May 2023 arXiv papers — page 176

Ruopeng Zhang, Zixuan Xu, Sibo Zheng

Gravitational freeze-in is a mechanism to explain the observed dark matter relic density if dark matter neither couples to inflation nor to standard model sector. In this work, we study gravitational freeze-in dark matter production during Higgs preheating based on non-perturbative resonance. Using reliable lattice method to handle this non-perturbative proc

Honghyok Kim, Michelle Bell

Defining the effect of exposure of interest and selecting an appropriate estimation method are prerequisite for causal inference. Understanding the ways in which association between heatwaves (i.e., consecutive days of extreme high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted, is lim

Anderson de Andrade, Alon Harell, Yalda Foroutan, Ivan V. Bajić

We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the reconstruction task using the information available in the computer vision task. We include an information analysis of both approaches to provide baselines and also propose an entr

Nils Loose, Felix Mächtle, Claudius Pott, Volodymyr Bezsmertnyi

WebAssembly (Wasm) is a low-level binary format for web applications, which has found widespread adoption due to its improved performance and compatibility with existing software. However, the popularity of Wasm has also led to its exploitation for malicious purposes, such as cryptojacking, where malicious actors use a victim's computing resources to min

Ritu Garg, Pallavi Gupta, A. Upadhyay

We have employed HQET to give the spin-parity quantum numbers for recently observed bottom strange states $B_{sJ}(6063)$ and $B_{sJ}(6114)$ by LHCb collaborations. By exploring flavour independent parameters $ \Delta_{F}^{(c)} =\Delta_{F}^{(b)}$ and $ \lambda_{F}^{(c)} = \lambda_{F}^{(b)}$, we calculated masses of experimentally missing bottom strange meson

Dong Zhang

With various AI tools such as ChatGPT becoming increasingly popular, we are entering a true AI era. We can foresee that exceptional AI tools will soon reap considerable profits. A crucial question arise: should AI tools share revenue with their training data providers in additional to traditional stakeholders and shareholders? The answer is Yes. Large AI too

Masaomi Ono, Takaya Nozawa, Shigehiro Nagataki, Alexandra Kozyreva

To investigate the impact of matter mixing on the formation of molecules in the ejecta of SN 1987A, time-dependent rate equations for chemical reactions are solved for one-zone and one-dimensional ejecta models of SN 1987A. The latter models are based on the one-dimensional profiles obtained by angle-averaging of the three-dimensional hydrodynamical models (

Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat

The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we in

Pavel E. Kornilovitch

Two-particle lattice states are important for physics of magnetism, superconducting oxides, and cold quantum gases. The quantum-mechanical lattice problem is exactly solvable for finite-range interaction potentials. A two-body Schroedinder equation can be reduced to a system of linear equations whose numbers scale with the number of interacting sites. For th

Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal

Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits. We consider studying the behavior of LLM-based agents which we refer to as LLM personas and present

Xinmiao Lin, Yikang Li, Jenhao Hsiao, Chiuman Ho

The popular VQ-VAE models reconstruct images through learning a discrete codebook but suffer from a significant issue in the rapid quality degradation of image reconstruction as the compression rate rises. One major reason is that a higher compression rate induces more loss of visual signals on the higher frequency spectrum which reflect the details on pixel

Di Zhou, H. Jane Bae

We propose a framework for developing wall models for large-eddy simulation that is able to capture pressure-gradient effects using multi-agent reinforcement learning. Within this framework, the distributed reinforcement learning agents receive off-wall environmental states including pressure gradient and turbulence strain rate, ensuring adaptability to a wi

Lei Jiao, Xuefeng Zhang

TianQin is a proposed space-based gravitational wave detector designed to operate in circular high Earth orbits. As a sequel to [Zhang et al. Phys. Rev. D 103, 062001 (2021)], this work provides an analytical model to account for the perturbing effect of the Earth's gravity field on the range acceleration noise between two TianQin satellites. For such an ``o

Hongyi Wang, Saurabh Agarwal, Pongsakorn U-chupala, Yoshiki Tanaka

Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting in end-to-end speedups. However, low-rank model training necessitates adjusting several additional factorization hyperparameters, such as the rank of the factorization at each laye

