October 2022 arXiv papers — page 126

Yang Liu, Hongwei Yu, Puxun Wu

We establish a cosmological-model-independent method to determine the Hubble constant $H_0$ from the localized fast radio bursts (FRBs) and the Hubble parameter measurements from cosmic chronometers and obtain a first such determination $H_0=71\pm 3~\mathrm{km/s/Mpc}$, with an uncertainty of 4\%, from the eighteen localized FRBs and nineteen Hubble parameter

Li-Yang Chen, Hongwei Yu, Puxun Wu

The gravitationally enhanced friction can reduce the speed of the inflaton to realize an ultra-slow-roll inflation, which will amplify the curvature perturbations. The amplified perturbations can generate a sizable amount of primordial black holes (PBHs) and induce simultaneously a significant background gravitational waves (SIGWs). In this paper, we investi

Brian Yan, Siddharth Dalmia, Yosuke Higuchi, Graham Neubig

Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment. However for translation, CTC exhibits clear limitations due to the contextual and non-monotonic nature of the task and thus lags behind attentional decoder approaches in terms of translation

Simon Bang Kristensen, Katrine Bødkergaard, Bo Martin Bibby

An adaptive design adjusts dynamically as information is accrued and a consequence of applying an adaptive design is the potential for inducing small-sample bias in estimates. In psychometrics and psychophysics, a common class of studies investigate a subject's ability to perform a task as a function of the stimulus intensity, meaning the amount or clarity o

Xiaoke Lou, Weixu Su, Dong Tan

The Gardiner-Masur compactification of Teichm\"uller space is homeomorphic to the horofunction compactification of the Teichm\"uller metric. Let $\xi$ and $\eta$ be a pair of boundary points in the Gardiner-Masur compactification that fill up the surface. We show that there is a unique Teichm\"uller geodesic which is optimal for the horofunctions correspondi

Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong

Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information. However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem. To address the above challenges, we introduce an opti

Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng

News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations. First, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. Second, existing graph-based NR methods are promising but lack ef

Park Jun Yeong, Shin Su Jong, Choi Chang Hwan, Lee Jung Jae

In this paper, we propose a natural language processing architecture that can handle tasks that previously required two models as one model. With a single model, we analyze the language patterns and conversational context of Alzheimer's patients and derive answers from two results: patient classification and chatbot. If the patient's language characteristics

Qiayuan Liao, Zhefeng Cao, Hua Chen, Wei Zhang

This paper studies real-time motion planning and control for ball bumping motion with quadruped robots. To enable the quadruped to bump the flying ball with different initializations, we develop a nonlinear trajectory optimization-based planning scheme that jointly identifies the take-off time and state to achieve accurate ball hitting during the flight phas

Ziling Heng, Xinran Wang

In ``Infinite families of near MDS codes holding $t$-designs, IEEE Trans. Inform. Theory, 2020, 66(9), pp. 5419-5428'', Ding and Tang made a breakthrough in constructing the first two infinite families of NMDS codes holding $2$-designs or $3$-designs. Up to now, there are only a few known infinite families of NMDS codes holding $t$-designs in the literature.

Chenze Shao, Zhengrui Ma, Yang Feng

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, whi

Rasmus Ros, Elizabeth Bjarnason, Per Runeson

Continuous experimentation (CE) is used by many companies with internet-facing products to improve their software based on user data. Some companies deliberately adopt an experiment-driven approach to software development while some companies use CE in a more ad-hoc fashion. The goal of the study is to identify factors that explain the variations in the util

Renjun Duan, Zongguang Li

In this paper we consider the Boltzmann equation modelling the motion of a polyatomic gas where the integration collision operator in comparison with the classical one involves an additional internal energy variable $I\in\mathbb{R}_+$ and a parameter $\delta\geq 2$ standing for the degree of freedom. In perturbation framework, we establish the global well-po

Adam Hibberd, T. Marshall Eubanks

Comet C/2014 UN$_{271}$, alternative designation 'BB' after its discoverers 'Bernardinelli/Bernstein', and commonly referred to as UN$_{271}$, is an extreme case on two fronts, firstly its solar distance on discovery ($>$ 29 au) and secondly the size of its nucleus (137$\pm$ 15 km). With an aphelion distance of $\sim$33,000 au (w.r.t. the solar system baryce

