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An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

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Urban informatics 2022年第1期1卷 6页

作者： Fangzheng Lyu Shaohua Wang Su Yeon Han Charlie Catlett Shaowen Wang cyberGIS Center for Advanced Digital and Spatial Studies University of Illinois at Urbana-Champaign Urbana IL USA. Department of Geography and Geographic Information Science University of Illinois at Urbana-Champaign Urbana IL USA. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. Computing Environment and Life Sciences Argonne National Laboratory Chicago IL USA.

Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.

关键词： Machine learning Remote sensing Urban Heat Island Urban sensor network cyberGIS

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Association of structural MRI with Braak staging using neural network-based risk scoring

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Matthew I. Miller Shangran Qiu Prajakta S. Joshi Joyce Lee Rhoda Au Vijaya B. Kolachalama Boston University School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University School of Public Health Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background The gold standard of Alzheimer’s disease (AD) diagnosis remains histopathologic examination of neuropathologic changes. However, the dependence on post-mortem microscopic analysis limits the role of pathology in clinical care. Magnetic resonance imaging (MRI) is routinely used to diagnose suspected dementia (DE) due to AD, but correlating neuroimaging to underlying pathologic changes remains challenging. Herein, we report the development of two risk scores derived from convolutional neural network (CNN) processing of MRI scans, which we then validate against Braak staging of neurofibrillary tangles from autopsy reports. Method Using T1-weighted MRIs from the National Alzheimer’s Coordinating Center (NACC), we trained two CNNs (Figure 1) to predict continuous scores for overall cognitive status (“COG” score) and AD probability (“ALZ” score). The COG score, ranging from 0-2, graded a subject’s cognitive status on a spectrum from normal cognition (NC, 0), to mild cognitive impairment (MCI, 1), and DE due to any cause (2). The ALZ score then graded each DE individual’s probability of AD from 0 to 1. These CNNs were then applied to a subset of participants with autopsy reports available within 6 months of MRI. CNN performance was evaluated by area under receiver operating characteristic curve (AUC). Differences in risk scores were assessed by Kruskal-Wallis H testing with post hoc Dunn testing. Result The CNN models for COG and ALZ score estimation were trained on 4,822 NACC participants. Thresholding of COG scores distinguished individuals with DE from those with NC and MCI (AUC = 0.87). ALZ scores distinguished individuals with AD from those with non-AD causes of DE (AUC = 0.77). Among 74 persons with available neuropathology data, COG scores differed significantly among Braak staging groups (p<0.001), increasing from a mean of 0.99±0.36 in Stage 1 to 1.47±0.41 in Stage 6 (p = 0.010). Significant differences in ALZ scores were also observed (p = 0.039) betwe

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A quantum-inspired classical algorithm for separable non-negative matrix factorization

arXiv

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arXiv 2019年

作者： Chen, Zhihuai Li, Yinan Sun, Xiaoming Yuan, Pei Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Centrum Wiskunde & Informatica and QuSoft Science Park 123 Amsterdam1098XG Netherlands

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Quantum speedup for twin support vector machine

arXiv

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arXiv 2019年

作者： Ye, Zekun Li, Lvzhou Situ, Haozhen Institute of Computer Science Theory School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Center for Quantum Computing Pengcheng Laboratory Shenzhen518055 China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

In this work, we investigate how to speed up machine learning tasks based on quantum computation. We show an important classifier in machine learning, the twin support vector machine, can be exponentially speeded up on quantum computers. Specifically, for a training set with m samples which are represented by n-dimensional vectors, the proposed quantum algorithm can learn two non-parallel hyperplanes in O(log mn) time, and then classify a new sample in O(log n) time by comparing the distances from the simple to the two hyperplanes. Note that the classical algorithm requires polynomial time both in the training and classification procedures. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Inventory models for short life cycle clothing products use a logistic growth model

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： V Lukitosari A P Subriadi Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia Department of Information System Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Clothes are products that follow short-life fashion and market demand. Products with a short lifetime occur due to technological developments and/or changes in market tastes. The clothing industry is one of the industries that has a short and obsolete sales period in stages. The excess number of products that accumulate in the warehouse due to obsolescence can cause the company to get a profit that is not optimal. One strategy to increase demand for the product is to apply discounts. There are two types of discounts used, namely a single discount and multiple discounts. The demand function of inventory model is considered analogous to the logistic growth model. The results show profit is maximized to get optimal order quantity and optimal discount price.

