Review: Deep Learning in Electron Microscopy
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Open Source Used in Influx1.8 Influx 1.9
Open Source Used In Influx1.8 Influx 1.9 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-1178791953 Open Source Used In Influx1.8 Influx 1.9 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-1178791953 Contents 1.1 golang-protobuf-extensions v1.0.1 1.1.1 Available under license 1.2 prometheus-client v0.2.0 1.2.1 Available under license 1.3 gopkg.in-asn1-ber v1.0.0-20170511165959-379148ca0225 1.3.1 Available under license 1.4 influxdata-raft-boltdb v0.0.0-20210323121340-465fcd3eb4d8 1.4.1 Available under license 1.5 fwd v1.1.1 1.5.1 Available under license 1.6 jaeger-client-go v2.23.0+incompatible 1.6.1 Available under license 1.7 golang-genproto v0.0.0-20210122163508-8081c04a3579 1.7.1 Available under license 1.8 influxdata-roaring v0.4.13-0.20180809181101-fc520f41fab6 1.8.1 Available under license 1.9 influxdata-flux v0.113.0 1.9.1 Available under license 1.10 apache-arrow-go-arrow v0.0.0-20200923215132-ac86123a3f01 1.10.1 Available under -
Getting Started with Machine Learning
Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course -
Easybuild Documentation Release 20210907.0
EasyBuild Documentation Release 20210907.0 Ghent University Tue, 07 Sep 2021 08:55:41 Contents 1 What is EasyBuild? 3 2 Concepts and terminology 5 2.1 EasyBuild framework..........................................5 2.2 Easyblocks................................................6 2.3 Toolchains................................................7 2.3.1 system toolchain.......................................7 2.3.2 dummy toolchain (DEPRECATED) ..............................7 2.3.3 Common toolchains.......................................7 2.4 Easyconfig files..............................................7 2.5 Extensions................................................8 3 Typical workflow example: building and installing WRF9 3.1 Searching for available easyconfigs files.................................9 3.2 Getting an overview of planned installations.............................. 10 3.3 Installing a software stack........................................ 11 4 Getting started 13 4.1 Installing EasyBuild........................................... 13 4.1.1 Requirements.......................................... 14 4.1.2 Using pip to Install EasyBuild................................. 14 4.1.3 Installing EasyBuild with EasyBuild.............................. 17 4.1.4 Dependencies.......................................... 19 4.1.5 Sources............................................. 21 4.1.6 In case of installation issues. .................................. 22 4.2 Configuring EasyBuild.......................................... 22 4.2.1 Supported configuration -
Bringing Probabilistic Programming to Scientific Simulators at Scale
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, and Frank Wood SC19, Denver, CO, United States 19 November 2019 Simulation and HPC Computational models and simulation are key to scientific advance at all scales Particle physics Nuclear physics Material design Drug discovery Weather Climate science Cosmology 2 Introducing a new way to use existing simulators Probabilistic programming Simulation Supercomputing (machine learning) 3 Simulators Parameters Outputs (data) Simulator 4 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output 5 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output 6 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output WE NEED THE INVERSE! 7 Simulators Parameters Outputs (data) Simulator Prediction: ● Simulate forward evolution of the system ● Generate samples of output Inference: ● Find parameters that can produce (explain) observed data ● Inverse problem ● Often a manual process 8 Simulators Parameters Outputs (data) Inferred Simulator Observed data parameters Gene network Gene expression 9 Simulators Parameters Outputs (data) -
Rethinking the Recall Measure in Appraising Information Retrieval Systems and Providing a New Measure by Using Persian Search Engines
Archive of SID International Journal of Information Science and Management Vol. 17, No. 1, 2019, 1-16 Rethinking the Recall Measure in Appraising Information Retrieval Systems and Providing a New Measure by Using Persian Search Engines Mohsen Nowkarizi Mahdi Zeynali Tazehkandi Associate Prof. Department of Knowledge & M.A. Department of Knowledge & Information Information Sciences, Faculty of Education Sciences, Faculty of Education Sciences & Sciences & Psychology, Ferdowsi University Psychology, Ferdowsi University of Mashhad, of Mashhad, Mashhad, Iran Mashhad, Iran Corresponding Author: [email protected] Abstract The aim of the study was to improve Persian search engines’ retrieval performance by using the new measure. In this regard, consulting three experts from the Department of Knowledge and Information Science (KIS) at Ferdowsi University of Mashhad, 192 FUM students of different degrees from different fields of