Caglar Aytekin

In this manuscript, we show that any neural network with any activation function can be represented as a decision tree. The representation is equivalence and not an approximation, thus keeping the accuracy of the neural network exactly as is. We believe that this work provides better understanding of neural networks and paves the way to tackle their black-bo

Zhaowei Wang

Legal case retrieval, which aims to retrieve relevant cases given a query case, plays an essential role in the legal system. While recent research efforts improve the performance of traditional ad-hoc retrieval models, legal case retrieval is still challenging since queries are legal cases, which contain hundreds of tokens. Legal cases are much longer and mo

Francisco Cruz, Adam Bignold, Hung Son Nguyen, Richard Dazeley

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained an

Gopal-Krishna, Krishan Chand, Hum Chand, Vibhore Negi

This study aims to characterise, for the first time, intranight optical variability (INOV) of low-mass active galactic nuclei (LMAGN) which host a black hole (BH) of mass $M_{BH} \sim 10^6 M_{\odot}$, i.e., even less massive than the Galactic centre black hole Sgr A* and 2-3 orders of magnitude below the supermassive black holes (SMBH, $M_{BH}$ $\sim$ $10^8

Jihoon Tack, Jongjin Park, Hankook Lee, Jaeho Lee

The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains, and meta-learning is no exception; a recent discovery shows that utilizing task-wise target models can significantly boost the generalization performance. However, obtaining a targ

Anpeng Zhang, Xiutao Feng, Shengyuan Xu

Quantum computers in practice today require strict memory constraints, where 2-qubit operations can only be performed between the qubits closest to each other in a graph structure. So a quantum circuit must undergo a transformation to the graph before it can be implemented. In this paper, we study the optimization of the CNOT circuits on some noisy intermedi

Suman Mondal, Mintu Karmakar, Prabir Dutta, Saurav Giri

We observe multiple step-like jumps in a Dy-Fe-Ga-based ferrimagnetic alloy in its magnetic hysteresis curve at 2 K. The observed jumps have a stochastic character with respect to their magnitude and the critical field of occurrence, and the jumps do not show any temporal effect. The jump size distribution follows a power law variation indicating the scale i

Tinghao Zhang, Zhijun Li, Yongrui Chen, Kwok-Yan Lam

Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accurac

Haoyun Wang, Yao Xie

Change-point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large-scale, high-dimensional, and complex streaming data call for computationally (memory) efficient sequential change-point detection algorithms that are also statistically powerful. This

Xiaowu Sun, Yasser Shoukry

This paper presents a neurosymbolic framework to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described using temporal logic formulas such as Linear Temporal Logic (LTL) to capture complex tasks. The proposed framework trains Neural Network (NN)-based planners that enjoy strong correctness guarantees when

Linbo Wang

Applied researchers often claim that the risk difference is more heterogeneous than the relative risk and the odds ratio. Some also argue that there are theoretical grounds for why this claim is true. In this note, we point out that these arguments reflect certain effect measures are variation independent of a nuisance parameter that is easier to interpret,

Aviral Kumar, Anikait Singh, Frederik Ebert, Mitsuhiko Nakamoto

Progress in deep learning highlights the tremendous potential of utilizing diverse robotic datasets for attaining effective generalization and makes it enticing to consider leveraging broad datasets for attaining robust generalization in robotic learning as well. However, in practice, we often want to learn a new skill in a new environment that is unlikely t

Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou

Deep neural networks often suffer from poor generalization caused by complex and non-convex loss landscapes. One of the popular solutions is Sharpness-Aware Minimization (SAM), which smooths the loss landscape via minimizing the maximized change of training loss when adding a perturbation to the weight. However, we find the indiscriminate perturbation of SAM

Jianbo Wang, Huan Yang, Jianlong Fu, Toshihiko Yamasaki

With the development of the convolutional neural network, image style transfer has drawn increasing attention. However, most existing approaches adopt a global feature transformation to transfer style patterns into content images (e.g., AdaIN and WCT). Such a design usually destroys the spatial information of the input images and fails to transfer fine-grain

