July 2023 arXiv papers — page 127

Sandipan Mohanta, Bijay Kumar Agarwalla

We consider a generic four-stroke quantum Otto engine consisting of two unitary and two thermalization strokes with an arbitrary many-body working medium. Using the Schwinger-Keldysh non-equilibrium Green's function formalism, we provide an analytical expression for the cumulant generating function corresponding to the joint probability distribution of non-e

Zhun Yang, Joohyung Lee, Chiyoun Park

Injecting discrete logical constraints into neural network learning is one of the main challenges in neuro-symbolic AI. We find that a straight-through-estimator, a method introduced to train binary neural networks, could effectively be applied to incorporate logical constraints into neural network learning. More specifically, we design a systematic way to r

Vasudev Vikram, Caroline Lemieux, Joshua Sunshine, Rohan Padhye

Property-based testing (PBT), while an established technique in the software testing research community, is still relatively underused in real-world software. Pain points in writing property-based tests include implementing diverse random input generators and thinking of meaningful properties to test. Developers, however, are more amenable to writing documen

Saurabh Kumar, Henrik Marklund, Ashish Rao, Yifan Zhu

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities. The design of such agents, which remains a long-standing challenge of artificial intelligence, is addressed by the subject of continual learning. This monograph clarifies and for

Yan He, Dai Li, Zhanghao Yu, Kaiyuan Yang

Physically unclonable functions (PUFs) are increasingly adopted for low-cost and secure secret key and chip ID generations for embedded and IoT devices. Achieving 100% reproducible keys across wide temperature and voltage variations over the lifetime of a device is critical and conventionally requires large masking or Error Correction Code (ECC) overhead to

Darij Grinberg, Richard P. Stanley

Let $D=\left( V,A\right) $ be a digraph with $n$ vertices, where each arc $a\in A$ is a pair $\left( u,v\right) $ of two vertices. We study the \emph{Redei--Berge symmetric function} $U_{D}$, defined as the quasisymmetric function% \[ \sum L_{\operatorname*{Des}\left( w,D\right) ,\ n}\in\operatorname*{QSym}. \] Here, the sum ranges over all lists $w=\left( w

Haixing Dai, Lu Zhang, Lin Zhao, Zihao Wu

With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these methods can only discover the patterns or rule

Kangheun Kim, Fan Yang, Klaus Mølmer, Jaewook Ahn

Strong mutual interactions correlate elementary excitations of quantum matter and plays a key role in a range of emergent phenomena, from binding and condensation to quantum thermalization and many-body localization. Here, we employ a Rydberg quantum simulator to experimentally demonstrate strongly correlated spin transport in anisotropic Heisenberg magnets,

Meng Li, Yahan Yu, Yi Yang, Guanghao Ren

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In t

Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo

The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating and predicting inorganic crystal structures is important for the discovery of new materials, which can target applications such as energy or electronic devices. However, most current

Zhihe Zhao, Neiwen Ling, Nan Guan, Guoliang Xing

Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with varying levels of criticality on edge GPUs remains an area of limited study. Unlike server-level GPUs, edge G

Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex,

Yuta Hirasaki, Shunsuke Daimon, Toshinari Itoko, Naoki Kanazawa

We have investigated instability of a superconducting quantum computer by continuously monitoring the qubit output. We found that qubits exhibit a step-like change in the error rates. This change is repeatedly observed, and each step persists for several minutes. By analyzing the correlation between the increased errors and anomalous variance of the output,

Tsai Hor Chan, Chi Ho Wong, Jiajun Shen, Guosheng Yin

Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing HIN representation learning methods can effectively learn the semantic and structural features in the network, little awareness was given to the distribution discrepancy of subgrap

Laurent Bulteau, Louxin Zhang

One strategy for reconstruction of phylogenetic networks is to solve the phylogenetic network problem, which involves inferring phylogenetic trees first and subsequently computing the smallest phylogenetic network that displays all the trees. This approach capitalizes on exceptional tools available for inferring phylogenetic trees from biomolecular sequences

Rui Chen, Bin Zhou, Dong-Hui Xu

Three-dimensional higher-order topological semimetals in crystalline systems exhibit higher-order Fermi arcs on one-dimensional hinges, challenging the conventional bulk-boundary correspondence. However, the existence of higher-order Fermi arc states in aperiodic quasicrystalline systems remains uncertain. In this work, we present the emergence of three-dime

