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DS 200 (AUG) 0:1 Research Methods
This course will develop the soft skills required for the CDS students. The modules (each spanning 3 hours) that each student needs to complete include: Seminar attendance, literature review, technical writing (reading, writing, reviewing), technical presentation, CV/resume preparation, grant writing, Intellectual property generation (patenting), incubation/start-up opportunities, and academia/industry job search.
Compulsory for all CDS students and all modules needs to be completed by all students (more information)
DS 201 (AUG) 2:0 Bioinformatics
K. Sekar and D. Pal
Unix utilities, overview of various biological databases (Protein Data Bank, structural classification of proteins, genome database and Cambridge structural database for small molecules), introduction to protein structures, introduction to how to solve macromolecular structure using various biophysical methods, protein structure analysis, visualization of biological macro molecules, data mining techniques using protein sequences and structures. short sequence alignments, multiple sequence alignments, genome alignments, phylogenetic analysis, genome context-based methods, RNA and transcriptome analysis, mass spectrometry applications in proteome and metabolome analysis, molecular modeling, protein docking and dynamics simulation. Algorithms, scaling challenges and order of computing in big biological data.
Pre-requisites: Undergraduate level familiarity in Physics, Chemistry and Maths.
*C. Branden and J. Tooze (eds) Introduction to Protein Structure, Garland, 1991
*Mount, D.W., Bioinformatics: Sequence and Genome Analysis, Cold. Spring Harbor Laboratory Press, 2001.
*Baxevanis, A.D., and Ouellette, B.F.F. (Eds), Bioinformatics: A practical guide to the analysis of the genes and proteins, Wiley-Interscience, 1998
DS 202 (Jan) 2:1 Algorithmic Foundations of Big Data Biology (www)
Chirag Jain
This course will cover computer science techniques involved in the analysis of biological big data. The focus will be on understanding the algorithmic and mathematical foundations of the methods, and how these methods get implemented in associated tools to support biological applications. Hands-on programming assignments will be offered to appreciate the complexities of real sequencing data.
(0) Introduction- basics of biological data, high-throughput DNA/RNA sequencing and associated biotechnological breakthroughs, data structures and algorithms warm-up
(1) Exact string pattern matching: Z algorithm, Knuth-Morris-Pratt and Boyer-Moore
(2) Genome-scale index structures: suffix tries and suffix trees, Burrows-Wheeler Transform, FM-Index
(3) Approximate string pattern matching: Hamming distance, edit distance, dynamic programming, pairwise and multiple sequence alignment
(4) Alignment-free sequence comparison: co-linear chaining problem, whole-genome comparison
(5) Genome assembly: de Bruijn graphs, overlap graphs, haplotype assembly and phasing
(6) Pattern discovery: Hidden Markov models, gene finding
(7) Phylogenetics – algorithms for evolutionary tree reconstruction, distance-based phylogeny, neighbour-joining algorithm
(8) Trending topics – cancer genomics, deep learning in genomics, transcriptomics, single-cell omics, population genomics
Knowledge of basic data structures, algorithms, programming experience, and DS-221 (or) DS-201 (or) E0-251 (or) E0-225 (or) consent from the Instructor
· Gusfield, Dan. "Algorithms on strings, trees, and sequences: Computer science and computational biology." Acm Sigact News 28.4 (1997): 41-60.
· Durbin, Richard, et al. Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge university press, 1998.
· Jones, Neil C. and Pavel Pevzner. An introduction to bioinformatics algorithms. MIT press, 2004.
· Mäkinen, Veli, et al. Genome-scale algorithm design. Cambridge University Press, 2015.
· Aluru, Srinivas, ed. Handbook of computational molecular biology. CRC Press, 2005.
DS 211 (AUG) 3:0 Numerical Optimization
Deepak Subramani
Introduces numerical optimization with emphasis on convergence and numerical analysis of algorithms as well as applying them in problems of practical interest. Topics include: Methods for solving matrix problems and linear systems that arise in the context of optimization algorithms. Major algorithms in unconstrained optimization (e.g., modified Newton, quasi-Newton, steepest descent, nonlinear conjugate gradient, trust-region methods, line search methods), constrained optimization (e.g., simplex, barrier, penalty, sequential gradient, augmented Lagrangian, sequential linear constrained, interior point methods), derivative-free methods (e.g., simulated annealing, Bayesian optimization, Surrogate-assisted optimization), dynamic programming, and optimal control.
Pre-requisites: Basic knowledge of Numerical Methods, linear algebra, and/or consent from the advisor
*Numerical Optimization, J. Nocedal and S. Wright, Springer Series in Operations Research and Financial Engineering, 2006.
*Linear Programming with MATLAB, M. Ferris, O. Mangasarian, and S. Wright, MPS-SIAM Series on Optimization, 2007.
* Practical Methods of Optimization by R. Fletcher 2nd edition, Wiley, 1987.
DS 215 (AUG) 3:0 Introduction to Data Science
Anirban Chakraborty
Probability and Statistics Primer: Discrete and continuous random variables and their probability distributions, Theoretical distributions, Conditional probability distribution, Correlation, Independence, Vector random variables, Convergence of random sequences, Sums of random variables, Central limit theorem, Laws of large numbers, Random Processes and examples, Statistics as a function of time, Statistics between two processes, Sum processes, Independent increment and Markov property, Random telegraph signal, Poisson and Gaussian processes, Stationarity, Wide-sense stationarity, Time averages and Ergodic theorems, Statistical Inference: Estimation Theory, MVUE, CRLB, General linear models, Sufficient statistic, RBLS theorem, BLUEs, Gauss-Markov theorem, Maximum Likelihood (ML), Least-squares (LS), MMSE and Bayesian MSE, Bayesian ML, MAP, Statistical hypothesis testing.
Machine Learning (Unsupervised): Data clustering: K-means, Expectation-Maximization and Gaussian Mixture Model, Agglomerative clustering, Evaluation of clustering performance, Dimensionality reduction: Principal Component Analysis (PCA) and Random projection, Sparse coding and dictionary learning, K-SVD, Hands-on tutorial with Numpy, Scipy, Scikit-learn.
Machine Learning (Supervised): Linear regression, Overfitting, Bias-variance tradeoff, Regularization, Ridge regression, LASSO, Gaussian processes regression, K-NN, Logistic regression, Decision tree and random forest, Introduction to neural networks, Building blocks of ANN and modern deep nets, Forward propagation, Cost functions, Back propagation, Training by gradient descent, Regularization and dropout, Practical applications, Hands-on tutorial with Scikit-learn, Tensorflow/Pytorch.
Pre-requisites: Undergraduate level knowledge of linear algebra, multivariate calculus, numerical methods, basic programming skills (in any programming language).
* Athanasios Papoulis and S. Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes, McGraw Hill Education, 2017.
* Alberto Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Edition, Pearson, 2008.
* Steven M. Kay, Fundamentals of Statistical Signal Processing, VolumeI: Estimation Theory, Pearson, 1993.
* Jerome H. Friedman, Robert Tibshirani and Trevor Hastie, The Elements of Statistical Learning, Springer, 2001.
* Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
* Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016.
DS 221 (AUG) 3:1 Introduction to Scalable Systems (www)
Matthew Jacob, Sathish Vadhiyar and Chirag Jain
Architecture: computer organization, single-core optimizations including exploiting cache hierarchy and vectorization, parallel architectures including multi-core, shared memory, distributed memory and GPU architectures; Algorithms and Data Structures: algorithmic analysis, overview of trees and graphs, algorithmic strategies, concurrent data structures; Parallelization Principles: motivation, challenges, metrics, parallelization steps, data distribution, PRAM model; Parallel Programming Models and Languages: OpenMP, MPI, CUDA; Distributed Computing: Commodity cluster and cloud computing; Distributed Programming: MapReduce/Hadoop model.
Pre-requisites: Basic knowledge of system science and/or consent from the advisor
* Parallel Computing Architecture. A Hardware/Software Approach. David Culler, Jaswant Singh. Publisher: Morgan Kauffman. ISBN: 981-4033-103. 1999.
* Parallel Computing. Theory and Practice. Michael J. Quinn. Publisher: Tata: McGraw-Hill. ISBN: 0-07-049546-7. 2002.
* Computer Systems – A Programmer's Perspective. Bryant and O'Hallaron. Publisher: Pearson Education. ISBN: 81-297-0026-3. 2003.
* Data Structures, Algorithms, and Applications in C++, 2nd Edition, Sartaj Sahni
* Introduction to Parallel Computing. Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar. Publisher: Addison Wesley. ISBN: 0-201-64865-2. 2003.
* An Introduction to Parallel Programming. Peter S Pacheco. Publisher: Morgan Kauffman. ISBN: 978-93-80931-75-3. 2011.
* Online references for OpenMP, MPI, CUDA
* Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, Kai Hwang, Jack Dongarra and Geoffrey Fox, Morgan Kaufmann, 2011
* Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer, 2010
DS 222 (AUG) 3:1 Machine Learning with Large Datasets
P P Talukdar
Streaming algorithms and Naive Bayes, fast nearest neighbor, parallel perceptrons, parallel SVM, randomized algorithms, hashing, sketching, scalable SGD, parameter servers, graph-based semi-supervised learning, scalable link analysis, large-scale matrix factorization, speeding up topic modeling, big learning and data platforms, learning with GPUs.
