July 2023 arXiv papers — page 19

Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu

This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. Hallucination, wherein these models generate plausible yet unverified or incorrect information, can have serious consequences in healthcare applications. We propose a new benchmark and dataset, Med-HALT (Me

Maria Eckardt, Christina Surulescu

Starting from a mesoscopic description of cell migration and intraspecific interactions we obtain by upscaling an effective reaction-difusion-taxis equation for the cell population density involving spatial nonlocalities in the source term and biasing its motility and growth behavior according to environmental acidity. We prove global existence, uniqueness,

Camélia Guerraoui, Paul Reisert, Naoya Inoue, Farjana Sultana Mim

The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process. Although these models are useful for evaluating the quality of an argument, they oftentimes cannot explain why a particular argument is considered po

Benjamin Bode

Let $f:\mathbb{C}^2\to\mathbb{C}$ be an inner non-degenerate mixed polynomial with a nice Newton boundary with $N$ compact 1-faces. In the first part of this series of papers we showed that $f$ has a weakly isolated singularity and that its link can be constructed from a sequence of links $L_1, L_2,\ldots,L_N$, each of which is associated with a compact 1-fa

Le Gong, Shiying Li, Naqib Sad Pathan, Mohammad Shifat-E-Rabbi

Here we describe a new image representation technique based on the mathematics of transport and optimal transport. The method relies on the combination of the well-known Radon transform for images and a recent signal representation method called the Signed Cumulative Distribution Transform. The newly proposed method generalizes previous transport-related ima

Vishal D. Jadhav, Narahari N. Moudhgalya, Tapabrata Sen, T. V. Prabhakar

Intelligent Electronic Devices (IEDs) are vital components in modern electrical substations, collectively responsible for monitoring electrical parameters and performing protective functions. As a result, ensuring the integrity of IEDs is an essential criteria. While standards like IEC 61850 and IEC 60870-5-104 establish cyber-security protocols for secure i

Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides

Keisuke Kawata, Mizuki Komura

This study explores the marriage matching of only-child individuals and its outcome. Specifically, we analyze two aspects. First, we investigate how marital status (i.e., marriage with an only child, that with a non-only child and remaining single) differs between only children and non-only children. This analysis allows us to know whether people choose mate

Khiem Vinh Tran, Kiet Van Nguyen, Ngan Luu Thuy Nguyen

Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers. The English language, renowned for its wealth of resources, has witnessed notable advancements in both datasets and models designed for VQA. However, there is a lack of models th

Carlo Bellavita, Nikolaos Chalmoukis, Vassilis Daskalogiannis, Georgios Stylogiannis

For a finite, positive, Borel measure $\mu$ on $(0,1)$ we consider an infinite matrix $\Gamma_\mu$, related to the classical Hausdorff matrix defined by the same measure $\mu$, in the same algebraic way that the Hilbert matrix is related to the Ces\'aro matrix. When $\mu$ is the Lebesgue measure, $\Gamma_\mu$ reduces to the classical Hilbert matrix. We prove

Haotian Bai, Yiqi Lin, Yize Chen, Lin Wang

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. Howeve

Tadayoshi Adachi, Yuta Tsujii

We study one of multidimensional inverse scattering problems for quantum systems in a constant electric field, by utilization of the Enss-Weder time-dependent method. The main purpose of this paper is to propose some methods of sharpening key estimates in the analysis, which are much simpler than those in the previous works. Our methods give an appropriate c

Yun-Shiuan Chuang

This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs). The first tutorial explains BERT architecture and tokenization, guiding users through training, tuning, and evaluating standard and domain-specific BERT models with HuggingFace transformers. The second focuse

Younghoon Kim, Zachary F. Fisher, Vladas Pipiras

This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes inter-subject similarities and differences between two pre-determined groups by considering a combination of group spatial informat