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Lattice Blind Signatures with Forward Security

arXiv

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arXiv 2020年

作者： Le, Huy Quoc Duong, Dung Hoang Susilo, Willy Tran, Ha Thanh Nguyen Trinh, Viet Cuong Pieprzyk, Josef Plantard, Thomas Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia Department of Mathematical and Physical Sciences Concordia University of Edmonton 7128 Ada Blvd NW EdmontonABT5B 4E4 Canada Faculty of Information and Communication Technology Hong Duc University 565 Quang Trung Thanh Hoa Viet Nam CSIRO Data61 SydneyNSW Australia Institute of Computer Science Polish Academy of Sciences Warsaw Poland

Blind signatures play an important role in both electronic cash and electronic voting systems. Blind signatures should be secure against various attacks (such as signature forgeries). The work puts a special attention to secret key exposure attacks, which totally break digital signatures. Signatures that resist secret key exposure attacks are called forward secure in the sense that disclosure of a current secret key does not compromise past secret keys. This means that forward-secure signatures must include a mechanism for secret-key evolution over time periods. This paper gives a construction of the first blind signature that is forward secure. The construction is based on the SIS assumption in the lattice setting. The core techniques applied are the binary tree data structure for the time periods and the trapdoor delegation for the key-evolution mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

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GPU-acceleration of the ELPA2 distributed eigensolver for dense symmetric and hermitian eigenproblems

arXiv

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arXiv 2020年

作者： Yu, Victor Wen-Zhe Moussa, Jonathan Kůs, Pavel Marek, Andreas Messmer, Peter Yoon, Mina Lederer, Hermann Blum, Volker Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Molecular Sciences Software Institute BlacksburgVA24060 United States Max Planck Computing and Data Facility GarchingD-85747 Germany Institute of Mathematics of the Czech Academy of Sciences Žitná 25 Prague115 67 Czech Republic NVIDIA Switzerland Technoparkstr 1 Zürich8005 Switzerland Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

The solution of eigenproblems is often a key computational bottleneck that limits the tractable system size of numerical algorithms, among them electronic structure theory in chemistry and in condensed matter physics. Large eigenproblems can easily exceed the capacity of a single compute node, thus must be solved on distributed-memory parallel computers. We here present GPU-oriented optimizations of the ELPA two-stage tridiagonalization eigensolver (ELPA2). On top of cuBLAS-based GPU offloading, we add a CUDA kernel to speed up the back-transformation of eigenvectors, which can be the computationally most expensive part of the two-stage tridiagonalization algorithm. We benchmark the performance of this GPU-accelerated eigensolver on two hybrid CPU-GPU architectures, namely a compute cluster based on Intel Xeon Gold CPUs and NVIDIA Volta GPUs, and the Summit supercomputer based on IBM POWER9 CPUs and NVIDIA Volta GPUs. Consistent with previous benchmarks on CPU-only architectures, the GPU-accelerated two-stage solver exhibits a parallel performance superior to the one-stage counterpart. Finally, we demonstrate the performance of the GPU-accelerated eigensolver developed in this work for routine semi-local KS-DFT calculations comprising thousands of atoms. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphics processing unit

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Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and markov random field

arXiv

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arXiv 2020年

作者： Bi, Haixia Xu, Lin Cao, Xiangyong Xue, Yong Xu, Zongben Faculty of Engineering University of Bristol BristolBS8 1UB United Kingdom Institute of Artificial Intelligence Shanghai Em-Data Technology Co. Ltd. Shanghai200000 China School of Environment Science and Spatial Informatics China University of Mining and Technology XuzhouJiangsu221116 China School of Electronics Computing and Mathematics University of Derby DerbyDE22 1GB United Kingdom Institute for Information and System Sciences Xi’an Jiaotong University Xi’an China

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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A survey of classification methods for chest x-ray pathologies utilizing deep learning

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AIP Conference Proceedings 2023年第1期3015卷

作者： Israa Faisal Jassam Saleh Mesbah Elkaffas Adel A. El-Zoghabi Ayman Samir Beden Muhammad M. Saad Mohammed Tali Almalchy Registration and Students Affairs University Headquarter University of Anbar Anbar City 31001 Iraq. College of Computing and IT Arab Academy for Science Technology and Maritime Transport Alexandria Egypt. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt. Department of Computer Technology Engineering Faculty of Information Technology Imam Ja’afar Al-Sadiq University Maysan 10011 Iraq. Lead Data Scientist Head of Nawah.ai Stuttgart Germany. Department of Computer Engineering Faculty of Information Technology Imam Ja’afar Al-Sadiq University Maysan 10011 Iraq.

Any urgent diagnosis of pneumonia can be affected by a chest x-ray (CXR henceforth), which is one of the most common types of radiography. Artificial intelligence (AI) has been employed by most researchers for the automatic detection of the complicated patterns in imaging data and to provide quantitative analysis of radiographic properties as well. In recent years, many advances have taken place, namely in the computer vision domain. As a consequence, these advances enable the researchers to provide in-depth analysis and investigation, especially with the existence of thousands of samples of CXR images. CXR samples and techniques provide a huge support for the radiologists in making crucial decisions while examining hundreds of images from different perspectives. Moreover, time consumption will be reduced because the most urgent cases will be correctly identified in a short period of time. The current study will analyze ten of the well-known deep learning (DL)techniques that tackled identifying CXR images in relation to their actual diagnosis. Each technique will be briefly addressed regarding data preparation, methodology, and model design. Furthermore, the accuracy measure generated by each model will be compared to the dataset used. It is hoped that the current study will be a reliable resource for DL to strategies in depicting CXR images in the medical domain.

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