Cholmin Kang, Chunggi Lee, Heon Song, Minuk Ma

Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. In particular, annotating different types of cells in histopathology suffer from high inter- and intra-rater variability due to the ambiguity of the task. Under this setting, the re

Tianheng Cheng, Xinggang Wang, Shaoyu Chen, Qian Zhang

Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes. While, we find that box-supervised methods can produce some fine segmentation masks and we wonder whether the det

Simon Daniel Duque Anton, Daniel Schneider, Hans Dieter Schotten

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it

W. Horiuchi, N. Itagaki

We investigate the degree of $\alpha$ ($^4$He nucleus) clustering in the ground-state density profiles of $^{44}$Ti and $^{52}$Ti. Two types of density distributions, shell- and cluster-model configurations, are generated fully microscopically with the antisymmetrized quasi-cluster model, which can describe both the j-j coupling shell and $\alpha$-cluster co

Man Zhou, Hu Yu, Jie Huang, Feng Zhao

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (\emph{e.g.}, interpolation, transposed convolution, and un-pooling) heavily depend on local pixel attention, incapably exploring the global dependency. In contrast, the Fourier domain obeys the nature of global model

Ziling Heng, Xinran Wang, Xiaoru Li

Linear codes with a few weights have many nice applications including combinatorial design, distributed storage system, secret sharing schemes and so on. In this paper, we construct two families of linear codes with a few weights based on special polynomials over finite fields. The first family of linear codes are extended primitive cyclic codes which are af

Samrat Roy, Marina Bogomolov, Ruth Heller, Amy M. Claridge

There has been increasing interest in studying the effect of giving births to unintended pregnancies on later life physical and mental health. In this article, we provide the protocol for our planned observational study on the long-term mental and physical health consequences for mothers who bear children resulting from unintended pregnancies. We aim to use

Andrei V. Konstantinov, Lev V. Utkin

New models of the attention-based random forests called LARF (Leaf Attention-based Random Forest) are proposed. The first idea behind the models is to introduce a two-level attention, where one of the levels is the "leaf" attention and the attention mechanism is applied to every leaf of trees. The second level is the tree attention depending on the "leaf" at

Mohamad Taghizadeh, Karim Mohammadi

Melanoma is one of the most serious skin cancers that can occur in any part of the human skin. Early diagnosis of melanoma lesions will significantly increase their chances of being cured. Improving melanoma segmentation will help doctors or surgical robots remove the lesion more accurately from body parts. Recently, the learning-based segmentation methods a

Ariyan Javanpeykar, Thomas Krämer, Christian Lehn, Marco Maculan

Motivated by recent work of Lawrence-Venkatesh and Lawrence-Sawin, we show that non-isotrivial families of subvarieties in abelian varieties have big monodromy when twisted by generic rank one local systems. While Lawrence-Sawin discuss the case of subvarieties of codimension one, our results hold for subvarieties of codimension at least half the dimension o

Thu Nguyen, Rabindra Khadka, Nhan Phan, Anis Yazidi

For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from at least one of the datasets is small. However, a potential challenge in such cases is that the features from these datasets are not identical, even though there are some commonly s

Xiaojun Chen, Yifan He, Zaikun Zhang

The sign-constrained Stiefel manifold in $\mathbb{R}^{n\times r}$ is a segment of the Stiefel manifold with fixed signs (nonnegative or nonpositive) for some columns of the matrices. It includes the nonnegative Stiefel manifold as a special case. We present global and local error bounds that provide an inequality with easily computable residual functions and

Manoj Ghising, Mohammed Tobrej, Binay Rai, Ruchi Tamang

In this paper, we report on the hard X-ray observation of the X-ray pulsar 1E 1145.1-6141 performed with the Nuclear Spectroscopic Telescope Array mission (NuSTAR). The coherent pulsation of the source with a period of $\sim296.653\;\pm\;0.021\;s $ is detected. The source may be in the equilibrium phase, according to the most recent measurements of its pulse

Anjali Mittal, Rhythm Grover, Debasis Kundu, Amit Mitra

In this paper, we propose some estimation techniques to estimate the elementary chirp model parameters, which are encountered in sonar, radar, acoustics, and other areas. We derive asymptotic theoretical properties of least squares estimators and approximate least squares estimators for the one-component elementary chirp model. It is proved that the proposed

