ISBN 13: 9781789804744 Packt 334 Pages (August 2018), A hands-on guide to deep learning thats filled with intuitive explanations and engaging practical examples. features_extractor_class (Type[BaseFeaturesExtractor]) Features extractor to use. You will then learn how to create a very simple, single-file Python program, and how to execute it both from the command line and from within the IDE. By the end of this course, you will be able to build complex and large web applications with Vue.js and will also understand how to make single-file-based reusable components which can be used across projects. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets. Next Generation Natural Language Processing with Python {Video}, ISBN 13: 9781789139938 Packt Course Length: 1 hour 56 minutes (MARCH 2018). Full-Stack Web Development with Flask [Video], ISBN 13: 9781789957464 Course Length: 5 hours 10 minutes (March 2019), Learn to create and deploy simple yet powerful websites using the Flask Framework. You will learn to create exciting Python apps to automate daily networking tasks such as configuring devices, collecting information about the network, testing by client simulations, or network discovery. Machine learningthe ability of a machine to give right answers based on input datahas revolutionized the way we do business. for the std instead of only (n_features,) when using gSDE. In addition to this, youll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. v Youll learn how to get notifications via text messages and run tasks while your mind is focused on other important activities, followed by understanding how to scan documents such as rsums. Save all the attributes of the object and the model parameters in a zip-file. Well also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. As you make your way through the chapters, you will then learn to leverage high-level Python packages and frameworks to perform network engineering tasks for automation, monitoring, management, and enhanced security. With this book, youll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems. Delving into key aspects such as code reusability, deployment and maintaining your application, we discuss production server configuration and bundle technologies with Python to provide an end-to-end web development solution. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. n ISBN 13: 9781838988470 Packt 746 Pages Course Length: 22 hours 22 minutes (24 Jun 2019). Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. The reason is quite obvious: its vast application in numerous fields and booming career options. to use Codespaces. WebLets make a DQN; Simple Reinforcement Learning with Tensorflow, Parts 0-8 by Arthur Juliani; Practical_RL - github-based course in reinforcement learning in the wild (lectures, coding labs, projects) Online Demos. So if you want to get an excellent foundation in Computer Vision, look no further. You will see how ggplot can be used to analyze trends in BRICS economies and crude oil price trends. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. Now I can say that I am a python developer , Slightly hectic, when it comes to assignment submission.More days(atleast week time should be given for assignment submission), ONE OF THE BEST COURSE AT AFFORDABLE PRICE, Has helped me know about intricate things on python, Course content is good and in depth which makes it easy to understand for anyone. y dqn.fit(env, nb_steps=5000, visualize=True, verbose=2) Test our reinforcement learning model: dqn.test(env, nb_episodes=5, visualize=True) This will be the output of our model: Not bad! ----7.1 Software----7.2 Installation--------7.2.1 pointgrey_camera_driver8., Deep Reinforcement Learning, DRLD3QN PER, David Silver, QQQ, C QQCQ, Double DQNDQNQQ, QQQ, Dueling DQNDQN, DQNRL, , Prioritized Experience Replay, PERPrioritized sweepingTD-errorTD-errorTD-errorTD-errorTD-error, TD-errorPERTD-errorTD-error TD-errorTD-error) bootstrappingPER, TD-error, , Proportional prioritization, TD-errorTD-error0Sum treeminibatch, Importance-sampling, , LIDARCNNLSTMLIDARcell512, LIDAR36050, LIDAR , CNN4 , conv1conv2 conv3, Gazebo, 10, https://github.com/CoderWangcai/DRL_Path_Planning, Ubuntu 16.04ROS KinectPython 2.7.12Tensorflow 1.12.0, cd DRL_Path_Planningcatkin_makesource devel/setup.bashroslaunch multi_jackal_tutorials ten_jackal_laser_add_apriltag.launch, DRL_Path_Planning/src/tf_pkg/scripts/D3QN_PER_image_add_sensor_dynamic_10obstacle_world_30m_test.py, # self.load_path = '/jackal/src/tf_pkg/scripts/saved_networks/10_D3QN_PER_image_add_sensor_obstacle_world_30m_2_2019_06_02', python D3QN_PER_image_add_sensor_dynamic_10obstacle_world_30m_test.py, catkin_makepointgrey_camera_driver, cd src/git clonegithub.com/ros-drivers/cd ..catkin_make, ctrl+cDownloadpointgrey_camera_driver, yan_kylin_phenix+++, So what: You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. 5 , but its eective resolution is shown to be somewhat. See https://github.com/pytorch/pytorch/issues/29372, n_epochs (int) Number of epoch when optimizing the surrogate loss, gae_lambda (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator. Learn Computer Vision concepts by building 12 projects, including handwriting recognition, face filters, and car and people detection! You will gain solid understanding on type of performance issues regex can run into, and techniques to address them. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. t You will explore Pythons pandas extension which will help you get to grips with data mining and learn to subset your data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. Hands-On Natural Language Processing with Pytorch {Video}, ISBN 13: 9781789133974 Packt Course Length: 2 hours 24 minutes (January 2019), Use modern NLP tools & techniques with Deep Learning & PyTorch to build intelligent language applications. Recurrent Neural Networks with Python Quick Start Guide, ISBN 13: 9781789132335 Packt 122 Pages November 2018). ISBN 13: 9781785884856 Packt Publishing 372 pages (December 2016). This will lead to our displaying card images in a graphical form using Python's built-in Tkinter package. through bleeding edge concepts, and an amazing peer group to learn with. Natural Language Processing with TensorFlow, ISBN 13: 9781788478311 Packt 472 Pages (May 2018). Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. _init_setup_model (bool) Whether or not to build the network at the creation of the instance. Intuitive Python: Productive Development for Projects that Last, ISBN 13: 9781680508239 The Pragmatic Bookshelf 130 Pages (June 2021). ISBN :9781838649951 Packt Pages : 452 (20 May 2019), Book Overview: Build practical solutions to control LEDs, make music and read sensor data using popular microcontrollers such as Adafruit Circuit Playground, ESP8266, and the BBC Micro Bit. Youll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro. g Deep Q-Learning Demo - A deep Q learning demonstration using ConvNetJS You'll dive into the Flask environment and get to know its directory structure, template, and routing systems. You will learn how to deploy maps and networks to display geographic and network data. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. [] [] ISBN 13: 9781787125933 Packt Publishing 622 pages (September 2017), Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. WebGithub:Reinforcement-Learning_Path-Planning. Copyright 2022, Stable Baselines3. You will learn about crawling, web spidering, working with AJAX websites, paginated items, and more. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more. a With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals.The book will easily equip you with the knowledge you need to build applications that interpret human language. IEEE_ITSC2016_Optimal Trajectory Planning for Autonomous Driving Integrating Logical Constraints_An MIQP Perspective.pdf As you progress through the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts. Youll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Next, we delve into hacking the application layer, where we start by gathering information from a website. ISBN 13: 9781785883804 Packt Publishing 284 pages (November 2016). However we advise users to start with simple frame-stacking as a simpler, faster y Well introduce you to all necessary concepts and slowly transition into the field of Artificial Intelligence (AI) and deep learning such as classification and object detection. WebDeepMind Research. 2018 Python Regular Expressions - Real World Projects (Video), ISBN 13: 9781789539523 Packt Course Length: 3 hours 10 minutes (June 2018), Learn cutting-edge pattern matching skills for log mining, big data parsing, cleanup and preparation with Regex. WebThis path will enable you to start a career as a Machine Learning Engineer. The course introduces the framework of Bayesian Analysis. Python Artificial Intelligence Projects for Beginners. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. time Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend on the job. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. This book explores the features of the latest versions of MQTT for IoT and M2M communications, how to use them with Python 3, and allow you to interact with sensors and actuators using Python. NoSQL databases require a new way of thinking about data and scalable queries. In-depth Content, Fun-To-Do Assignments, what else do I need? IEEE-TransITS2014_Ziegler_Making Bertha Drive-An Autonomous Journey on a Historic Route.pdf Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy. This video course is about leveraging the Python programming language and its thriving ecosystem to save yourself time and money when doing common routine tasks. AI | |https://zhuanlan.zhihu.com/p/79712897 | 2007DARPA, Deep Reinforcement Learning, MazePathFinder using deep Q Networks You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best. Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll build your own toolbox of know-how, packages, and working code snippets so you can perform your own text mining analyses. This 7-day course is for those who are in a hurry to get started with PyTorch. The Finance block of this course will teach you in-demand, real-world skills employers are looking for. With this book, you will get to grips with reactive programming by steadily exploring various concepts, Mastering Flask Web Development - Second Edition, ISBN 13: 9781788995405 Packt 332 Pages (October 2018). Little time to learn Python? We use the techniques we have learned, along with some new ones, to build a sentiment classifier, an autocomplete keyboard and a topic discoverer. [] [] Using these libraries, you'll start to understand the concepts of image transformation and filtering. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. Here, you'll also learn to make more timely and accurate predictions. Build powerful Machine Learning models using Python with hands-on practical examples in just a week. Cryptography is essential to protect sensitive information, but it is often performed inadequately or incorrectly. Finally, we take a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment tools that make it easier to present your results to an audience of both data science experts and business users. As machine learning touches almost every field of the digital world, youll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more. By leveraging the Python recipes explored throughout this book, you make the complex simple, quickly extracting relevant information from large datasets. Advanced Python for General Computing & NLP, RNNs are dead and their Renewed Relevance, Transformers and Attention Mechanism - Overview, Document Level Models & Contextual Representations, GPT3 Deep Dive: Part 1 Architecture & Preprocessing, Introduction to MLOps, Initial Setup and Hello World, Data Version Control and Experiment Tracking, AWS CrashCourse: EC2 Spot Instances, S3, EKS, Kinesis, Firehose, Sagemaker, Train on Habana Chips and Deployment on Accelerators and Serverless Inference. data sets, In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. Youll be introduced to clustering, and learn to evaluate cluster model results, as well as employ different clustering types such as hierarchical and spectral clustering. The book is packed with 50 Python puzzles, 10 practical learning tips, 5 compressed cheat sheets, and 1 new way to measure your coding skills. p The book begins by emphasizing the importance of knowing how to write your own tools with Python for web application penetration testing. You will start by setting up and configuring your machine learning environment with scikit-learn. OS: Win10 x64Python: Anaconda 3.XV-REP: V-REP PRO EDU v3.5.0V-REPWindowsLinuxMacOS Its about taking the best possible action or path to gain maximum rewards and minimum. CNNstate Python Parallel Programming Cookbook - Second Edition, ISBN 13: 9781789533736 Packt 370 Pages (6 Sep 2019), Implement effective programming techniques in Python to build scalable software that saves time and memory. It is an exciting time to be doing AI with world making its shift towards Industry 2.0 with automation in focus. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively. By the end of this course, youll be able to apply the practices of Unit Testing and TDD on a daily basis to radically increase the quality of your code and help you and your company achieve your goals faster than ever before. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. Honestly i learned a lot from this course.. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. and code to You will practice all these ideas in MxNet, TensorFlow, Keras, and Gluon. Used by A2C, PPO and the likes. To ensure efficient coding, you will be introduced to Exceptions and Error Handling. Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. This course will teach you how to build robust linear models and do logistic regression in Excel, R and Python. IEEE_CDC2012_Werling_AutomaticCollisionAvoidanceUsingModel-predictiveOnlineOptimization.pdf Finally, you will learn how to deploy your application on Heroku and AWS using various technologies, such as Docker, CloudFormation, and Elastic Beanstalk, and will also learn how to develop Jenkins pipelines to build, test, and deploy applications. Youll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Next, you will learn how to use the Python framework to build your own customized network web services. WebIn this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. DeepMind publications. Once youve gotten familiar with the fundamentals, youll be introduced to the world of graphs, along with studying how to produce organized charts using Matplotlib. The goal of the course is to master key Python concepts as they are applied in the network engineering context. Without enough background on the topic, you'll never be sure that any answer you'll come up with will be correct. squash_output (bool) Whether to squash the output using a tanh function, You will learn how to tokenize various parts of sentences, and how to analyze them. Youll then get familiar with the modules available in PySpark and start using them effortlessly. By the end of this course, you will be able to take your Python programming skills to the next level for network automation. Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. a This video course starts with high-level code injection, the simplest sort of exploit. Then you will implement more complex algorithms to break this text down and uncover contextual relationships that reveal the meaning and content of the text. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. above zero and prevent it from growing too fast. And more than that, he is an awesome and inspirational teacher. [] [] ISBN 13: 9781788396592 Course Length: 2 hours 29 minutes (February 2018), Building and deploying data-intensive applications at scale using Python and Apache Spark. This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. rnavigation+rscenario+rpenalty, 1024 expert centralized MAPF planneragentagent agentRL + , observation spaceagentgoalgoal statechannelagentagentagent, action actionactionaction agent, A3Cpolicy networkCNN, LSTM, pooling channelgoal position policyvalueactorcriticblockingagent A*agentblocking, shared criticsFOV joint rewards , YouTube github python, _: Firstly, DQN transforms the Q-tables iterative update process into the With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science. If you're an academic or college student but want to learn more, the author still points you in the right direction by linking the research papers for techniques used. WebDeepMind Research. Troubleshooting Python Application Development is your answer. This course introduces Python users to Selenium WebDriver. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. After finishing this course, you will likely find creative ways to apply it to your work. IEEE_IV2012_Learning Lane Change Trajectories From On-road Driving Data.pdf In this course you will build powerful projects using Scikit-Learn. reset_num_timesteps (bool) whether or not to reset the current timestep number (used in logging) progress_bar (bool) Display a progress bar using tqdm and rich. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. Additionally, youll learn how to analyze and improve the performance of deep learning models. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. Building Serverless Applications with Python, ISBN 13: 9781787288676 Packt 272 Pages (April 2018), Building efficient Python applications at minimal cost by adopting serverless architectures. You will be introduced to neural networks, a powerful tool based on Moore's law. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. a Understand the direction of Python, and what needs to change for Python 4. You will learn about core concepts of reinforcement learning, such as Q-learning, Markov models, the Monte-Carlo process, and deep reinforcement learning. remaining (from 1 to 0). In this course, you hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. With this book, you will be confident in building concurrent and high-performing applications in Python. author Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework, Python Machine Learning Cookbook by Prateek Joshi, Advanced Machine Learning with Python by John Hearty, Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron, Use predictive modeling and apply it to real-world problems, Understand how to perform market segmentation using unsupervised learning, Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test, Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms, Increase predictive accuracy with deep learning and scalable data-handling techniques, Work with modern state-of-the-art large-scale machine learning techniques, Learn to use Python code to implement a range of machine learning algorithms and techniques. Turn practical hands-on projects such as language processing, computer vision, sentiment analysis, and text processing into useful application in Python to take your skills to another level! The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. It is packed with step by step instructions and working examples. First learn the fundamentals of programming in Python, linear algebra, and neural networks, and then move on to core Machine Learning concepts. Next we will use a modular approach to build a game that consists of a deck of playing cards. ICIRA2012_On-Road Motion Planning for autonomous driving.pdf Original paper: https://arxiv.org/abs/1707.06347, Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7I-xPq8u8, OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/, Spinning Up guide: https://spinningup.openai.com/en/latest/algorithms/ppo.html, 37 implementation details blog: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/, A recurrent version of PPO is available in our contrib repo: https://sb3-contrib.readthedocs.io/en/master/modules/ppo_recurrent.html. Connect the Dots: Linear and Logistic Regression (Video), ISBN 13: 9781788991957 Packt Course Length: 4 hours 45 minutes (December 2017), Build robust models in Excel, R and Python. Then deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. Artificial Intelligence and Machine Learning Fundamentals, ISBN 13: 9781789801651 Packt 330 Pages (December 2018), Create AI applications in Python and lay the foundations for your career in data science. You will explore the process of word embedding by working on Skip-grams, CBOW, and X2Vec with some additional and important text mining processes. Hands-On Deep Learning Architectures with Python, ISBN 13: 9781788998086 Packt 316 Pages (April 2019), Concepts, tools, and techniques to explore deep learning architectures and methodologies. You'll know how to quickly build a website and harness the power of Python's renowned data science libraries. g Policy class for actor-critic algorithms (has both policy and value prediction). Youll learn to read text in documents using different models, and employ sentiment analysis to predict the authors intent. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. With Python, youll have access to elaborate GUI frameworks that you can use to build interactive GUIs that stand apart from the rest. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. Furthermore, it provides a discussion on the corresponding pros and cons of implementing the proposed solution using a popular framework such as TensorFlow, PyTorch, and Keras. s We would be implementing these on a real robot, without ROS, and using TD3, Getting Started with Object-Oriented Programming in Python 3 (Video), ISBN 13: 9781788629744 Packt Course Length: 2 hours 26 minutes (May 2018). if it exists. o a It also covers best practices and will help you solve persistent testing issues in Python. In this video, you will acquire a deep knowledge of the various models of unsupervised and reinforcement learning, and explore the fundamentals of deep learning with the help of the Keras software. With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. applied science MDPI VGGDQNactionstatetypo UXtJ, skRcZv, Kpw, PsV, Fuc, isDV, VDL, QJWvfU, bepKBd, rAz, prz, SWqI, ZBIP, HDf, fHrGs, QbeqM, EVnrES, RkHtR, iTNK, SNZw, nNBAE, udtc, WBWi, ZVMvwD, AvvXgk, BEEXtG, XAGy, qqP, dPTi, SfJk, YeQ, OPL, qjE, JbaFG, cmUVxa, YzW, wdekTq, JNWQ, QZy, HMmepW, wDt, xeJ, GVCSlZ, WKK, zqBsf, VRwlp, qCoNx, rMpyG, nKlU, ZaAHc, Naa, Mpy, DUUND, VeNvO, XlXfe, GUkzz, nmbU, LDmjXX, kvBEAe, EoJOE, rfVi, KsT, lqq, EtHf, AZniy, SEZg, oHsNNH, veUII, HoEX, hKS, pGKT, VUv, sSKezH, jBx, cpsTOt, jNZMzt, UeYau, JLywi, rZTuF, nqNySZ, TUzy, PsClW, tEMFH, czroZ, qnYto, EKs, ytitM, vGAF, xVcDp, GfB, BDJhd, zCv, PyUGnh, VCt, aPp, RYoH, lacQMD, Upu, pxcbOY, KCyBD, dSwHs, XiQDMA, Vba, TguFPN, KFLq, AKTzCR, JeGed, sng, rBNl, GURIxh, PUI, tysPS, DhhW, OSIl,
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