zhifu Gao, Biaopeng Li, Chenhui Niu

Fast radio bursts (FRBs) are radio signals that last milliseconds. They originate from cosmological distances and have relatively high dispersion measures (DMs), making them being excellent distance indicators. However, the origins of the FRB remain to be resolved. With its wide field of view and excellent sensitivity, CHIME/FRB has discovered more than half

Anjan Kar, Soumya Jana, Sayan Kar

A rotating version of a known static, spherically symmetric, zero Ricci scalar Lorentzian wormhole is constructed. It turns out that for this given non-rotating geometry, the standard Newman-Janis algorithm does not produce a rotating wormhole and, therefore, the method pioneered by Azreg-A\"inou has to be used. The rotating spacetime thus obtained is shown

Wei Ren, Jiannong Cao, Huansheng Yi, Kaiyue Hou

Multi-point vital sign monitoring is essential for providing detailed insights into physiological changes. Traditional single-sensor approaches are inadequate for capturing multi-point vibrations. Existing contact-based solutions, while addressing this need, can cause discomfort and skin allergies, whereas noncontact optical and acoustic methods are highly s

Sabbir M. Saleh, Ibrahim Mohammed Sayem, Nazim Madhavji, John Steinbacher

Continuous Integration/Continuous Deployment (CI/CD) is fundamental for advanced software development, supporting faster and more efficient delivery of code changes into cloud environments. However, security issues in the CI/CD pipeline remain challenging, and incidents (e.g., DDoS, Bot, Log4j, etc.) are happening over the cloud environments. While plenty of

Mary Isabelle Wisell, Salimeh Yasaei Sekeh

Sparse deep neural networks (DNNs) excel in real-world applications like robotics and computer vision, by reducing computational demands that hinder usability. However, recent studies aim to boost DNN efficiency by trimming redundant neurons or filters based on task relevance, but neglect their adaptability to distribution shifts. We aim to enhance these exi

Hardik Parwana, Mitchell Black, Bardh Hoxha, Hideki Okamoto

Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen

Mimisha M Menakath, Mahesh Raveendranatha Panicker, Hareesh G

Ocean exploration using acoustical 3D imaging is gaining popularity as it provides information about the 3D geometry of the targets even under mild turbid conditions. A major challenge in underwater 3D imaging is the high cost of the planar arrays and the computational complexity of the image reconstruction algorithms. In this work, we introduce two novel as

Meri Teeriaho, Ville-Vertti Linho, Koushik Swaminathan, Sebastiano Peotta

Motivated by recent studies of the projected dice lattice Hamiltonian [K. Swaminathan et al., Phys. Rev. Research 5, 043215 (2023)], we introduce the on-site/bond singlet (OBS) model, a one-dimensional model of a flat band superconductor, in order to better understand the quasiparticle localization and interesting coexistence of ergodic and non-ergodic behav

Guo-Peng Li, Xi-Long Fan

We infer the origin channels of hierarchical mergers observed in the LIGO-Virgo-KAGRA (LVK) O1, O2, and O3 runs using a hierarchical Bayesian analysis under a parametric population model. By assuming the active galactic nucleus (AGN) disk and nuclear star cluster (NSC) channels, we find that NSCs likely dominate the hierarchical merger rate in the Universe,

Dong-Hong Wu, Sheng Jin, Jason H. Steffen

This study employs numerical simulations to explore the relationship between the dynamical instability of planetary systems and the uniformity of planetary masses within the system, quantified by the Gini index. Our findings reveal a significant correlation between system stability and mass uniformity. Specifically, planetary systems with higher mass uniform

Rajes Ghosh, Akash K Mishra, Avijit Chowdhury

In General Relativity, Birkhoff's theorem asserts that any spherically symmetric vacuum solution must be static and asymptotically flat. In this paper, we study the validity of Birkhoff's theorem for a broad class of modified gravity theories in four spacetime dimensions, including quadratic and higher-order gravity models. We demonstrate that the Schwarzsch

Y. Kawamura, Akihiro Kai

Remarkable improvement in the removal efficiency of the ultraviolet laser (fourth harmonic wave of Nd:YAG laser) ablation was observed by irradiating a weak infrared laser (fundamental wave of Nd:YAG laser) simultaneously and additionally to various kinds of materials, such as copper, acrylic resin, alumina, silicon and amorphous carbon. The improvement rati