Pre-requisites: Prior exposure to machine learning.
* Mining of Massive Dataset. Jure Leskovec, Anand Rajaraman, Jeff Ullman
* Scaling up Machine Learning: Parallel and Distributed Approaches. Ron Bekkerman, Mikhail Bilenko, John Langford
* Foundations of Data Science. Avrim Blum, John Hopcroft, Ravi Kannan
* Research literature
DS 226 (AUG) 2:1 Introduction to Computing for AI & Machine Learning
Sashikumaar Ganesan
This course is aimed at building the foundation of computational thinking with applications to Artificial Intelligence and Machine learning (AI&ML). Besides, how to build a neural network and how to train, evaluate and optimize it with TensorFlow will also be covered in this course.
Programming Foundation: Fundamentals of digital storage of data,Performance of a computer, Caches, Debugging and Profiling, Basic optimization techniques for serial codes.
Introduction to Object oriented programming: Object and Data Structure Basics, Python Statements, Methods and Functions,
Object-oriented programming (OOP): Inheritance,Encapsulation, Abstraction, Polymorphism. OOP concepts in Python. OOP concepts in C++. Python tools for Data Science: Pandas, NumPy, Matplotlib, Scikit-Learn, Just-in-Time(JIT) compilers, Numba
Computational Thinking: Arrays, Matrix-Vector, Matrix multiplication, Solving dense andsparse systems. Basic machine learning algorithms.
Deep Learning with Opensource AI/MLPackages: Tensors,Tensor Flow basics, mlpack, Interface to mlpack, Simple statistics and plotting, Loading and exploring data, Learning with Tensor Flow and Keras, Mini-project.
Prerequisites: Basic knowledge of mathematics, data structures, and algorithms.
1. John Hennessy David Patterson. Computer Architecture .A Quantitative Approach. 6th edition,Morgan Kauffman, 2017. https://www.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1
2. Shaw, Zed A.Learn python 3 the hard way: A very simpleintroduction to the terrifyinglybeautiful world of computers and code. Addison-WesleyProfessional, 2017.
3. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn,Keras, and TensorFlow, 2ndEdition, O'Reilly Media, Inc. 2019
DS 244 (JAN) 2:1 Hardware-aware Scientific Computing
Sashikumaar Ganesan
This course is focused on building hardware aware hybrid parallel libraries for Exascale computing. It is at the intersection of Computer architecture, Object-oriented parallel programming and Numerical algorithms.
Parallel Computer Architecture: Fundamentals of Parallel Multicore Architecture, Communication networks. Debugging and Profiling, GDB, Profiling with GProf.
Hybrid Parallel Programming Models: Shared Memory Programming basics, OpenMP with accelerator devices, C++ threads, Intel Thread Building Blocks, Message-passing, MPI with threads, OpenCL, CUDA with multi-GPU, CUDA-Aware MPI, Kokkos, Task-based programming, StarPU, MPI+X programming model.
Parallel Algorithms: Speedup & scalability, Roofline model, Performance measurements, Likwid. Computational LinearAlgebra, Sparse linear algebra; Iterative Solution of Linear Sparse Systems: SELL-C-sigma, Varying precision algorithms. High-Performance Libraries: MAGMA, MANDALA.
Course Plan: https://indianinstituteofscience.sharepoint.com/sites/hasc
Prerequisites: Good knowledge of C/C++ and Consent from the instructor
1. Frédéric Magoules, François-Xavier Roux, GuillaumeHouzeaux. Parallel Scientific Computing,Wiley, 2016, doi: 10.1002/9781118761687
2. Georg Hager, Gerhard Wellen. Introduction to HighPerformance Computing for Scientists andEngineers. CRC Press, 2010.
3. John Hennessy David Patterson. Computer Architecture.A Quantitative Approach. 6th edition,Morgan Kauffman, 2017. https://www.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1
4. Ananth Grama, Anshul Gupta, George Karypis, VipinKumar. Introduction to ParallelComputing. 2nd edition, Benjamin/Cummings, 2003.https://www-users.cs.umn.edu/~karypis/parbo
DS 250 (JAN) 3:1 Multigrid Methods
Sashikumaar Ganesan
Classical iterative methods, convergence of classical iterative methods, Richardson iteration method, Krylov subspace methods: Generalized minimal residual (GMRES), Conjugate Gradient (CG), Bi-CG method. Geometric Multigrid Method: Grid transfer, Prolongation and restriction operators, two-level method, Convergence of coarse grid approximation, Smoothing analysis. Multigrid Cycles: Vcycle, W-cycle, F-cycle, convergence of multigrid cycles, remarks on computational complexity. Algebraic Multigrid Method: Hierarchy of levels, Algebraic smoother, Coarsening, Interpolation, remarks on parallel implementation.
Pre-requisites: Good knowledge of Linear Algebra and/or consent from the instructor.
* Pieter Wesseling, An Introduction to Multigrid Methods, R.T. Edwards, Inc., 2004.
* William L. Briggs, Van Emden Henson and Steve F. McCormick, A Multigrid Tutorial, SIAM, 2nd edition, 2000.
DS 252 (AUG) 3:1 Cloud Computing
Yogesh Simmhan
Context: Shared/distributed memory computing; Data/task parallel computing; Role of Cloud computing.
Technology: Cloud Virtualization, Elastic computing; Infrastructure/Platform/Software as a Service (IaaS/PaaS/SaaS); Public/Private Clouds; Service oriented architectures; Mobile, Edge and Fog computing; Multi-clouds.
Application Design Patterns: Workflow and dataflow; Batch, transactional and continuous; Scaling, locality and speedup; Cloud, Mobile and Internet of Things (IoT) applications.
Execution Models: Synchronous/asynchronous patterns; Scale up/Scale out; Data marshalling/unmarshalling; Load balancing; stateful/stateless applications; Performance metrics; Consistency, Availability and Partitioning (CAP theorem).
Programming project using public Cloud infrastructure, e.g. Amazon AWS, Microsoft Azure Cloud resources provided.
Pre-requisites: Data Structures, Programming and Algorithm concepts. Programming experience.
* Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, Kai Hwang, Jack Dongarra and Geoffrey Fox, Morgan Kaufmann, 2011
* Current literature.
DS 255 (JAN) 3:1 System Virtualization
J. Lakshmi
Virtualization as a construct for resource sharing; Re-emergence of virtualization and it's importance for Cloud computing; System abstraction layers and modes of virtualization; Mechanisms for system virtualization – binary translation, emulation, para-virtualization and hardware virtualization; Virtualization using HAL layer – Exposing physical hardware through HAL (example of x86 architecture) from an OS perspective; System bootup process; Virtual Machine Monitor; Processor virtualization; Memory Virtualization; NIC virtualization; Disk virtualization; Graphics card virtualization; OS-level virtualization and the container model; OS resource abstractions and virtualization constructs (Linux Dockers example) ; Virtualization using APIs – JVM example.
Pre-requisites: Basic course on operating systems and consent of the instructor.
* J. Smith, R. Nair, Virtual Machines: Versatile Platforms for Systems and Processess, Morgan Kaufman, 2005.
* D. Bovet, M. Casti, Understanding the Linux Kernel, Third Edition, O'Reilly, 2005.
* Wolfgang Mauerer, Linux Kernel Architecture, Wiley India, 2012.
* D. Chisnall, The Definitive Guide to the Xen Hypervisor, Prentice Hall, 2007
* R. Bryant, D. O'Hallaron, Computer Systems: A Programmer's Perspective (2nd Edition), Addison Wesley, 2010
* Current literature.
DS 256 (JAN) 3:1 Scalable Systems for Data Science
Yogesh Simmhan
Design of distributed program models and abstractions, such as MapReduce, Dataflow and Vertex-centric models, for processing volume, velocity and linked datasets, and for storing and querying over NoSQL datasets.
Approaches and design patterns to translate existing data-intensive algorithms and analytics into these distributed programming abstractions.
Distributed software architectures, runtime and storage strategies used by Big Data platforms such as Apache Hadoop, Spark, Storm, Giraph and Hive to execute applications developed using these models on commodity clusters and Clouds in a scalable manner.
This course has a hands-on project where students will work with real, large datasets and commodity clusters, and use scalable algorithms and platforms to develop a Big Data application.
Pre-requisites: Data Structures, Programming and Algorithm concepts with strong programming experience, and DS 221 (or) DS 222 (or) DS 252 (or) consent from the Instructor
* Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer, 2010
* Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman and Jeff Ullman, 2nd Edition (v2.1), 2014.