Boya Zhang, Weijian Luo, Zhihua Zhang

Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial intelligence. Recent studies have suggested that score-based diffusion models are effective in adversarial defenses. Howe

Yiwen Ding

Let $D_1\subset D_2$ be $(\varphi, \Gamma)$-modules of rank $2$ over the Robba ring, and $\pi(D_1)$, $\pi(D_2)$ be the associated locally analytic representations of $\rm{GL}_2(\mathbb{Q}_p)$ via the $p$-adic local Langlands correspondence. We describe the relation between $\pi(D_1)$ and $\pi(D_2)$.

Yuxin Gou, Junrong Zhang, Akihito Kiyama, Zhao Pan

The dynamics of cavitation bubbles in the vicinity of a solid cylinder or fibre are seen in water treatment, demolition and/or cleaning of composite materials, as well as bio-medical scenarios such as ultrasound-induced bubbles near the tubular structures in the body. When the bubble collapses near the surface, violent fluid jets may be generated. Understand

Seulip Lee, Lin Mu

This paper presents a pressure-robust enriched Galerkin (EG) method for the Brinkman equations with minimal degrees of freedom based on EG velocity and pressure spaces. The velocity space consists of linear Lagrange polynomials enriched by a discontinuous, piecewise linear, and mean-zero vector function per element, while piecewise constant functions approxi

Sepideh Mahabadi, Shyam Narayanan

In this work we consider the diversity maximization problem, where given a data set $X$ of $n$ elements, and a parameter $k$, the goal is to pick a subset of $X$ of size $k$ maximizing a certain diversity measure. [CH01] defined a variety of diversity measures based on pairwise distances between the points. A constant factor approximation algorithm was known

Chak Lam Shek, Guangyao Shi, Ahmad Bilal Asghar, Pratap Tokekar

We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic wind disturbances. In prior work on mobile sensor routing problems, sensors and their carrier are one integrated platform, and sensors are assumed to be able to take measurements at

Xiaohan Ma, Wenhao Yu, Chengxiang Zhao, Changjun Wang

Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even

Yanbing Li, Weichuan Zhang, Lianying Ji

With the development of autonomous driving technology, automotive radar has received unprecedented attention due to its day-and-night and all-weather working capability. It is worthwhile to note that more and more vehicles are equipped with automotive radars, resulting in mutual interference between radars. The interference reduces radar target detection per

M. Sharif, Tayyab Naseer

This paper studies charged star models associated with anisotropic matter distribution in $f(\mathcal{R},\mathcal{T},\mathcal{Q})$ theory, where $\mathcal{Q}=\mathcal{R}_{\phi\psi}\mathcal{T}^{\phi\psi}$. For this purpose, we take a linear model of this gravity as $\mathcal{R}+\zeta\mathcal{Q}$, where $\zeta$ represents a coupling constant. We consider a sel

Xin-Qiang Li, Li-Juan Liu, En Wang, Le-Le Wei

We study the $B^- \to K^- \eta \eta_c$ decay by taking into account the $S$-wave contributions from the pseudoscalar meson-pseudoscalar meson interactions within the unitary coupled-channel approach, where the $D\bar{D}$ bound state is dynamically generated. In addition, the contribution from the intermediate resonance $K_0^*(1430)^-$, with $K_0^*(1430)^- \t

Yuan Gao, Weijun Fang, Jingke Xu, Dong Wang

Locally repairable codes (LRCs) have attracted a lot of attention due to their applications in distributed storage systems. In this paper, we provide new constructions of optimal $(2, \delta)$-LRCs over $\mathbb{F}_q$ with flexible parameters. Firstly, employing techniques from finite geometry, we introduce a simple yet useful condition to ensure that a punc

Longbin Li, Chao Zhang, Sen Li, Yun Zhong

In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by the representation learning paradigm, e.g., embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods do not sufficiently exploit the useful collaborative signal

Hyesu Jang, Minwoo Jung, Ayoung Kim

Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance d

Kamalesh Jana, Amit D. Lad, Guo-Bo Zhang, Bo-Yuan Li

We investigate the production of hot electrons from the interaction of relativistically intense ($I> 10^{18} W/cm^{2}$) ultra-short (25 fs) laser pulses with sub-wavelength grating target. We measure the hot electron angular distribution and energy spectra for grating target and compare them with those from a planar mirror target. We observe that hot electro

