Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
Fpga implementation of optimal step size nlms algorithm and its performance a...eSAT Journals
Abstract The Normalized Least Mean Square error (NLMS) algorithm is most popular due to its simplicity. The conflicts of fast convergence and low excess mean square error associated with a fixed step size NLMS are solved by using an optimal step size NLMS algorithm. The main objective of this paper is to derive a new nonparametric algorithm to control the step size and also the theoretical performance analysis of the steady state behavior is presented in the paper. The simulation experiments are performed in Matlab. The simulation results show that the proposed algorithm as superior performance in Fast convergence rate, low error rate, and has superior performance in noise cancellation. Index Terms: Least Mean square algorithm (LMS), Normalized least mean square algorithm (NLMS)
A Tale of Data Pattern Discovery in ParallelJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
Fpga implementation of optimal step size nlms algorithm and its performance a...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
Fpga implementation of optimal step size nlms algorithm and its performance a...eSAT Journals
Abstract The Normalized Least Mean Square error (NLMS) algorithm is most popular due to its simplicity. The conflicts of fast convergence and low excess mean square error associated with a fixed step size NLMS are solved by using an optimal step size NLMS algorithm. The main objective of this paper is to derive a new nonparametric algorithm to control the step size and also the theoretical performance analysis of the steady state behavior is presented in the paper. The simulation experiments are performed in Matlab. The simulation results show that the proposed algorithm as superior performance in Fast convergence rate, low error rate, and has superior performance in noise cancellation. Index Terms: Least Mean square algorithm (LMS), Normalized least mean square algorithm (NLMS)
A Tale of Data Pattern Discovery in ParallelJenny Liu
In the era of IoTs and A.I., distributed and parallel computing is embracing big data driven and algorithm focused applications and services. With rapid progress and development on parallel frameworks, algorithms and accelerated computing capacities, it still remains challenging on deliver an efficient and scalable data analysis solution. This talk shares a research experience on data pattern discovery in domain applications. In particular, the research scrutinizes key factors in analysis workflow design and data parallelism improvement on cloud.
Fpga implementation of optimal step size nlms algorithm and its performance a...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
From the perspective of Design and Analysis of Algorithm. I made these slide by collecting data from many sites.
I am Danish Javed. Student of BSCS Hons. at ITU Information Technology University Lahore, Punjab, Pakistan.
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Large data with Scikit-learn - Boston Data Mining Meetup - Alex PerrierAlexis Perrier
A presentation of adaptive classification and regression algorithms available in scikit-learn with a Focus on Stochastic Gradient Descent and KNN. Performance examples on 2 Large datasets are presented for SGD, Multinomial Naive Bayes, Perceptron and Passive Aggressive Algorithms.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
The caret package is a unified interface to a large number of predictive mode...odsc
The caret package is a unified interface to a large number of predictive model functions in R.
First created in 2005, the home for the source code and documentation has changed several times.
In this talk, we will outline the somewhat unique aspects of the package and how it impacts the development environment (including documentation and testing). Friction points with CRAN and their resolution will also be discussed.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This slide represents topics on PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) where I tried to cover basic PCA, application, and use of PCA and SVD, Important keywords to know about PCA briefly, PCA algorithm and implementation, Basic SVD, SVD calculation, SVD implementation, Performance comparison of SVD and PCA regarding one publicly available dataset.
N.B. Information in this slide are gathered from
1. Machine Learning course by Andrew NG,
2. Mining of Massive Dataset | Stanford University | Artificial Intelligence - All in One (youtube channel)
3. and many more they are described in the slide.
Transfer Learning for Improving Model Predictions in Robotic SystemsPooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Automated Parameterization of Performance Models from MeasurementsWeikun Wang
This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.
This presentation contains information about the divide and conquer algorithm. It includes discussion regarding its part, technique, skill, advantages and implementation issues.
