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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

arXiv

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arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics Data by Interpretable Contrastive Learning

CellMirror: Deciphering Cell Populations from Spatial Transc...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Junjie Xia Jiangnan Cui Zhenyu Huang Shuangquan Zhang Fang Yao Yijian Zhang Chunman Zuo Department of Applied Mathematics Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Institute Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Affiliated Shenzhen Maternity and Child Healthcare Hospital Institute of Maternal and Child Medicine Southern Medical University Shenzhen China Department of General Surgery Xinhua Hospital Affiliated to Shanghai Jiao Tong University of Medicine Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data.

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All-Optical Initialization, Readout, and Coherent Preparation of Single Silicon-Vacancy Spins in Diamond

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Physical Review Letters 2014年第26期113卷 263602-263602页

作者： Lachlan J. Rogers Kay D. Jahnke Mathias H. Metsch Alp Sipahigil Jan M. Binder Tokuyuki Teraji Hitoshi Sumiya Junichi Isoya Mikhail D. Lukin Philip Hemmer Fedor Jelezko Institute for Quantum Optics and Center for Integrated Quantum Science and Technology (IQst) Ulm University Ulm D-89081 Germany Department of Physics Harvard University 17 Oxford Street Cambridge Massachusetts 02138 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Advanced Materials R & D Laboratories Sumitomo Electric Industries Limited Itami Hyogo 664-0016 Japan Research Center for Knowledge Communities University of Tsukuba 1-2 Kasuga Tsukuba Ibaraki 305-8550 Japan Electrical & Computer Engineering Department Texas A&M University College Station Texas 77843 USA

The silicon-vacancy (SiV−) color center in diamond has attracted attention because of its unique optical properties. It exhibits spectral stability and indistinguishability that facilitate efficient generation of photons capable of demonstrating quantum interference. Here we show optical initialization and readout of electronic spin in a single SiV− center with a spin relaxation time of T1=2.4±0.2 ms. Coherent population trapping (CPT) is used to demonstrate coherent preparation of dark superposition states with a spin coherence time of T2⋆=35±3 ns. This is fundamentally limited by orbital relaxation, and an understanding of this process opens the way to extend coherence by engineering interactions with phonons. Hyperfine structure is observed in CPT measurements with the Si29 isotope which allows access to nuclear spin. These results establish the SiV− center as a solid-state spin-photon interface.

关键词： SILICON -- Optical properties ELECTRON spin states PHOTONIC crystals DIAMONDS PHONONS FINE structure (Physics)

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Micdir: Multi-Scale Inverse-Consistent Deformable Image Registration Using Unetmss with Self-Constructing Graph Latent

SSRN

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SSRN 2022年

作者： Chatterjee, Soumick Bajaj, Himanshi Siddiquee, Istiyak H. Subbarayappa, Nandish Bandi Simon, Steve Shashidhar, Suraj Bangalore Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and therefore can not track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions have been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet - which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013±0.0243 for intramodal and 0.6211±0.0309 for intermodal, while VoxelMorph achieved 0.7747±0.0260 and0.6071±0.0510, respectively. © 2022, The Authors. All rights reserved.

关键词： Image registration

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Corrections to “Toward Near-Ground Localization: Modeling and Applications for TOA Ranging Error” [Oct 17 5658-5662]

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IEEE Transactions on Antennas and Propagation 2018年第2期66卷 1052-1052页

作者： Cheng Xu Jie He Xiaotong Zhang Po-Hsuan Tseng Shihong Duan School of Computer and Communication Engineering University of Science and Technology Beijing and the Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

The authors of [1] would like to make the following corrections.

关键词： Antennas Distance measurement Time of arrival estimation Cramer-Rao bounds

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A Novel Texture Generation Super Resolution Model

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Procedia computer science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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Keeping speed and distance for aligned motion

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Physical Review E 2015年第1期91卷 012807-012807页

作者： Illés J. Farkas Jeromos Kun Yi Jin (金祎) Gaoqi He (何高奇) Mingliang Xu (徐明亮) MTA-ELTE Statistical and Biological Physics Research Group (Hungarian Academy of Sciences) Pázmány Péter sétány 1A Budapest 1117 Hungary Regional Knowledge Center ELTE Faculty of Sciences Irányi Dániel u. 4. Székesfehérvár 8000 Hungary Department of Biological Physics Eötvös University Pázmány Péter sétány 1A Budapest 1117 Hungary Department of Computer Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China School of Information Engineering Zhengzhou University 100 Science Avenue Zhengzhou 450001 China