Hamid Nazari

The co-localization problem is a model that simultaneously localizes objects of the same class within a series of images or videos. In \cite{joulin2014efficient}, authors present new variants of the Frank-Wolfe algorithm (aka conditional gradient) that increase the efficiency in solving the image and video co-localization problems. The authors show the effic

Feiyu Jiang, Changbo Zhu, Xiaofeng Shao

Data objects taking value in a general metric space have become increasingly common in modern data analysis. In this paper, we study two important statistical inference problems, namely, two-sample testing and change-point detection, for such non-Euclidean data under temporal dependence. Typical examples of non-Euclidean valued time series include yearly mor

Zhili Feng, Anna Bair, J. Zico Kolter

Modern image classification is based upon directly predicting classes via large discriminative networks, which do not directly contain information about the intuitive visual features that may constitute a classification decision. Recently, work in vision-language models (VLM) such as CLIP has provided ways to specify natural language descriptions of image cl

Xiangman Li, Jianbing Ni

Secure data deletion enables data owners to fully control the erasure of their data stored on local or cloud data centers and is essential for preventing data leakage, especially for cloud storage. However, traditional data deletion based on unlinking, overwriting, and cryptographic key management either ineffectiveness in cloud storage or rely on unpractica

Yun-Ling Chen, Tian-Run Lin, Guang-Yuan Zhang

In this paper, we will prove a result which is used by Guang-Yuan Zhang in another paper in which the existence of extremal surfaces for covering surfaces is proved and the sharp form of Ahlfors' Second Fundamental Theorem is given.

Dmitriy Bilyk, Alan Chang, Otte Heinävaara, Ryan W. Matzke

We consider random lines in $\mathbb{R}^3$ (random with respect to the kinematic measure) and how they intersect $\mathbb{S}^2$. It is known that the entry point and the exit point behave like \textit{independent} uniformly distributed random variables. We give a new proof using bilinear integral geometry and use this approach to show that this property is e

Jie Wu, Jin Li

There are tens of millions of compact binary systems in the Milky Way, called galactic binaries (GBs), most of which are unresolved, and the Gravitational waves (GWs) emitted overlap to form foreground confusion. By simulating such foreground confusion, we have studied how LISA, Taiji and TianQin, including their alternative orbital configurations, subtract

Subhankar Dey, Veronica King, Colby T. Shaw, Bülent Tosun

We consider homologically essential simple closed curves on Seifert surfaces of genus one knots in $S^3$, and in particular those that are unknotted or slice in $S^3$. We completely characterize all such curves for most twist knots: they are either positive or negative braid closures; moreover, we determine exactly which of those are unknotted. A surprising

Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an eff

Guang-Juan Wang, Makoto Oka, Daisuke Jido

A new color basis system and confinement mechanism for multi-quark systems are proposed according to the string-type picture of QCD. The color string configurations in the strong coupling QCD are implemented in the set of color basis states. The extended color Hilbert space for $QQ\bar Q\bar Q$ systems includes a ''hidden color'' state, which mixes with two-

SiHun Lee, Sangmin Lee, Kijoo Jang, Haeseong Cho

A data-driven parametric model order reduction (MOR) method using a deep artificial neural network is proposed. The present network, which is the least-squares hierarchical variational autoencoder (LSH-VAE), is capable of performing nonlinear MOR for the parametric interpolation of a nonlinear dynamic system with a significant number of degrees of freedom. L

Éva Czabarka, Junsheng Liu, László A. Székely

A tanglegram $\cal T$ consists of two rooted binary trees with the same number of leaves, and a perfect matching between the two leaf sets. In a layout, the tanglegrams is drawn with the leaves on two parallel lines, the trees on either side of the strip created by these lines are drawn as plane trees, and the perfect matching is drawn in straight line segme

Chao Ye, Guoshan Lu, Haobo Wang, Liyao Li

Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information. Given the recent influence of large-scale pretrained models like ChatGPT and SAM across various domains, exploring the application of pretraining techniques for mining tabular data on the web has emerged as a h