G. S. Da Costa, M. S. Bessell, Thomas Nordlander, Arvind C. N. Hughes

The advent of large-scale stellar spectroscopic surveys naturally leads to the implementation of machine learning techniques to isolate, for example, small sub-samples of potentially interesting stars from the full data set. A recent example is the application of the t-SNE statistical method to $\sim$600,000 stellar spectra from the GALAH survey in order to

Deepthi S. Prabhu, Annapurni Subramaniam, Snehalata Sahu, Chul Chung

We present the first comprehensive study of the most massive globular cluster Omega Centauri in the far-ultraviolet (FUV) extending from the center to ~ 28% of the tidal radius using the Ultraviolet Imaging Telescope aboard AstroSat. A comparison of the FUV-optical color-magnitude diagrams with available canonical models reveals that the horizontal branch (H

Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu

"He is a person", "Paris is located on the earth". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the language of pre-trained language models (PLMs) is. To achieve this, we introduce a novel approach to build a benchmark for specificity testing by forming masked token prediction tas

Tung Nguyen, Qinqing Zheng, Aditya Grover

Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et al., 2021; Emmons et al., 2021) have shown that by conditioning on desired future returns, BC can perform competitively to their value-based counterparts, while enjoying much more s

Sho Ejiri

In this note, we generalize slightly Popa--Schnell's theorem regarding to direct images of pluricanonical bundles to the case when the ample line bundle is not globally generated. We also treat the case of positive characteristic.

Hao Cheng, Hao Fang, Xiaodong Liu, Jianfeng Gao

Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular. Specifically, the de-facto architecture for open-domain question answering uses two isomorphic encoders that are initialized from the same pretrained model but separately parameterized for questions and passages. This bi-

Yanchuan Chang, Jianzhong Qi, Yuxuan Liang, Egemen Tanin

Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results. They also have a heavy impact on the query efficiency. An ideal measure should have the capability to accurately evaluate the similarity between any two trajectories in a very short amount of time. Towards this aim, we

Anna Freni-Sterrantino, Thomas P Prescott, Greg Ceely, Myer Glickman

Composite indicators are a useful tool to summarize, measure and compare changes among different communities. The UK Office for National Statistics has created an annual England Health Index (starting from 2015) comprised of three main health domains - lives, places and people - to monitor health measures, over time and across different geographical areas (1

Jiaxi Wang, Ji Wu, Lei Huang

Batch Normalization (BN) is a core and prevalent technique in accelerating the training of deep neural networks and improving the generalization on Computer Vision (CV) tasks. However, it fails to defend its position in Natural Language Processing (NLP), which is dominated by Layer Normalization (LN). In this paper, we are trying to answer why BN usually per

Dan Zhang, Rui Zheng, Luosang Gadeng, Pei Yang

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown promise. However, implicit cross-task consistency learning in multi-task networks is limited. To address this, we pro

Tianyi Liu, Size Hou, Jiayuan Zhu, Zilong Zhao

Thanks to the capacity for long-range dependencies and robustness to irregular shapes, vision transformers and deformable convolutions are emerging as powerful vision techniques of segmentation.Meanwhile, Graph Convolution Networks (GCN) optimize local features based on global topological relationship modeling. Particularly, they have been proved to be effec

Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim

Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction. However, previous HRL algorithms often suffer from serious data inefficiency as environments get large. The extended components, $i.e.$, goal space and length of episodes, impose a burden on either one or both high-level and low-le

Tianzhen Wang, Haixiang Zhang, Liuquan Sun

When large amounts of data continuously arrive in streams, online updating is an effective way to reduce storage and computational burden. The key idea of online updating is that the previous estimators are sequentially updated only using the current data and some summary statistics of historical raw data. In this article, we develop a renewable learning met

Kin Wai Cheuk, Ryosuke Sawata, Toshimitsu Uesaka, Naoki Murata

In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model is trained to convert spectrograms into piano rolls, we think of it as a conditional generative task where we train our model to generate realistic looking piano rolls from pure Gau