The cohesive collective motion (flocking, swarming) of autonomous agents is ubiquitously observed and exploited in both natural and man-made settings, thus, minimal models for its description are essential. In a model with continuous space and time we find that if two particles arrive symmetrically in a plane at a large angle, then (i) radial repulsion and (ii) linear self-propelling toward a fixed preferred speed are sufficient for them to depart at a smaller angle. For this local gain of momentum explicit velocity alignment is not necessary, nor are adhesion or attraction, inelasticity or anisotropy of the particles, or nonlinear drag. With many particles obeying these microscopic rules of motion we find that their spatial confinement to a square with periodic boundaries (which is an indirect form of attraction) leads to stable macroscopic ordering. As a function of the strength of added noise we see—at finite system sizes—a critical slowing down close to the order-disorder boundary and a discontinuous transition. After varying the density of particles at constant system size and varying the size of the system with constant particle density we predict that in the infinite system size (or density) limit the hysteresis loop disappears and the transition becomes continuous. We note that animals, humans, drones, etc., tend to move asynchronously and are often more responsive to motion than positions. Thus, for them velocity-based continuous models can provide higher precision than coordinate-based models. An additional characteristic and realistic feature of the model is that convergence to the ordered state is fastest at a finite density, which is in contrast to models applying (discontinuous) explicit velocity alignments and discretized time. To summarize, we find that the investigated model can provide a minimal description of flocking.

关键词： ELECTROSTATIC flocking ANISOTROPY INTELLIGENT agents (computer software) UBIQUITOUS computing DISCRETIZATION methods computer engineering

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Face Recognition System Using Local Features Fusion for Multi-Masks

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Journal of Physics: Conference Series 2021年第1期2107卷

作者： Mustafa Zuhaer Nayef Al-Dabagh Muhammad Imran Ahmad Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Department of Computer Science College of Science Knowledge University Kirkuk Road 44001 Erbil Iraq

Face recognition is a relatively novel research field, and its application is closely related to numerous other areas. Moreover, it is emerging as a critical research theme due to its broad range of applications. Thus, many face recognition methods use a variety of feature extraction approaches. Nonetheless, the issue continues to be challenging, particularly identifying non-biological entities. This paper proposes an extended descriptor for local features of an effectual facial recognition system using a local directional pattern operator. This technique combines the Frei-Chen and Robinson masks' strengths by fusion of the directional features of LDP for these two masks; this elicits a robust feature extraction method for distinguishing faces. Experimental results using the Yale database show that the extended descriptor considerably improved recognition rate and better performance than traditional local feature descriptors.

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Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Yassin, Hadya Dubost, Florian Nürnberger, Andreas Speck, Oliver Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Faculty of Nature Sciences Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Faculty of Electrical Engineering and Information Technology Otto von Guericke University Magdeburg Germany Department of Biomedical Data Science Stanford University StanfordCA United States Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany

Deep learning models have shown their potential in recent years for several applications. However, most of the models are opaque in nature and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, there is a movement towards creating explainable/interpretable models or applying post-hoc explainability/interpretability methods on classifiers to build trust in deep learning models. Moreover, deep learning methods can be used for segmentation tasks, which typically require hard-to-obtain, time-consuming manually-annotated segmentation labels for training. This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The localisation heatmaps provided by the networks - representing the models' focus areas and being used in classification decision-making - can be directly interpreted, without requiring any post-hoc methods to derive information for model explanation. The models are trained by using the input image and only the classification labels as groundtruth in a supervised fashion - without using any information about the location of the region of interest (i.e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels. The final segmentation of the regions of interest is obtained by thresholding these heatmaps. The models were employed for the task of multi-class brain tumour classification using two different datasets, resulting in the best F1-score of 0.93 for the supervised classification task while securing a median Dice score of 0.67 ± 0.08 for the weakly-supervised segmentation task. Furthermore, the obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7% accuracy. Copyright © 2022, The Autho

关键词： Magnetic resonance imaging

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