Mu Li, Kanglong Fan, Kede Ma

Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output. Most previous methods fail to give a complete treatment of these characteristics, and thus are prone to errors. In this paper, we present a simple new crit

Raphael A. Meyer, Cameron Musco, Christopher Musco

Krylov subspace methods are a ubiquitous tool for computing near-optimal rank $k$ approximations of large matrices. While "large block" Krylov methods with block size at least $k$ give the best known theoretical guarantees, block size one (a single vector) or a small constant is often preferred in practice. Despite their popularity, we lack theoretical bound

Ali Goli, Amandeep Singh

We explore the viability of Large Language Models (LLMs), specifically OpenAI's GPT-3.5 and GPT-4, in emulating human survey respondents and eliciting preferences, with a focus on intertemporal choices. Leveraging the extensive literature on intertemporal discounting for benchmarking, we examine responses from LLMs across various languages and compare them t

Jiandong Zhang, Liying Yang, Zhesi Shen

In the field of scientometrics, the subject classification system of academic journals holds great importance. Accurate identification and classification of "multidisciplinary" journals are crucial in revealing the scientific structure and evaluating journals. Based on data from the Web of Science database from 2016 to 2020, we calculated the discipl

Yi-teng Hu, Murat Sat

This paper deals with differential pencils possessing a term depending on the unknown function with a fixed argument. We deduce the so called main equation together with its fine structure for the spectral problem. Then, according to the boundary conditions and the position of argument, we describe two cases: degenerate and non-degenerate. For these two case

Washim Uddin Mondal, Vaneet Aggarwal

We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback. The delay and compositeness of rewards mean that rewards generated as a result of taking an action at a given state are fragmented into different components, and they are sequentially realized at delayed time insta

Nicola Cotumaccio

In the past thirty years, numerous algorithms for building the suffix array of a string have been proposed. In 2021, the notion of suffix array was extended from strings to DFAs, and it was shown that the resulting data structure can be built in $ O(m^2 + n^{5/2}) $ time, where $ n $ is the number of states and $ m $ is the number of edges [SODA 2021]. Recen

Haichen Xu, Matteo Baggioli, Tom Keyes

Contrary to the case of solids and gases, where Debye theory and kinetic theory offer a good description for most of the physical properties, a complete theoretical understanding of the vibrational and thermodynamic properties of liquids is still missing. Liquids exhibit a vibrational density of states (VDOS) which does not obey Debye law, and a heat capacit

Ke Wan, Alain Kornhauser

Travel time derivatives are financial instruments that derive their value from road travel times, serving as an underlying asset that cannot be directly traded. Within the transportation domain, these derivatives are proposed as a more comprehensive approach to value pricing. They enable road pricing based not only on the level of travel time but also its vo

Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen, Sutanay Choudhury

Recent studies have shown that Binary Graph Neural Networks (GNNs) are promising for saving computations of GNNs through binarized tensors. Prior work, however, mainly focused on algorithm designs or training techniques, leaving it open to how to materialize the performance potential on accelerator hardware fully. This work redesigns the binary GNN inference

Jason Gross, Andres Erbsen, Jade Philipoom, Rajashree Agrawal

We address the challenges of scaling verification efforts to match the increasing complexity and size of systems. We propose a research agenda aimed at building a performant proof engine by studying the asymptotic performance of proof engines and redesigning their building blocks. As a case study, we explore equational rewriting and introduce a novel prototy

Jingman Pang, Hongjia Wang, Yufei Tang, Yun Zhang

Electric manipulation of skyrmions in 2D magnetic materials has garnered significant attention due to the potential in energy-efficient spintronic devices. In this work, using first-principles calculations and Monte Carlo simulations, we report the electric-field-tunable magnetic skyrmions in MnIn2Te4 monolayer. By adjusting the magnetic parameters, includin

Constantine Yannouleas, Uzi Landman

The few-body problem (with $N \leq 6$ fermionic charge carriers) in isolated moir\'{e} quantum dots (MQDs) in transition metal dichalcogenide (TMD) bilayer materials with integer fillings, $\nu \geq 2$, is investigated by employing large-scale full configuration interaction (FCI, also termed exact-diagonalization) computations, and by performing a comparativ