Changkun Ye, Nick Barnes, Lars Petersson, Russell Tsuchida

Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predi

Yuntian Deng, Noriyuki Kojima, Alexander M. Rush

Building on recent advances in image generation, we present a fully data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations on top of a Gaussian noise distribution. We view the diffusion denoising process as a sequential decision maki

Haoning Zhang, Junwei Bao, Haipeng Sun, Huaishao Luo

Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from external labeled dialogue data (e.g., from question answering, dialogue summarization, machine reading comprehension tasks, etc.) into DST, whereas collecting a large amount of ext

Kai Hui, Tao Chen, Zhen Qin, Honglei Zhuang

Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We investigate how to augment T5-based re-rankers using high-quality information retrieved from two external corpora -- a commercial web search engine and Wikipedia. We empirically d

Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou

Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with

Byeongki Jeong, Janghyeok Yoon, Jaewoong Choi

Recent extensively competitive business environment makes companies to keep their eyes on social media, as there is a growing recognition over customer languages (e.g., needs, interests, and complaints) as source of future opportunities. This research avenue analysing social media data has received much attention in academia, but their utilities are limited

Jeong Woo Kim, Jin Gyu Lee, Donggil Lee, Hyungbo Shim

We develop a discrete-time version of the blended dynamics theorem for the use of designing distributed computation algorithms. The blended dynamics theorem enables to predict the behavior of heterogeneous multi-agent systems. Therefore, once we get a blended dynamics for a particular computational task, design idea of node dynamics for individual heterogene

Qianzhong Ou

Under the assumption of finite energy, positive solutions to the critical p-Laplace equation in $\mathbb{R}^n$ for $1< p<n$ have been classified completely by moving plane method. In this paper, the author provide a new approach to obtain the same classification results for $\frac{n+1}{3}\leq p<n$, without any further assumptions.

Arka Banerjee, Tom Abel

In astronomy and cosmology, significant effort is devoted to characterizing and understanding spatial cross-correlations between points - e.g. galaxy positions, high energy neutrino arrival directions, X-ray and AGN sources, and continuous field - e.g. weak lensing and Cosmic Microwave Background (CMB) maps. Recently, we introduced the $k$-nearest neighbor f

Ion Santra

We study the induced dynamics of an inertial tracer particle elastically coupled to passive or active Brownian particles. We integrate out the environment degrees of freedom to obtain generalized Langevin equation for the tracer dynamics in both cases. In particular, we find the exact form of the dissipation kernel and effective noise experienced by the trac

Sam Gunn, Nathan Ju, Fermi Ma, Mark Zhandry

What does it mean to commit to a quantum state? In this work, we propose a simple answer: a commitment to quantum messages is binding if, after the commit phase, the committed state is hidden from the sender's view. We accompany this new definition with several instantiations. We build the first non-interactive succinct quantum state commitments, which can b

Saumya Choudhary, A. Nicholas Black, Aku Antikainen, Robert W. Boyd

Weak phase noise present on an optical field can be amplified by a self-focusing nonlinearity and form intense "rogue wave" features. Here, we study the effect of the coherence length (or grain size) of this phase noise on the likelihood of rogue wave formation in the presence of a self-focusing nonlinearity. We show that while the likelihood of rogue wave f

Aadi Gupta, Priya Gulati, Siddhartha P. Chakrabarty

In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit

Haoyi Zhu, Hao-Shu Fang, Cewu Lu

Neural Radiance Fields (NeRFs), despite their outstanding performance on novel view synthesis, often need dense input views. Many papers train one model for each scene respectively and few of them explore incorporating multi-modal data into this problem. In this paper, we focus on a rarely discussed but important setting: can we train one model that can repr

Yuta Hamada, Hikaru Kawai, Kiyoharu Kawana

The validity of the Coleman mechanism, which automatically tunes the fundamental constants, is examined in two-dimensional and four-dimensional quantum gravity theories. First, we consider two-dimensional Euclidean quantum gravity on orientable closed manifolds coupled to conformal matter of central charge $c \leq1$. The proper time Hamiltonian of this syste

Kazuki Ikeda

Using von Neumann algebras, we extend the theory of quantum computation on a graph to a theory of computation on an arbitrary topological space.