Katsunori Kubo

We investigate the Hubbard model with the Rashba spin-orbit coupling on a square lattice. The Rashba spin-orbit coupling generates two-dimensional Weyl points in the band dispersion. In a system with edges along [11] direction, zero-energy edge states appear, while no edge state exists for a system with edges along an axis direction. The zero-energy edge sta

Juan Camilo Arias, Vyacheslav Futorny, André de Oliveira

For an arbitrary affine Lie algebra we study an analog of the category O for the natural Borel subalgebra and zero central charge. We show that such category is semisimple having the reduced imaginary Verma modules as its simple objects. This generalizes the result of Cox, Futorny, Misra in the case of affine sl(2).

Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida

Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content. In this case, we may rely on the capability of self-attention mechanism in Transformers to capture these long-term dependencies

Yuwen Cheng, Lili Wu, Shu Yang

Randomized experiments (REs) are the cornerstone for treatment effect evaluation. However, due to practical considerations, REs may encounter difficulty recruiting sufficient patients. External controls (ECs) can supplement REs to boost estimation efficiency. Yet, there may be incomparability between ECs and concurrent controls (CCs), resulting in misleading

Di Lu, Shihao Ran, Joel Tetreault, Alejandro Jaimes

Recent work on Event Extraction has reframed the task as Question Answering (QA), with promising results. The advantage of this approach is that it addresses the error propagation issue found in traditional token-based classification approaches by directly predicting event arguments without extracting candidates first. However, the questions are typically ba

Anthony Sicilia, Malihe Alikhani

The ingrained principles of fairness in a dialogue system's decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. For example, misusing

Pinyan Lu, Chenyang Xu, Ruilong Zhang

The paper designs revenue-maximizing auction mechanisms for agents who aim to maximize their total obtained values rather than the classical quasi-linear utilities. Several models have been proposed to capture the behaviors of such agents in the literature. In the paper, we consider the model where agents are subject to budget and return-on-spend constraints

Adnan Eghtesad, Jan Niklas Fuhg, Nikolaos Bouklas

We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP) constitutive modeling framework for predicting the flow response in metals as a function of underlying grain size. The developed NN-EVP algorithm is based on input convex neural networks as a means to strictly enforce thermodynamic consistency, while allowing high expressivit

Yan Liang, Ming-Jie Liang, Sai Li, Z. D. Wang

High-fidelity universal quantum gates are widely acknowledged as essential for scalable quantum computation. However, in solid-state quantum systems, which hold promise as physical implementation platforms for quantum computation, the inevitable $ZZ$ crosstalk resulting from interqubit interactions significantly impairs quantum operation performance. Here we

Minu J. Bae, Nitish K. Panigrahy, Prajit Dhara, Walter O. Krawec

Free-space satellite communication has significantly lower photon loss than terrestrial communication via optical fibers. Satellite-based quantum key distribution (QKD) leverages this advantage and provides a promising direction in achieving long-distance inter-continental QKD. Satellite channels, however, can be highly dynamic due to various environmental f

Oleg P. Sushkov

Stimulated by recent suggestion of Cosmic Axion Spin Precession Experiment with Eu contained compound we develop a new method for accurate calculation of Schiff moments of even-odd deformed nuclei. The method is essentially based on experimental data on magnetic moments and E1,E3-amplitudes in the given even-odd nucleus and in adjacent even-even nuclei. Unfo

YeongHyeon Park, Uju Gim, Myung Jin Kim

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for eff

Jiazheng Zhu, Ning Jiang, Tinggui Wang, Shifeng Huang

We report the discovery of a faint optical tidal disruption event (TDE) in the nearby star-forming galaxy NGC 3799. Identification of the TDE is based on its position at the galaxy nucleus, a light curve declining as t^-5/3, a blue continuum with an almost constant blackbody temperature of ~12,000K, and broad (~15,000kms^-1) Balmer lines and characteristic H

Guoyang Xie, Jinbao Wang, Yawen Huang, Jiayi Lyu

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this c

O. S. Stashko, O. V. Savchuk, V. I. Zhdanov

We study linear perturbations against static spherically symmetric background configurations of General Relativity with a real scalar field (SF), which is minimally coupled with gravity; it is non-linear due to the presence of the self-action potential. The background solutions have a naked singularity at the center of the configuration. The focus is on the

Yash Aggarwal

The Bondi accretion rate of black holes in our and nearby galaxies Messier 87, NGC 3115, NGC 1600, and Cygnus A have been determined or constrained using Chandra or other observations. It, however, remains unknown how much mass from the Bondi radius reaches each black hole and how much is accreted. We determine the accretion rate and radiative efficiency for

Tomas Kojar

In this article, we study the analogue of the integration by parts formula from "Hitting times for Gaussian processes" in the context of GMC and its inverse.