Xiao-Long Wang, Min Fang, Gregory J. Herczeg, Yu Gao

We present an analysis of 288 young stellar objects (YSOs) in the Perseus Molecular Cloud that have well defined $g$ and $r$-band lightcurves from the Zwicky Transient Facility. Of the 288 YSOs, 238 sources (83% of our working sample) are identified as variables based on the normalized peak-to-peak variability metric, with variability fraction of 92% for sta

Cecilia Machado, Germán Reyes, Evan Riehl

We examine the effects of an affirmative action policy at an elite Brazilian university that reserved 45 percent of admission slots for Black and low-income students. We find that marginally-admitted students who enrolled through the affirmative action tracks experienced a 14 percent increase in early-career earnings. But the adoption of affirmative action a

Louis Golowich

In this paper, we present a new construction of simplicial complexes of subpolynomial degree with arbitrarily good local spectral expansion. Previously, the only known high-dimensional expanders (HDXs) with arbitrarily good expansion and less than polynomial degree were based on one of two constructions, namely Ramanujan complexes and coset complexes. In con

Anil Carie, Abdur Rashid Sangi, Satish Anamalamudi, Murali Krishna Enduri

Scheduling and Channel Access at the MAC layer of the IoT network plays a pivotal role in enhancing the performance of IoT networks. State-of-the-art Omni-directional antenna based application data transmission has relatively less achievable throughput in comparison with directional antenna based scheduling protocols. To enhance the performance of the IoT ne

Eli Sennesh, Jan-Willem van de Meent

A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models. Both probabilistic programs and causal models define a joint probability density over a set of random variables, and exhibit sparse structure that can be used to reason about caus

Swaroop Ghosh, Suryansh Upadhyay, Abdullah Ash Saki

Quantum computing is an emerging computing paradigm that can potentially transform several application areas by solving some of the intractable problems from classical domain. Similar to classical computing systems, quantum computing stack including software and hardware rely extensively on third parties many of them could be untrusted or less-trusted or unr

Manika Bag, Tania Biswas, Sheetal Dharmatti

In this work, we study an optimal boundary control problem for a Cahn - Hilliard -Navier-Stokes (CHNS) system in a two dimensional bounded domain. The CHNS system consists of a Navier-Stokes equation governing the fluid velocity field coupled with a convective Cahn - Hilliard equation for the relative concentration of the fluids. An optimal control problem i

Fengming Dong, Meiqiao Zhang

In [J. Combin. Theory Ser. B 161 (2023), 109--119], the authors showed that the list-color function $P_l(G,k)$ of any simple graph $G$ of size $m$ coincides with its chromatic polynomial $P(G,k)$ for all integers $k\ge m-1$. In this article, we extend this conclusion to any uniform hypergraph. Furthermore, we show that for any $r$-uniform hypergraph ${\cal H

Kento Osuga

For any (possibly singular) hyperelliptic curve, we give the definition of a hyperelliptic refined spectral curve and the hyperelliptic refined topological recursion, generalising the formulation for a special class of genus-zero curves by Kidwai and the author, and also improving the proposal by Chekhov and Eynard. Along the way, we uncover a fundamental ge

Ziqing Zhu, Ka Wing Chan, Siqi Bu, Ze Hu

The Nash Equilibrium (NE) estimation in bidding games of electricity markets is the key concern of both generation companies (GENCOs) for bidding strategy optimization and the Independent System Operator (ISO) for market surveillance. However, existing methods for NE estimation in emerging modern electricity markets (FEM) are inaccurate and inefficient becau

Xiaoyang Huang

We present effective field theories for dipole symmetric topological matters that can be described by the Chern-Simons theory. Unlike most studies using higher-rank gauge theory, we develop a framework with both U(1) and dipole gauge fields. As a result, only the highest multipole symmetry can support the 't Hooft anomaly. We show that with appropriate point