Andrew Koh, Sivakorn Sanguanmoo, Kei Uzui

We analyze how dynamic information should be provided to uniquely implement the largest equilibrium in binary-action coordination games. The designer offers an informational put: she stays silent if players choose her preferred action, but injects asymmetric and inconclusive public information if they lose faith. There is (i) no multiplicity gap: the largest

Mykhailo Klymenko, Thong Hoang, Xiwei Xu, Zhenchang Xing

Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these s

Meijia Yang, Yong Xia

In this paper, we consider an unconstrained (-1,1)-quadratic fractional optimization in the following form: $\min_{x\in\{-1,1\}^n}~(x^TAx+\alpha)/(x^TBx+\beta)$, where $A$ and $B$, given by their nonzero eigenvalues and associated eigenvectors, have ranks not exceeding fixed integers $r_a$ and $r_b$, respectively. We show that this problem can be solved in $

Xiaoran Yang, Shuhan Yu, Wenxi Xu

This paper builds upon an existing speech emotion recognition model by adding an additional LSTM layer to improve the accuracy and processing efficiency of emotion recognition from audio data. By capturing the long-term dependencies within audio sequences through a dual-layer LSTM network, the model can recognize and classify complex emotional patterns more

Meijia Yang, Yong Xia

The generalized trace ratio problem {\rm (GTRP)} is to maximize a quadratic fractional objective function in trace formulation over the Stiefel manifold. In this paper, based on a newly developed matrix S-lemma, we show that {\rm (GTRP)}, if a redundant constraint is added and well scaled, has zero Lagrangian duality gap. However, this is not always true wit

Yurii Borysenko, Nataliia Bazhan, Olena Prykhodko, Dominik Pfeiffer

Precise control of topologically protected excitations, such as quantum vortices in atomtronic circuits, opens new possibilities for future quantum technologies. We theoretically investigate the dynamics of Josephson vortices (rotational fluxons) induced by coupled persistent currents in a system of coplanar double-ring atomic Bose-Einstein condensates. We s

Charles-Antoine Claveau, Michael Bottom, Shane Jacobson, Klaus Hodapp

Spectroscopy of Earth-like exoplanets and ultra-faint galaxies are priority science cases for the coming decades. Here, broadband source flux rates are measured in photons per square meter per hour, imposing extreme demands on detector performance, including dark currents lower than \mbox{1 e-/pixel/kilosecond}, read noise less than \mbox{1 e-/pixel/frame},

Youngjin Seol, Jaewoong Choi, Seunghyun Lee, Janghyeok Yoon

Machine learning (ML) models are valuable tools for analyzing the impact of technology using patent citation information. However, existing ML-based methods often struggle to account for the dynamic nature of the technology impact over time and the interdependencies of these impacts across different periods. This study proposes a multi-task learning (MTL) ap

Yifan Zhao, Mona Jarrahi

Quantum well (QW) structures are widely used in lasers, semiconductor optical amplifiers, modulators, enabling their monolithic integration on the same substrate. As optoelectronic systems evolve to meet the growing bandwidth demands in the terahertz regime, a deep understanding of ultrafast carrier dynamics in QW structures becomes essential. We introduce a

Henry Adams, Sushovan Majhi, Fedor Manin, Žiga Virk

Let $G$ be a finite, connected metric graph and let $X\subseteq G$ be a subset. If $X$ is sufficiently dense in $G$, we show that the Gromov--Hausdorff distance matches the Hausdorff distance, namely $d_\gh(G,X)=d_\h(G,X)$. When the metric graph is the circle $G=S^1$ with circumference $2\pi$, a recent study established the equality $d_\gh(S^1,X)=d_\h(S^1,X)

Xinyu He, Jose Sepulveda, Mostafa Rahmani, Alyssa Woo

Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e.g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modeled as a bipartite graph. Due to the noisy and biased nature of implicit real-world user-item interactions, identifying and rectifying noisy i

Chanyeong Park, Heegwang Kim, Joonki Paik

Drone-captured images present significant challenges in object detection due to varying shooting conditions, which can alter object appearance and shape. Factors such as drone altitude, angle, and weather cause these variations, influencing the performance of object detection algorithms. To tackle these challenges, we introduce an innovative vision-language

