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SSRN 2024年

作者： Famiyeh, Lord Xu, Honghui Tesema, Fiseha Berhanu Kelly, Caleb Ji, Dongsheng Xiao, Hang Tong, Lei Chen, Ke He, Jun Department of Chemical and Environmental Engineering University of Nottingham Ningbo China Ningbo315100 China Zhejiang Institute of Meteorological Sciences Hangzhou310017 China School of Computer Science University of Nottingham Ningbo China Ningbo315100 China Department of Civil Engineering University of Nottingham Ningbo China Ningbo315100 China Beijing100029 China Zhongke Haixi Industrial Technology Innovation Center Ningbo318825 China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Nottingham Ningbo China Beacon of Excellence Research and Innovation Institute Ningbo315100 China Department of Chemical and Environmental Engineering China

The component-based potency factor approach, along with benzopyrene (BaP) unit risk values from the World Health Organization (WHO), is commonly used to assess lung cancer risk from polycyclic aromatic hydrocarbons (PAHs). However, there are concerns about potential overestimation of PAH-related lung cancer risk with this method. Additionally, multiple linear regression (MLR) models may not adequately capture the nonlinear interactions within PAH mixtures. In this study, we used BaP unit risk values from both WHO and the U.S. Environmental Protection Agency (USEPA) to estimate lung excess cancer risk (LECR) from PAHs. Our analysis indicated a moderate risk level (>1.0×10−6) of PAH exposure in Ningbo, China, identifying gasoline and diesel engine emissions, industrial emissions/coal combustion, natural gas combustion, and biomass burning/volatilization of unburnt fuel as primary sources. We employed machine learning (ML) algorithms, including random forest (RF), extremely randomized tree (ERT), and extreme gradient boosting (XGBoost), to estimate source-specific LECR of PAHs. Our findings highlighted industrial emissions, coal combustion, and gasoline engine exhaust emissions as the predominant contributors to the elevated LECR in Ningbo. © 2024, The Authors. All rights reserved.

关键词： Polycyclic aromatic hydrocarbons

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JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion

arXiv

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arXiv 2022年

作者： Qian, Cheng Huang, Kejun Glass, Lucas Srinivasa, Rakshith S. Sun, Jimeng Analytics Center of Excellence IQVIA Inc. United States Dept. of Computer and Information Science and Engineering University of Florida United States Department of Computer Science University of Illinois at Urbana-Champaign United States

Tensor completion aims at imputing missing entries from a partially observed tensor. Existing tensor completion methods often assume either multi-linear or nonlinear relationships between latent components. However, real-world tensors have much more complex patterns where both multi-linear and nonlinear relationships may coexist. In such cases, the existing methods are insufficient to describe the data structure. This paper proposes a Joint mUlti-linear and nonLinear IdentificAtion (JULIA) framework for large-scale tensor completion. JULIA unifies the multi-linear and nonlinear tensor completion models with several advantages over the existing methods: 1) Flexible model selection, i.e., it fits a tensor by assigning its values as a combination of multi-linear and nonlinear components;2) Compatible with existing nonlinear tensor completion methods;3) Efficient training based on a well-designed alternating optimization approach. Experiments on six real large-scale tensors demonstrate that JULIA outperforms many existing tensor completion algorithms. Furthermore, JULIA can improve the performance of a class of nonlinear tensor completion methods. The results show that in some large-scale tensor completion scenarios, baseline methods with JULIA are able to obtain up to 55% lower root mean-squared-error and save 67% computational complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

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Path generation based on convex lifting: optimization of the corridors

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IFAC-PapersOnLine 2022年第16期55卷 260-265页

作者： Marco Mirabilio Sorin Olaru Carlos E.T. Dórea Alessio Iovine Maria Domenica Di Benedetto Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L'Aquila Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes 91190 Gif-sur-Yvette France Department of Computer Engineering and Automation Universidade Federal do Rio Grande do Norte UFRN-CT-DCA 59078-900 Natal RN Brazil

The capability to compute a partition of a cluttered environment starting from the obstacles that lie in it enables the construction of a route connecting an initial point to a desired final one. In this paper, we revisit the convex lifting method for the computation of a suitable partition of the cluttered environment with the objective to propose a systematic procedure of reorganization of the cells within the partition in order to repel their boundaries from the obstacles. The ultimate goal is the construction of a connecting-path between an initial point and a final point characterized by a corridor with improved width guaranteeing the collision avoidance. The qualities of the corridors will impact the constraints on the motion of the controlled agent and consequently the real-time performance and robustness of the navigation in a cluttered environment.

关键词： obstacle avoidance linear systems optimization

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Field extraction and logo recognition on indian bank cheques using convolution neural networks 8th

Field extraction and logo recognition on indian bank cheques...

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8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Vishnuvardhan, Gopireddy Ravi, Vadlamani Mallik, Amiya Ranjan Center of Excellence in Analytics Institute for Development and Research in Banking Technology Castle Hills Road 1 Masab Tank Hyderabad500057 India School of Computer and Information Sciences University of Hyderabad Hyderabad500046 India Department of Computer Science Engineering IIIT Bhubaneswar Bhubaneswar751003 India

ISBN: (纸本)9789811556784

A large number of bank checks are processed manually every day, across the world. In a developing nation like India, cheques are significant instruments for achieving cashless transactions. Cheque processing is a tedious task that can be automated with advanced deep learning architectures. Cheque automation involves selecting the Regions Of Interest (ROI) and then analyzing the contents in the ROI. In this paper, we propose a novel approach to extract ROI (fields) on the cheque using a Convolutional Neural Network (CNN)-based object detection algorithms like YOLO. By virtue of employing a CNN-based model, our approach turns out to be scale, skew, and shift invariant. We achieved a mean average precision (mAP) score of 86.6% across all the fields on a publicly available database of cheques. On the extracted logo field from YOLO, we performed logo recognition using VGGnet as a feature extractor and achieved an accuracy of 99.01%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Extraction