Hiroshi Kobayashi, Hidekazu Tanaka

Thanks to ``dust-to-planet'' simulations (DTPSs), which treat the collisional evolution directly from dust to giant-planet cores in a protoplanetary disk, we showed that giant-planet cores are formed in $\lesssim 10\,$au in several $10^5$ years, because porous pebbles grow into planetesimals via collisions prior to drift in 10 au (Kobayashi & Tanaka 2021, Pa

Ashkan Zarezadeh, Behrooz Khosravi, Zeinab Akhlaghi

The average order of a finite group G is denoted by o(G). In this note, we classify groups whose average orders are less than o(S4), where S4 is the symmetric group on four elements. Moreover, we prove that G \cong S4 if and only if o(G) = o(S4). As a consequence of our results we give a characterization for some finite groups by the average order. In [9, Th

Jessie Thwaites, Aswathi Balagopal V., Sam Hori, M. J. Romfoe

Searches for neutrinos from gravitational wave events have been performed utilizing the wide energy range of the IceCube Neutrino Observatory. We discuss results from these searches during the third observing run (O3) of the advanced LIGO and Virgo detectors, including a low-latency follow-up of public candidate alert events in O3, an archival search on high

Yueh-Ning Ku, Mikhail Kuznetsov, Shaunak Mishra, Paloma de Juan

Online ads showing e-commerce products typically rely on the product images in a catalog sent to the advertising platform by an e-commerce platform. In the broader ads industry such ads are called dynamic product ads (DPA). It is common for DPA catalogs to be in the scale of millions (corresponding to the scale of products which can be bought from the e-comm

Sebastian Peitz, Hans Harder, Feliks Nüske, Friedrich Philipp

The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems. The main reason is the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. This equally applies to ordinary, stochastic, and partial differential equations (PDEs). Until now, with

Hanrong Ye, Dan Xu

Learning discriminative task-specific features simultaneously for multiple distinct tasks is a fundamental problem in multi-task learning. Recent state-of-the-art models consider directly decoding task-specific features from one shared task-generic feature (e.g., feature from a backbone layer), and utilize carefully designed decoders to produce multi-task fe

Cheng He, Jiaqi Liu, Changzheng Qu

In this paper the long-time dynamics of the massive Thirring model is investigated. Firstly the nonlinear steepest descent method for Riemann-Hilbert problem is explored to obtain the soliton resolution of the solutions to the massive Thirring model whose initial data belong to some weighted-Sobolev spaces. Secondly, the asymptotic stability of multi-soliton

Abhishek Desai, Jessie Thwaites, Justin Vandenbroucke

Astrophysical neutrinos detected by the IceCube observatory can be of Galactic or extragalactic origin. The collective contribution of all the detected neutrinos allows us to measure the total diffuse neutrino Galactic and extragalactic signal. In this work, we describe a simulation package that makes use of this diffuse Galactic contribution information to

Yutao Ma, Yong-Hua Mao, Siyu Wang

We consider $\beta$-Jacobi ensembles with parameters $p_1, p_2\geq n.$ We prove that the empirical measure of the rescaled Jacobi ensembles converges weakly to a modified Watcher law via the spectral measure method, which revisits the weak limits obtained in \cite{MaLDPJ} while replacing the condition $\beta n\!>\!> \log n$ by $\beta n\!>\!>1.$ We also provi

Ricardo Garcia, Robin Strudel, Shizhe Chen, Etienne Arlaud

Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR). While previous work mainly evaluate

Vinay Chamola, Gaurang Bansal, Tridib Kumar Das, Vikas Hassija

Imagine stepping into a virtual world that's as rich, dynamic, and interactive as our physical one. This is the promise of the Metaverse, and it's being brought to life by the transformative power of Generative Artificial Intelligence (AI). This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming

Charumathi Badrinath, Weiwei Pan, Finale Doshi-Velez

A common way to explore text corpora is through low-dimensional projections of the documents, where one hopes that thematically similar documents will be clustered together in the projected space. However, popular algorithms for dimensionality reduction of text corpora, like Latent Dirichlet Allocation (LDA), often produce projections that do not capture hum