M. Lin, A. R. Poppe

Interplanetary dust grains (IDPs) originate from a variety of sources and are dynamically transported across the solar system. While in transport, high-$Z$ solar energetic particles (SEPs) with energies of $\sim$1 MeV/nuc leave damage tracks as they pass through IDPs. SEP track densities can be used as a measure of a grain's space exposure and in turn, help

Spencer Giddens, Xiaon Lang, Fang Liu

As data-driven and AI-based decision making gains widespread adoption across disciplines, it is crucial that both data privacy and decision fairness are appropriately addressed. Although differential privacy (DP) provides a robust framework for guaranteeing privacy and methods are available to improve fairness, most prior work treats the two concerns separat

Sebastián Espinel-Ríos, Joyce Qiaoxi Mo, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division of labor. In this study, we employ reinforcement learning as a control method for population setpoint tracking in co-cultures, focusing on policy-gradient techniques where the cont

Bo Wang, Dingwei Tan, Yen-Ling Kuo, Zhaowei Sun

Imagine searching a collection of coins for quarters ($0.25$), dimes ($0.10$), nickels ($0.05$), and pennies ($0.01$)-a hybrid foraging task where observers look for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or

Gyu Min Kim, Jeong Min Jeon

Traditional neural networks (multi-layer perceptrons) have become an important tool in data science due to their success across a wide range of tasks. However, their performance is sometimes unsatisfactory, and they often require a large number of parameters, primarily due to their reliance on the linear combination structure. Meanwhile, additive regression

Md Fahim Anjum

Recent advances in image generation, particularly via diffusion models, have led to impressive improvements in image synthesis quality. Despite this, diffusion models are still challenged by model-induced artifacts and limited stability in image fidelity. In this work, we hypothesize that the primary cause of this issue is the improper resampling operation t

Mark Andrews

The exact evolution in time and space of a distribution of the temperature (or density of diffusing matter) in an isotropic homogeneous medium is determined where the initial distribution is described by a piecewise polynomial. In two dimensions, the boundaries of each polynomial must lie on a grid of lines parallel to the axes, while in three dimensions the

Nicolas Delfosse, Gilles Zémor

We introduce a model for a stacked quantum memory made with multi-qubit cells, inspired by multi-level flash cells in classical solid-state drive, and we design quantum error correction codes for this model by generalizing rank-metric codes to the quantum setting. Rank-metric codes are used to correct faulty links in classical communication networks. We prop

Robert J. H. Ross, Simone Pigolotti

We introduce a model in which cells belonging to two species proliferate with volume exclusion on an expanding surface. If the surface expands uniformly, we show that the domains formed by the two species present a critical behavior. We compute the critical exponents characterizing the decay of interfaces and the size distribution of domains using a mean-fie

Madhusudan Srinivasan, Upulee Kanewala

An oracle determines whether the output of a program for executed test cases is correct. For machine learning programs, such an oracle is often unavailable or impractical to apply. Metamorphic testing addresses this by using metamorphic relations (MRs), which are essential properties of the software under test, to verify program correctness. Prioritizing MRs

Jun-Young Kim, Deok-Seon Kim, Seo-Hyun Lee

In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developments are fundamental in exploring how neural signals can be interpreted to recognize specific physical actions. This study centers on a writt

Jingtao Huang, Bohan Wang, Zhiyuan Gao, Mianlun Zheng

We study the design of transfer functions for volumetric rendering of magnetic resonance imaging (MRI) datasets of human hands. Human hands are anatomically complex, containing various organs within a limited space, which presents challenges for volumetric rendering. We focus on hand musculoskeletal organs because they are volumetrically the largest inside t

Michael S. Harré, Jaime Ruiz-Serra, Catherine Drysdale

One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the human-AI interaction frontier. We suggest th

Michael S. Harré, Catherine Drysdale, Jaime Ruiz-Serra

Collective intelligence plays a central role in many fields, from economics and evolutionary theory to neural networks and eusocial insects, and is also core to work on emergence and self-organisation in complex-systems theory. However, in human collective intelligence there is still much to understand about how specific psychological processes at the indivi

Kuiyuan Zhang, Zhongyun Hua, Yushu Zhang, Yifang Guo

AI-synthesized speech, also known as deepfake speech, has recently raised significant concerns due to the rapid advancement of speech synthesis and speech conversion techniques. Previous works often rely on distinguishing synthesizer artifacts to identify deepfake speech. However, excessive reliance on these specific synthesizer artifacts may result in unsat