* Current literature
DS 260 (JAN) 3:0 Medical Imaging
Phaneendra Yalavarthy
X-ray Physics, interaction of radiation with matter, X-ray production, X-ray tubes, dose, exposure, screen-filmradiography, digital radiography, X-ray mammography, X-ray Computed Tomography (CT). Basic principles of CT, single and multi-slice CT. Tomographic image reconstruction, filtering, image quality, contrast
resolution, CT artifacts. Magnetic Resonance Imaging (MRI): brief history, MRI major components. Nuclear
Magnetic Resonance: basics, localization of MR signal, gradient selection, encoding of MR signal, T1 and T2
relaxation, k-space filling, MR artifacts. Ultrasound basics, interaction of ultrasound with matter, generation
and detection of ultrasound, resolution. Doppler ultrasound, nuclear medicine (PET/SPECT), multi-modal
imaging, PET/CT, SPECT/CT, oncological imaging, medical image processing and analysis, image fusion,
contouring, segmentation, and registration.
Pre-requisites: Basic knowledge of system theory and Consent from the instructor.
* The Essential Physics of Medical Imaging, J. T. Bushberg, J. A. Seibert, E. M. Leidholdt Jr., and J. M. Boone, Second Edition, Lippincott Williams & Wilkins Publishers, 2002.
* Physics of Radiology, A. B. Wolbarst, Second Edition, Medical Physics Publishing, 2005.
* Current Literature
DS 261 (AUG) 3:1 Artificial Intelligence for Medical Image Analysis
Phaneendra K Yalavarthy
1). Overview of biological and medical imaging modalities and research/clinical applications
2). Quick introduction to: (a) medical imaging tools (viewers, formats, etc.) and (b) Pytorch/Tensorflow
3). Basic Mathematics (Linear Algebra, Probability, and Optimization)
4). Challenges in biomedical image data handling and curation
5). Detection / Segmentation / Image classification for biomedical images
6). Machine Learning Methods: SVM, PCA, KNN, FCM, etc. applied to medical image analysis
7). Overview of Neural networks and Deep Learning: Principle of learning, CNNs, Loss Functions, etc. for medical image analysis
8). Transfer learning, fine tuning, and generalization in medical image analysis
9). Evaluation methodology in medical image analysis: metrics, calibration, uncertainty, bias, etc.
10). Challenges in healthcare AI deployment: reproducibility, interpretability and regulatory
11). Unsupervised/self-supervised learning in biomedical image analysis
12). Generative models and Inverse problems in medical imaging
B. Laboratory Component:
1). Systematically test a number of methods for medical image analysis using artificial intelligence methods
2). Mini Project to solve a test problem in medical image analysis (Ex:- Segmentation of Hepatic vessels in kidney using X-ray CT images)
Pre-requisites: Basic knowledge of Systems and Signals, Proficiency in Python, C/C++ and Consent from the instructor.
* Kevin Zhou, Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches, Elsevier, 1st Edition – December 2, 2015.
* Jon Krohn, Grant Beyleveld, Aglaé Bassens, Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence, Addison Wesley, 2019.
* Current Literature
DS 263 (AUG) 3:1 Video Analytics
R. Venkatesh Babu, Anirban Chakrabarty and Arjun Jain
Revisit to Digital Image and Video Processing, Camera Models, Background Modelling, Object Detection and Recognition, Local Feature Extraction, Biologically Inspired Vision, Object Classification, Segmentation, Object Tracking, Activity Recognition, Anomaly Detection, Handling Occlusion, Scale and Appearance changes, Other Applications.
Pre-requisites: Image Processing, Probability, Linear Algebra.
* Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
* Forsyth, D.A., and Ponce, J., Computer Vision: A Modern Approach, Pearson Education, 2003.
* Current Literature
DS 265 (JAN) 3:1 Deep Learning for Computer Vision
R. Venkatesh Babu and Anirban Chakrabarty
Computer vision – brief overview; Machine Learning – overview of selected topics ; Introduction to Neural Networks, Backpropagation, Multi-layer Perceptrons ; Convolutional Neural Networks ; Training Neural Networks ; Deep Learning Software Frameworks ; Popular CNN Architectures ; Recurrent Neural Networks ; Applications of CNNs- Classification, Detection, Segmentation, Visualization, Model compression ; Unsupervised learning ; Generative Adversarial Networks.
Prerequisites: Basic knowledge of Computer Vision and Machine Learning, Proficiency in Python, C/C++.
- Current Literature
DS 269 (JAN) 2:1 Computational Methods for Reacting Flows
Aditya Konduri
Course description: This is an advanced elective course for research students. The first part of this course would train students in developing detail chemistry based reacting flow solvers, specifically relevant to combustion processes. Dimensionality reduction concepts coupled with neural networks for parametrising thermo-chemical properties that would significantly lower computational costs will be introduced. The second part of the course focuses on data analysis methods for combustion datasets. Both standard and machine learning based analyses methods would be covered. In addition to the theoretical background, the course would involve programming of a one-dimensional solver and hands-on data analysis exercises.
- Solver design:
- Governing equations: conservation of mass, momentum, energy and species. Low mach number and fully compressible formulations. Non-dimensional numbers. (1 week)
- Discretisation methods: finite difference and finite volume. (1 weeks)
Introduction to chemical kinetics: global and elementary reactions, Arrhenius equation, chemical time scales, stiffness. (1 week)
- Elements of a solver development: initial and boundary conditions, simulation algorithms, verification and validation (3 weeks)
- Dimensionality reduction: principal component analysis, higher order moment tensors (1 week)
- Regression methods for thermo-chemical coefficients (1.5 weeks)
- Data analytics:
- DNS database analysis: premixed and non-premixed turbulent flames, modes of combustion, flame structure, turbulence-chemistry interactions, chemical explosive mode analysis (3.5 weeks)
- Machine learning based analysis: flame surface extraction, detection of combustion instabilities (2 weeks)
Prerequisites: Basic knowledge in combustion (AE 241 or equivalent), numerical methods for differential equations (DS 289 or equivalent) and machine learning (E0 229 or equivalent), or a consent from the instructor. Good proficiency in programming.
Evaluation: assignments, paper presentation, project, final exam.
Reference Text books:
- An Introduction to Combustion, Stephen R. Turns, McGraw Hill, 2011.
- Theoretical and numerical combustion, Thierry Poinsot and Denis Veynante, RT Edwards Inc., 2005.
- Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control content, J. Nathan Kutz and Steven L. Brunton, Cambridge University Press, 2019.
- Research papers, material/notes provided by instructor.
DS 284 (AUG) 2:1 Numerical Linear Algebra
Venue: CDS 202 (Join Course Page for Aug 2022 Term)
Phani Motamarri
A foundational course on computational linear algebra and is fundamental to computational and data science research conducted in many emerging scientific disciplines including AI/ML and Quantum computing
Topics: Matrix and vector norms, floating points arithmetic, forward and backward stability of algorithms, conditioning of a problem, perturbation analysis, Solving linear systems, Gaussian elimination, LU factorization, Pivoting, Cholesky decomposition, Iterative refinement, QR factorization, Gram-Schmidt orthogonalization, Projections, Householder reflectors, Givens rotation, Singular Value Decomposition, Rank and matrix approximations, image compression using SVD, Least squares and least norm solution of linear systems, pseudoinverse, normal equations, Eigenvalue problems, Gershgorin theorem, Similarity transform, Eigenvalue & eigenvector computations and sensitivity, Power method, Schur decomposition, QR iteration with & without shifts, Hessenberg transformation, Rayleigh quotient, Symmetric eigenvalue problem, Computing the Singular Value Decomposition, Golub-Kahan-Reinsch algorithm, Chan SVD algorithm, Krylov subspace methods: Lanczos, Arnoldi, MINRES, GMRES, Conjugate Gradient.
Pre-requisites: Basic knowledge of multivariate calculus and elementary real analysis
* Biswa Nath Datta, Numerical Linear Algebra and Applications, 2nd Edition, 2004
* Lloyd N. Trefethen and David Bau, III, Numerical linear algebra, SIAM, 1997.
* C. G. Cullen, An Introduction to numerical linear algebra, Charles PWS Publishing, 1994.
* David C. Lay, Linear Algebra and its Applications, Pearson, 2013.
* Golub, G., Van Loan C.F., Matrix Computation, John Hopkins, 1996.
* Saad, Y., Iterative Methods for Sparse Linear Systems, Second Edition, SIAM, 2003
DS 288 (AUG) 3:0 Numerical Methods (Equivalent to UE 201 : Introduction to Scientific Computing)
Sashikumaar Ganesan/Phaneendra K Yalavarthy
Root finding: Functions and polynomials, zeros of a function, roots of a nonlinear equation, bracketing, bisection, secant, and Newton-Raphson methods. Interpolation, splines, polynomial fits, Chebyshev approximation. Numerical Integration and Differentiation: Evaluation of integrals, elementary analytical methods, trapezoidal and Simpson's rules, Romberg integration, Gaussian quadrature and orthogonal polynomials, multidimensional integrals, summation of series, Euler-Maclaurin summation formula, numerical differentiation and estimation of errors. Optimization: Extremization of functions, simple search, Nelder-Mead simplex method, Powell's method, gradient-based methods, simulated annealing. Complex analysis: Complex numbers, functions of a complex variable, analytic functions, conformal mapping, Cauchy's theorem. Calculus of residues. Fourier and Laplace Transforms, Discrete Fourier Transform, z transform, Fast Fourier Transform (FFT), multidimensional FFT, basics of numerical optimization.