Carys Chase-Mayoral, L. Q. English, Yeongjun Kim, Sanghoon Lee

We generate compact localized states in an electrical diamond lattice, comprised of only capacitors and inductors, via local driving near its flatband frequency. We compare experimental results to numerical simulations and find very good agreement. We also examine the stub lattice, which features a flatband of a different class where neighboring compact loca

Ziyang Zhou, Yingtie Lei, Xuhang Chen, Shenghong Luo

Shadows in scanned documents pose significant challenges for document analysis and recognition tasks due to their negative impact on visual quality and readability. Current shadow removal techniques, including traditional methods and deep learning approaches, face limitations in handling varying shadow intensities and preserving document details. To address

Kaipeng Zheng, Huishuai Zhang, Weiran Huang

Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories were not seen by the model before. This often leads to a relatively uniform distribution of feature values across channels on novel classes, posing challenges in determining channel importance for novel tasks. Standard few-shot learning methods employ geome

Hai Wu, Qunsong Zeng, Kaibin Huang

For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simultaneous downloading of diverse and high-dimensional models to multiple devices over wireless links presents a significant communication bottleneck. To overcome the bottleneck, we pr

Miran Jeong, Sejong Kim

As non-commutative versions of the quasi-arithmetic mean, we consider the Lim-P\'{a}lfia's power mean, R\'{e}nyi right mean and R\'{e}nyi power means. We prove that the Lim-P\'{a}lfia's power mean of order $t \in [-1,0)$ is weakly log-majorized by the log-Euclidean mean and fulfills the Ando-Hiai inequality. We establish the log-majorization relationship bet

Alessio Quinci, Gianmario Merisio, Francesco Topputo

Lagrangian descriptors reveal the dynamical skeleton governing transport mechanisms of a generic flow. In doing so, they unveil geometrical structures in the phase space that separate regions with different qualitative behavior. This work investigates to what extent Lagrangian descriptors provide information about non-Keplerian motion in Mars proximity, whic

Songnian Chen, Junlong Feng

We develop new changes-in-changes (CIC) and distributional synthetic controls (DSC) types of methods when there exists group-level heterogeneity. For CIC, we allow individuals to belong to heterogeneous groups, extending Athey and Imbens (2006) by finding appropriate control groups that share similar group-level unobserved characteristics to the treatment gr

Rui Li, Jacob Santiago, Daniel Salas, Ibrahim Karaman

We studied Ni50+xTi50-x with compositions up to x = 2, performing 47Ti and 49Ti nuclear magnetic resonance (NMR) measurements from 4 K to 400 K. For large x in this system, a strain-glass appears in which frozen ferroelastic nano-domains replace the displacive martensite structural transition. Here we demonstrate that NMR can provide an extremely effective p

Ou Zheng, Mohamed Abdel-Aty, Dongdong Wang, Chenzhu Wang

Large Language Models (LLMs) have shown remarkable effectiveness in various general-domain natural language processing (NLP) tasks. However, their performance in transportation safety domain tasks has been suboptimal, primarily attributed to the requirement for specialized transportation safety expertise in generating accurate responses [1]. To address this

Dong-Dong Pu, Ji-Guo Wang, Ya-Fei Song, Xiao-Dong Bai

The study of ultracold atomic spin systems with long-range interaction provides the possibility of searching for magnetic supersolid phases in quantum many-body scenarios. In this paper, we consider two-species Bose gases with spin-orbit coupling and nearest-neighbor interaction confined in a two-dimensional optical lattice. The competition between spin-orbi

Anna Gardner, Eric Baxter, Srinivasan Raghunathan, Weiguang Cui

Protoclusters are the progenitors of massive galaxy clusters. Understanding the properties of these structures is important for building a complete picture of cluster formation and for understanding the impact of environment on galaxy evolution. Future cosmic microwave background (CMB) surveys may provide insight into the properties of protoclusters via obse

S. Polosatkin

A new method for measuring the electron temperature of the plasma in GOL-NB facility is proposed. The proposed method is based on measuring the ratio of intensities of spectral lines emitted by fast atoms injected into the plasma. The beams of fast hydrogen atoms used for plasma heating or diagnostics contain atoms with full energy as well as atoms with frac