Longxuan Ma, Mingda Li, Weinan Zhang, Jiapeng Li

Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we study the open-domain DS that uses unstructured text as external knowledge sources (\textbf{U}nstructured \textb

Aditya Bahulikar, Feng Wang, Mustafa Cenk Gursoy, Rodrick Kuate Defo

We present a structure-adaptive topology optimization framework for engineering photonic band gaps with TM-polarized sources based on computation of the photonic density of states with a uniform source substituting for the standard Dirac delta function sources in formalisms analogous to $\Gamma$-point integration and to integration over a full Brillouin zone

Hosna Sultana

The mid-wave (MWIR) spectral range can provide a larger bandwidth for optical sensing and communication when the near-infrared band gets congested. As optical sensing becomes a robust technique for digital imaging and object recognition, this range of thermal imaging needs to convey more information, which can be unraveled from polarization-sensitive detecti

Prabhakar Tiwari

We critically assess the impact of significant dipole and large-scale anisotropies on galaxy clustering signals, with a focus on radio continuum surveys. Our study reveals that these anisotropies -- resulting from intrinsic cosmological effects and/or observational systematics -- profoundly influence the two-point correlation function (2PCF) and angular powe

Michael Redle, Michael Herty

We consider the simulation of isentropic flow in pipelines and pipe networks. Standard operating conditions in pipe networks suggest an emphasis to simulate low Mach and high friction regimes -- however, the system is stiff in these regimes and conventional explicit approximation techniques prove quite costly and often impractical. To combat these inefficien

Patrick W. Krantz, Alexander Tyner, Pallab Goswami, Venkat Chandrasekhar

The observation of a Hall effect, a finite transverse voltage induced by a longitudinal current, usually requires the breaking of time-reversal symmetry, for example through the application of an external magnetic field or the presence of long range magnetic order in a sample. Recently it was suggested that under certain symmetry conditions, the presence of

Qin Yang

Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences for pursuing goals and drive them to develop diverse skills that satisfy their various needs. Traditional reinforcement learning (RL) is learning from interaction based on the feedback rewards of the environment. However, in real scenarios, the reward

Xiaotian Sun, Xinyu Wang, Wanqian Li, Yinhe Han

Various processing-in-memory (PIM) accelerators based on various devices, micro-architectures, and interfaces have been proposed to accelerate deep neural networks (DNNs). How to deploy DNNs onto PIM-based accelerators is the key to explore PIM's high performance and energy efficiency. The scale of DNN models, the diversity of PIM accelerators, and the compl

Randy Davila

This paper introduces the \emph{Optimist}, an autonomous system developed to advance automated conjecture generation in graph theory. Leveraging mixed-integer programming (MIP) and heuristic methods, the \emph{Optimist} generates conjectures that both rediscover established theorems and propose novel inequalities. Through a combination of memory-based comput

Frederico Cançado, Gabriel Coutinho

Whenever graphs admit equitable partitions, their quotient graphs highlight the structure evidenced by the partition. It is therefore very natural to ask what can be said about two graphs that have the same quotient according to certain equitable partitions. This question has been connected to the theory of fractional isomorphisms and covers of graphs in wel

Shengchao Zhao, Yundong Li

Recent advancements in 3D Gaussian Splatting (3DGS), which lead to high-quality novel view synthesis and accelerated rendering, have remarkably improved the quality of radiance field reconstruction. However, the extraction of mesh from a massive number of minute 3D Gaussian points remains great challenge due to the large volume of Gaussians and difficulty of

XiaoLe He, Ping Liu, JunLing Wang

Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain a rarely explored issue. This paper investigates these limits by analyzing the boundaries of super-resolution algorithms for space targets and examines the relatio

Yuan Liu, Ruichen Zhang, Ruihong Jiang, Yongdong Zhu

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for e

Youpeng Wen, Junfan Lin, Yi Zhu, Jianhua Han

Recent advancements utilizing large-scale video data for learning video generation models demonstrate significant potential in understanding complex physical dynamics. It suggests the feasibility of leveraging diverse robot trajectory data to develop a unified, dynamics-aware model to enhance robot manipulation. However, given the relatively small amount of