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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Deep convolutional neural network based classification of alzheimer’s disease using MRI data

arXiv

引用

arXiv 2021年

作者： Nawaz, Ali Iqbal, Javid Anwar, Syed Muhammad Bagci, Ulas Liaqat, Rehan Majid, Muhammad Department of Software Engineering University of Engineering and Technology Taxila Pakistan Center for Research in Computer Vision University of Central Florida OrlandoFL United States Department of Electrical Engineering University of Engineering and Technology Taxila Pakistan Department of Computer Engineering University of Engineering and Technology Taxila Pakistan

Alzheimer’s disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient’s memory. Early detection can prevent the patient from further damage to the brain cells and hence avoid permanent memory loss. In the past few years, various automatic tools and techniques have been proposed for the diagnosis of AD. Several methods focus on fast, accurate, and early detection of the disease to minimize the loss to a patient’s mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to the human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using an imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer's Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control;and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to state-of-the-art methods. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Evaluation of machine learning techniques for Barret's and dysplasia discrimination of the esophagus from in vivo optical coherence tomography images 25

Evaluation of machine learning techniques for Barret's and d...

引用

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021

作者： Photiou, Christos Plastiras, George Tearney, Guillermo Pitris, Costas Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus 75 Kallipoleos St Nicosia1678 Cyprus Wellman Center for Photomedicine Massachusetts General Hospital and Harvard Medical School 55 Fruit Street BostonMA02114 United States

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

Optical Coherence Tomography (EOCT) systems can acquire high-resolution in vivo, real-time images of the human esophagus and so, they can be an important tool for the early diagnosis and prognosis of serious esophageal diseases such as Barrett's, dysplasia and esophageal cancer. In this study, we compare various machine learning methods for tissue segmentation and classification of esophageal tissue using in vivo OCT images. An automated algorithm is proposed able to detect normal tissue from Barrett's Esophagus (BE) and dysplasia. The classification was based on several features extracted from each A-Scan of EOCT images. The areas of the epithelium were annotated as normal, BE or dysplasia by an *** results of various machine learning (ML) classifiers have shown that a neural network based approach provided the best performance, separating BE from dysplasia, for individual A-Scans, with an accuracy of 89%. © 2021 SPIE.

关键词： Optical tomography

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A hybrid pulsed laser deposition approach to grow thin films of chalcogenides

arXiv

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arXiv 2023年

作者： Surendran, Mythili Singh, Shantanu Chen, Huandong Wu, Claire Avishai, Amir Shao, Yu-Tsun Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Core Center for Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

Vapor-pressure mismatched materials such as transition metal chalcogenides have emerged as electronic, photonic, and quantum materials with scientific and technological importance. However, epitaxial growth of vapor-pressure mismatched materials are challenging due to differences in the reactivity, sticking coefficient, and surface adatom mobility of the mismatched species constituting the material, especially sulfur containing compounds. Here, we report a novel approach to grow chalcogenides - hybrid pulsed laser deposition - wherein an organosulfur precursor is used as a sulfur source in conjunction with pulsed laser deposition to regulate the stoichiometry of the deposited films. Epitaxial or textured thin films of sulfides with variety of structure and chemistry such as alkaline metal chalcogenides, main group chalcogenides, transition metal chalcogenides and chalcogenide perovskites are demonstrated, and structural characterization reveal improvement in thin film crystallinity, and surface and interface roughness compared to the state-of-the-art. The growth method can be broadened to other vapor-pressure mismatched chalcogenides such as selenides and tellurides. Our work opens up opportunities for broader epitaxial growth of chalcogenides, especially sulfide-based thin film technological applications. © 2023, CC BY.

关键词： Thin films

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A Fall Detection Algorithm for Thigh mounted Smartphones using Random Forest and Feature Selection Techniques

A Fall Detection Algorithm for Thigh mounted Smartphones usi...

引用

International Conference on Emerging Technologies, ICET

作者： Rida Maryam Ahsan Shahzad Mariam Bashir Lismer Andres Caceres-Najarro Minseok Kim Department of Computer & Software Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Computer Science National Excellence Institute (NEI) Islamabad Pakistan Gwangju Institute of Science and Technology (GIST) Gwangju South Korea Bootcamp Company Ltd. Gwangju South Korea

ISBN: (纸本)9781665459938

Falls are sudden incidents that cause the body to go down onto the ground or toward the ground without intention or by accident. Falls can result in physical injuries and mental distress, especially in the elderly. Such results can cause serious disabilities and in the worst cases even death. Consequently, falls among the elderly are a widespread concern. That is one of the reasons why research on fall detection and prevention systems has gained considerable attention. Recently, smartphone (SP) based fall detection systems have attracted a lot of research efforts. Although there are several works using SPs for fall detection systems, most of them need to use additional external devices in addition to the SP and/or require the SPs to be positioned with a fixed orientation limiting their applicability. In daily life, people, most of the time, carry the SP in thigh locations such as the pockets of the pants. Therefore, this study focuses on the development of a reliable and highly accurate fall detection algorithm using thigh position SPs signals. The proposed algorithm employs a random forest classifier that incorporates feature selection techniques for an adequate selection of the best features. The proposed algorithm is trained and tested on the SP dataset used in a recent algorithm called FallDroid. Our simulation results show that the proposed approach achieves excellent fall detection scores outperforming the FallDroid.

关键词： Simulation Transfer learning Thigh Feature extraction Entropy Classification algorithms Fall detection

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Quantum reversible circuits for multiplicative inverse

引用

EPJ Quantum Technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

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