Woowon Byun, Minjin Kim, Yun-Kyeong Sheen, Dongseob Lee

We search for quasi-stellar objects (QSOs) in a wide area of the south ecliptic pole (SEP) field, which has been and will continue to be intensively explored through various space missions. For this purpose, we obtain deep broadband optical images of the SEP field covering an area of $\sim$$14.5\times14.5$ deg$^2$ with the Korea Microlensing Telescope Networ

Kenji Bekki, Takuji Tsujimoto

Recent JWST observations of the z=10.6 galaxy GN-z11 have revealed a very high gas-phase nitrogen abundance (higher than four times the solar value), a very small half-light radius(~ 60 pc), and a large stellar mass (M_s ~ 10^9 M_sun) for its size. We consider that this object is a forming galactic nucleus or ultra-compact dwarf galaxy rather than a proto gl

Guochao Sun, Claude-André Faucher-Giguère, Christopher C. Hayward, Xuejian Shen

Recent discoveries of a significant population of bright galaxies at cosmic dawn $\left(z \gtrsim 10\right)$ have enabled critical tests of cosmological galaxy formation models. In particular, the bright end of the galaxy UV luminosity function (UVLF) appears higher than predicted by many models. Using approximately 25,000 galaxy snapshots at $8 \leq z \leq

Hiroto Takada, Daiki Suenaga, Masayasu Harada, Atsushi Hosaka

Effects of the $U(1)_A$ axial anomaly on the mass spectrum of singly heavy baryons (SHBs) is studied in terms of the chiral effective theory based on the chiral linear representation for light flavors. We consider SHBs made of both three quarks ($Qqq$) and five quarks ($Qqqq\bar{q}$). For the three-quark SHBs we prove that the inverse mass hierarchy for the

Noriaki Kawaguchi

For any continuous self-map of a compact metric space, we consider the space of chain components and prove that the s-limit shadowing implies the denseness of chain components with the shadowing property. It gives a partial answer to a question raised by Moothathu [Topology Appl. 158 (2011) 2232--2239]. We also prove that the s-limit shadowing is not generic

Yoon-Soo Jang, Sunghoon Eom, Jungjae Park, Jonghan Jin

Comb-mode resolved spectral domain interferometry (CORE-SDI), which is capable of measuring length of kilometers or more with precision on the order of nanometers, is considered to be a promising technology for next-generation length standards, replacing laser displacement interferometers. In this study, we aim to improve the measurement precision of CORE-SD

Youjie Zhou, Guofeng Mei, Yiming Wang, Fabio Poiesi

This paper presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in dark environments. Existing methods typically rely solely on RGB images or fuse the modalities at later stages, which can result in lower accuracy when the RGB information is unrelia

Ruyi Liu, Jingzhi Tie, Zhen Wu, Qing Zhang

The focus of this paper is on identifying the most effective selling strategy for pairs trading of stocks. In pairs trading, a long position is held in one stock while a short position is held in another. The goal is to determine the optimal time to sell the long position and repurchase the short position in order to close the pairs position. The paper prese

Anuvab Sen, Arul Rhik Mazumder, Udayon Sen

Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper,

Philipp Eller, Martin Rongen

The IceCube Neutrino Observatory deployed 5160 digital optical modules (DOMs) in a cubic kilometer of deep, glacial ice below the geographic South Pole, recording the Cherenkov light of passing charged particles. While the optical properties of the undisturbed ice are nowadays well understood, the properties of the refrozen drill holes still pose a challenge

Takeshi Kato, Jyunichi Miyakoshi, Tadayuki Matsumura, Yasuyuki Kudo

The philosophical world has proposed the concept of "mixbiotic society," in which individuals with freedom and diverse values mix and mingle to recognize their respective "fundamental incapability" each other and sublimate into solidarity, toward solving the issues of social isolation and fragmentation. Based on this concept, the mixbiotic society measures h