Pre-requisites: Basic knowledge of multivariate calculus and elementary real analysis
* Richard L. Burden and J. Douglas Faires, Numerical Analysis: Theory and Applications, India Edition, Cengage Brooks-Cole Publishers, 2010.
* Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P., Numerical Recipes in C/FORTRAN, Prentice Hall of India, New Delhi, 1994.
* Borse, G.J., Numerical Methods with MATLAB: A Resource for Scientists and Engineers, PWS Publishing Co., Boston, 1997.
DS 289 (JAN) 3:1 Numerical Solution of Differential Equations
Aditya Konduri
Ordinary differential equations: Lipschitz condition, solutions in closed form, power series method. Numerical methods: error analysis, stability and convergence, Euler and Runge-Kutta methods, multistep methods, Adams-Bashforth and Adams-Moulton methods, Gear's open and closed methods, predictor-corrector methods. Sturm-Liouville problem: eigenvalue problems, special functions, Legendre, Bessel, and Hermite functions. Partial differential equations: classification, elliptic, parabolic and hyperbolic PDEs, Dirichlet, Neumann and mixed boundary value problems, separation of variables, Green's functions for inhomogeneous problems. Numerical solution of PDEs: relaxation methods for elliptic PDEs, Crank-Nicholson method for parabolic PDEs, Lax-Wendroff method for hyperbolic PDEs. Calculus of variations and variational techniques for PDEs, integral equations. Finite element method and finite difference time domain method, method of weighted residuals, weak and Galerkin forms, ordinary and weighted/general least squares. Fitting models to data, parameter estimation using PDEs.
Pre-requisites: Basic course on numerical methods and consent of the instructor.
* Arfken, G.B., and Weber, H.J., Mathematical Methods for Physicists, Sixth Edition, Academic Press, 2005.
* Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P., Numerical Recipes in C/FORTRAN – The art of Scientific Computing, Second Edn, Cambridge University Press, 1998.
* Lynch, D.R., Numerical Partial Differential Equations for Environmental Scientists and Engineers – A First Practical Course, Springer, New York, 2005.
DS 290 (AUG) 3:0 Modelling and Simulation
Soumyendu Raha
Statistical description of data, data-fitting methods, regression analysis, analysis of variance, goodness of fit. Probability and random processes, discrete and continuous distributions, Central Limit theorem, measure of randomness, Monte Carlo methods. Stochastic Processes and Markov Chains, Time Series Models. Modelling and simulation concepts,Discrete-event simulation: Event scheduling/Time advance algorithms verification and validation of simulation models. Continuous Simulation: Modelling with and Simulation of Stochastic Differential Equations
Pre-requisites: Basic course on numerical methods and consent of the instructor.
* Banks, J., Carson, J.S., and Nelson, B., Discrete-Event System Simulation, Second Edn, Prentice Hall of India, 1996.
* Francois E. Cellier, Ernesto Kofman, Continuous System Simulation, Springer, 2006, ISBN: 0387261028.
* Peter E. Kloden, Eckhard Platen, Numerical Solutions of Stochastic Differential Equations, Springer, Verlog, 1999.
* Peter E. Kloden, Eckhard Platen, Henri Schurz, Numerical Solution of SDE through Computer Experiments, Springer Verlog, 1994
DS 291 (AUG) 3:1 Finite Elements: Theory and Algorithms
Sashikumaar Ganesan
Generalized (weak) derivatives, Sobolev norms and associated spaces, inner-product spaces, Hilbert spaces, construction of finite element spaces, mapped finite elements, two- and three-dimensional finite elements,Interpolation and discretization error, variational formulation of second order elliptic boundary value problems, finite element algorithms and implementation for linear elasticity, Mindlin-Reissner plate problem, systems in fluid mechanics
Pre-requisites: Good knowledge of numerical analysis along with basic programming background and/or consent from the instructor.
* Sashikumaar Ganesan, Lutz Tobiska: Finite elements: Theory and Algorithms, Cambridge-IISc Series, Cambridge University Press, 2017
* Dietrich Braess, Finite Elements: Theory, Fast Solvers, and Applications in Solid Mechanics, Cambridge University Press, 3rd ed., 2007.
* Susanne C. Brenner, Ridgway Scott, The Mathematical Theory of Finite Element Methods, Springer-Verlag, 3rd ed., 2008.
* Current literature
DS 294 (JAN) 3:0 Data Analysis and Visualization
Anirban Chakraborty
Data pre-processing, data representation, data reconstruction, machine learning for data processing, convolutional neural networks, visualization pipeline, isosurfaces, volume rendering, vector field visualization, applications to biological and medical data, OpenGL, visualization toolkit, linear models, principal components, clustering, multidimensional scaling, information visualization.
Pre-requisites: Basic knowledge of numerical methods and consent from instructor
* Hansen, C.D., and Johnson, C.R., Visualization Handbook, Academic Press, 2004.
* Ware, C., Information Visualization: Perception for Design, Morgan Kaufmann, Second Edn, 2004.
* Current literature
DS 295 (JAN) 3:1 Parallel Programming (www)
Sathish Vadhiyar
Parallel Algorithms: MPI collective communication algorithms including prefix computations, sorting, graph algorithms, GPU algorithms; Parallel Matrix computations: dense and sparse linear algebra, GPU matrix computations; Algorithm models: Divide-and-conquer, Mesh-based communications, BSP model; Advanced Parallel Programming Models and Languages: advanced MPI including MPI-2 and MPI-3, advanced concepts in CUDA programming; Scientific Applications: sample applications include molecular dynamics, evolutionary studies, N-Body simulations, adaptive mesh reinements, bioinformatics; System Software: sample topics include scheduling, mapping, performance modeling, fault tolerance.
Pre-requisites: Introduction to Scalable Systems course (or)
Students are expected to be prepared on the slides that will be provided on introduction to parallel computing, OpenMP, MPI, CUDA.
* Parallel Computing. Theory and Practice. Michael J. Quinn. Publisher: Tata: McGraw-Hill. ISBN: 0-07-049546-7. 2002.
* Introduction to Parallel Computing. Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar. Publisher: Addison Wesley. ISBN: 0-201-64865-2. 2003.
* An Introduction to Parallel Programming. Peter S Pacheco. Publisher: Morgan Kauffman. ISBN: 978-93-80931-75-3. 2011.
* Online references for OpenMP, MPI, CUDA
* Literature: relevant conference and journal papers.
DS 299 0:28 Dissertation Project
This includes the analysis, design of hardware/software construction of an apparatus/instruments and testing and evaluation of its performance. The project work is usually based on a scientific/engineering problem of current interest. Every student has to complete the work in the specified period and should submit the Project Report for final evaluation. The students will be evaluated at the end first year summer for 4 credits. The split of credits term wise is as follows 0:4 Summer, 0:8 AUG, 0:16 JAN.
DS 303 (AUG) 2:0 Chemoinformatics
Debnath Pal
Exploring current chemoinformatics resources for synthetic polymers, pigments, pesticides, herbicides, diagnostic markers, biodegradable materials, biomimetics. Primary, secondary and tertiary sources of chemical information. Database search methods: chemical indexing, proximity searching, 2D and 3D structure and substructure searching. Introduction to quantum methods, combinatorial chemistry (library design, synthesis and deconvolution), spectroscopic methods and analytical techniques. Analysis and use of chemical reaction information, chemical property information, spectroscopic information, analytical chemistry information, chemical safety information. Representing intermolecular forces: ab initio potentials, statistical potentials, forcefields, molecular mechanics. Monte Carlo methods, simulated annealing, molecular dynamics. High throughput synthesis of molecules and automated analysis of NMR spectra. Predicting reactivity of biologically important molecules, combining screening and structure 'SAR by NMR'. Computer storage of chemical information, data formats, OLE, XML, web design and delivery.
Pre-requisites: Basic knowlege of chemistry and background in Maths.
* Current Scientific Literature and Web lectures: Lectures posted online.
* Maizell, R.E., How to find Chemical Information: A guide for Practicing Chemists, Educators, and students, John Wiley and Sons, 1998. ISBN 0-471-12579-2.
* Gasteiger, J., and Engel, T., Chemoinformatics. A Textbook, Wiley-VCH, 2003. ISBN: 3-527-30681-1
DS 305 (AUG) 3:1 Topics in Web-scale Knowledge Harvesting
P P Talukdar
Entity extraction, entity normalization, entity categorization, relation extraction, distant supervision, curriculum learning, knowledge base (KB) inference, open information extraction (OpenIE), temporal inference,ontology evolution, bootstrapped learning, learning from limited supervision in KBs, scalable learning and inference over large datasets for KB construction, recent KB construction systems, multilingual knowledge acquisition, knowledge acquisition from multiple modalities, representation learning for knowledge harvesting.
Pre-requisites: Basic knowledge of machine learning and/or natural languageprocessing will be helpful although not mandatory.
Current Literature.
