May 2024 arXiv papers — page 108

Daiju Funakawa, Satoshi Okumura, Yuki Ueda

We investigate the existence of discrete positive or negative energy ground states of the Dirac operator $H$ which describe the fermion scattering on topological solitons in the nonlinear $O(3)$ $\sigma$-model. Additionally, we provide a sufficient condition to ensure that the positive and negative energies of the Dirac operator $H$ are non-zero.

Anwoy Chatterjee, Eshaan Tanwar, Subhabrata Dutta, Tanmoy Chakraborty

Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; however, adapting them to novel tasks is still challenging. While colossal models excel in zero-shot performance, their computational demands limit widespread use, and smaller language mode

Benjamin Zi Hao Zhao, Muhammad Ikram, Mohamed Ali Kaafar

OpenAI's ChatGPT initiated a wave of technical iterations in the space of Large Language Models (LLMs) by demonstrating the capability and disruptive power of LLMs. OpenAI has prompted large organizations to respond with their own advancements and models to push the LLM performance envelope. OpenAI has prompted large organizations to respond with their own a

MIT Hardness Group, Erik D. Demaine, Lilly Hall, Hayashi Layers

We prove RE-completeness (and thus undecidability) of several 2D games in the Super Mario Bros. platform video game series: the New Super Mario Bros. series (original, Wii, U, and 2), and both Super Mario Maker games in all five game styles (Super Mario Bros. 1 and 3, Super Mario World, New Super Mario Bros. U, and Super Mario 3D World). These results hold e

Kai Huang, Luca Gioacchini, Marco Mellia, Luca Vassio

In the context of cybersecurity, tracking the activities of coordinated hosts over time is a daunting task because both participants and their behaviours evolve at a fast pace. We address this scenario by solving a dynamic novelty discovery problem with the aim of both re-identifying patterns seen in the past and highlighting new patterns. We focus on traffi

Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee

We introduce the state-of-the-art semi-analytic model {\small FEGA} (Formation and Evolution of GAlaxies), which incorporates updated prescriptions for key physical processes in galaxy formation. Notably, {\small FEGA} features an unprecedented semi-analytic modeling of positive Active Galactic Nuclei (AGN) feedback. The model combines the latest prescriptio

Tanoy Debnath, Shadman Wadith, Anichur Rahman

Agriculture has a significant role in a country's economy. The "SMARD" project aims to strengthen the country's agricultural sector by giving farmers with the information and tools they need to solve common difficulties and increase productivity. The project provides farmers with information on crop care, seed selection, and disease management best practices

Jason M. H. Beedle, Daniel J. Gershman, Vadim M. Uritsky, Jason R. Shuster

Dayside magnetic reconnection allows for the transfer of the solar wind's energy into Earth's magnetosphere. This process takes place in electron diffusion regions (EDRs) embedded in ion diffusion regions (IDRs), which form in the magnetopause boundary's current sheet. A significant out-of-plane parallel current contribution in the diffusion regions was repo

Jie Zhu, Junhui Li, Yalong Wen, Lifan Guo

In light of recent breakthroughs in large language models (LLMs) that have revolutionized natural language processing (NLP), there is an urgent need for new benchmarks to keep pace with the fast development of LLMs. In this paper, we propose CFLUE, the Chinese Financial Language Understanding Evaluation benchmark, designed to assess the capability of LLMs ac

Nalini Joshi, Marta Mazzocco, Pieter Roffelsen

In this paper, we consider a six parameter family of affine Segre surfaces embedded in $\mathbb C^6$. For generic values of the parameters, this family is associated to the $q$-difference sixth Painlev\'e equation. We show that different limiting forms of this family give Segre surfaces that are isomorphic as affine varieties to the the monodromy manifolds o

Takehito Koshisaka, Takuya Sakamoto

This study proposes a sensing method using a millimeter-wave array radar and a depth camera to measure pulse waves at multiple sites on the human body. Using a three-dimensional shape model of the target human body measured by the depth camera, the method identifies reflection sites on the body through electromagnetic scattering simulation. On the basis of t