Sarah Henry, Chenghao Wang

This study analyzes heat waves (HWs), air pollution (AP) episodes, and compound HW and AP events (CE) in the urban environment and provides a comparison between events in urban areas (UAs) and rural areas (RAs). A 1-km gridded daily minimum temperature dataset and a 1-km gridded daily PM2.5 concentration dataset were used along with geospatial data to charac

Zhiwei Chong

This paper examines the details of an inelastic collision when a bullet shoots a block vertically upward from below. With the assumption of constant interaction force between them, we obtain quantities of interest including the displacement for the block at the end of collision, the collision time, and in particular, the bullet's depth inside the block, whic

Philipp Eller, Kayla Leonard DeHolton, Jan Weldert, Rasmus Ørsøe

IceCube DeepCore, the existing low-energy extension of the IceCube Neutrino Observatory, was designed to lower the neutrino detection energy threshold to the GeV range. A new extension, called the IceCube Upgrade, will consist of seven additional strings installed within the DeepCore fiducial volume. The new modules will have spacings of about 20 m horizonta

Pawan Kumar, Paz Beniamini, Om Gupta, James M. Cordes

Most FRB models can be divided into two groups based on the distance of the radio emission region from the central engine. The first group of models, the so-called `nearby' or magnetospheric models, invoke FRB emission at distances of 10$^9$ cm or less from the central engine, while the second `far-away' models involve emission from distances of 10$^{11}$ cm

Te-Yu Chi, Yu-Meng Tang, Chia-Wen Lu, Qiu-Xia Zhang

Our research focuses on solving the zero-shot text classification problem in NLP, with a particular emphasis on innovative self-training strategies. To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time. Specifically, we use categories from Wikiped

Jonathan A. Hillman

We show that the homotopy type of a 4-manifold $M$ whose fundamental group is a finitely presentable $PD_3$-group $\pi$ and with $w_1(M)=w_1(\pi)$ is determined by $\pi$, $\pi_2(M)$, $k_1(M)$ and the equivariant intersection pairing $\lambda_M$.

Gregory LeClaire Wagner, Nick Pizzo, Luc Lenain, Fabrice Veron

A light breeze rising over calm water initiates an intricate chain of events that culminates in a centimeters-deep turbulent shear layer capped by gravity-capillary ripples. At first, viscous stress accelerates a laminar wind-drift layer until small surface ripples appear. Then a second "wave-catalyzed" instability grows in the wind-drift layer, before sharp

Cheng Wen, Xianghui Sun, Shuaijiang Zhao, Xiaoquan Fang

This paper presents the development and evaluation of ChatHome, a domain-specific language model (DSLM) designed for the intricate field of home renovation. Considering the proven competencies of large language models (LLMs) like GPT-4 and the escalating fascination with home renovation, this study endeavors to reconcile these aspects by generating a dedicat

Philipp Eller

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube -- Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutr

Samuel E. Otto, Gregory R. Macchio, Clarence W. Rowley

Recently developed reduced-order modeling techniques aim to approximate nonlinear dynamical systems on low-dimensional manifolds learned from data. This is an effective approach for modeling dynamics in a post-transient regime where the effects of initial conditions and other disturbances have decayed. However, modeling transient dynamics near an underlying

Zhiwei Chong

This paper aims to show how to guide students with a familiar example to extract as much physics as possible before jumping into mathematical calculation. The period for a physical pendulum made up of a uniform rod is changed by attaching a piece of putty on it. The period for the combined system depends on the location of the putty. Simple reasoning without

Sepehr Samavi, Florian Shkurti, Angela P. Schoellig

In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse Reinforcement Learning to model reward functions for an ego-car conducting a lane change in a highway setting. We define a new feature based on the unpredictability of surrounding cars and use it in t

Kang Liu, Jipeng Qiang, Yun Li, Yunhao Yuan

Lexical simplification (LS) methods based on pretrained language models have made remarkable progress, generating potential substitutes for a complex word through analysis of its contextual surroundings. However, these methods require separate pretrained models for different languages and disregard the preservation of sentence meaning. In this paper, we prop