Sangshin Oh, Minsung Kang, Hyeongi Moon, Keunwoo Choi

To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professional audio engineers, in order to determine research priorities and define various research tasks. We also summarize the current challenges in audio quality and controllability base

Wen Siang Tan, Markus Wagner, Christoph Treude

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to a

Konstantinos Mamis, Mohammad Farazmand

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social b

Vincent Graves, Christoph Sünderhauf, Nick S. Blunt, Róbert Izsák

In this educational paper, we will discuss calculations on the hydrogen molecule both on classical and quantum computers. In the former case, we will discuss the calculation of molecular integrals that can then be used to calculate potential energy curves at the Hartree--Fock level and to correct them by obtaining the exact results for all states in the mini

Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou

Today's online advertisers procure digital ad impressions through interacting with autobidding platforms: advertisers convey high level procurement goals via setting levers such as budget, target return-on-investment, max cost per click, etc.. Then ads platforms subsequently procure impressions on advertisers' behalf, and report final procurement conversions

Yuma Nishida, Masahiro Shimizu, Tatsuya Okuno, Jun Matsuoka

The Soret effect, temperature gradient driven diffusion, in silicate melts has been investigated intensively in the earth sciences from the 1980s. The SiO2 component is generally concentrated in the hotter region of silicate melts under a temperature gradient. Here, we report that at ultra-high temperatures above approximately 3000 K, SiO2 becomes concentrat

Gunhee Cho, David R. Morrison

We show that the non-measure hyperbolicity of K3 surfaces -- which M. Green and P. Griffiths verified for certain cases in 1980 -- holds for all K3 surfaces. As a byproduct, we prove the non-measure hyperbolicity of any Hilbert schemes of points on K3 surfaces. We also obtain a new proof of the non-measure hyperbolicity of any Enriques surface.

Zijie Huang, Yizhou Sun, Wei Wang

Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across d

Yalu Zhu, Feng Liu, William A. Sirignano

A finite-volume method for the steady, compressible, reacting, turbulent Navier-Stokes equations is developed by using a steady-state preserving splitting scheme for the stiff source terms in chemical reaction. Laminar and turbulent reacting flows in a mixing layer with large streamwise pressure gradient are studied and compared to boundary-layer solutions.

Soyoung Yang, Minseok Choi, Youngwoo Cho, Jaegul Choo

Despite the extensive applications of relation extraction (RE) tasks in various domains, little has been explored in the historical context, which contains promising data across hundreds and thousands of years. To promote the historical RE research, we present HistRED constructed from Yeonhaengnok. Yeonhaengnok is a collection of records originally written i

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren

Human collective tasks in teams and organizations increasingly require participation of members with diverse backgrounds working in networked social environments. However, little is known about how network structure and the functional diversity of member backgrounds would interact with each other and affect collective processes. Here we conducted three sets

Ruby Annette, Aisha Banu, Sharon Priya, Subash Chandran

Cloud render farm services are the animation domain specific cloud services Platform-as-a-Service (PaaS) type of cloud services that provides a complete platform to render the animation files. However, identifying the render farm services that is cost effective and also matches the functional requirements that changes for almost every project like the animat

Qi-Jun Hong

Empowering the creation of thermodynamic and property databases, the CALPHAD (CALculation of PHAse Diagrams) methodology plays a vital role in enhancing materials and manufacturing process design. In this study, we propose a deep learning approach to train parameters in CALPHAD models solely based on chemical formula. We demonstrate its application through a

Jiahang Li, Hitoshi Mikada, Junichi Takekawa

Full-waveform inversion (FWI) is known as a seismic data processing method that achieves high-resolution imaging. In the inversion part of the method that brings high resolution in finding a convergence point in the model space, a local numerical optimization algorithm minimizes the objective function based on the norm using the least-square form. Since the