study, both male and female, were asked to conduct the search based on 32 simulated work tasks (SWT) on the selected search engines and report the results by citing the related URLs. The Findings indicated that to measure recall, one does not focus on how documents are selecting, but the retrieval of related documents that are indexed in the information retrieval system database is considered While to measure comprehensiveness, in addition to considering the related documents' retrieval in the system's database, the performance of the documents selecting on the web (performance of crawler) was also measured. At the practical level, there was no strong correlation between the two measures (recall and comprehensiveness) however, these two measure different features. Also, the test of repeated measures design showed that with the change of the measure from recall to comprehensiveness, the search engine’s performance score is varied. -
Advances in Electron Microscopy with Deep Learning
Advances in Electron Microscopy with Deep Learning by Jeffrey Mark Ede Thesis To be submitted to the University of Warwick for the degree of Doctor of Philosophy in Physics Department of Physics January 2021 Contents Contents i List of Abbreviations iii List of Figures viii List of Tables xvii Acknowledgments xix Declarations xx Research Training xxv Abstract xxvi Preface xxvii I Initial Motivation......................................... xxvii II Thesis Structure.......................................... xxvii III Connections............................................ xxix Chapter 1 Review: Deep Learning in Electron Microscopy1 1.1 Scientific Paper..........................................1 1.2 Reflection............................................. 100 Chapter 2 Warwick Electron Microscopy Datasets 101 2.1 Scientific Paper.......................................... 101 2.2 Amendments and Corrections................................... 133 2.3 Reflection............................................. 133 Chapter 3 Adaptive Learning Rate Clipping Stabilizes Learning 136 3.1 Scientific Paper.......................................... 136 3.2 Amendments and Corrections................................... 147 3.3 Reflection............................................. 147 Chapter 4 Partial Scanning Transmission Electron Microscopy with Deep Learning 149 4.1 Scientific Paper.......................................... 149 4.2 Amendments and Corrections................................... 176 4.3 Reflection............................................. 176 -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
CV, Dlib, Flask, Opencl, CUDA, Matlab/Octave, Assembly/Intrinsics, Cmake, Make, Git, Linux CLI
Arun Arun Ponnusamy Visakhapatnam, India. Ponnusamy Computer Vision [email protected] Research Engineer www.arunponnusamy.com github.com/arunponnusamy ㅡ Skills Passionate about Image Processing, Computer Vision and Machine Learning. Key skills - C/C++, Python, Keras,TensorFlow, PyTorch, OpenCV, dlib, Flask, OpenCL, CUDA, Matlab/Octave, Assembly/Intrinsics, CMake, Make, Git, Linux CLI. ㅡ Experience care.ai (5 years) Computer Vision Research Engineer JAN 2019 - PRESENT, VISAKHAPATNAM Key areas worked on - image classification, object detection, action recognition, face detection and face recognition for edge devices. Tools and technologies - TensorFlow/Keras, TensorFlow Lite, TensorRT, OpenCV, dlib, Python, Google Cloud. Devices - Nvidia Jetson Nano / TX2, Google Coral Edge TPU. 1000Lookz Senior Computer Vision Engineer JAN 2018 - JAN 2019, CHENNAI Key areas worked on - head pose estimation, face analysis, image classification and object detection. Tools and technologies - TensorFlow/Keras, OpenCV, dlib, Flask, Python, AWS, Google Cloud. MulticoreWare Inc. Software Engineer JULY 2014 - DEC 2017, CHENNAI Key areas worked on - image processing, computer vision, parallel processing, software optimization and porting. Tools and technologies - OpenCV, dlib, C/C++, OpenCL, CUDA. ㅡ PSG College of Technology / Bachelor of Engineering Education JULY 2010 - MAY 2014, COIMBATORE Bachelor’s degree in Electronics and Communication Engineering with CGPA of 8.42 (out of 10). Favourite subjects - Digital Electronics, Embedded Systems and C Programming. ㅡ Notable Served as one of the Joint Secretaries of Institution of Engineers(I) (students’ chapter) of PSG College of Technology. Efforts Secured first place in Line Tracer Event in Kriya ‘13, a Techno management fest conducted by Students Union of PSG College of Technology. Actively participated in FIRST Tech Challenge, a robotics competition, conducted by Caterpillar India. -
March 2012 Version 2