Wei-Qi Peng, Yong Chen

In this work, we consider the Cauchy problem for the defocusing Hirota equation with a nonzero background \begin{align} \begin{cases} iq_{t}+\alpha\left[q_{xx}-2\left(\left\vert q\right\vert^{2}-1\right)q\right]+i\beta\left(q_{xxx}-6\left\vert q\right\vert^{2}q_{x}\right)=0,\quad (x,t)\in \mathbb{R}\times(0,+\infty),\\ q(x,0)=q_{0}(x),\qquad \underset{x\righ

Jonathan W. Siegel

We study the following two related problems. The first is to determine to what error an arbitrary zonoid in $\mathbb{R}^{d+1}$ can be approximated in the Hausdorff distance by a sum of $n$ line segments. The second is to determine optimal approximation rates in the uniform norm for shallow ReLU$^k$ neural networks on their variation spaces. The first of thes

Wanting Yang, Xuefen Chi, Linlin Zhao, Zehui Xiong

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the i

Aqin Xiao, Junfeng Yin

A class of averaging block nonlinear Kaczmarz methods is developed for the solution of the nonlinear system of equations. The convergence theory of the proposed method is established under suitable assumptions and the upper bounds of the convergence rate for the proposed method with both constant stepsize and adaptive stepsize are derived. Numerical experime

Chuyan Zhang, Yuncheng Yang, Hao Zheng, Yun Gu

Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations. Previous literature on model finetuning has mainly focused on regularization terms and specific policy models, while the misalignment of ch

K. Z. Nanjo, Y. Yamamoto, K. Ariyoshi, H. Horikawa

We studied the earthquake detection capacity of DONET (Dense Oceanfloor Network system for Earthquakes and Tsunamis) operating in the Nankai Trough, a target region monitored for future megathrust earthquakes. The focus of this paper was to evaluate the impact on this capacity from the malfunction of parts of the network. For this purpose, the completeness m

De-Ming Chian, Feng-Ji Chen, Yu-Chen Chang, Chao-Kai Wen

In this study, we develop an active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) prototype compliant with the 5G New Radio standard at 3.5~GHz. The experimental results clearly indicate that active RIS plays a vital role in enhancing MIMO performance, surpassing passiv

Piyush Jangid, Felix Ulrich Richter, Ming Lun Tseng, Ivan Sinev

High-index dielectric subwavelength structures and metasurfaces are capable of enhancing light-matter interaction by orders of magnitude via geometry-dependent optical resonances. This enhancement, however, comes with a fundamental limitation of a narrow spectral range of operation in the vicinity of one or few resonant frequencies. Here we tackle this limit

Christian Adamcewicz, Paul D. Lasky, Eric Thrane

The astrophysical origins of the binary black hole systems seen with gravitational waves are still not well understood. However, features in the distribution of black-hole masses, spins, redshifts, and eccentricities provide clues into how these systems form. Much has been learned by investigating these distributions one parameter at a time. However, we can

Vladimir Dzhunushaliev, Vladimir Folomeev, Yakov Shnir

We study fermionic modes localized on the static spherically symmetric self-gravitating non-Abelian monopole in the $SU(2)$ Einstein-Dirac-Yang-Mills-Higgs theory. We consider dependence of the spectral flow on the effective gravitational coupling constant and show that, in the limiting case of transition to the Reissner-Nordstr\"{o}m black hole, the fermion

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Thomas A. Maier

Motivated by the recently discovered high-$T_c$ superconductor La$_3$Ni$_2$O$_7$, we comprehensively study this system using density functional theory and random phase approximation calculations. At low pressures, the Amam phase is stable, containing the Y$^{2-}$ mode distortion from the Fmmm phase, while the Fmmm phase is unstable. Because of small differen