Ye-Peng Yan, Guo-Jian Wang, Si-Yu Li, Jun-Qing Xia

Primordial B-mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B-modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB photons with large-scale structures will distort the primordial E modes, adding a lensing B-mode component to t

Andre Oestereich, Marcelo Pires, Nuno Crokidakis, Daniel Cajueiro

In this work, we study an epidemic model with vaccination coupled with opinion dynamics in a dynamic network. The network structure evolves as agents with differing opinions disconnect from one another and connect with agents that share similar opinions about vaccination. We consider a SIS-like model with an extra vaccinated state. Agents can have continuous

Ziqing Zhu, Siqi Bu, Ka Wing Chan, Bin Zhou

This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service

Ziqing Zhu, Siqi Bu, Ka Wing Chan, Bin Zhou

In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer

Venkatesan Guruswami, Shilun Li

We present an explicit construction of a sequence of rate $1/2$ Wozencraft ensemble codes (over any fixed finite field $\mathbb{F}_q$) that achieve minimum distance $\Omega(\sqrt{k})$ where $k$ is the message length. The coefficients of the Wozencraft ensemble codes are constructed using Sidon Sets and the cyclic structure of $\mathbb{F}_{q^{k}}$ where $k+1$

Wen Xiao, Yujia Xie, Giuseppe Carenini, Pengcheng He

Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we propose a tri-agent generation pipeline comprising a generator, an instructor, and an editor to enhance output personalization. The generator produces an initial output, the instructo

Bin Xiao, Murat Simsek, Burak Kantarci, Ala Abu Alkheir

Table Detection has become a fundamental task for visually rich document understanding with the surging number of electronic documents. However, popular public datasets widely used in related studies have inherent limitations, including noisy and inconsistent samples, limited training samples, and limited data sources. These limitations make these datasets u

Reese Kuper, Ipoom Jeong, Yifan Yuan, Jiayu Hu

As semiconductor power density is no longer constant with the technology process scaling down, modern CPUs are integrating capable data accelerators on chip, aiming to improve performance and efficiency for a wide range of applications and usages. One such accelerator is the Intel Data Streaming Accelerator (DSA) introduced in Intel 4th Generation Xeon Scala

Yuanhang Zheng, Zhixing Tan, Peng Li, Yang Liu

Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large Language Models (LLMs). Recent studies reveal that black-box prompt tuning lacks versatility across tasks and LLMs, which we believe is related to the suboptimal choice of subspace

Li Li

Ein, Niu and Park showed in [ENP20] that if the degree of the line bundle $L$ on a curve of genus $g$ is at least $2g+2k+1$, the $k$-th secant variety of the curve via the embedding defined by the complete linear system of $L$ is normal, projectively normal and arithmetically Cohen-Macaulay, and they also proved some vanishing of the Betti diagrams. However,

Kristoffer Larsena, Zhuo He, Chen Zhao, Xinwei Zhang

Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters

Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park

Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors follow some geometric structure in the latent space. In this paper, we consider random dot product graphs, in which an e

Hamed Valizadegan, Miguel J. S. Martinho, Jon M. Jenkins, Douglas A. Caldwell

Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet (y(x)=exoplanet) given some information related to that signal (represented by x). Except for the validation technique in Ro

Namo Bang, Jeehyun Lee, Myoung-Wan Koo

Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks by tracking dialogue states and generating appropriate responses to help users achieve defined goals. Recently, end-to-end dialogue models pre-trained based on large datasets have shown promising performance in the conversational system. However, they share the same parameters to t

Zeming Sun, Zhaslan Baraissov, Catherine A. Dukes, Darrah K. Dare

Surface structures on radio-frequency (RF) superconductors are crucially important in determining their interaction with the RF field. Here we investigate the surface compositions, structural profiles, and valence distributions of oxides, carbides, and impurities on niobium (Nb) and niobium-tin (Nb3Sn) in situ under different processing conditions. We establ

Farhad Moghimifar, Fatemeh Shiri, Van Nguyen, Reza Haffari

Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data

Andrej Leško

In this note we prove an inequality involving primes and the product of consecutive primes.