DS 323 (AUG) 1:1 Parallel Computing for Finite Element Methods
Sashikumaar Ganesan
This course will provide an introduction to parallel finite element data structure and its efficient implementation in ParMooN (Parallel Mathematics and object oriented Numerics), an open source parallel finite element package. Further, the implementation of the parallel (MPI/OpenMPI) geometric multigrid solver will also be taught. Parallel finite element solution of scalar and incompressible Navier-Stokes equations in two- and three-dimensions using ParMooN (cmg.cds.iisc.ac.in/parmoon/) will also be a part of this course..
Pre-requisites: Good knowledge of finite element methods and C/C++.
- Sashikumaar Ganesan, Lutz Tobiska: Finite elements: Theory and Algorithms, Cambridge-IISc Series, Cambridge University Press, 2017
- An Introduction to Parallel Programming. Peter S Pacheco. Publisher: Morgan Kauffman. ISBN: 978-93-80931-75-3. 2011
- Current literature
DS 360 (JAN) 3:0 Topics in Medical Imaging
Phaneendra K Yalavarthy
Three-dimensional Medical Image Processing, Medical Image reconstruction using high performance computing, General Purpose Graphics Processing Units (GP-GPU) computing for Medical Image processing, reconstruction, and Analysis, Computer Aided Detection (CAD) systems – Algorithms, Analysis, Medical Image Registration: rigid and non-rigid registration, Volume based image analysis, Medical Image Enhancement: Deblurring techniques, Four-dimensional Medical Imaging, Molecular Imaging, Diffuse Optical Tomography, and Medical Image Informatics.
Pre-requisites: DS 260 or E9 241 or consent from the Instructor.
* Current Literature
DS 391 (JAN) 3:0 Data Assimilation to Dynamical Systems
Soumyendu Raha
Quick introduction to nonlinear dynamics: bifurcations, unstable manifolds and attractors, Lyapunov exponents, sensitivity to initial conditions and concept of predictability. Markov chains, evolution of probabilities (Fokker-Planck equation), state estimation problems. An introduction to the problem of data assimilation (with examples) Bayesian viewpoint, discrete and continuous time cases Kalman filter (linear estimation theory) Least squares formulation (possibly PDE examples) Nonlinear Filtering: Particle filtering and MCMC sampling methods. Introduction to Advanced topics (as and when time permits): Parameter estimation, Relations to control theory, Relations to synchronization.
Pre-requisites: Consent from the Instructor.
* Edward Ott, Chaos in Dynamical Systems, Camridge press, 2nd Edition, 2002.(or one of the many excellent books on dynamical systems)
* Van Leeuwen, Peter Jan, Cheng, Yuan, Reich, Sebastian, Nonlinear Data Assimilation, Springer Verlag, July 2015.
* Sebastian Reich, Colin Cotter, Probabilistic Forecasting and Bayesian Data Assimilation, Cambridge University Press, August 2015
* Law, Kody, and Stuart, Andrew, and Zygalakis, Konstantinos, Data Assimilation, A Mathematical Introduction, Springer Texts in Applied Mathematics, September 2015.
DS 392 (JAN) 3:1 Environmental Data Analytics
Deepak Subramani
The course introduces and trains students in different Data Analytics techniques used in the Geosciences including Machine Learning and Deep Learning algorithms. Emphasis is laid on understanding the algorithms as well as using them in practice. The course is designed as research case studies from Ocean Modeling, Remote Sensing and Study of the Natural Environment with significant hands-on activity. At the end of the course, students would be able to recognize, model and solve geoscience problems that require application of data analytics methods.
Syllabus: Data-Driven Modelling in the Geosciences: Problem Formulation and Computational Modeling Approaches. Handling and Analysing Spatiotemporal Geoscience Data (Remote Sensing, In-situ instruments, Primitive Equation Models). [1 Week]
Hands-on Applications of Supervised (Linear Methods, Nonlinear Methods) and Unsupervised Learning (Clustering, Dimensionality Reduction) in Environmental Analytics. [5 Weeks]
Hands-on Applications of Deep Learning (Multi-Layer Perceptron, Convolutional and Recurrent Architectures) in Remote Sensing of the Natural Environment. [3 Weeks]
Bayesian Inference and Data Assimilation for Physics-based Dynamic Data-driven Environmental Systems. [3 Weeks]
Reinforcement Learning for Ocean Sensing. [2 Weeks]
Prerequisites: DS 211 (Numerical Optimization), DS 221 (Introduction to Scalable Systems), DS 284 (Numerical Linear Algebra), Consent of Instructor
Evaluation: Case studies based on recent literature and research from QUEST Lab; Fortnightly Quizzes; Coding Competitions (Mid-Term and End-Term); Course Project (which must be suitable for a publication)
1. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. Second Edition. O'Reilly Media, 2019.
2. Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013.
3. Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
4. Recent Literature, Selected Chapters, Material/Notes Provided by Instructor
DS 393 (JAN) 3:1 High-performance computing for Quantum Modeling of Materials
Phani Motamarri
Course Description: This is an advanced elective course for research students. The course is designed to train research students with the relevant theory, numerical algorithms and implementation aspects underlying the state-of-the-art computational techniques for quantum modelling of materials. Emphasis will be laid on the computational methods pertinent to today's exascale era. Students will be exposed to research problems in the broad area of quantum modelling of materials that require a paradigm shift in the design and implementation of computational methods to exploit the extreme levels of parallelism offered by today's heterogeneous architectures. At the end of the course, students will appreciate key computational and implementation aspects relevant to behind-the-scene algorithms of black-box codes used for quantum modelling of materials.
Review of classical mechanics and postulates of quantum mechanics, Many-body Schrodinger equation, System of identical articles, Notion of Slater Determinant, Hartree Fock Theory, Density Functional Theory equations (DFT), PAW formalism for pseudopotential DFT, Density Functional Perturbation Theory.(3 weeks)
Review of state-of-the-art numerical discretization strategies for electronic structure calculations — Plane waves, Atomic-orbital basis sets, LAPW approach, Finite-difference, Finite-elements (2.5 weeks)
Iterative eigenvalue algorithms for large-scale electronic structure calculations — Davidson type methods, Preconditioned Conjugate Gradient methods, LOBPCG approach, Chebyshev filtered subspace iteration (2 weeks)
Scalable methods for DFT using finite-elements (DFT-FE) — Review of variational calculus, Local real-space variational formulation for DFT, Smeared floating charge approach, Finite-element discretization of DFT equations, Mixed precision subspace filtering approach for FE discretized eigenproblem on hybrid CPU-GPU architectures, Hands-on DFT-FE code exercises (4 weeks)
Configurational force approach for atomic forces and stresses — Notion of generalized forces and its application for DFT functional, Computationally efficient evaluation of atomic forces and stresses in a finite element setting (2.5 weeks)
Prerequisites: Background in Numerical Methods/Linear Algebra/Parallel programming/Quantum Mechanics and/or consent from instructor
Evaluation: Periodic Quizzes, student presentations on the above topics, Case studies based on research conducted at MATRIX lab at CDS, Course Project
- Richard M Martin, Electronic Structure – Basic Theory and Practical Methods, Second Edition, Cambridge University Press, 2020.
- Zhaojun Bai, James Demmel, Jack Dongorra, Axel Ruhe — Templates for the
Solution of Algebraic Eigenvalue Problems, A Practical Guide, SIAM Publishers http://www.netlib.org/utk/people/JackDongarra/etemplates/index.html
- Morton E. Gurtin — Configurational Forces as Basic Concepts of Continuum Physics
- Research Papers, Material/Notes Provided by Instructor
DS 397 (JAN) 2:1 Topics in Embedded Computing
S K Nandy
Introduction to embedded processing, dataflow architectures, architecture of embedded SoC platforms, dataflow process networks, compiling techniques/optimizations for stream processing, architecture of runtime reconfigurable SoC platforms, simulation, design space exploration and synthesis of applications on runtime reconfigurable SoC platforms, additional topics including but not limited to computation models for coarse grain reconfigurable architectures (CGRA), readings and case study of REDEFINE architecture, compiler back-ends for CGRAs.
Pre-requisites: Basic knowledge of digital electronics, computer organization and design, computer architecture, data structures and algorithms, and consent of instructor.
* Current literature.


text:
Rules & Criteria
Discipline is a sine qua non of any successful academic life. Student are expected to maintain proper discipline and peace in the class room, in the college office, in the library and reading room, in the meeting hall, in the play ground and college campus is general as well as in all spheres of college life. No society/No association/No club or other organizing shall be formed without the prior permission of the college authorities.
No meeting of the students shall be concerned and no outside person shall be invited in the college campus without the permission of the college authorities.
Any function or social shall be organized by the college authorities.
Any student found quality of the breach of discipline rules or regulations shall be liable to disciplinary action including expulsion from the college as decided by the college authorities.
A student failing to attend less than 90% of the practical & 85% of the theory classes held be declared as DIS-COLLEGIATE. i.e. he / she will be debarred from appearing at any examination under the university or Primary Board.
A student attending less than 75% of the classes held is declared NON-COLLEGIATE. She / he in that case has to pay the required fine as non-Collegiate fee for appearing at the University/Primary Board Examination.