Shailja Thakur, Baleegh Ahmad, Hammond Pearce, Benjamin Tan

In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Veril

Jhon Manuel Portella Delgado, Ankit Goel

It is well known that zeros and poles of a single-input, single-output system in the transfer function form are the roots of the transfer function's numerator and the denominator polynomial, respectively. However, in the state-space form, where the poles are a subset of the eigenvalue of the dynamics matrix and thus can be computed by solving an eigenvalue p

Kentaro Iio, Gulshan Noorsumar, Dominique Lord, Yunlong Zhang

In recent years, passively recorded probe traffic volumes have increasingly been used to estimate traffic volumes. However, it is not always possible to count probe traffic volume in a spatial dataset when probe trajectories cannot be fully reconstructed from raw probe point location data due to sparse recording intervals, lack of pseudonyms or timestamps. A

J Bartlett, C E Davey, L A Johnston, J Duan

Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decreasing total imaging time. Diffusion acquisition invariant representations of the DWI signals are typically used as input to these methods to ensure that they can be applied flexibly

Chong Yao, Youjun Zhang

For enhancing power flow control in power transmission, a simplified new structure of direct power flow controller with continuous full regulation range (F-DPFC) was proposed. It has only one-stage power conversion and comprises of a three-phase transformer in parallel and a three-phase trans-former in series with grid, three single-phase full-bridge ac unit

Ke Yan, Dakai Jin, Dazhou Guo, Minfeng Xu

Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are small glands scattered throughout the body. They are grouped or defined to various LN stations according to their anatomical locations. The CT imaging appearance and context of LN

Jin Zhang, Jin Ke, Bing Wang, Ximing Chen

The compressive properties of metal-organic framework (MOF) crystals are not only crucial for their densification but also key in determining their performance in many applications. We herein investigated the mechanical responses of a classic crystalline MOF, HKUST-1 by using in situ compression tests. A serrated flow accompanied by the unique strain avalanc

Keyang Liu, Maxim Jourenko, Mario Larangeira

DAG-based consensus has attracted significant interest due to its high throughput in asynchronous network settings. However, existing protocols such as DAG-rider (Keidar et al., PODC 2021) and ``Narwhal and Tusk'' (Danezis et al., Eurosys 2022) face two undesired practical issues: (1) high transaction latency and (2) high cost to verify transaction outcomes.

Shuya Ota, Greg Christian, Ben J. Reed, Wilton N. Catford

BlueSTEAl, the Blue (aluminum chamber of) Silicon TElescope Arrays for light nuclei,has been developed to study direct reactions in inverse kinematics, as well as scattering and breakup reactions using radioactive ion beams. It is a detector system consisting of a pair of annular silicon detector arrays and a zero-degree phoswich plastic scintillator. For ty

Hoseung Song, Michael C. Wu

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether the relationships between two

Cécile Huneau, Annalaura Stingo, Zoe Wyatt

In this paper we show the classical global stability of the flat Kaluza-Klein spacetime, which corresponds to Minkowski spacetime in $\m R^{1+4}$ with one direction compactified on a circle. We consider small perturbations which are allowed to vary in all directions including the compact direction. These perturbations lead to the creation of massless modes a

Yuan Hu, Jianlong Yuan, Congcong Wen, Xiaonan Lu

The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision language models (VLMs) specifically t

Mohammadhasan Dinpajooh, Elisa Biasin, Christopher J. Mundy, Gregory K. Schenter

Ion-ion correlations, screening, and equilibrium bulk structure in various concentrated electrolytes are investigated using synchrotron small angle X-ray scattering (SAXS), theory, and molecular simulation. Utilizing SAXS measurements we provide estimates of the Kirkwood Transition (KT) for a variety of aqueous electrolytes (NaCl, CaCl$_2$, SrCl$_2$, and ErC

Lei Cheng, Arindam Sengupta, Siyang Cao

Advances in autonomous driving are inseparable from sensor fusion. Heterogeneous sensors are widely used for sensor fusion due to their complementary properties, with radar and camera being the most equipped sensors. Intrinsic and extrinsic calibration are essential steps in sensor fusion. The extrinsic calibration, independent of the sensor's own parameters