Ziyi Wang, Lijia Wei, Lian Xue

This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary

Zile Huang, Chong Zhang, Mingyu Jin, Fangyu Wu

While deep learning-based general object detection has made significant strides in recent years, the effectiveness and efficiency of small object detection remain unsatisfactory. This is primarily attributed not only to the limited characteristics of such small targets but also to the high density and mutual overlap among these targets. The existing transfor

Anish Ghoshal, Nobuchika Okada, Arnab Paul, Digesh Raut

It has recently been shown in Ref. [1] that the double-inflation scenario based on the Coleman-Weinberg potential can successfully generate primordial black holes (PBHs) with the inflationary predictions consistent with the Planck measurements. These PBHs can play the role of dark matter in our universe. In this paper, we propose the classically conformal mi

Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park

Energy storage devices, such as batteries, thermal energy storages, and hydrogen systems, can help mitigate climate change by ensuring a more stable and sustainable power supply. To maximize the effectiveness of such energy storage, determining the appropriate charging and discharging amounts for each time period is crucial. Reinforcement learning is preferr

Peiyao Sheng, Chenyuan Wu, Dahlia Malkhi, Michael K. Reiter

This paper introduces and develops the concept of ``ticketing'', through which atomic broadcasts are orchestrated by nodes in a distributed system. The paper studies different ticketing regimes that allow parallelism, yet prevent slow nodes from hampering overall progress. It introduces a hybrid scheme which combines managed and unmanaged ticketing regimes,

Wanting Lyu, Songjie Yang, Yue Xiu, Xinyi Chen

A dual-robust design of beamforming is investigated in an integrated sensing and communication (ISAC) system.Existing research on robust ISAC waveform design, while proposing solutions to imperfect channel state information (CSI), generally depends on prior knowledge of the target's approximate location to design waveforms. This approach, however, limits the

Kento Uchida, Ryoki Hamano, Masahiro Nomura, Shota Saito

In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as safe optimization and formulated as a specialized type of constrained optimization problem with constraints for safety functions. Safe optimization requires performing efficient optim

Sungwook Jang

Birkar and Hu showed that if a pair $(X,\Delta)$ is lc and $K_{X}+\Delta$ admits a birational Zariski decomposition, then $(X,\Delta)$ has a minimal model. Analogously, we prove that if a pair $(X,\Delta)$ is pklt and $-(K_{X}+\Delta)$ admits a birational Zariski decomposition, then $(X,\Delta)$ has an anticanonical minimal

Sameer Iyer, Yasunori Maekawa

The Triple-Deck equations are a classical boundary layer model which describes the asymptotics of a viscous flow near the separation point, and the Couette flow is an exact stationary solution to the Triple-Deck equations. In this paper we prove the local rigidity of the Couette flow in the sense that there are no other stationary solutions near the Couette

Chen Zhang, Steven Tin Sui Luo, Jason Chun Lok Li, Yik-Chung Wu

We investigate the learning of implicit neural representation (INR) using an overparameterized multilayer perceptron (MLP) via a novel nonparametric teaching perspective. The latter offers an efficient example selection framework for teaching nonparametrically defined (viz. non-closed-form) target functions, such as image functions defined by 2D grids of pix

Mushui Liu, Jun Dan, Ziqian Lu, Yunlong Yu

Due to the large-scale image size and object variations, current CNN-based and Transformer-based approaches for remote sensing image semantic segmentation are suboptimal for capturing the long-range dependency or limited to the complex computational complexity. In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image featu

Jiachen Sun, Changsheng Wang, Jiongxiao Wang, Yiwei Zhang

Large language models have become increasingly prominent, also signaling a shift towards multimodality as the next frontier in artificial intelligence, where their embeddings are harnessed as prompts to generate textual content. Vision-language models (VLMs) stand at the forefront of this advancement, offering innovative ways to combine visual and textual da

Chong Hian Chee, Daniel Leykam, Adrian M. Mak, Kishor Bharti

Digital quantum computers promise exponential speedups in performing quantum time-evolution, providing an opportunity to simulate quantum dynamics of complex systems in physics and chemistry. However, the task of extracting desired quantum properties at intermediate time steps remains a computational bottleneck due to wavefunction collapse and no-fast-forwar

Patrick J. Laub, Young Lee, Philip K. Pollett, Thomas Taimre

The Hawkes process is a model for counting the number of arrivals to a system which exhibits the self-exciting property - that one arrival creates a heightened chance of further arrivals in the near future. The model, and its generalizations, have been applied in a plethora of disparate domains, though two particularly developed applications are in seismolog

Ana Isabel Garcia Guerra, Teng Sung Shiuan

To support the testing of AVs, CETRAN has created a guideline for the evaluation of complex multi agent test scenarios presented in this report. This allows for a clear structured manner in evaluating complexity elements based on the corresponding difficulties an AV might encounter in Singapore traffic. This study aims to understand the source of complexity

Jianchao Zhang, Jun Suzuki

Bayesian approach for quantum parameter estimation has gained a renewed interest from practical applications of quantum estimation theory. Recently, a lower bound, called the Bayesian Nagaoka-Hayashi bound for the Bayes risk in quantum domain was proposed, which is an extension of a new approach to point estimation of quantum states by Conlon et al. (2021).

Ming-Chun Jiang, Ryota Masuki, Guang-Yu Guo, Ryotaro Arita

We perform a systematic ab initio study on phonon-mediated superconductivity in the transition-metal-based superconductors Sc$_6M$Te$_2$ ($M$ = Fe, Co, Ni). Firstly, our charge analysis reveals significant electron transfer from Sc to $M$ due to the substantial difference in the electronegativity, filling the 3$d$ orbitals of $M$ and suppressing magnetic ins

Zhiqiang Wang, Yiran Pang, Yanbin Lin, Xingquan Zhu

Text classification is fundamental in Natural Language Processing (NLP), and the advent of Large Language Models (LLMs) has revolutionized the field. This paper introduces an adaptable and reliable text classification paradigm, which leverages LLMs as the core component to address text classification tasks. Our system simplifies the traditional text classifi

Tetsuya Sato, Shinichi Watanabe, Mamoru Matsuo, Takeo Kato

We theoretically investigate nonequilibrium spin fluctuations in a ferromagnet induced by a light pulse. Using a Lindblad equation consistent with the Landau-Lifshitz-Gilbert equation, we compute the autocorrelation function of magnetization. Our analysis reveals that this function comprises both thermal and nonequilibrium components. To examine the latter i

Yaoqi Yang, Bangning Zhang, Daoxing Guo, Hongyang Du

Recently, generative AI has attracted much attention from both academic and industrial fields, which has shown its potential, especially in the data generation and synthesis aspects. Simultaneously, secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement due to an advantage on low deployment cost, fl

Kwangho Choi, Junho Lee

Using a Morse function and a Witten deformation argument, we obtain an upper bound for the dimension of the space of divergence-free symmetric Killing $p$-tensors on a closed Riemannian manifold, and calculate it explicitly for $p=2$.

K. Lakshmanan

We investigate the computational problem of determining whether a bivariate polynomial with non-negative coefficients and no constant term can attain a prime value. While classical conjectures such as Bouniakowsky's provide necessary conditions for univariate prime-representing polynomials, we introduce a new recursive algorithm that efficiently certifies wh

Junhui Li, Xingsong Hou

Decoding remote sensing images to achieve high perceptual quality, particularly at low bitrates, remains a significant challenge. To address this problem, we propose the invertible neural network-based remote sensing image compression (INN-RSIC) method. Specifically, we capture compression distortion from an existing image compression algorithm and encode it

Zijin Hong, Jian Liu

Event Extraction (EE) is an essential information extraction task that aims to extract event-related information from unstructured texts. The paradigm of this task has shifted from conventional classification-based methods to more contemporary question-answering-based (QA-based) approaches. However, in QA-based EE, the quality of the questions dramatically a