WWRF VIP WG CONNECTED VEHICLES White Paper Connected Vehicles: The Role of Emerging Standards, Security and Privacy, and Machine Learning Editor: SESHADRI MOHAN, CHAIR CONNECTED VEHICLES WORKING GROUP PROFESSOR, SYSTEMS ENGINEERING DEPARTMENT UA LITTLE ROCK, AR 72204, USA Project website address: www.wwrf.ch This publication is partly based on work performed in the framework of the WWRF. It represents the views of the authors(s) and not necessarily those of the WWRF. EXECUTIVE SUMMARY The Internet of Vehicles (IoV) is an emerging technology that provides secure vehicle-to-vehicle (V2V) communication and safety for drivers and their passengers. It stands at the confluence of many evolving disciplines, including: evolving wireless technologies, V2X standards, the Internet of Things (IoT) consisting of a multitude of sensors that are housed in a vehicle, on the roadside, and in the devices worn by pedestrians, the radio technology along with the protocols that can establish an ad-hoc vehicular network, the cloud technology, the field of Big Data, and machine intelligence tools. WWRF is presenting this white paper inspired by the developments that have taken place in recent years in standards organizations such as IEEE and 3GPP and industry consortia efforts as well as current research in academia. This white paper provides insights into the state-of-the-art regarding 3GPP C-V2X as well as security and privacy of ETSI ITS, IEEE DSRC WAVE, 3GPP C-V2X. The White Paper further discusses spectrum allocation worldwide for ITS applications and connected vehicles. A section is devoted to a discussion on providing connected vehicles communication over a heterogonous set of wireless access technologies as it is imperative that the connectivity of vehicles be maintained even when the vehicles are out of coverage and/or the need to maintain vehicular connectivity as a vehicle traverses multiple wireless access technology for access to V2X applications. -
Advanced Data Mining with Weka (Class 5)
Advanced Data Mining with Weka Class 5 – Lesson 1 Invoking Python from Weka Peter Reutemann Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Lesson 5.1: Invoking Python from Weka Class 1 Time series forecasting Lesson 5.1 Invoking Python from Weka Class 2 Data stream mining Lesson 5.2 Building models in Weka and MOA Lesson 5.3 Visualization Class 3 Interfacing to R and other data mining packages Lesson 5.4 Invoking Weka from Python Class 4 Distributed processing with Apache Spark Lesson 5.5 A challenge, and some Groovy Class 5 Scripting Weka in Python Lesson 5.6 Course summary Lesson 5.1: Invoking Python from Weka Scripting Pros script captures preprocessing, modeling, evaluation, etc. write script once, run multiple times easy to create variants to test theories no compilation involved like with Java Cons programming involved need to familiarize yourself with APIs of libraries writing code is slower than clicking in the GUI Invoking Python from Weka Scripting languages Jython - https://docs.python.org/2/tutorial/ - pure-Java implementation of Python 2.7 - runs in JVM - access to all Java libraries on CLASSPATH - only pure-Python libraries can be used Python - invoking Weka from Python 2.7 - access to full Python library ecosystem Groovy (briefly) - http://www.groovy-lang.org/documentation.html - Java-like syntax - runs in JVM - access to all Java libraries on CLASSPATH Invoking Python from Weka Java vs Python Java Output public class Blah { 1: Hello WekaMOOC! public static void main(String[] -
ML Cheatsheet Documentation
ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions. -
Comparativa De Tecnologías De Deep Learning
Universidad Carlos III de Madrid Departamento de Informática Proyecto Final de Carrera Comparativa de Tecnologías de Deep Learning Author: Alberto Lozano Benjumea Supervisors: Alejandro Baldominos Gómez Ignacio Navarro Martín Leganés.Septiembre de 2017 Alberto Lozano Benjumea Universidad Carlos III de Madrid Avenida de la Universidad, 30 28911 Leganés, Madrid (Spain) Email: [email protected] “Just because it’s not nice doesn’t mean it’s not miraculous” Terry Pratchett This page has been intentionally left blank. Abstract Este proyecto se localiza en la rama de aprendizaje automático del campo de la in- teligencia artificial. El objetivo del mismo es realizar una comparativa de los diferentes frameworks y bibliotecas existentes en el mercado, a día de la realización del presente documento, sobre aprendizaje profundo. Esta comparativa, pretende compararlos us- ando diferentes métricas y una batería heterogénea de pruebas. Adicionalmente se realizará un detallado estudio del arte indicando las técnicas sobre las que sustentan dichas bibliotecas. Se indicarán con ejemplos cómo usar cada una de estas técnicas, las ventajas e inconvenientes de las mismas y sus relaciones. Keywords: aprendizaje profundo, aprendizaje automático frameworks, comparativa v This page has been intentionally left blank. Agradecimientos Probablemente se podría escribir la mitad del documento con agradecimientos a las personas que han influido en que este documento pueda existir. Podría partir de mi abuelo quién me enseñó la informática y lo que se podía lograr con el Amstrad y de ahí hasta el día de hoy, a familia, compañeros, amigos que te influyen, incluso las personas que influyen negativamente, con los que ves que que camino no debes escoger.