Xichao Zhang, Jing Xia, Oleg A. Tretiakov, Guoping Zhao

Nanoscale magnetic domains with controllable configurations could be used for classical and quantum applications, where the switching of magnetization configurations is an essential operation for information processing. Here, we report that the magnetic domain reorientation in a notched ferromagnetic nanotrack can be realized and effectively controlled by ap

Rishabh Singh Chauhan, Uttara Sutradhar, Anton Rozhkov, Sybil Derrible

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables bo

Takahiro Sanada, Ryota Kojima, Yuichi Komorida, Koko Muroya

Algorithms for partition refinement are actively studied for a variety of systems, often with the optimisation called Hopcroft's trick. However, the low-level description of those algorithms in the literature often obscures the essence of Hopcroft's trick. Our contribution is twofold. Firstly, we present a novel formulation of Hopcroft's trick in terms of ge

Yiren Lu, Guojing Tian, Xiaoming Sun

Maximum cut (Max-Cut) problem is one of the most important combinatorial optimization problems because of its various applications in real life, and recently Quantum Approximate Optimization Algorithm (QAOA) has been widely employed to solve it. However, as the size of the problem increases, the number of qubits required will become larger. With the aim of s

Jennifer Hults, Karin Reinhold-Larsson

$T$ is a Ritt operator in $L^p$ if $\sup_n n\|T^n-T^{n+1}\|<\infty$. From \cite{LeMX-Vq}, if $T$ is a positive contraction and a Ritt operator in $L^p$, $1<p<\infty$, the square function $\left( \sum_n n^{2m+1} |T^n(I-T)^{m+1}f|^2 \right)^{1/2}$ is bounded. We show that if $T$ is a Ritt operator in $L^1$, \[Q_{\alpha,s,m}f=\left( \sum_n n^{\alpha} |T^n(I-T)^

Yung-Cheng Li, Yu-Hao Huang, Chao-Chung Huang, Yan-Ting Liu

It is crucial to realize field-free, deterministic, current-induced switching in spin-orbit torque magnetic random-access memory (SOT-MRAM) with perpendicular magnetic anisotropy (PMA). A tentative solution has emerged recently, which employs the interlayer chiral exchange coupling or the interlayer Dzyaloshinskii-Moriya interaction (i-DMI) to achieve symmet

Yahui Fu, Koji Inoue, Chenhui Chu, Tatsuya Kawahara

Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user's experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user's perspective, ignoring the system's perspective. In this paper, we pr

Risheng Liu, Jiaxin Gao, Xuan Liu, Xin Fan

The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified pe

Jiale Linghu, Hao Dong, Junzhi Cui, Yufeng Nie

The direct deep learning simulation for multi-scale problems remains a challenging issue. In this work, a novel higher-order multi-scale deep Ritz method (HOMS-DRM) is developed for thermal transfer equation of authentic composite materials with highly oscillatory and discontinuous coefficients. In this novel HOMS-DRM, higher-order multi-scale analysis and m

Yifei Yang, Hangdong Zhao, Xiangyao Yu, Paraschos Koutris

This paper presents predicate transfer, a novel method that optimizes join performance by pre-filtering tables to reduce the join input sizes. Predicate transfer generalizes Bloom join, which conducts pre-filtering within a single join operation, to multi-table joins such that the filtering benefits can be significantly increased. Predicate transfer is inspi

Wenhao Tang, Sheng Huang, Xiaoxian Zhang, Fengtao Zhou

The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI, existing MIL methods intuitively focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecti

Izuru Mori, Adam Nyman

In this paper, we study a generalization of the notion of AS-regularity for connected $\mathbb{Z}$-algebras. Our main result is a characterization of those categories equivalent to noncommutative projective schemes associated to right coherent regular $\mathbb{Z}$-algebras, which we call quantum projective $\mathbb{Z}$-spaces in this paper. As an application

Yibo Zhou, Hai-Miao Hu, Jinzuo Yu, Zhenbo Xu

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto s