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arXiv 2023年

作者： Yong, Suk Yee Ong, Cheng Soon Australia Astrophysics and Space Technologies Research Centre Macquarie University SydneyNSW2109 Australia School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia Machine Learning and Artificial Intelligence Future Science Platform CSIRO Australia Data61 CSIRO CanberraACT2601 Australia School of Computing The Australian National University CanberraACT2601 Australia

Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjected to biases and uncertainties as it is reliant on the scaling relation from a small sample of local active galactic nuclei. In this study, we propose the application of conformalised quantile regression (CQR) to quantify the uncertainties of the black hole predictions in a machine learning setting. We compare CQR with various prediction interval techniques and demonstrated that CQR can provide a more useful prediction interval indicator. In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties. That is it yields a tighter constraint on the prediction interval (hence more certain) for a larger black hole mass, and accordingly, bright and broad spectral line width source. Using a combination of neural network model and CQR framework, the recovered virial black hole mass predictions and uncertainties are comparable to those measured from the Sloan Digital Sky Survey. The code is publicly available at https://***/yongsukyee/uncertain_blackholemass. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia Computer science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

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Procedia Computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics

arXiv

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arXiv 2022年

作者： Negrea, Jeffrey Yang, Jun Feng, Haoyue Roy, Daniel M. Huggins, Jonathan H. Department of Statistics and Actuarial Science University of Waterloo Canada Vector Institute Canada Department of Mathematical Sciences University of Copenhagen Denmark Department of Mathematics & Statistics Boston University United States Department of Statistical Sciences University of Toronto Canada Faculty of Computing & Data Sciences Boston University United States

The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this theory–practice gap by characterizing the large-sample statistical asymptotics of SGAs via a joint step-size–sample-size scaling limit. We show that iterate averaging with a large fixed step size is robust to the choice of tuning parameters and asymptotically has covariance proportional to that of the MLE sampling distribution. We also prove a Bernstein–von Mises-like theorem to guide tuning, including for generalized posteriors that are robust to model misspecification. Numerical experiments validate our results and recommendations in realistic finite-sample regimes. Our work lays the foundation for a systematic analysis of other stochastic gradient Markov chain Monte Carlo algorithms for a wide range of models. © 2022, CC BY.

关键词： Sampling

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Existence of periodic and S-asymptotically periodic solutions to fractional diffusion equations with analytic semigroups

Existence of periodic and S-asymptotically periodic solution...

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作者： Mu, Jia Nan, Jiecuo Zhou, Yong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Xiangtan University Hunan411105 China Nonlinear Analysis and Applied Mathematics Research Group Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-Liouville fractional derivatives. Even so, in this paper, periodic (S-asymptotically periodic) mild or classical solutions for FDE with Weyl-Liouville fractional derivatives could be obtained in various fractional power spaces. In addition, a numerical simulation example and a specific example of fractional diffusion equation are given to verify the main theoretical results. © 2019 John Wiley & Sons, Ltd.

关键词： Diffusion

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Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

Sequence-to-Sequence Knowledge Graph Completion Based On Gat...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fengge Yi Xiumei Wei Xiaojing Liu Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

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Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

arXiv

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arXiv 2023年

作者： Horry, Michael James Chakraborty, Subrata Pradhan, Biswajeet Paul, Manoranjan Zhu, Jing Barua, Prabal Datta Rajendra Acharya, U. Chen, Fang Zhou, Jianlong School of Civil and Environmental Engineering Faculty of Engineering and IT University of Technology Sydney UltimoNSW2007 Australia IBM Australia Limited SydneyNSW2000 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Earth Observation Centre Institute of Climate Change Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia Department of Radiology Westmead Hospital WestmeadNSW2145 Australia Cogninet Brain Team Cogninet Australia SydneyNSW2010 Australia Faculty of Business Education Law & Arts University of Southern Queensland ToowoombaQLD4350 Australia School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia Data Science Institute University of Technology Sydney UltimoNSW2007 Australia

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray provides an inexpensive imaging mode for lung cancer diagnosis, especially for remote populations with limited healthcare access. Unfortunately, suspicious nodules are difficult to distinguish from pulmonary vascular and bone structures using Chest X-ray. Computer vision algorithms have previously been proposed to assist human radiologists in this task, however, the leading studies use down-sampled images and highly parameterized, computationally expensive methods with unproven generalization capability to unseen datasets. Instead, this study localizes lung nodules from chest X-ray images using highly efficient encoder-decoder neural networks that have been crafted to process full resolution input images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested internally using the Japanese Society of Radiological Technology lung nodule dataset. The trained networks are used to localize lung nodules from an independent external chest x-ray dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization from chest x-ray images. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the conventional validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and

关键词： Image resolution

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Variational secure cloud quantum computing

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Physical Review A 2022年第2期105卷 022603-022603页

作者： Yuta Shingu Yuki Takeuchi Suguru Endo Shiro Kawabata Shohei Watabe Tetsuro Nikuni Hideaki Hakoshima Yuichiro Matsuzaki Department of Physics Faculty of Science Division I Tokyo University of Science Shinjuku Tokyo 162-8601 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan NTT Communication Science Laboratories NTT Corporation3-1 Morinosato Wakamiya Atsugi Kanagawa 243-0198 Japan NTT Computer and Data Science Laboratories NTT corporation Musashino Tokyo 180-8585 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan

Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in VQAs, a parametrized ansatz circuit is used to generate a trial wave function, and the parameters are optimized to minimize a cost function. On the other hand, blind quantum computing (BQC) has been studied in order to provide a quantum algorithm with security by using cloud networks. A client with a limited ability to perform quantum operations hopes to have access to a quantum computer of a server, and BQC allows the client to use the server's computer without leakage of the client's information (such as input, running quantum algorithms, and output) to the server. However, BQC is designed for fault-tolerant quantum computing, and this requires many ancillary qubits, which may not be suitable for NISQ devices. Here, we propose an efficient way to implement the NISQ computing with guaranteed security for the client. In our architecture, only N+1 qubits are required, under an assumption that the form of ansätze is known to the server, where N denotes the necessary number of the qubits in the original NISQ algorithms. The client only performs single-qubit measurements on an ancillary qubit sent from the server, and the measurement angles can specify the parameters for the ansätze of the NISQ algorithms. The no-signaling principle guarantees that neither parameters chosen by the client nor the outputs of the algorithm are leaked to the server. This work paves the way for new applications of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum networks

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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological data Analysis

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Journal of Chemical Information and Modeling 2025年第11期65卷 5567-5575页

作者： Koseki, Jun Motono, Chie Yanagisawa, Keisuke Kudo, Genki Yoshino, Ryunosuke Hirokawa, Takatsugu Imai, Kenichiro Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Integrated Research Center for Self-Care Technology (irc-sct) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) Waseda University Shinjuku-ku Tokyo 169-8555 Japan Department of Computer Science School of Computing Institute of Science Tokyo Tokyo 152-8550 Japan Middle Molecule IT-based Drug Discovery Laboratory (MIDL) Institute of Science Tokyo Tokyo 152-8550 Japan Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba Ibaraki 305-8571 Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba Ibaraki 305-8575 Japan Transborder Medical Research Center University of Tsukuba Ibaraki 305-8577 Japan Global Research and Development Center for Business By Quantum-AI Technology (G-QuAT) National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8560 Japan

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein’s conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy. © 2025 The Authors. Published by American Chemical Society.

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