Iulia Cristian, Juan J. L. Velázquez

In this work, we study the long time asymptotics of a coagulation model which describes the evolution of a system of particles characterized by their volume and surface area. The aggregation mechanism takes place in two stages: collision and fusion of particles. During the collision stage, the two particles merge at a contact point. The newly formed particle

Haicheng Mei, Jingsong Gao, Kailu Wang, Jiahao Dong

Nitrogen ions pumped by intense femtosecond laser pulses give rise to optical amplification in the ultraviolet range. Here, we demonstrated that a seed light pulse carrying orbital angular momentum (OAM) can be significantly amplified in nitrogen plasma excited by a Gaussian femtosecond laser pulse. With the topological charge of +1 and -1, we observed an en

Hokuto Uehara, Tomonobu Watanabe

The first author explicitly describes the set of Fourier--Mukai partners of elliptic ruled surfaces over the complex number field in \cite{Ue17}. In this article, we generalize it over arbitrary characteristic fields. We also obtain a partial evidence of the Popa--Schnell conjecture in the proof.

Zachary P. Bradshaw, Christophe Vignat

We contribute to the zoo of dubious identities established by J.M. and P.B. Borwein in their 1992 paper, "Strange Series and High Precision Fraud" with five new entries, each of a different variety than the last. Some of these identities are again a high precision fraud and picking out the true from the bogus can be a challenging task with many unexpected tw

Hong Shen, Tianshi Li, Toby Jia-Jun Li, Joon Sung Park

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regardi

Boris Kruglikov, Omid Makhmali

We extend the recent paradigm "Integrability via Geometry" from dimensions 3 and 4 to higher dimensions, relating dispersionless integrability of partial differential equations to curvature constraints of the background geometry. We observe that in higher dimensions on any solution manifold the symbol defines a vector distribution equipped with a subconforma

Xiangyue Cui, Xuefei Yan, Bowen Wang, Yongqing Cai

Molybdenum ditelluride (MoTe2) is an unique transition metal dichalcogenide owing to its energetically comparable 1H and 1T prime phases. This implies a high chance of coexistence of 1H-1T prime heterostructures which poses great complexity in the measurement of the intrinsic lattice thermal conductivities (kappa). In this work, via first-principles calculat

Jhonathan O. Murcia Piñeros, Rodolpho Vilhena de Moraes, Antônio F. Bertachini de Almeida Prado

The Gravity Assisted Maneuver has been applied in lots of space missions, to change the spacecraft heliocentric velocity vector and the geometry of the orbit, after the close approach to a celestial body, saving propellant consumption. It is possible to take advantage of additional forces to improve the maneuver, like the forces generated by the spacecraft-a

Yann Hicke, Tonghua Tian, Karan Jha, Choong Hee Kim

Automated Essay scoring has been explored as a research and industry problem for over 50 years. It has drawn a lot of attention from the NLP community because of its clear educational value as a research area that can engender the creation of valuable time-saving tools for educators around the world. Yet, these tools are generally focused on detecting good g

Jhonathan O. Murcia Piñeros, Antônio F. Bertachini de Almeida Prado, Walter Abrahão dos Santos, Rodolpho Vilhena de Moraes

The perturbative integral method was applied to quantify the contribution of external forces during a specific interval of time in trajectories of spacecraft around asteroids and under the Luni-solar influence. However, this method has not been used to quantify the contributions of drag in aerocapture and aerobraking. For this reason, the planet Mars is sele

Jie S. Li, Yow-Ting Shiue, Yong-Siang Shih, Jonas Geiping

This paper describes our zero-shot approaches for the Visual Word Sense Disambiguation (VWSD) Task in English. Our preliminary study shows that the simple approach of matching candidate images with the phrase using CLIP suffers from the many-to-many nature of image-text pairs. We find that the CLIP text encoder may have limited abilities in capturing the com

Yann Hicke, Abhishek Masand, Wentao Guo, Tushaar Gangavarapu

(Tack et al., 2023) organized the shared task hosted by the 18th Workshop on Innovative Use of NLP for Building Educational Applications on generation of teacher language in educational dialogues. Following the structure of the shared task, in this study, we attempt to assess the generative abilities of large language models in providing informative and help