Qingya Li, Shengcai Liu, Juan Zou, Ke Tang

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitat

Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang

In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in their answers if they are more adept, as opposed to their less skilled counterparts. Expanding on this foundational insight, we propose a new self-evaluation method ProbDiff for a

Wenhan Fan, Zhicheng Ding, Ruixin Huang, Chang Zhou

A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually re

Yingjie Pei, Wanli Ni, Jin Xu, Xinwei Yue

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the p

Kai Zhang, Xuanyu Cao, Khaled B. Letaief

Federated learning (FL) necessitates that edge devices conduct local training and communicate with a parameter server, resulting in significant energy consumption. A key challenge in practical FL systems is the rapid depletion of battery-limited edge devices, which limits their operational lifespan and impacts learning performance. To tackle this issue, we i

Chao Liang, Wei Xiang, Bang Wang

Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate task

Ying Xing, Mengci Zhao, Bin Yang, Yuwei Zhang

In recent years, defect prediction techniques based on deep learning have become a prominent research topic in the field of software engineering. These techniques can identify potential defects without executing the code. However, existing approaches mostly concentrate on determining the presence of defects at the method-level code, lacking the ability to pr

Boxiang Wang

The multichannel virtual sensing active noise control (MVANC) methodology is an advanced approach that may provide a wide area of silence at specific virtual positions that are distant from the physical error microphones. Currently, there is a scarcity of open-source programs available for the MVANC algorithm. This work presents a MATLAB code for the MVANC a

Koyo Kubota, Makoto Takagi, Tsubasa Ikami, Yasuhiro Egami

Triple decomposition is a useful analytical method for extracting the mean value, organized coherent motion, and stochastic part from a fluctuating quantity. Although the pressure-sensitive paint (PSP) method is widely used to measure the pressure distribution on a surface, the PSP data measuring near atmospheric pressure contain significant noise. Here, we

Pengzhi Li, Chengshuai Tang, Qinxuan Huang, Zhiheng Li

In this paper, we explore the existing challenges in 3D artistic scene generation by introducing ART3D, a novel framework that combines diffusion models and 3D Gaussian splatting techniques. Our method effectively bridges the gap between artistic and realistic images through an innovative image semantic transfer algorithm. By leveraging depth information and

Wanting Lyu, Songjie Yang, Yue Xiu, Zhongpei Zhang

This paper investigates flexible beamforming design in an integrated sensing and communication (ISAC) network with movable antennas (MAs). A bistatic radar system is integrated into a multi-user multiple-input-single-output (MU-MISO) system, with the base station (BS) equipped with MAs. This enables array response reconfiguration by adjusting the positions o

Panagiota Fatourou, Eric Ruppert

Augmenting an existing sequential data structure with extra information to support greater functionality is a widely used technique. For example, search trees are augmented to build sequential data structures like order-statistic trees, interval trees, tango trees, link/cut trees and many others. We study how to design concurrent augmented tree data structur

Jeremy R. Lilly, Giacomo Capodaglio, Darren Engwirda, Robert L. Higdon

The Courant-Friedrichs-Lewy (CFL) condition is a well known, necessary condition for the stability of explicit time-stepping schemes that effectively places a limit on the size of the largest admittable time-step for a given problem. We formulate and present a new local time-stepping (LTS) scheme optimized, in the CFL sense, for the shallow water equations (

Jianshun Zeng, Wang Li, Yanjie Lv, Shuai Gao

Street-view image has been widely applied as a crucial mobile mapping data source. The inpainting of street-view images is a critical step for street-view image processing, not only for the privacy protection, but also for the urban environment mapping applications. This paper presents a novel Deep Neural Network (DNN), multi-scale semantic prior Feature gui

Tai-Xing Liu, Ri-Guang Huang, Dong-Liang Fang

The fundamental process of neutrinoless double beta decay, $nn\to ppe^-e^-$, dominated by the exchange of light Majorana neutrinos, is studied in the framework of chiral effective field theory. Considering neutrinos as virtual states, we evaluate the contributions of finite nucleon size to the transition amplitude in a non-perturbative manner, as opposed to

Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci

Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving. This has motivated many studies in applying LLMs to wireless communication networks. In this paper, we propose a pre-trained LLM-empowered framework to perform fully automatic ne

Ziyue Piao, Christian Frisson, Bavo Van Kerrebroeck, Marcelo M. Wanderley

This paper investigates the integration of force feedback in Digital Musical Instruments (DMI), specifically evaluating the reproduction of intricate vibrato techniques using haptic feedback controllers. We introduce our system for vibrato modulation using force feedback, composed of Bend-aid (a web-based sequencer platform using pre-designed haptic feedback

Weitian Wu, Xinmin Yang

Many existing branch and bound algorithms for multiobjective optimization problems require a significant computational cost to approximate the entire Pareto optimal solution set. In this paper, we propose a new branch and bound algorithm that approximates a part of the Pareto optimal solution set by introducing the additional preference information in the fo

Zhendong Ang, Umang Mathur

Runtime predictive analyses enhance coverage of traditional dynamic analyses based bug detection techniques by identifying a space of feasible reorderings of the observed execution and determining if any of these witnesses the violation of some desired safety property. The most popular approach for modelling the space of feasible reorderings is through Mazur

Pranjal Rawat

Aesthetics drives product differentiation in industries such as fashion, interior decor, luxury goods, real estate and hospitality. However, visual differentiation is hard to encode in formal economic analysis. This paper analyses millions of purchase records from H\&M in the Netherlands, including product images, text descriptions, prices, and consumer demo

Bo Wu, Peiye Liu, Wen-Huang Cheng, Bei Liu

Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensi

Wei Hu, Pei Li, Arno Rogg, Alexander Schepelmann

Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state-of-the-art Earth-based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of t

Li Wei, Tierui Gong, Chongwen Huang, Zhaoyang Zhang

Holographic multiple-input multiple-output (HMIMO) utilizes a compact antenna array to form a nearly continuous aperture, thereby enhancing higher capacity and more flexible configurations compared with conventional MIMO systems, making it attractive in current scientific research. Key questions naturally arise regarding the potential of HMIMO to surpass Sha

Chang Li, Huaiyu Zhang, Xi Zhang

In this paper, we study the rigidity of eigenvalues of shring Ricci solitons. It is known that the drifted Laplacian on shrinking Ricci solitons has discrete spectrum, its eigenvalues have a lower bound and a rigidity result holds. Firstly, we show that if the $n^\text{th}$ eigenvalue is close to this lower bound, then the $n$-soliton must be the trivial Gau

Ege Erdil, Tamay Besiroglu, Anson Ho

Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategies for estimating idea production functions. We explore various methods, including naive approaches, linear regression, maximum likelihood estimation, and Bayesian inference, each

Tian Xu, Lingxiao Wang, Lianyi He, Kai Zhou

In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on system configurations governed by the Boltzmann factor, $e^{-S}$ at different temperatures within the imaginary time formalism of thermal field theory. We focus on 0+1 dimensional

Xirui Peng, Qiming Xu, Zheng Feng, Haopeng Zhao

This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rapid development of Natural Language Processing (NLP) and deep learning technologies, automatic news generation

Kazumasa Nomura, Paul Terwilliger

Let ${\mathcal X} = (X, \{R_i\}_{i=0}^d)$ denote a symmetric association scheme. Fix an ordering $\{E_i\}_{i=0}^d$ of the primitive idempotents of $\mathcal{X}$, and let $P$ (resp.\ $Q$) denote the corresponding first eigenmatrix (resp.\ second eigenmatrix) of $\mathcal X$. The scheme $\mathcal X$ is said to be formally self-dual (with respect to the orderin

Changshuai Wei, Benjamin Zelditch, Joyce Chen, Andre Assuncao Silva T Ribeiro

Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets. In this paper, we propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (NOAH) framework. NOAH is the first g

