Factor Graphs and the Sum-Product Algorithm
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A tutorial paper that presents a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes either exactly or approximately various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms
Application Fundamentals (Android)
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The provided text discusses various aspects of Android app development fundamentals. It covers key concepts related to app components, the AndroidManifest.xml file, and app resources. Android apps are built using various components, including Activities, Services, Broadcast Receivers, and Content Providers. These components serve different purposes and have distinct lifecycles. Activities are used for user interaction, services for background tasks, broadcast receivers for system-wide event handling, and content providers for managing shared data.The AndroidManifest.xml file is essential for declaring app components, permissions, and other settings. It informs the Android system about the app's components and capabilities. For instance, it specifies the minimum API level, declares hardware and software requirements, and defines intent filters to enable components to respond to specific actions.It's crucial to declare app requirements, such as device features and minimum Android API levels, to ensure compatibility with different devices and configurations. These declarations help in filtering the app's availability on Google Play for users with compatible devices.Android apps rely on resources separate from code, including images, layouts, strings, and more. These resources are stored in various directories and can be tailored for different device configurations. Providing alternative resources allows for optimization across different languages, screen sizes, orientations, and other factors.
Understanding these fundamentals is essential for developing Android applications effectively, ensuring compatibility, and providing a consistent user experience across a wide range of devices and configurations.
GIS: Projections and their distortions
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In GIS, projections are helpful to take something plotted on a globe and convert it to a flat map that we can print or show on a screen. Unfortunately it also introduces distortions to the objects and features on the map. This not only distorts the objects visually, but the results for any spatial attribute calculations will also reflect this distortion (such as distance and area ). Below is a link to a quick primer on projections, types of distortions that can occur, and suggestions on how to choose a correct projection for your work.
Numpy - a Python Library
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Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.
Regulated Research Community of Practice
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The daily news clearly shows the increasing threat to safety and privacy of data, personal as well as intellectual property. While the requirements such as DFARS 7012, HIPAA, and Cybersecurity Maturity Model Certification (CMMC) improve the consistency of data handling between agencies and contractors and grantees, it leaves academic institutions to figure out how to meet such requirements in a cost-effective way that fits the research and education mission of the institution. Most institutions, agencies, and companies act in isolation with one-off contract language to address data security and safeguarding concerns. Even though cybersecurity has a clear and uniform goal of protecting data, a onesize solution does not fit all academic institutions.
By supporting this community with development of a community strategic roadmap, regular discussions and workshops, and a repository of generalized and specific resources for handling regulated research programs RRCoP lowers the barrier to entry for institutions handling new regulations.
Bash shell tutorial
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Training materials for using the bash (and zsh) shell.
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
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A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
UCLA Extended Reality (XR) collaboration resources and Workshop
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Comprehensive Extended Reality (XR) collaboration resources for building a high performance extended reality (XR), augmented reality (AR), virtual reality (VR) and mixed reality campus teams. The tags set are a small subset of the the topics covered.
Weka
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Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
Vulkan Support Survey across Systems
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It's not uncommon to see beautiful visualizations in HPC center galleries, but the majority of these are either rendered off the HPC or created using programs that run on OpenGL or custom rasterization techniques. To put it simply the next generation of graphics provided by OpenGL's successor Vulkan is strangely absent in the super computing world. The aim of this survey of available resources is to determine the systems that can support Vulkan workflows and programs. This will assist users in getting past some of the first hurdles in using Vulkan in HPC contexts.
Feed Forward NNs and Gradient Descent
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Feed-forward neural networks are a simple type of network that simply rely on data to be "fed-forward" through a series of layers that makes decisions on how to categorize datum. Gradient descent is a type of optimization tool that is often used to train machines. These two areas in ML are good starting points and are the easiest types of neural network/optimization to understand.
MATLAB bioinformatics toolbox
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Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.
Bridges-2 Home Page
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Landing Page for Bridges-2 information
Ask.CI Q&A Platform for Research Computing
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CHARMM Links to Install, Run, and Troubleshoot MD Simulations
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CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a widely distributed molecular simulation program with a broad array of applications. CHARMM has the capabilities to setup and run simulations on both biological and materials systems, contains a comprehensive set of analysis and tools, and has high performance on a variety of platforms. Here you will find links to the CHARMM website, forum, and registration/download page.
Gaussian 16
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Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.
Data Imputation Methods for Climate Data and Mortality Data
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This slices and videos introduced how to use K-Nearest-Neighbors method to impute climate data and how to use Bayesian Spatio-Temporal models in R-INLA to impute mortality data. The demos will be added soon.
Astronomy data analysis with astropy
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Astropy is a community-driven package that offers core functionalities needed for astrophysical computations and data analysis. From coordinate transformations to time and date handling, unit conversions, and cosmological calculations, Astropy ensures that astronomers can focus on their research without getting bogged down by the intricacies of programming. This guide walks you through practical usage of astropy from CCD data reduction to computing galactic orbits of stars.
Thrust resources
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Thrust is a CUDA library that optimizes parallelization on the GPU for you. The Thrust tutorial is great for beginners. The documentation is helpful for anyone using Thrust.
Federated CI Resources
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Discussion about contributing cycles to the Open Science Grid.
Horovod: Distributed deep learning training framework
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Horovod is a distributed deep learning training framework. Using horovod, a single-GPU training script can be scaled to train across many GPUs in parallel. The library supports popular deep learning framework such as TensorFlow, Keras, PyTorch, and Apache MXNet.
AI for improved HPC research - Cursor and Termius - Powerpoint
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These slides provide an introduction on how Termius and Cursor, two new and freemium apps that use AI to perform more efficient work, can be used for faster HPC research.
NITRC
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The Neuroimaging Tools and Resources Collaboratory (NITRC) is a neuroimaging informatics knowledge environment for MR, PET/SPECT, CT, EEG/MEG, optical imaging, clinical neuroinformatics, imaging genomics, and computational neuroscience tools and resources.
Pandas - Python
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pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.
Educause HEISC-800-171 Community Group
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The purpose of this group is to provide a forum to discuss NIST 800-171 compliance. Participants are encouraged to collaborate and share effective practices and resources that help higher education institutions prepare for and comply with the NIST 800-171 standard as it relates to Federal Student Aid (FSA), CMMC, DFARS, NIH, and NSF activities.