Alexei Filinkov, Ian G. Fuss

An axiomatic quantum field theory applied to the self-interacting boson field is realised in terms of generalised operators that allows us to form products and take derivatives of the fields in simple and mathematically rigorous ways. Various spaces are explored for representation of these operators with this exploration culminating with a Hida-Colombeau alg

J. Vazquez, C. D. Kilpatrick, G. Dimitriadis, R. J. Foley

We present pre- and post-explosion observations of the Type II-P supernova (SN~II-P) 2019mhm located in NGC~6753. Based on optical spectroscopy and photometry, we show that SN\,2019mhm exhibits broad lines of hydrogen with a velocity of $-8500\pm200$~km~s$^{-1}$ and a $111\pm2$~day extended plateau in its luminosity, typical of the Type II-P subclass. We als

Boce Hu, Chenfei Zhu, Xupeng Ai, Sunil K. Agrawal

Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine has long received attention. Although the development of deep learning has made HPE methods in telemedicine simpler and easier to use, addressing low accuracy and high latency remains a big challenge. In this paper, we propose a novel multi-view Attention Cube Regression Network (ACRN

Pierre Marza, Laetitia Matignon, Olivier Simonin, Christian Wolf

Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, end-to-end learning of navigation keeps some form of memory in a neural network. Networks are typically imbued w

Aqin Xiao, Junfeng Yin, Ning Zheng

A class of fast greedy block Kaczmarz methods combined with general greedy strategy and average technique are proposed for solving large consistent linear systems. Theoretical analysis of the convergence of the proposed method is given in detail. Numerical experiments show that the proposed methods are efficient and faster than the existing methods.

Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu

We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its efficacy with changes in clinical landscapes. Our study involved 1328 prostate cancer patients who underwent definitive radiotherapy from January 2006 to August 2022 at the University

Wai Hong Ronald Chan

Breaking surface waves generate drops of a broad range of sizes that have a significant influence on regional and global climates, as well as the identification of ship movements. Characterizing these phenomena requires a fundamental understanding of the underlying mechanisms behind drop production. The interscale nature of these mechanisms also influences t

Manyi Zhang, Yuxin Ren, Zihao Wang, Chun Yuan

Instance-dependent label noise is realistic but rather challenging, where the label-corruption process depends on instances directly. It causes a severe distribution shift between the distributions of training and test data, which impairs the generalization of trained models. Prior works put great effort into tackling the issue. Unfortunately, these works al

Isaac Liao, Rumen R. Dangovski, Jakob N. Foerster, Marin Soljačić

Fast gradient-based optimization algorithms have become increasingly essential for the computationally efficient training of machine learning models. One technique is to multiply the gradient by a preconditioner matrix to produce a step, but it is unclear what the best preconditioner matrix is. This paper introduces a novel machine learning optimizer called

Hengyuan Hu, David J Wu, Adam Lerer, Jakob Foerster

We consider the problem of making AI agents that collaborate well with humans in partially observable fully cooperative environments given datasets of human behavior. Inspired by piKL, a human-data-regularized search method that improves upon a behavioral cloning policy without diverging far away from it, we develop a three-step algorithm that achieve strong

Abigail Hickok

I introduce the concept of a persistence diagram (PD) bundle, which is the space of PDs for a fibered filtration function (a set $\{f_p: \mathcal{K}^p \to \mathbb{R}\}_{p \in B}$ of filtrations that is parameterized by a topological space $B$). Special cases include vineyards, the persistent homology transform, and fibered barcodes for multiparameter persist

N. E. Djienbekov, A. M. Bekbussyn, N. Kh. Bastykova, T. S. Ramazanov

The results of modeling shear flows in classical two-dimensional dipole systems are presented. We used the method of non-equilibrium molecular dynamics to calculate the viscosity at various shear rates. The coefficients of shear viscosity are given in the limit of low shear rates for various regimes of interparticle correlation from a weakly correlated gaseo

Jia-Qi Dong, Wen-Hui Han, Yisen Wang, Xiao-Song Chen

The cover-time problem, i.e., time to visit every site in a system, is one of the key issues of random walks with wide applications in natural, social, and engineered systems. Addressing the full distribution of cover times for random walk on complex structures has been a long-standing challenge and has attracted persistent efforts. Yet, the known results ar