Jiaxiang Yu, Yiyang Liu, Ruiyang Fan, Guobing Sun

In the facial expression recognition task, researchers always get low accuracy of expression classification due to a small amount of training samples. In order to solve this kind of problem, we proposes a new data augmentation method named MixCut. In this method, we firstly interpolate the two original training samples at the pixel level in a random ratio to

Rossen I. Ivanov

The propagation of water waves of finite depth and flat bottom is studied in the case when the depth is not small in comparison to the wavelength. This propagation regime is complementary to the long-wave regime described by the famous KdV equation. The Hamiltonian approach is employed in the derivation of a model equation in evolutionary form, which is both

Logan Thrasher Collins, Todd Huffman, Randal Koene

Mammalian whole-brain connectomes are a foundational ingredient for holistic understanding of brains. Indeed, imaging connectomes at sufficient resolution to densely reconstruct cellular morphology and synapses represents a longstanding goal in neuroscience. Mouse connectomes could soon come within reach while human connectomes remain a more distant yet stil

Tamitake Itoh, Yuko S. Yamamoto

One-dimensional junctions between parallel and closely arranged multiple silver nanowires (NWs) exhibit a large electromagnetic (EM) enhancement factor (FR) owing to both localized and surface plasmon resonances. Such junctions are referred to as one-dimensional (1D) hotspots (HSs). This study found that two-photon excited emissions, such as hyper-Rayleigh,

Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao

Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSul

Jefferson A. Peña Torres, Raúl E. Gutiérrez De Piñerez

We introduce CNER, an ensemble of capable tools for extraction of semantic relationships between named entities in Spanish language. Built upon a container-based architecture, CNER integrates different Named entity recognition and relation extraction tools with a user-friendly interface that allows users to input free text or files effortlessly, facilitating

Rohta Takahashi, Masayuki Umemura, Ken Ohsuga, Yuta Asahina

We have discovered analytical expressions for the probability density function (PDF) of photons that are multiply scattered in relativistic flows, under the assumption of isotropic and inelastic scattering. These expressions characterize the collective dynamics of these photons, ranging from free-streaming to diffusion regions. The PDF, defined within the li

Wenxi Lai, Yu-Quan Ma, Qiaoxin Li

Previously in vibration sensors, optical glass plates, optical fibres, carbon nanotubes, semiconductor materials, piezoelectric materials and molecules are proved to be effective transducers for sensing vibrations. In this work, for the first time, we will propose a model of vibration sensor using single atom transport in an open optical lattice. In this app

Yi Hu, Fanyu Meng, Hechang Lei, Qi-Kun Xue

Superconductivity and long-range ferromagnetism hardly coexist in a uniform manner. The counter-example has been observed, in uranium-based superconductors for instance, with a coexisting temperature limited to about 1 K. Here, we report the coexistence of high temperature superconductivity and itinerant ferromagnetism in lithium intercalated FeSe flakes. In

Yuqing Lan, Zhenghao Liu, Yu Gu, Xiaoyuan Yi

Fact verification tasks aim to identify the integrity of textual contents according to the truthful corpus. Existing fact verification models usually build a fully connected reasoning graph, which regards claim-evidence pairs as nodes and connects them with edges. They employ the graph to propagate the semantics of the nodes. Nevertheless, the noisy nodes us

Rya Sanovar, Srikant Bharadwaj, Renee St. Amant, Victor Rühle

Transformer-based models have emerged as one of the most widely used architectures for natural language processing, natural language generation, and image generation. The size of the state-of-the-art models has increased steadily reaching billions of parameters. These huge models are memory hungry and incur significant inference latency even on cutting edge

Han Zhang, Joshua J. Sanchez, Jiun-Haw Chu, Jian Liu

Anisotropic lattice deformation plays an important role in the quantum mechanics of solid state physics. The possibility of mediating the competition and cooperation among different order parameters by applying in situ strain/stress on quantum materials has led to discoveries of a variety of elasto-quantum effects on emergent phenomena. It has become increas