Tao Xu, Bo Wu, Ruilong Fan, Yun Zhou

Gaze estimation is a crucial task in computer vision, however, existing methods suffer from high computational costs, which limit their practical deployment in resource-limited environments. In this paper, we propose a novel lightweight model, FR-Net, for accurate gaze angle estimation while significantly reducing computational complexity. FR-Net utilizes th

Bill Tomlinson, Andrew W. Torrance, Rebecca W. Black, Donald J. Patterson

Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifac

Oluwasegun Isaac Akinboye, Yu Zhang, Vamsi Krishna Reddy Kondapalli, Lars Alexander Olivan

There is a growing momentum in recent thermoelectric materials research for flexible materials which enhance power output and efficiency at human wearable temperatures. In our previous work, we established the method to improve thermoelectric properties with co-embedding microparticles in carbon nanotube (CNT) network-based composites. In this work, we inves

Okan Düzyel

The quality of GAN-generated images on the MNIST dataset was explored in this paper by comparing them to the original images using t-distributed stochastic neighbor embedding (t- SNE) visualization. A GAN was trained with the dataset to generate images and the result of generating all synthetic images, the corresponding labels were saved. The dimensionality

Param Ahir, Hiteishi M. Diwanji

Visual question answering (VQA) usesimage processing algorithms to process the image and natural language processing methods to understand and answer the question. VQA is helpful to a visually impaired person, can be used for the security surveillance system and online chatbots that learn from the web. It uses NLP methods to learn the semantic of the questio

Isaac Filella-Merce, Alexis Molina, Marek Orzechowski, Lucía Díaz

Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their ability to design new molecules and enhance specific properties of existing ones. However, current GM methods have limitations, such as low affinity towards the target, unknown ADME/PK

Yuanming Shi, Shuhao Xia, Yong Zhou, Yijie Mao

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wirel

Anant Khandelwal

Existing Question Answering (QA) systems limited by the capability of answering questions from unseen domain or any out-of-domain distributions making them less reliable for deployment to real scenarios. Most importantly all the existing QA domain adaptation methods are either based on generating synthetic data or pseudo labeling the target domain data. The

Roberto Insabella, Martin Gonzalez, Lucas Riobo, Klaus Hass

In this work we present the first application of software-defined optoelectronics (SDO) for bidimensional optoacoustic tomography (OAT). The SDO concept refers to optoelectronic systems where the functionality associated with the conditioning and processing of optical and electrical signals are digitally implemented and controlled by software. This paradigm

Juan Ignacio García-García, Daniel Marín-Aragón, Adrián Sánchez-Loureiro, Alberto Vigneron-Tenorio

Numerical semigroups have been extensively studied throughout the literature, and many of their invariants have been characterized. In this work, we generalize some of the most important results about symmetry, pseudo-symmetry, or fundamental gaps, to affine $\mathcal C$-semigroups. In addition, we give algorithms to compute the tree of irreducible $\mathcal

Zhixin Pan, Prabhat Mishra

Machine learning (ML) is successful in achieving human-level artificial intelligence in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While recent efforts on explainable AI (XAI) has received significant attention, most of the existing solutions are not applicable in real-time systems since they map interpre

Mahmood Khalsan, Mu Mu, Eman Salih Al-Shamery, Lee Machado

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data is still complicated because of the high dimensionality of the datasets employed. We developed a new fuzzy gene selectio

Zlata Tabachová, Christian Diem, András Borsos, Csaba Burger

Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters d

Gabriele Pozzato, Simona Onori

Lithium-ion batteries are playing a key role in the sustainable energy transition. To fully exploit the potential of this technology, a variety of modeling, estimation, and prediction problems need to be addressed to enhance its design and optimize its utilization. Batteries are complex electrochemical systems whose behavior drastically changes as a function

John Kalung Leung, Igor Griva, William G. Kennedy, Jason M. Kinser

This paper presents an innovative approach to address the problems researchers face in Emotion Aware Recommender Systems (EARS): the difficulty and cumbersome collecting voluminously good quality emotion-tagged datasets and an effective way to protect users' emotional data privacy. Without enough good-quality emotion-tagged datasets, researchers cannot c

Jawid Ahmad Baktash, Mursal Dawodi, Mohammad Zarif Joya, Nematullah Hassanzada

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish

Gabriel Abellán, Nelson Bolívar, Ivaylo Vasilev

In this work we study the influence of isotropic and anisotropic fluids on the spherically symmetric warp metric. We evaluate the energy conditions and the influence of including a cosmological constant type term. We find that, considering this term, there is a trade-off between the weak and strong energy conditions. The obtained solutions are numerical and

Boling Yang, Liyuan Zheng, Lillian J. Ratliff, Byron Boots

Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asy

Bill Tomlinson, Andrew W. Torrance, Rebecca W. Black

Recent advances in artificial intelligence (AI) have raised questions about whether the use of AI is appropriate and legal in various professional contexts. Here, we present a perspective on how scholars may approach writing in conjunction with AI, and offer approaches to evaluating whether or not such AI-writing violates copyright or falls within the safe h

Andrew W. Torrance, Bill Tomlinson

To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use of copyrighted works as training sets for AI is merely a transitory and non-consumptive use that does not materially interfe

Andrew W. Torrance, Bill Tomlinson

Over the past half century, there have been several false dawns during which the "arrival" of world-changing artificial intelligence (AI) has been heralded. Tempting fate, the authors believe the age of AI has, indeed, finally arrived. Powerful image generators, such as DALL-E2 and Midjourney have suddenly allowed anyone with access the ability easil

Muhammad AL-Qurishi

Given the number of Arabic speakers worldwide and the notably large amount of content in the web today in some fields such as law, medicine, or even news, documents of considerable length are produced regularly. Classifying those documents using traditional learning models is often impractical since extended length of the documents increases computational re

Valérie Gillot, Philippe Langevin

We propose an effective version of the lift by derivation, an invariant that allows us to provide the classification of B(5,6,8)=RM(6, 8)/RM(4,8). The main consequence is to establish that the covering radius of the Reed-Muller RM(4,8) is equal to 26.

Adrian S. Lewis, Tonghua Tian

A central tool for understanding first-order optimization algorithms is the Kurdyka-Lojasiewicz inequality. Standard approaches to such methods rely crucially on this inequality to leverage sufficient decrease conditions involving gradients or subgradients. However, the KL property fundamentally concerns not subgradients but rather "slope", a purely

Sonal Sannigrahi, Rachel Bawden

Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and finer-grained segmentation into characters as opposed to subwords. In this work, we investigate lexical sharing in multilingual machine

Luis F. Alcerro

Recent results on inclusive jet production and production of a W boson in association with a charm quark by the CMS Collaboration are presented in this proceeding. The impact of these measurements on proton PDFs are also discussed.

Xilun Chen, Lili Yu, Wenhan Xiong, Barlas Oğuz

We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to

Mursal Dawodi, Jawid Ahmad Baktash

Text classification has become a crucial task in various fields, leading to a significant amount of research on developing automated text classification systems for national and international languages. However, there is a growing need for automated text classification systems that can handle local languages. This study aims to establish an automated classif

Jawid Ahmad Baktash, Mursal Dawodi

This paper is an extension of our previous conference paper. In recent years, there has been a growing interest among researchers in developing and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) systems have become ubiquitous, used in everything from games to translation sy

Jun-Sik Yoo, Tanmoy Bhattacharya, Rajan Gupta, Santanu Mondal

One of the sensitive probes of physics beyond the standard model is the test of the unitarity of the Cabbibo-Kobyashi-Maskawa (CKM) matrix. Current analysis of the first row is based on $|V_{ud}|$ from fourteen superallowed $0^+ \to 0^+$ nuclear $β$ decays and $|V_{ud}|$ from the kaon semileptonic decay, $K \to π\ell ν_\ell$. Modeling the nuclear effects in

Zexin Sun, John Baillieul

Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved by model predictive control (MPC), but because the optimization step