Shahriar Ferdous, Christiana Chamon, Laszlo B. Kish

In this paper, the vulnerability of the Vadai, Mingesz and Gingl (VMG)- Kirchhoff-Law-Johnson-Noise (KLJN) Key Exchanger (Nature, Science Report 5 (2015) 13653) against two active attacks is demonstrated. The security vulnerability arises from the fact that the effective driving impedances are different between the HL and LH cases for the VMG-KLJN scheme; wh

Jinbi Zhang, Junling Zheng

We show that the difference of the extension dimensions of two derived equivalent algebras is bounded above by the minimal length of a tilting complex associated with a derived equivalence, and that the extension dimension is an invariant under the stable equivalence. In addition, we provide two sufficient conditions such that the extension dimension is an i

Ying Dai

In this paper, we proposed a framework of constructing two types of the automatic image aesthetics assessment models with different CNN architectures and improving the performance of the image's aesthetic score prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in

Ziquan Liu, Antoni B. Chan

The adversarial vulnerability of deep neural networks (DNNs) has been actively investigated in the past several years. This paper investigates the scale-variant property of cross-entropy loss, which is the most commonly used loss function in classification tasks, and its impact on the effective margin and adversarial robustness of deep neural networks. Since

Cheng Peng, S. Kevin Zhou, Rama Chellappa

Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc. However, applying deep-learning-based SR approaches for clinical applications often encounters issues of domain inconsistency, as the test data may b

Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues. In this paper, we present MARLlib, a library designed to add

Siyao Liu, Yong Wang

In [10], Wears defined and studied algebraic T-solitons. In this paper, we give the definition of algebraic Schouten solitons as a special T-soliton and classify algebraic Schouten solitons associated to Levi-Civita connections, canonical connections and Kobayashi-Nomizu connections on three-dimensional Lorentzian Lie groups with some product structure.

Kazuhiko Kakamu

This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Ther

D. Freeman, T. Oikhberg, B. Pineau, M. A. Taylor

Let $(\Omega,\Sigma,\mu)$ be a measure space, and $1\leq p\leq \infty$. A subspace $E\subseteq L_p(\mu)$ is said to do stable phase retrieval (SPR) if there exists a constant $C\geq 1$ such that for any $f,g\in E$ we have $$ \inf_{|\lambda|=1} \|f-\lambda g\|\leq C\||f|-|g|\|. $$ In this case, if $|f|$ is known, then $f$ is uniquely determined up to an unavo

Mayank Gupta, Martin Wild, Subimal Ghosh

Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the turbulent heat flux only from the four inputs of incoming an

Haneul Yoo, Jiho Jin, Juhee Son, JinYeong Bak

Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers. Historians with expertise in this time period have been analyzing the documents, but that process is very difficult and time-consuming, and language models would signific

Madhumitha Sakthi, Niranjan Yadla, Raj Pawate

Deep learning model compression is an improving and important field for the edge deployment of deep learning models. Given the increasing size of the models and their corresponding power consumption, it is vital to decrease the model size and compute requirement without a significant drop in the model's performance. In this paper, we present model compressio

Daniel Hickox-Young, Danilo Puggioni, James M. Rondinelli

Over the past decade, materials that combine broken inversion symmetry with metallic conductivity have gone from a thought experiment to one of the fastest growing research topics. In 2013, the observation of the first uncontested polar transition in a metal, LiOsO$_3$, inspired a surge of theoretical and experimental work on the subject, uncovering a host o

Ajwad Akil, Najrin Sultana, Abhik Bhattacharjee, Rifat Shahriyar

In this work, we present BanglaParaphrase, a high-quality synthetic Bangla Paraphrase dataset curated by a novel filtering pipeline. We aim to take a step towards alleviating the low resource status of the Bangla language in the NLP domain through the introduction of BanglaParaphrase, which ensures quality by preserving both semantics and diversity, making i

Yi Cheng, Guanghui Lan, Saeed Masiha, H. Edwin Romeijn

We study projection-free methods for functional constrained optimization with convex or smooth nonconvex objectives. Such problems arise in applications such as portfolio optimization and radiation therapy planning, where risk-aware criteria and sparsity frequently appear together. For the convex setting, we propose a Level Conditional Gradient (LCG) method