Michael V. Klibanov, Jingzhi Li, Zhipeng Yang

A system of two coupled nonlinear parabolic partial differential equations with two opposite directions of time is considered. In fact, this is the so-called "Mean Field Games System" (MFGS), which is derived in the mean field games (MFG) theory. This theory has numerous applications in social sciences. The topic of Coefficient Inverse Problems (CIPs) in the

Zachary J. Wegert, Jordi Manyer, Connor Mallon, Santiago Badia

In this paper we present GridapTopOpt, an extendable framework for level set-based topology optimisation that can be readily distributed across a personal computer or high-performance computing cluster. The package is written in Julia and uses the Gridap package ecosystem for parallel finite element assembly from arbitrary weak formulations of partial differ

Davide Moia

The understanding and optimization of solar energy conversion and light emitting devices can greatly benefit from equivalent circuit models describing their response. However, a general model of electron-hole recombination in semiconductors is currently missing. This study presents equivalent circuit models of radiative and non-radiative electron-hole recomb

Sanjaya Khanal, Shiva Raj Pokhrel

This research analyzes, models and develops a novel Digital Learning Environment (DLE) fortified by the innovative Private Learning Intelligence (PLI) framework. The proposed PLI framework leverages federated machine learning (FL) techniques to autonomously construct and continuously refine personalized learning models for individual learners, ensuring robus

S. K. Kadam, Satish S. Sonkamble, N. D. Vagshette, M. K. Patil

This paper presents properties of the intracluster medium (ICM) in the environment of a cool core cluster Abell 2566 (redshift $z$ = 0.08247) based on the analysis of 20 ks Chandra X-ray data. 2D imaging analysis of the Chandra data from this cluster revealed spiral structures in the morphology of X-ray emission from within the central 109 kpc formed due to

Xinzhe Li, Ming Liu

Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs (AR-LLMs) have catalyzed a significant surge in Artificial Intelligence (AI). This paper aims to emphasize the significance of utilizing free-form modalities (forms of input and o

Vanessa Sochat

Research has become increasingly reliant on software, serving as the driving force behind bioinformatics, high performance computing, physics, machine learning and artificial intelligence, to name a few. While substantial progress has been made in advocating for the research software engineer, a kind of software engineer that typically works directly on soft

Hamed Taghavian, Malin Andersson, Mikael Johansson

A data-driven solution is provided for the fast-charging problem of lithium-ion batteries with multiple safety and aging constraints. The proposed method optimizes the charging current based on the observed history of measurable battery quantities, such as the input current, terminal voltage, and temperature. The proposed method does not need any detailed ba

Bingran You, Qiming Wu, David Miron, Wenjun Ke

High-rate remote entanglement between photon and matter-based qubits is essential for distributed quantum information processing. A key technique to increase the modest entangling rates of existing long-distance quantum networking approaches is multiplexing. Here, we demonstrate a temporally multiplexed ion-photon interface via rapid transport of a chain of

Björn Schäfer, Moritz Weber

We partially characterize nuclearity for the recently introduced class of hypergraph C*-algebras using a tailor-made hypergraph minor relation. The latter is generated by certain operations on hypergraphs which resemble the moves on directed graphs used by Eilers, Restorff, Ruiz and Sørensen to classify unital graph C*-algebras. In particular, we obtain a ne

Wuming Yang, Zhijia Tian

The ratio of metal abundance to hydrogen abundance of the solar photosphere, $(Z/X)_{s}$, has been revised several times. Standard solar models, based on these revised solar abundances, are in disagreement with seismically inferred results. Recently, Magg et al. introduced a new value for $(Z/X)_{s}$, which is still in debate in the community. The solar abun

Akhlesh Lakhtakia

The geometric phase has acquired interest for optical devices such as achromatic phase shifters, spatial light modulators, frequency shifters, and planar lenses for wavefront engineering. Numerical work with Reusch piles with a large number of layers per period suggests that the geometric phase of the reflected/transmitted plane wave may be sensitive to nano

Tian-Xiao He, Nikolai A. Krylov

We discuss examples of linear representations of finite groups as subgroups of the Riordan group. In particular, we show that the symmetric group of degree three has no faithful representation as a subgroup of the Riordan group over the complex numbers, but can be embedded as a subgroup of the Riordan group over a field of characteristic three.