Tomography is important for network design and routing optimization. Prior approaches require either
precise time synchronization or complex cooperation. Furthermore, active tomography consumes explicit
probing resulting in limited scalability. To address the first issue we propose a novel Delay Correlation
Estimation methodology named DCE with no need of synchronization and special cooperation. For the
second issue we develop a passive realization mechanism merely using regular data flow without explicit
bandwidth consumption. Extensive simulations in OMNeT++ are made to evaluate its accuracy where we
show that DCE measurement is highly identical with the true value. Also from test result we find that
mechanism of passive realization is able to achieve both regular data transmission and purpose of
tomography with excellent robustness versus different background traffic and package size.
From the perspective of Design and Analysis of Algorithm. I made these slide by collecting data from many sites.
I am Danish Javed. Student of BSCS Hons. at ITU Information Technology University Lahore, Punjab, Pakistan.
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Large data with Scikit-learn - Boston Data Mining Meetup - Alex PerrierAlexis Perrier
A presentation of adaptive classification and regression algorithms available in scikit-learn with a Focus on Stochastic Gradient Descent and KNN. Performance examples on 2 Large datasets are presented for SGD, Multinomial Naive Bayes, Perceptron and Passive Aggressive Algorithms.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
The caret package is a unified interface to a large number of predictive mode...odsc
The caret package is a unified interface to a large number of predictive model functions in R.
First created in 2005, the home for the source code and documentation has changed several times.
In this talk, we will outline the somewhat unique aspects of the package and how it impacts the development environment (including documentation and testing). Friction points with CRAN and their resolution will also be discussed.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This slide represents topics on PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) where I tried to cover basic PCA, application, and use of PCA and SVD, Important keywords to know about PCA briefly, PCA algorithm and implementation, Basic SVD, SVD calculation, SVD implementation, Performance comparison of SVD and PCA regarding one publicly available dataset.
N.B. Information in this slide are gathered from
1. Machine Learning course by Andrew NG,
2. Mining of Massive Dataset | Stanford University | Artificial Intelligence - All in One (youtube channel)
3. and many more they are described in the slide.
Transfer Learning for Improving Model Predictions in Robotic SystemsPooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Automated Parameterization of Performance Models from MeasurementsWeikun Wang
This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.
This presentation contains information about the divide and conquer algorithm. It includes discussion regarding its part, technique, skill, advantages and implementation issues.
Tomography is important for network design and routing optimization. Prior approaches require either
precise time synchronization or complex cooperation. Furthermore, active tomography consumes explicit
probing resulting in limited scalability. To address the first issue we propose a novel Delay Correlation
Estimation methodology named DCE with no need of synchronization and special cooperation. For the
second issue we develop a passive realization mechanism merely using regular data flow without explicit
bandwidth consumption. Extensive simulations in OMNeT++ are made to evaluate its accuracy where we
show that DCE measurement is highly identical with the true value. Also from test result we find that
mechanism of passive realization is able to achieve both regular data transmission and purpose of
tomography with excellent robustness versus different background traffic and package size.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Parallel External Memory Algorithms Applied to Generalized Linear ModelsRevolution Analytics
Presentation by Lee Edlefsen, Revolution Analytics to JSM 2012, San Diego CA, July 30 2012
For the past several decades the rising tide of technology has allowed the same data analysis code to handle the increase in sizes of typical data sets. That era is ending. The size of data sets is increasing much more rapidly than the speed of single cores, of RAM, and of hard drives. To deal with this, statistical software must be able to use multiple cores and computers. Parallel external memory algorithms (PEMA's) provide the foundation for such software. External memory algorithms (EMA's) are those that do not require all data to be in RAM, and are widely available. Parallel implementations of EMA's allow them to run on multiple cores and computers, and to process unlimited rows of data. This paper describes a general approach to efficiently parallelizing EMA's, using an R and C++ implementation of GLM as a detailed example. It examines the requirements for efficient PEMA's; the arrangement of code for automatic parallelization; efficient threading; and efficient inter-process communication. It includes billion row benchmarks showing linear scaling with rows and nodes, and demonstrating that extremely high performance is achievable.