Uniform & IDENTITY CARD
During the entire course every student should wear college uniform as prescribed by the college authority & will be provided with an identity card. Students must come to the college with proper uniform & their identity cards.
Some other necessary information
- Attendance in all the curricular or co-curricular activities is compulsory. If he/she fails to attend the programme, he/she must inform to the Principal. Appearing in the Internal Examinations is compulsory for every student.
- Outsiders are not permitted within the college premises and students should not bring outsiders to the college without prior permission of the college authority.
- Cell phone should be switched off during class hours and inside the library.
- Malpractices of any type in examination will lead immediate expulsions from the examination hall.
- The college is recognized by the ERC-NCTE and affiliated to the University of Burdwan and the WBBPE.

subset: commoncrawl


text: Table of contents Upgrade to Aternity on-premise 12.2 Update your Aternity on-premise for a significant step forward in new features and resolved issues. Always check the sizing before updating. This version uses different database sizes, dashboard requirements and new servers. Learn more. Update the Aternity components in the prescribed order. Update Aternity in the correct order Before you begin Before upgrading Aternity on-premise: You must start with a fully functioning deployment of Aternity on-premise 11.0.3 or later. If you have an older version, we recommend performing a fresh deployment or upgrading first to Aternity 11.0.3. Check the correct sizing for your deployment before updating any of your Aternity servers. Learn more. All devices must already report using a compatible version of the Agent for End User Devices. Learn more about deploying the Agent. Important Aternity on-premise 12.2 requires that devices run Agent for End User Devices 12.2.x or below (Agent 11.0.x, 12.0.x or 12.1.x). Product Supported Agent for End User Devices Aternity 12.2 Agent 11.0.x, 12.0.x, 12.1.x, or 12.2.x Aternity 12.0-12.1 Agent 11.0.x, 12.0.x or Agent 12.1.x Aternity 11.x Agent 9.0, 9.1.7, 9.2.x, or Agent 11.0.x For a complete list of files to download ahead of time (especially important for air-gapped networks), learn more. File Name Description Link for download Aternity OnPremise <version> (64-bit).zip Download the package that includes the setup of the following Aternity servers: Management Server, Aggregation Server, Data Warehouse Server. Vertica Database Server, Dashboard Gateway, Aternity Docker Components Server, TableauServer-2021-3-7-Aternity-64bit.exe for the Aternity Dashboard Server Download the latest Aternity on-premise's main setup package from the Riverbed Support Site (learn more). TableauServer-2021-3-7-Aternity-64bit.exe The Aternity Dashboard Server uses Tableau, a third party product, as its main engine. Only this exact version of Tableau works with Aternity 12.2. Tableau setup and administration require a user with administrator rights on the host Windows machine. Tip Air-gapped networks make offline Tableau setup very lengthy. You must upload and download several files in sequence as a challenge response. Tableau does not allow this in one action. We do not control their setup procedures. Additional Packages page of the Riverbed Support Site Microsoft .NET for Dashboard Server Ensure this server has Microsoft .NET 3.5 (exactly) required for the database drivers, and .NET 4.5 or later, required for driver setup (both in parallel). To add .NET to a Windows Server, learn more. Search for these versions on the Microsoft Download Center. Chrony Synchronizes time and time zones between the Aternity components. If not included in your version of CentOS/RHEL, search for Chrony, and download the version for your Linux OS. ProcedureStep 1 Plan the strategy of your Aternity on-premise upgrade Considerations when planning your upgrade of Aternity on-premise Decide on the size of your deployment, based on the number of devices to monitor. This directly determines the number of servers you need, and their hardware specifications. For example, if the updated requirements for your sizing state you must have a new standalone Aggregation Server, you must use the custom update to remove the legacy embedded Aggregation Server from within the Management Server, then run the setup again on a standalone server to a fresh setup of a new Aggregation Server there. Remove an embedded component during upgrade, then run setup on a standalone server Ensure you have an existing fully functional Aternity 11.0.3 or later before starting your upgrade. All devices MUST be reporting to Aternity using a supported Agent (11.0.x, 12.0.x, 12.1.x or 12.2.x) Ensure the servers meet the software system requirements for all components in the solution. Choose the network topology of your deployment, based on the location of your monitored devices. Set the firewalls to open the required ports between components, if you plan any of them to be on opposite sides of a firewall. Decide the security, whether you want to encrypt connections between some or all of the Aternity components. Synchronize all Aternity components to have the same date, time AND time zone. Strengthen robustness by determining the backup strategy and whether to have high availability with redundant servers. Step 2 Download the latest Aternity on-premise's main setup package from the Riverbed Support Site (learn more). Sign in to Riverbed Support Site, then select Software & Documentation > Application Performance Management > Aternity > Aternity Server. Select the required version to download. Note Only logged in users with certain permissions can see the download option. The setup package is for all Aternity servers. Download Aternity on-premise from the website Step 3 (One week before upgrading Aternity) Prepare the Aternity Oracle Database Server for Aternity by verifying its configuration. Learn more. You must run the Aternity Database Verification Utility several times before and after updating the database (learn more). The verification may return a fatal error if the Aternity schema user does not have the MANAGE SCHEDULER privilege. To resolve, as the database's SYSTEM level user (who created the schema in the Aternity database), connect to Oracle Database and run the command: GRANT MANAGE SCHEDULER TO <db_aternity_user> This is mandatory for upgrading the Aternity Management Server. You can remove this privilege once the upgrade is completed. Step 4 Perform an upgrade to Vertica 10.1.1-10. Learn more. Vertica is a third-party column-store database optimized for large volume data analytics. The Aternity Vertica Database Server stores the performance data in the Vertica format, which is most efficient for displaying in Aternity dashboards. It receives its data from the Aternity Docker Components Server. Step 5 Log in to the Aternity Docker Components Server and stop it before proceeding with update by entering the command from the aternity-docker-admin directory: $ sudo ./aternity-docker-admin stop Step 6 Update the Dashboard Server if your base version is on-premise 12 or 12.1 (learn more), or Set up a new server on a new host if your base version is 11.0.3 (learn more). The Dashboard Server displays Aternity's intuitive dashboards using Tableau as its engine. Warning In both cases no matter from what version you are updating, first of all you must run the new dashboards Migration Tool. The tool updates the saved editable dashboards format so that they fit the new Dashboard Server version, and allows migration of dashboards between servers (learn more). Migration must happen BEFORE Dashboard Server update. Update the Dashboard Server component (Tableau Server) Step 7 (For on-premise 12.0/12.1) On the Dashboard Server, update the Dashboard Gateway component. Learn more. (For on-premise 11.0.3) Set up a new Dashboard Gateway component on the Dashboard Server. Learn more. The Dashboard Gateway acts as the interface between the Dashboard Server and the Aternity Management Server, performing background tasks like preprocessing the data for display in the dashboards, and maintaining logs, audits and cleanup. It must be on the same computer as the Dashboard Server. While it has no user interface of its own, it is crucial to the proper functioning of Aternity. Update the Dashboard Gateway on the same computer as the Dashboard Server Step 8 Update the Aternity Management Server by running its express setup or custom setup over the previous version. To setup the Aternity Management Server, you must register with Aternity's online activation form. Enter your contact details and serial number to send an activation key to your email (learn more). If you previously had several Aternity components on this server, recheck the sizing, as you may need to create new standalone servers and remove legacy embedded components in this server. If you are keeping the same topology or security, choose the express update, which keeps your legacy settings in the updated servers. If you are changing your topology or security, choose the custom update. Remove an embedded component during upgrade, then run setup on a standalone server For example, if the updated requirements for your sizing state you must have a new standalone Aggregation Server, you must use the custom update to remove the legacy embedded Aggregation Server from within the Management Server, then run the setup again on a standalone server to a fresh setup of a new Aggregation Server there. Use the updated sizing to determine whether a component is embedded or standalone Step 9 Update the Aternity dashboard layouts from the Management Server. Learn more. The Aternity dashboard layouts contain the names, calculations and layouts of the system's dashboards for this Aternity version. Important Add the dashboard layouts ONLY from the Management Server. Add the layouts from the Management Server into the Dashboard Server via Dashboard Gateway Step 10 Restore saved dashboards (learn more). Step 11 Update the Aternity Docker Components Server that includes various Aternity components, mandatory and optional. Learn more. Secure communication between Docker Components and other Aternity servers. Learn more. (Optional) Open the System Health dashboard and check the state of all Docker Components. The System Health dashboard displays the overall topology and health of your Aternity deployment. Step 12 (For deployments above 30,000 devices only) Run the express update or the custom update of a standalone Data Warehouse Server on its own computer, or create a new one. Create a new server only if changing the deployment size and the new size requires several servers that did not previously exist. The Data Warehouse Server stores the raw data gathered from the Aggregation Servers, and aggregates (summarizes) it for the Oracle Database Server and the Aternity Vertica Database Server. Step 13 (For deployments above 1,000 devices only) Run the express update or the custom update of a standalone Aggregation Server on its own computer, or create a new one. As your deployment grows in size, add more identical Aggregation Servers (see deployment sizing for details). If your sizing requires that you deploy more than one dedicated Aternity Aggregation Server, you must deploy a third party load balancer (LB). Then configure the Agent for End User Devices of all devices to point to the LB's virtual IP address (learn more). Configure the LB with persistent (sticky) sessions to ensure the Agent maintains its connection with the same server. Aternity also supports sticky sessions when the LB implements stickiness via cookies. Aggregation Server collects and consolidates data from monitored devices Step 14 (Optional) To view Aternity data in Portal™ connect the Aternity Data Source to Portal. Configure the Portal™ to connect to your Aternity Data Source to view Aternity data in the Portal alongside data from other products in the Alluvio Suite. Step 15 (Optional) Import the legacy translated MS Office activities from the backup file. Aternity on-premise version 12.2 update rewrites the OOTB MS Office activities. As a result, if you localized them to your language, once version 12.2 is up and running, you need to merge the translations into the updated OOTB MS Office activities. Prior to upgrade, the translated activities were automatically backed up to <Aternity_Home_folder>/monitorsBackup folder in the Management Server. The file name is Export_Microsoft_Monitors_{dd-MMM-yyyy-_HH_mm_ss}.zip where the date changes with the actual date and time of the backup. Update the Aternity Oracle Database ServerUpdate the Aternity Vertica Database ServerBack up and Restore the Edited DashboardsUpdate the Aternity Dashboard ServerUpdate the Aternity Dashboard Gateway ServerQuickly Update the Aternity Management ServerUpdate (Publish) Dashboard Layouts for AternityUpdate the Aternity Docker Components ServerQuickly Update Aternity Data Warehouse Server on a Dedicated ComputerQuickly Update an Aternity Aggregation Server on a Dedicated Computer SavePDF Selected topic Selected topic and subtopics All content Related Links


text:
New York Woman Faces Up to Eight Years Behind Bars for Selling Abortion Pills Online
Ursula Wing ran a business providing medication abortion pills to customers who needed them. Then the FDA showed up at her door.