Manan Shukla, Oshani Seneviratne

Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devic

E. Nácher, J. A. Briz, A. N. Nerio, A. Perea

Treatment planning systems at proton-therapy centres generally use X-ray computed tomography (CT) as primary imaging technique to infer the proton treatment doses to tumour and healthy tissues. However, proton stopping powers in the body, as derived from X-ray images, suffer from important proton-range uncertainties. In order to reduce this uncertainty in ra

Bhavin Jawade, Deen Dayal Mohan, Srirangaraj Setlur, Nalini Ratha

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availability of large scale real-world datasets

Victor Aguirregabiria, Francis Guiton

This paper investigates the impact of decentralizing inventory decision-making in multi-establishment firms using data from a large retail chain. Analyzing two years of daily data, we find significant heterogeneity among the inventory decisions made by 634 store managers. By estimating a dynamic structural model, we reveal substantial heterogeneity in manage

D. Xiao, H. B. Tran Tan, A. Derevianko

The Stark-interference technique is commonly used to amplify the feeble parity-violating signal in atomic experiments. As a result, interpretation of these experiments in terms of electroweak observables requires knowledge of the Stark-induced $E1$ transition amplitudes or, equivalently, transition polarizabilities. While the literature assumes that these tr

Zhaoyang Qiu, Hui Liu, Chengfeng Sun

In this paper, we consider the large deviations of invariant measure for the 3D stochastic hyperdissipative Navier-Stokes equations driven by additive noise. The unique ergodicity of invariant measure as a preliminary result is proved using a deterministic argument by the exponential moment and exponential stability estimates. Then, the uniform large deviati

Jan A Bergstra, John V Tucker

We analyse abstract data types that model numerical structures with a concept of error. Specifically, we focus on arithmetic data types that contain an error value $\bot$ whose main purpose is to always return a value for division. To rings and fields, we add a division operator $x/y$ and study a class of algebras called common meadows wherein $x/0 = \bot$.

V. G. Gueorguiev, A. Maeder

The Scale Invariant Vacuum (SIV) paradigm is applied to the Big-Bang Nucleosynthesis using the known analytic expressions for the expansion factor $a$ and the plasma temperature $T$ as functions of the SIV time $\tau$ since the Big-Bang when $a(\tau=0)=0$. The results are compared to the known standard BBNS model as calculated with the PRIMAT code. Potential

Martik Aghajanian, Arash A. Mostofi, Johannes Lischner

We present theoretical calculations of the optical spectrum of monolayer MoS$_2$ with a charged defect. In particular, we solve the Bethe-Salpeter equation based on an atomistic tight-binding model of the MoS$_2$ electronic structure which allows calculations for large supercells. The defect is modelled as a point charge whose potential is screened by the Mo

Subhayan Sahu, Shao-Kai Jian

We study the emergence of anticoncentration and approximate unitary design behavior in local Brownian circuits. The dynamics of circuit averaged moments of the probability distribution and entropies of the output state can be represented as imaginary time evolution with an effective local Hamiltonian in the replica space. This facilitates large scale numeric

Dan Betea, Jérémie Bouttier, Harriet Walsh

We introduce multicritical Schur measures, which are probability laws on integer partitions which give rise to non-generic fluctuations at their edge. They are in the same universality classes as one-dimensional momentum-space models of free fermions in flat confining potentials, studied by Le Doussal, Majumdar and Schehr. These universality classes involve

Dongmeng Xi

Firstly, we propose our conjectured Reverse-log-Brunn-Minkowski inequality (RLBM). Secondly, we show that the (RLBM) conjecture is equivalent to the log-Brunn-Minkowski (LBM) conjecture proposed by B\"or\"oczky-Lutwak-Yang-Zhang. We name this as ``reverse-to-forward" principle. Using this principle, we give a very simple new proof of the log-Brunn-Minkowski

Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin

Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and cl

J. Czarnecki, A. Bertoni, G. Goldoni, P. Wójcik

We investigate the effective Lande factor in semiconductor nanowires with strong Rashba spin-orbit coupling. Using the $\mathbf{k}\cdot\mathbf{p}$ theory and the envelope function approach we derive a conduction band Hamiltonian where the tensor $g^*$ is explicitly related to the spin-orbit coupling constant $\alpha_R$. Our model includes orbital effects fro