AI optimizing HPC simulations (presentation from 6th EULAG Workshop)byteLAKE
See our presentation from the 6th International EULAG Users Workshop. We talked about taking HPC to the "Industry 4.0" by implementing smart techniques to optimize the codes in terms of performance and energy consumption. It explains how Machine Learning can dynamically optimize HPC simulations and byteLAKE's software autotuning solution.
Find out more about byteLAKE at: www.byteLAKE.com
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Data Analytics and Simulation in Parallel with MATLAB*Intel® Software
This talk covers the current parallel capabilities in MATLAB*. Learn about its parallel language and distributed and tall arrays. Interact with GPUs both on the desktop and in the cluster. Combine this information into an interesting algorithmic framework for data analysis and simulation.
HDFS-HC: A Data Placement Module for Heterogeneous Hadoop ClustersXiao Qin
An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System.
The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into
account for launching speculative map tasks, because it is
assumed that most maps are data-local. Unfortunately, both
the homogeneity and data locality assumptions are not satisfied
in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably
reduce the MapReduce performance. In this paper, we address
the problem of how to place data across nodes in a way that
each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster,
our data placement scheme adaptively balances the amount of
data stored in each node to achieve improved data-processing
performance. Experimental results on two real data-intensive
applications show that our data placement strategy can always
improve the MapReduce performance by rebalancing data
across nodes before performing a data-intensive application
in a heterogeneous Hadoop cluster.
(Slides) Efficient Evaluation Methods of Elementary Functions Suitable for SI...Naoki Shibata
Naoki Shibata : Efficient Evaluation Methods of Elementary Functions Suitable for SIMD Computation, Journal of Computer Science on Research and Development, Proceedings of the International Supercomputing Conference ISC10., Volume 25, Numbers 1-2, pp. 25-32, 2010, DOI: 10.1007/s00450-010-0108-2 (May. 2010).
http://www.springerlink.com/content/340228x165742104/
http://freshmeat.net/projects/sleef
Data-parallel architectures like SIMD (Single Instruction Multiple Data) or SIMT (Single Instruction Multiple Thread) have been adopted in many recent CPU and GPU architectures. Although some SIMD and SIMT instruction sets include double-precision arithmetic and bitwise operations, there are no instructions dedicated to evaluating elementary functions like trigonometric functions in double precision. Thus, these functions have to be evaluated one by one using an FPU or using a software library. However, traditional algorithms for evaluating these elementary functions involve heavy use of conditional branches and/or table look-ups, which are not suitable for SIMD computation. In this paper, efficient methods are proposed for evaluating the sine, cosine, arc tangent, exponential and logarithmic functions in double precision without table look-ups, scattering from, or gathering into SIMD registers, or conditional branches. We implemented these methods using the Intel SSE2 instruction set to evaluate their accuracy and speed. The results showed that the average error was less than 0.67 ulp, and the maximum error was 6 ulps. The computation speed was faster than the FPUs on Intel Core 2 and Core i7 processors.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
My Postdoctoral Research
1. Where Do We Need Derivatives?
Numerical Methods:
Solution of ODE, DAE, Optimization, Nonlinear equations.
Sensitivity Analysis:
How does a computer model react to perturbations in input parame-
ters or model constants?"
Design Optimization:
Choose parameters such that model computes better" design.
Data Assimilation & Inverse Problems:
Find values for model parameters such that model reproduces exper-
imentally obtained results.
Derivatives play a central role as the Taylor Series allows to
predict the eect of changes in input parameters, e.g.:
f(x + x) f(x) +
@ f
@ x
xT + O(jjxjj2)
2. Approaches to Computing Derivatives
By Hand:
Tedious and Error-Prone
Divided Dierences:
Can't assess reliability. Dicult to assess numerical accuracy (e.g.,
truncation and cancellation error) and expensive when computing
derivatives w.r.t. many independent variables.
one-sided dis:
@ f(x)
@ xi
jx=xo
f(xo h ei) f(xo)
h
central dis:
@ f(x)
@ xi
jx=xo
f(xo + h ei) f(xo h ei)
2h
Symbolic:
Infeasible for large codes. Not directly applicable to larger programs
with loops and branches. (e.g., Maple, Mathematica)
Automatic Dierentiation:
Requires little human time
Incurs no truncation error
Attractive computational complexity
Applicable to codes of arbitrary size
3. Hierarchical Structure of ADIFOR
Lots of
Alternatives
Program
Procedure
Loop Nest
Loop Body
Basic Block
Statement
Expression
ADIFOR Approach
4. Fortran
Analysis
Code
AD Intrinsics
Template
Expander
Fortran
Derivative
Code
Derivative
Computing
Code
The ADIFOR System
ADIFOR
Preprocessor
Compile
and Link
AD Intrinsics
Library
User’s
Derivative
Driver
SparsLinC
Library
Computational Differentiation
at Argonne National Laboratory
5. ODE’s, DAE’s
Optimization
Iterative
Solvers
C, C++
Fortran
(77,90,M,HPF)
MPI,PVM
Little
Languages
The Big Picture of AD Tools
Hessians
Non-smooth functions
New
Capabilities
New
Languages
Chain
Rule
Numerical
Methods
Associativity
Pseudo-Adjoints, Interface
Contraction, Breaking Dependencies
6. A Modular Approach to Building AD Tools
Input Program
Parsing and Canonicalization Program Analysis
Annotated
Intermediate Representation
Differentiation Executive
Derivative Augmentation
Unparsing
Parallel Output Program
Parallel
Derivative
Run-time
System
7. Time-Parallel Scheme for Derivative Computing
(FORTRAN-M Implementation)
Chain rule associativity breaks dependencies and generates new
task parallelism (in addition to existing one!).
x y
Ht Ht+1
dH t /dx dH t + 1 /dy dH t + 2 /dz
... Serial top-level
Manager
parallel_to_MM channel
Matrix-matrix
Master Wrapper
Multiplier
parallel_to_MM channel
Gradient Process 1
manager_to_parallel channel
manager_to_parallel channel
idle channel
idle channel
Gradient Process N
serial_to_manager channel
w
y z
z
x
y
dw/dx
proc. 0
proc. 1
proc. 2
Compute_Der Compute_Fun Compute_Mat Receive Send
7 22 36 50 65 79 94
0
1
2
3
4
5
6
7
8
8. Time-Parallel Scheme for Derivative Computing
(MPI Implementation)
Chain rule associativity breaks dependencies and generates new
task parallelism (in addition to existing one!).
x y Ht Hy t+1
x y
x Ht H z t+1
dH t /dx dH t + 1 /dy dH t + 2 /dz
dw/dx
w
proc. 0
proc. 1
proc. 2
y z
Master Wrapper
Manager
(option)
Gradient Process 1
Matrix-matrix
Multiplier
Gradient Process N
parallel_to_MM channel
parallel_to_MM channel
manager_to_parallel channel
manager_to_parallel channel
idle channel
idle channel
...
Compute_Der Compute_Fun Compute_Mat Receive Send
3.0 9.1 15.1 21.2 27.2 33.3 39.3
0
1
2
3
4
5
6
7
8
9
9. Parallel System Design with Task Manager
The parallel-task manager process will keep track of which pro-
cesses are active, and select an inactive process and send an
activations message to that process. This allows for a het-
erogeneous compute situation, where we might have a slower
processor.
Compute_Der Compute_Fun Compute_Mat Receive Send
4.9 14.6 24.3 34.0 43.7 53.4 63.1
0
1
2
3
4
(System Design without Task Manager)
Compute_Der Compute_Fun Compute_Mat Receive Send
5.0 15.0 25.0 35.0 45.0 55.0 65.0
0
1
2
3
4
5
(System Design with Task Manager)
For the parallel resource utilization, spawning parallel gradi-
ents computing can be done either by the round-robin scheme
statically (top), or by introducing a task manager dynamically
(bottom).