Jawid Ahmad Baktash, Mursal Dawodi

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have al

Rafael Puerta, Mahdieh Joharifar, Mengyao Han, Anders Djupsjöbacka

The sixth-generation (6G) mobile networks must increase coverage and improve spectral efficiency, especially for cell-edge users. Distributed multiple-input multiple-output (D-MIMO) networks can fulfill these requirements provided that transmission/reception points (TRxPs) of the network can be synchronized with sub nanosecond precision, however, synchroniza

Alen Adamyan, Erik Harutyunyan

There are various optimization techniques in the realm of 3D, including point cloud-based approaches that use mesh, texture, and voxels which optimize how you store, and how do calculate in 3D. These techniques employ methods such as feed-forward networks, 3D convolutions, graph neural networks, transformers, and sparse tensors. However, the field of 3D is o

Zikang Leng, Hyeokhyen Kwon, Thomas Plötz

The development of robust, generalized models in human activity recognition (HAR) has been hindered by the scarcity of large-scale, labeled data sets. Recent work has shown that virtual IMU data extracted from videos using computer vision techniques can lead to substantial performance improvements when training HAR models combined with small portions of real

Joao Faria Martins, Frank W Nijhoff, Daniel Riccombeni

In a previous paper by one of the authors, a Lagrangian 3-form structure was established for a generalised Darboux system, originally describing orthogonal curvilinear coordinate systems, which encodes the Kadomtsev-Petviashvili (KP) hierarchy. Here a hierarchy of Lagrangian multiforms is established for the same system, viewed as a hierarchy of Chern-Simons

Peyman Afshani, Pingan Cheng, Aniket Basu Roy, Zhewei Wei

We study the query version of the approximate heavy hitter and quantile problems. In the former problem, the input is a parameter $\varepsilon$ and a set $P$ of $n$ points in $\mathbb{R}^d$ where each point is assigned a color from a set $C$, and we want to build a structure s.t. given any geometric range $γ$, we can efficiently find a list of approximate he

M. H. Alqahtani

An open (resp., closed) subset A of a topological space (X, T ) is called C-open (resp., C-closed) set if cl(A) \ A (resp., A \ int(A)) is a countable set. This paper aims to present the concept of C-open and C-closed sets. We first investigate their basic properties. Then, we found some operators such as interior, closure, limit, border, and frontier using

Zhihao Jiang, Jinho Lim, Yi Li, Wolfgang Pfaff

Magnons, the quanta of collective spin excitations in magnetically ordered materials, have distinct properties that make them uniquely appealing for quantum information applications. They can have ultra-small wavelengths down to the nanometer scale even at microwave frequencies. They can provide coupling to a diverse set of other quantum excitations, and the

Ira M. Gessel

In the film Good Will Hunting, the main character, a janitor at MIT named Will Hunting, attacks the problem of drawing all the homeomorphically irreducible trees with 10 vertices. Although the film suggests that this is a difficult problem, it is in fact quite easy. A much more interesting problem is counting homeomorphically irreducible trees with $n$ verti

Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski, Tao B. Schardl

Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in applications such as recommendation systems and financial forensics. This paper is concerned with minibatch training and inference with GNNs that employ node-wise sampling in dist

Alberto Lastra, Cruz Prisuelos-Arribas

A generalized exponential matrix based on the construction of kernel operators for generalized summability is defined and analyzing its main properties, generalizing the classical exponential matrix and fractional exponential matrix. This object serves as a practical tool to express the solutions of linear systems of moment differential equations in a compac

Rohan Saha

With the advent of e-commerce platforms, reviews are crucial for customers to assess the credibility of a product. The star ratings do not always match the review text written by the customer. For example, a three star rating (out of five) may be incongruous with the review text, which may be more suitable for a five star review. A clustering approach can be

Yue Guan

Although ankle injuries resulting from postural instability are frequently observed during high-speed and intense physical activities, most current research has been limited to static or quasi-static models of the lower limb, or has focused solely on the ankle joint itself. In this study, to explain the kinetic mechanism underlying postural instability and a

Tobias Holck Colding, William P. Minicozzi

We will show that if a gradient shrinking Ricci soliton has an approximate symmetry on one scale, this symmetry propagates to larger scales. This is an example of the shrinker principle which roughly states that information radiates outwards for shrinking solitons.