A New York City woman who sold medication abortion pills to more than 2,000 people over two years has been indicted on federal charges of conspiracy to defraud the United States and introducing misbranded drugs into interstate commerce. If convicted, she could face up to eight years in prison.
In 2012, the woman, Ursula Wing, posted on her blog the Macrobiotic Stoner about her experience terminating her pregnancy using pills that she had bought online. Four years later, she found herself running a business providing medication abortion pills to customers who needed them, including a teenager who was afraid to tell her parents that she was pregnant, and a woman who hid her abortion from her abusive partner.
She did not see herself as an activist at first, according to an interview she gave Mother Jones earlier this year. But, she said, "it started changing into something political."
After Wing wrote on her blog about her experience buying abortion pills online, women in the comments section began to ask her where they could obtain them. It was the perfect storm: Wing, a single mother who needed to supplement her income as a web developer to help pay for legal fees related to a custody battle she was fighting, filled the demand she saw for self-managed abortion.
Given the anti-choice success in reducing abortion access, some pregnant people have turned toward self-managed abortion care to terminate their pregnancies. Many pregnant people prefer self-managed care, especially if don't feel safe going to abortion clinics—perhaps, for example, because of their gender identity or immigration status—or if they cannot take the time required to comply with various state regulations, like waiting periods and mandatory counseling and ultrasounds.
Wing launched a separate website in May 2016 and sold medication abortion kits there for $85 with expedited shipping. Medication abortion is a non-surgical procedure that involves a pregnant patient taking two drugs. The first drug, mifepristone, works by blocking the hormone progesterone, which causes the lining of the uterus to break down so that the pregnancy cannot continue. The second drug, misoprostol, which causes cramps and heavy bleeding that usually lasts for a few hours, induces contractions and ends the pregnancy.
Medication abortion in a clinical setting costs anywhere from $400 to $1,000, depending on one's health insurance (or lack thereof), the state, and the provider. Some overseas suppliers, meanwhile, can charge upward of $350 for medication abortion kits. But Wing bought the pills in bulk from India and marked them up 100 percent. This, in her view, was more than enough.
In 2017, a group called Plan C, which provides information on self-managed abortion, and Gynuity Health Projects, a group that works to ensure that reproductive health technologies are widely available at a reasonable cost, conducted a "mystery shopper" study that rated 11 medication abortion kit suppliers on price, shipping time, and product quality. Wing's service scored an A, the highest of the 11 services. The next best service scored an A-, but the price for the abortion kits cost $155 more than Wing's.
Elisa Wells, co-founder of Plan C, said that Wing "appeared to be a woman helping other women," according to Mother Jones. Wing would respond personally to customer emails, and her comment section became a place where women seeking self-managed abortion care could find support.
After the Plan C report card was published, Wing says her sales increased dramatically, from a handful per week to 60 or more orders per week.
For two years, Wing reportedly sold medication abortion kits to more than 2,000 people. As her site grew in popularity, so did her notoriety. When a Wisconsin man named Jeffrey Smith was arrested in February 2018 for allegedly dosing a woman who was pregnant with his child by slipping mifepristone into her drink, the U.S. Food and Drug Administration (FDA) was alerted to Wing's illicit business. Smith had apparently ordered packages from Wing's site twice, according to Mother Jones. Smith was charged with attempted first-degree homicide of an unborn baby and delivery of a prescription drug, according to the Wausau Daily Herald.
A year later, in February 2019, FDA agents showed up at Wing's door with an arrest warrant and seized her computer and phones, her daughter's iPad, boxes of medication abortion pills, and a dozen packages that she was set to mail.
After several months of awaiting her fate, Wing was indicted on June 26. The indictment alleges that from June 2016 through June 2018, Wing conspired to defraud the United States by impeding lawful functions of federal law enforcement and regulatory agencies, and that from January 2018 through February 2018, Wing caused the introduction of unapproved, misbranded prescription drugs mifepristone and misoprostol into interstate commerce by sending the drugs from the state of New York to Wisconsin.
The indictment further alleges that Wing "illegally smuggled into the United States misbranded prescription drugs from India, which were not approved by the Food and Drug Administration (FDA) for distribution in the United States, and that she sold these misbranded prescription drugs to customers in the United States and around the world" and that she dispensed these prescription drugs without a valid license to do so.
There isn't much precedent for this sort of thing. As Chelsea Conaboy notes for Mother Jones:
A New Jersey man was indicted in 2015 for importing and distributing prescription drugs, primarily erectile dysfunction pills but also mifepristone and misoprostol, but he died before the case was resolved. In 2010, a Virginia doctor who bought pills from a Chinese distributor to send to a family planning clinic in Mexico was sentenced to a one-year probation and fined $1,000.
People have, however, been prosecuted for self-managing their abortions. According to a report by If/When/How, since 1973, at least 21 people have been arrested for self-induced abortions in 20 states. They include Jennie Linn McCormack and Jennifer Whalen, who bought or were suspected of buying abortion pills online. McCormack's case was dismissed, and Whalen served time in prison. And then there's the egregious case involving Purvi Patel, who purchased abortion pills online and was sentenced to 41 years in prison for feticide and neglect of a dependent before a judge overturned her conviction, stating that she had already served enough time.
While uncertainty surrounds Wing's prosecution, what is certain is that she wants more people to get involved in providing medication abortion kits to pregnant people who need them.
"I want some copycats," she told Mother Jones. "There's not enough people doing this."
Wing's arraignment was Thursday, in Madison, Wisconsin.
"Ms. Wing and I are working with the United States Attorney's Office to resolve this case without a trial," her attorney, Peter Moyers, told Rewire.News in an email.
"I am confident that we'll reach an agreement that we can then present to the District Court for its approval. Criminal cases come in all shapes, sizes, and particulars. But the resolution process is constant. The government and the defense identify disputed issues and discuss what to do about them," Moyers, a federal defender, said. "Sometimes those disputes are numerous and profound. Based on what the defense knows now, it appears the disputes in Wing's case are few and mild."
Anti-choice legislators have continued to put forth increasing restrictions on abortions, claiming an interest in the health and safety of patients. In reality, however, such restrictions are an attempt to ban medication abortion or at least interfere with the delivery of medication abortion services. And as more people like Ursula Wing decide to flout the law in order to provide medication abortion kits to women like McCormack and Whalen, we can expect more federal prosecutions.


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WASHINGTON – Senate Republicans have confirmed that the crimes committed by US President Donald Trump, including abuse of power and obstruction of Congress, were not illegal.
In a 53-47 vote, criminal bribery, fraud, and criminal conspiracy were declared as decent and predictable actions for any normal leader of a democracy.
Majority Leader Mitch McConnell explained that while attempting to bribe a Ukrainian leader to dig up dirt on your opponent may look bad, it's perfectly safe in the eyes of partisan interests.
"These actions are good crimes, not bad crimes," explained McConnell cracking a champagne bottle to celebrate the victory of senselessness over the rule of law. "These good crimes will continue to be legal for a President so long as he's Donald Trump."
The Senate also voted 52-48 to pardon Trump for any of his future crimes, which are expected throughout the 2020 election and possible second term.