10. Parallel System Design with Task Manager
The parallel-task manager process will keep track of which pro-
cesses are active, and select an inactive process and send an
activations message to that process. This allows for a het-
erogeneous compute situation, where we might have a slower
processor.
Compute_Der Compute_Fun Compute_Mat Receive Send
4.2 12.5 20.8 29.1 37.4 45.7 54.0
0
1
2
3
4
(System Design without Task Manager)
Compute_Der Compute_Fun Compute_Mat Receive Send
4.2 12.6 21.0 29.4 37.8 46.2 54.6
0
1
2
3
4
5
(System Design with Task Manager)
For the parallel resource utilization, spawning parallel gradi-
ents computing can be done either by the round-robin scheme
statically (top), or by introducing a task manager dynamically
(bottom).
12. Speedup for ADIFOR Application:
Shallow Water Equations model (SWE)
The serial and parallel speedup for the ShallowWater Equations
model (SWE), which utilizes a time-dependent leapfrog scheme.
Shallow Water Equations model (SWE)
grid size = 21x21 n = 3*21*21 = 1323, p = 4, s = n + p = 1327
machine: IBM SP, time-loop: 40
160.00
140.00
120.00
100.00
80.00
60.00
40.00
20.00
0.00
ADIFOR Serial Parallel: 1 2 4 8 16 32
no. of derivative slaves
Speedup
Dense
Color
Sparse
Mixed-1
Mixed-2
The serial speedup has been done by employing the chain rule
and the sparsity patterns. Chain rule associativity breaks de-
pendencies and generates new task parallelism.
13. ADIFOR Application:
Shallow Water Equations model (SWE)
The Shallow Water Equations model (SWE), which utilizes a
time-dependent leapfrog scheme.
We let Z(t); Z(t 1) denote the current and previous state of
the time-dependent system. The next state is obtained by
Z(t + 1) = G(Z(t); Z(t + 1);W;B(t + 1);Obs(t + 1))
where G is the time-stepping operator, W are the time-
independent parameters, B(t + 1) are the next boundary con-
ditions, and Obs(t + 1) are observations of the next state.
0
5
10
15
20
25
0
5
10
15
20
20
10
0
−10
−20
−30
−40
−50
25
Shallow Water Equations model (SWE)
0
5
10
15
20
25
0
5
10
15
20
4
2
0
−2
−4
−6
−8
−10
25
x 106
Shallow Water Equations model (SWE) AD−Sensitivity
4-D variational data assimilation with shallow water equations
(SWE) when controlling both boundary and initial conditions
(left) and its sensitivity to a uniform relative change in the
observations and weights (right).
14. ADIFOR Application: MM5 PSU/NCAR
Mesoscale Weather Model
The Fifth-Generation Penn State/NCAR Mesoscale Weather
Model (MM5) is regional forecasting model. See A Description
of the Fifth-Generation Penn State/NCAR Mesoscale Weather
Model (MM5), G. A. Grell, J. Dudhia, and D. R. Stauer,
NCAR/TN-398+STR, 1994.
Water vapor mass fraction (left) and its sensitivity to a uniform
relative change in the surface pressure
16. MM5's Sensitivity to Initial Temperature
Grid size: 63 63 23.
Median distance of grid points: 101 km.
Radius of perturbation: 4.6 grid points.
Sensitivity of Temperature in deg/deg at
time t = 0h 30min (6th time step) on the
519 mb sigma-level.
17. ADIFOR Application:
High-Speed Civil Transport
MARSEN: 3-D marching Euler code - Vamshi Mohan Ko-
rivi and Art Taylor, Old Dominion University, Perry Newman,
NASA Langley
Aerodyn. Opt. Studies using a 3-D Supersonic
Euler Code with Ecient Calculation of Sensi-
tivity Derivatives, V. M. Korivi, P. Newman, A.
Taylor, AIAA-94-4270-CP, 1994.