Yu Xia, Sriram Narayanamoorthy, Zhengyuan Zhou, Joshua Mabry

The development of open benchmarking platforms could greatly accelerate the adoption of AI agents in retail. This paper presents comprehensive simulations of customer shopping behaviors for the purpose of benchmarking reinforcement learning (RL) agents that optimize coupon targeting. The difficulty of this learning problem is largely driven by the sparsity o

Ioannis Georgakilas, Rafał Mirek, Darius Urbonas, Michael Forster

In recent years, exciton-polariton microcavity arrays have emerged as a promising semiconductor-based platform for analogue simulations of model Hamiltonians and topological effects. To realize experimentally a variety of Hamiltonians and change their parameters, it is essential to have highly tunable and easily engineerable structures. Here, we demonstrate

Yue Liu, Sin Kit Lo, Qinghua Lu, Liming Zhu

Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue users' goals. Nevertheless, there is a lack of systematic knowledge to guide practitioners in designing the agents

Michał Dybowski, Przemyslaw Górka, Paul Howard

We show that the statement ``In every separable pseudometric space there is a maximal non-strictly \delta-separated set.'' implies the axiom of choice for countable families of sets. This gives answers to a question of Dybowski and G\'{o}rka in [M. Dybowski and P. G\'{o}rka, The axiom of choice in metric measure spaces and maximal \delta-separated sets, Arch

Robin Herkert, Patrick Buchfink, Bernard Haasdonk, Johannes Rettberg

Solving high-dimensional dynamical systems in multi-query or real-time applications requires efficient surrogate modelling techniques, as e.g., achieved via model order reduction (MOR). If these systems are Hamiltonian systems their physical structure should be preserved during the reduction, which can be ensured by applying symplectic basis generation techn

Shivasheesh Varshney, Martí Ramis, Sooho Choo, Mariona Coll

Water-soluble sacrificial layers based on epitaxially-grown, single crystalline (Ca, Sr, Ba)3Al2O6 layer are widely used for creating free-standing perovskite oxide membranes. However, obtaining these sacrificial layers with intricate stoichiometry remains a challenge, especially for molecular beam epitaxy (MBE). In this study, we demonstrate the hybrid MBE

Oumeng Zhang, Haowen Zhou, Brandon Y. Feng, Elin M. Larsson

Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction,

Yuan Li, Brendon J. Brewer, Geraint F. Lewis

We explore the globular cluster population of NGC 1052-DF4, a dark matter deficient galaxy, using Bayesian inference to search for the presence of rotation. The existence of such a rotating component is relevant to the estimation of the mass of the galaxy, and therefore the question of whether NGC 1052-DF4 is truly deficient of dark matter, similar to NGC 10

Fei Jiang, Yanyuan Ma

We study the well known difficult problem of prediction in measurement error models. By targeting directly at the prediction interval instead of the point prediction, we construct a prediction interval by providing estimators of both the center and the length of the interval which achieves a pre-determined prediction level. The constructing procedure require

Mohammad Amin Samadi, Spencer JaQuay, Jing Gu, Nia Nixon

AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create customized AI personas for diverse experimental setups using a user-friendly interface. This functionality is essential for simulating various interpersonal dynamics in team settings.

R. Ruffini, C. L. Bianco, M. Prakapenia, H. Quevedo

The concept of the irreducible mass ($M_{\rm irr}$) has led to the mass-energy ($M$) formula of a Kerr black hole (BH), in turn leading to its surface area $S=16\pi M_{\rm irr}^2$. This also allowed the coeval identification of the reversible and irreversible transformations, soon followed by the concepts of "extracted" and "extractable" energy. This new con