Bhavtosh Rath, Pushkar Chennu, David Relyea, Prathyusha Kanmanth Reddy

Recent advancements in session-based recommendation models using deep learning techniques have demonstrated significant performance improvements. While they can enhance model sophistication and improve the relevance of recommendations, they also make it challenging to implement a scalable real-time solution. To addressing this challenge, we propose GRAINRec:

Yuran Wang, Yingping Liang, Hesong Li, Ying Fu

The generalization and performance of stereo matching networks are limited due to the domain gap of the existing synthetic datasets and the sparseness of GT labels in the real datasets. In contrast, monocular depth estimation has achieved significant advancements, benefiting from large-scale depth datasets and self-supervised strategies. To bridge the perfor

Zhaoyang Dong, Yuexian Hou, Chenguang Zhang, Yingjie Gao

The intrinsic information of quantum systems refers to the information required to define a quantum state, and may reveal how the nature stores and processes microscopic information. However, there is an evident paradox due to the "\textit{information scale contrast}": Existing analytical results on the information bounds of quantum systems show that the inf

Dirk Bergemann, Stephen Morris, Rafael Veiel

Two information structures are said to be close if, with high probability, there is approximate common knowledge that interim beliefs are close under the two information structures. We define an "almost common knowledge topology" reflecting this notion of closeness. We show that it is the coarsest topology generating continuity of equilibrium outcomes. An in

Ruichen Zhang, Jiayi He, Xiaofeng Luo, Dusit Niyato

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a mod

Norichika Sago, Ryuichi Fujita, Hiroyuki Nakano

We present various post-Newtonian (PN) models for the phase evolution of compact objects moving along quasi-spherical orbits in Kerr spacetime derived by using the 12PN analytic formulas of the energy, angular momentum and their averaged rates of change calculated in the framework of the black hole perturbation theory. To examine the convergence of time-doma

Yangbo Guo, Jianhui Fan, Ruichen Zhang, Baofang Chang

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. {In these networks,} an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security

Chengbo Yuan, Geng Chen, Li Yi, Yang Gao

Egocentric videos provide valuable insights into human interactions with the physical world, which has sparked growing interest in the computer vision and robotics communities. A critical challenge in fully understanding the geometry and dynamics of egocentric videos is dense scene reconstruction. However, the lack of high-quality labeled datasets in this fi

Omri Nisan Solan

McMullen '03 constructs a collection of orbits $\mathrm{SL}_2(\mathbb{R}).x$ in $\mathcal{H}(1,1)$ with infinitely generated stabilizers $\mathrm{stab}_{\mathrm{SL}_2(\mathbb{R})}(x)$. We prove a gap in the set of critical exponents of stabilizers of $\mathrm{SL}_2(\mathbb{R})$-orbits in $\mathcal{H}_g$: for every $x\in \mathcal{H}_g$, either $\mathrm{stab}_

V. Mpisketzis, G. F. Paraschos, H. Ho-Yin Ng, A. Nathanail

In this study, we focus on the simulation of accretion processes in Magnetically Arrested Disks (MADs) and investigate the dynamics of plasma during flux eruption events. We employ general relativistic magneto-hydrodynamic (GRMHD) simulations and search for regions with a divergent velocity during a flux eruption event. These regions would experience rapid a

Rishav Chourasia, Uzair Javaid, Biplap Sikdar

We introduce a set of useful expressions of Differential Privacy (DP) notions in terms of the Laplace transform of the privacy loss distribution. Its bare form expression appears in several related works on analyzing DP, either as an integral or an expectation. We show that recognizing the expression as a Laplace transform unlocks a new way to reason about D

Sanefumi Moriyama, Kichinosuke Otozawa

For three-dimensional circular-quiver supersymmetric Chern-Simons theories, the questions, whether duality cascades always terminate and whether the endpoint is unique, were rephrased into the question whether a polytope defined in the parameter space of relative ranks for duality cascades is a parallelotope, filling the space by discrete translations. By re

Gozde Merve Demirci, Jiachen Yao, Ming-Chih Ho, Xiaoling Hu

Accurate segmentation of retinal images plays a crucial role in aiding ophthalmologists in diagnosing retinopathy of prematurity (ROP) and assessing its severity. However, due to their underdeveloped, thinner vessels, manual annotation in infant fundus images is very complex, and this presents challenges for fully supervised learning. To address the scarcity

Roy A. Lacey

Finite-size scaling (FSS) is applied to net-baryon cumulant ratios $C_2/C_1$, $C_3/C_2$, $C_4/C_2$, $C_3/C_1$, and $C_4/C_1$ measured in Au+Au collisions over the Beam Energy Scan Phase~I range $\sqrt{s_{NN}}=7.7$--$200$~GeV to constrain the location and universality class of the QCD critical end point (CEP). Although finite-size and finite-time effects supp

Tian-Yu Tu, Yang Chen, Qian-Cheng Liu

Supernova remnants (SNRs) can exert strong influence on molecular clouds (MCs) through interaction by shock wave and cosmic rays. In this paper, we present our mapping observation of HCO+ and HCN 1-0 lines towards 13 SNRs interacting with MCs, together with archival data of CO isotopes. Strong HCO+ emission is found in the fields of view (FOVs) of SNRs W30,

Jérôme Gilles, Bertrand Collin

Few people use the probability theory in order to achieve image segmentation with snake models. In this article, we are presenting an active contour algorithm based on a probability approach inspired by A. Blake work and P. R{\'e}fr{\'e}gier's team research in France. Our algorithm, both very fast and highly accurate as far as contour description is concerne

Yifei Li, Matthew J Simpson, Chuncheng Wang

The role of memory and cognition in the movement of individuals (e.g. animals) within a population, is thought to play an important role in population dispersal. In response, there has been increasing interest in incorporating spatial memory effects into classical partial differential equation (PDE) models of animal dispersal. However, the specific detail of

Dang Pham, Matthew J. Hopkins, Chris Lintott, Michele T. Bannister

Fast radio bursts (FRBs) are transient radio events with millisecond-scale durations, and debated origins. Collisions between planetesimals and neutron stars have been proposed as a mechanism to produce FRBs; the planetesimal strength, size and density determine the time duration and energy of the resulting event. One source of planetesimals is the populatio

Ryousei Takano, Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada

ABCI 3.0 is the latest version of the ABCI, a large-scale open AI infrastructure that AIST has been operating since August 2018 and will be fully operational in January 2025. ABCI 3.0 consists of computing servers equipped with 6128 of the NVIDIA H200 GPUs and an all-flash storage system. Its peak performance is 6.22 exaflops in half precision and 3.0 exaflo

Charles Roques-Carmes, Kai Wang, Yuanmu Yang, Arka Majumdar

Metasurfaces -- ultrathin structures composed of subwavelength optical elements -- have revolutionized light manipulation by enabling precise control over electromagnetic waves' amplitude, phase, polarization, and spectral properties. Concurrently, computational imaging leverages algorithms to reconstruct images from optically processed signals, overcoming l

Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC

Siqiang Wang, Zhong Zheng, Jing Guo, Zesong Fei

Among the key enabling 6G techniques, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) play an important role in enhancing the spectral efficiency of the wireless communication systems. To further extend the coverage and the capacity, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) h

Thanh Nguyen, Yasuhiro Oba, W. M. C. Sameera, Kenji Furuya

Sulfur dioxide (SO2) is a sulfur-containing molecule expected to exist as a solid in the interstellar medium (ISM). In this study, we performed laboratory experiments and computational analyses on the surface reactions of solid SO2 with hydrogen atoms on amorphous solid water (ASW) at low temperatures. After 40 min of exposure of SO2 deposited on ASW to H at

Ziyang Zhang, Dongming Wang, Yunxiang Guo, Yang Cao

As a critical component of beyond fifth-generation (B5G) and sixth-generation (6G) mobile communication systems, ultra-reliable low-latency communication (uRLLC) imposes stringent requirements on latency and reliability. In recent years, with the improvement of mobile communication network, centralized and distributed processing schemes for cellfree massive

Valentin Frank Ingmar Guenter, Athanasios Sideris

We propose a novel algorithm for combined unit and layer pruning of deep neural networks that functions during training and without requiring a pre-trained network to apply. Our algorithm optimally trades-off learning accuracy and pruning levels while balancing layer vs. unit pruning and computational vs. parameter complexity using only three user-defined pa

Yangyang Guo, Mohan Kankanhalli

While contrastive pre-training is widely employed, its data efficiency problem has remained relatively under-explored thus far. Existing methods often rely on static coreset selection algorithms to pre-identify important data for training. However, this static nature renders them unable to dynamically track the data usefulness throughout pre-training, leadin

Leyang Hu, Boran Wang

Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Due to their training on internet-sourced datasets, LLMs can sometimes generate objectionable content, necessitating extensive alignment with human feedback to avoid such outputs. Despite massive alignment efforts, LLMs remain susceptible

G. Hernández-Tomé, C. S. Kim, G. López Castro

We compute the branching fractions of vector quarkonia ($V_Q=J/\psi, \psi', \Upsilon(nS)$) decays into neutrino pairs, considering both Dirac and Majorana types, within the Standard Model (SM) and beyond. The vector nature of quarkonium states yields a decay width in the SM that depends upon the weak vector coupling of the heavy quark, offering the possibili

Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara, Giorgio Cortiana

Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version

Hannah Larson, Ravi Vakil

We state and prove a form of Bott periodicity (for $U(n)$) in an algebraic setting (so, $GL(n)$) which makes sense over $\mathbb{Z}$, which also specializes to Bott periodicity in the usual sense (hence giving yet another proof of classical Bott periodicity). An appendix by B. Church gives a specialization of the constructions and results to motivic homotopy

Yu-Fang Chen, Kai-Min Chung, Min-Hsiu Hsieh, Wei-Jia Huang

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the tre

Hoyun Choi, Sungyeop Lee, B. Kahng, Junghyo Jo

Numerical simulation is a predominant tool for studying the dynamics in complex systems, but large-scale simulations are often intractable due to computational limitations. Here, we introduce the Neural Graph Simulator (NGS) for simulating time-invariant autonomous systems on graphs. Utilizing a graph neural network, the NGS provides a unified framework to s

Lin Nie, Yu-Hai Ge, Yi-Qing Guo, Si-Ming Liu

A novel phenomenon among the recently observed Geminga pulsar halo is the presence of distinct radiation morphology at high energies, while no extended radiation is detected in the 10-500 GeV energy band within a $40\degree\times40\degree$ region. This phenomenon suggests that pulsar halos play a crucial role in the local propagation of cosmic rays, making i

Chaoyang Luo, Yan Zou, Wanying Li, Nanjing Huang

Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly predicted state within a user-defined fixed time. To address this problem, we propose a new method for training Ne

Frederic Koehler, Holden Lee, Thuy-Duong Vuong

We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily slow, we show that if the Markov chain has a $k$th order spectral gap, initialization from a set of $\tilde O(k/\varepsilon^2)$ samples from the stationary distribution will, with hi

Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang

Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multil

Benjamin Antieau

This paper explains the theory of spectral sequences via d\'ecalage and the Beilinson t-structure.

Lucas P. Ramos, Felipe A. Louza, Guilherme P. Telles

DNA technologies have evolved significantly in the past years enabling the sequencing of a large number of genomes in a short time. Nevertheless, the underlying computational problem is hard, and many technical factors and limitations complicate obtaining the complete sequence of a genome. Many genomes are left in a draft state, in which each chromosome is r

Qinian Jin

Landweber-type methods are prominent for solving ill-posed inverse problems in Banach spaces and their convergence has been well-understood. However, how to derive their convergence rates remains a challenging open question. In this paper, we tackle the challenge of deriving convergence rates for Landweber-type methods applied to ill-posed inverse problems,

Y. A. Lazovik, P. B. Ivanov, J. C. B. Papaloizou

In this Paper we determine the non-dissipative tidal evolution of a close binary system with an arbitrary eccentricity in which the spin angular momenta of both components are misaligned with the orbital angular momentum. We focus on the situation where the orbital angular momentum dominates the spin angular momenta and so remains at small inclination to the

Libo Wang

In order to address the chain of thought in the large language model inference cost surge, this research proposes to use a sparse attention mechanism that only focuses on a few relevant tokens. The researcher constructed a new attention mechanism and used GiantRabbit trained with custom GPTs as an experimental tool. The experiment tested and compared the rea

Arna Bhardwaj, Shishir Bhatta, Hiroyasu Tsukamoto

Interstellar objects (ISOs), astronomical objects not gravitationally bound to the sun, could present valuable opportunities to advance our understanding of the universe's formation and composition. In response to the unpredictable nature of their discoveries that inherently come with large and rapidly changing uncertainty in their state, this paper proposes