"I've always wanted to live in a Hobbesian state of nature," said Senator Lindsey Graham. "Now all of America gets to join me."
The Senate washroom will now use the 229-year-old US Constitution as its toilet paper.


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Commercial algaecide effective at removing cyanobacterium Planktothrix sp.
Most fish farmers operating in freshwater ponds will at some point encounter blooms of nuisance microalgae, which threaten the health and quality of their aquacultured products. Of particular concern are cyanobacteria because they produce toxins, as well as compounds (e.g., geosmin and 2-methyl isoborneol) that cause off-flavors in fish.
The main toxins are microcystins that can accumulate in the fish flesh, which poses safety concerns because these can be passed to human consumers. Preventive methods are perhaps the best management option, such as maintaining low nutrient levels – particularly phosphorus (P) – through water exchanges and/or the use of P-binding clay. However, some farmers observe that elevated temperatures often trigger nuisance microalgae blooms.
Off-flavors from nuisance microalgae cause serious economic losses to farmers due to the cost and delay involved to purge the fish prior to harvesting and/or strategies to remove cyanobacteria. Therefore, some farmers use algaecides, such as copper sulfate or alum. However, these tend to be less selective and can also cause toxicity to other beneficial microalgae and zooplankton.
The sudden die-off of the primary producers, including nuisance microalgae, can cause three problems. Firstly, without the main organisms to remove dissolved nitrogen, the ammonia and nitrite levels can subsequently spike. Secondly, the dead microalgae would contribute to increased biological oxygen demand that could strip oxygen out of the water. Thirdly, large amounts of microcystins would be released as the cyanobacteria die and are then taken up by animals.
A more selective compound to kill cyanobacteria includes using hydrogen peroxide (H2O2). Traditionally, this is added in liquid form but can pose safety concerns due to accidental spilling during transport, storage and application. Other H2O2-based products have emerged in the form of sodium carbonate peroxyhydrate (SCP) that, when added to water, is rapidly converted to H2O2 and sodium carbonate. Therefore, this is more stable, but largely untested in an aquaculture context.
In this article – adapted and summarized from the original publication (Science of the Total Environment (DOI:10.1016/j.scitotenv.2018.05.023) – we summarize results of a study to test a novel SCP-based product within small- and large-scale hypereutrophic tanks and ponds, respectively, that were both dominated with cyanobacteria. The small-scale trial was first conducted to narrow down the most appropriate doses to kill cyanobacteria without potentially harming beneficial microalgae and zooplankton. Based on these results, two optimal doses (2.5 and 4.0 mg/l H2O2) were tested within commercial ponds (large-scale trial) and over six weeks the cyanobacterial abundance, plankton abundance, microcystins and persistence of H2O2 levels were monitored.
Experimental design
Nutrients (inorganic fertilizer and de-oiled rice bran) were added, as needed, to pond water until hypereutrophic, cyanobacteria-dominated conditions were obtained (first image at the top). Identical nitrogen to total P conditions were used and the dominant cyanobacterium was Planktothrix sp.
In both trials, a granular SCP based algaecide (PAK® 27) was used, which is a USEPA-approved compound (SePRO Corporation, Carmel, IN, U.S.A.). Different dosages of H2O2 were prepared by dissolving the algaecide (active ingredient ~ 27 percent H2O2) granules in moderately hard water (~ 187 mg/l as CaCO3) analogous to local water conditions.
Small-scale trial
Three circular, 75-L barrels were installed in each of the six hypereutrophic ponds and filled with this water. The algaecide was added at 0, 5.56, 7.41, 9.26, 11.11, 12.96, 14.81, 18.52 and 29.63 mg/L to achieve final concentrations of 0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0 and 8.0 mg/L H2O2, respectively. Samples were daily taken between 10:00 and 12:00 over 10 days to monitor changes in cyanobacteria, eukaryotic microalgae and zooplankton abundance. Dissolved oxygen (DO), conductivity (µS/cm), pH and water temperature (degrees-C) were measured using a digital probe. Turbidity was measured using a Secchi disk and total phosphorus, ammonia nitrogen, nitrite-N, nitrate-N, total alkalinity and total hardness using commercial kits.
The average concentration of Planktothrix sp. prior to the algaecide applications was 1.08 × 106 cells/mL, while the combined taxa of various microalgae were 5.24 × 104 cells/mL. Among the tested concentrations, 2.5 mg/L H2O2 was the lowest dose that significantly reduced Planktothrix sp., and higher concentrations led to no further significant improvement at reducing cyanobacteria. At 4.0 mg/L H2O2 there was also no adverse effect to the plankton. However, concentrations of ≥ 4.0 mg/L adversely impacted the abundance of the non-targeted plankton community that included Synedra, Spirogyra and Cladophora sp. Therefore, 2.5 and 4.0 mg/L H2O2 were selected for testing in the large-scale trial.
Large-scale trial
A total of four ponds were randomly selected for the two H2O2 treatments, while the remaining two ponds were used as reference (control) ponds. Paddlewheels were used to facilitate an even dispersal of the algaecide. Similarly, as in the small-scale trial, the cyanobacterial / plankton abundance and water quality were measured along with assessing microcystins, chlorophyll-a and persistence of H2O2 over six weeks.
Both doses of 2.5 and 4.0 mg/L H2O2 significantly decreased Planktothrix sp. concentrations, but at different times of seven and five days post-application, respectively (Fig. 1). There was also a notable change in the appearance of the water after three days, where the cyanobacteria were dying and the water appeared to have a more greenish-brown coloration. A gradual recovery of Planktothrix sp. populations became apparent between weeks 5 and 6, although abundances were still far less than both the control or during the pre-application phase. In the 2.5 mg/L H2O2 treatment, the cyanobacterial decrease coincided with increased eukaryotic phytoplankton, particularly diatoms and green algae. Moreover, herbivorous zooplankton that included Brachionus sp., Daphnia sp., and calanoid and cyclopoid copepods were unaffected. In contrast, 4.0 mg/L H2O2 suppressed these phyto- and zooplankton, which was in contrast to the small-scale trial.
Both doses of 2.5 and 4.0 mg/L H2O2 significantly reduced total microcystins from seven and five days, respectively, until the end of the trial (Fig. 2). The added H2O2 rapidly degraded to below detectable levels after days 3 and 4, respectively (Fig. 3). The dissolved oxygen remained relatively stable over this study (Fig. 4a). The pH was lower in both H2O2 ponds but was more stable over time (Fig. 4b). There was a spike in ammonia within 2.5 and 4.0 mg/L H2O2 ponds on days 7 and 8, respectively, but these were within the tolerance of freshwater animals and decreased after week 3 (Fig. 4c). On the other hand, nitrite remained low and stable in all ponds (Fig. 4d).
Overall conclusions
Issues with extermination of cyanobacteria with traditional algaecides may include temporary deteriorations in water quality, unintended toxicity to beneficial plankton or high levels of microcystins. However, these were substantially mitigated by H2O2 at 2.5 mg/L in the form of SCP. In fact, this was slightly beneficial to zooplankton, likely due to the reemergence of eukaryotic microalgae as the cyanobacteria died. Moreover, the use of this product left no long-term traces of H2O2 in the water.
It is important to point out that, while the small-scale trial was an important step in this study, there were some discrepancies that included a concentration of 4.0 mg/L of the algaecide negatively affecting the plankton community within the ponds. Thus, caution should be used when small-scale trials are used to predict those in a large-scale commercial setting.
Overall, our results demonstrate that the granular SCP-based algaecide (PAK® 27) –corresponding to 2.5 mg/L H2O2 – can be recommended as an eco-friendly strategy to effectively remove populations of the cyanobacterium Planktothrix sp. without compromising water quality or other plankton communities.
Future research and perspectives
A limitation of this was study was not assessing the potential toxicity of H2O2 to fish. There are indications this would be safe, based on no apparent toxic effects to zooplankton at 2.5 mg/L and limited persistence in water; however, this will be tested in the future on juvenile channel catfish and largemouth bass.
This research will also extend to other cyanobacterial species as well as any potential effect this could have on compounds that cause "off-flavors" (e.g. geosmin and MIB), often a side-effect of hypereutrophic ponds that causes substantial economic losses to farmers.
Amit Kumar Sinha, Ph.D.
Aquaculture/Fisheries Center
University of Arkansas at Pine Bluff
1200 North University Drive
Pine Bluff, AR 71601 USA
Nicholas Romano, Ph.D.
Corresponding author
Aquaculture/Fisheries Center
University of Arkansas at Pine Bluff
1200 North University Drive
Pine Bluff, AR 71601 USA
Michael Eggleton, Ph.D.
Aquaculture/Fisheries Center
University of Arkansas at Pine Bluff
1200 North University Drive
Pine Bluff, AR 71601 USA
John Howe
Aquaculture/Fisheries Center
University of Arkansas at Pine Bluff
1200 North University Drive
Pine Bluff, AR 71601 USA
Rebecca Lochmann, Ph.D.
Aquaculture/Fisheries Center
University of Arkansas at Pine Bluff
1200 North University Drive
Pine Bluff, AR 71601 USA
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