Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013

•

1 like•503 views

Presentation given at the Integrated Computational Materials Engineering conference 2013. This presentation provides a brief survey of what spatial correlation functions can provide for point cloud microstructure datasets. This method is applicable to very large (~1,000,000 datapoints) both experimental and computational microstructure datasets. It is applied to Aluminum molecular dynamics simulations provided by Chandler Becker at NIST, molecular dynamics simulations of mechanical deformation of polymer materials provided by Karl Jacobs and Xin Dong at Georgia Tech, and lastly experimental datasets of the solidfication of Al-Cu alloys generated from X-ray Computed Tomography as provided by Peter Voorhees and John Gibbs at Northwestern University.

Spatially Resolved Pair Correlation Functions
for Structure-Processing Taxonomies
DATASOURCES
Chandler Becker
Al Molecular Dynamics
Peter Voorhees, John Gibbs
X-CT Al-Cu Solidification
Karl Jacobs, Xin Dong
Polymer MD
MAT
SCI
DATA
SCI
Tony Fast
Materials Data Analyst

μInformatics is material AGNOSTIC statistical framework aimed to distill
rich physical data into tractable forms that facilitate structural
taxonomies and bi-directional structure-property/processing
homogenization and localization relationships. It provides a foundation
for rigorous microstructure sensitive materials design.
3 Statistical Modules
5 Value Assessment
4
Data-Mining Modules
2
μS Signal Processing Modules
Experiment &
Simulation
Objective &
Subjective μS
metrics
DSP and image
segmentation
“HUGE influence on μI”
1
Physical Models
DSP
Spatial
Statistics
MKS Dimension
Reduction
MICROSTRUCTURE
INFORMATICS (μI)

Hey, I don’t
know what
direction to
hold this
microscope
image so I’m
going home!
MATERIAL / population RVE / sample
Materials science Statistics
?
? ?
Difference Between
Direct comparison of microstructures is most often
impractical which demands novel statistical interpretations.
Statistically speaking,
you probably never
will, so stay here and
use some statistics!

reveal
Statistical correlations between random points in space/time which reveal systematic patterns
in the microstructure. Contains the original μS within a translation & inversion.
Difference
Between
MaterialInformation
SpatialCorrelation Objective
Comparison
𝑚 𝑠
ℎ A digital signal of the microstructure at a position maybe voxel in the volume, s,
of S total positions for a channel, h, of H total channels. The channels describe
material features (e.g. phase, angle, curvature) using a prescribed basis function.
𝑓𝑟
ℎℎ′
=
1
𝑆
𝑚 𝑠
ℎ 𝑚 𝑠+𝑟
ℎ′
𝑆
𝑠=1

Evenly Gridded
Spatial Domain
& Outside Cell
Inside Cell
k-d tree range to
find point indices in
each partition
8
47
22
An Algorithm for Point
Cloud Spatial Statistics
Provides a look-up table
for material features
Build a kd-tree & partition the spatial domain
Build: O(N) & Search: O(log(N))

ot make dumb coding mistakes I will not make dumb coding mistakes I will not
ot make dumb coding mistakes I will not make dumb coding mistakes I will not
ot make dumb coding mistakes I will not make dumb coding mistakes I will not
ot make dumb coding mistakes I will not make dumb coding mistakes I will not
ot make dumb coding mistakes I will not make dumb coding mistakes I will not
ot make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
t make dumb coding mistakes I will not make dumb coding mistakes I will not
Combining Domains - The μS Function
𝑚 𝑠′
ℎ is the average of the weighted average of Legendre Polynomials of the
processed digital signal in each partition.
8
47
22
𝑚 𝑠′
ℎ =
𝐴𝑖 𝑚𝑖
ℎ
𝑖∈𝑃
𝐴𝑖𝑖∈𝑃
Position of the Partition(𝑠′
)
𝑑𝑥
Material Features: Orientation, Phase, Category, Curvature, Volume Fraction,…

HEAT TREATED α-β TITANIUMMicrostructure Taxonomies
Albeit these datasets are sampled from different
processing routes, the taxonomy is a structure-structure relationship that doesn’t
track processing history because the images are sampled after heat treatment.
Principal Components Analysis
Reduces D variables to d variables. Each axis corresponds to the
i-th greatest direction of variance.
Kalidindi, Surya R.; Niezgoda, Stephen R.; Salem, Ayman A
,"Microstructure informatics using higher-order statistics and efficient data-mining protocols", "JOM" , 2011

POLYMER SIMULATIONSXin Dong, Karl Jacobs,GA Tech
Each point indicates
the statistics, or a
structure, in a simulation.
Each color is a different
initial structure & lines
track history.
Initial
Stages
★Final
Structure

Al Molecular Dynamics
Chandler Becker, NIST
Liquid
Crystalline

$SOLIDIFICATION OF AL-CU ALLOYS20% Vf 15% Vf Peter Voorhees, John Gibbs Northwestern University Interfacial curvatures between Al & Cu during solidification rendered from X-CT 2 different volume fractions$

Chandler Becker
Al Molecular Dynamics
Peter Voorhees, John Gibbs
X-CT Al-Cu Solidification
Karl Jacobs, Xin Dong
Polymer MD
Le Fin

Improvements in algorithms, technology, and computation are directly impacting the landscape of information use in materials science. The 3 V’s of Big Data (volume, velocity, and variety) are becoming evermore apparent within all sectors of the field. Novel approaches will be required to confront the emerging data deluge and extract the richest knowledge from simulated and empirical information in complex evolving 3-D spaces. Microstructure Informatics (μInformatics) is an emerging suite of signal processing techniques, advanced statistical tools, and data science methods tailored specifically for this new frontier. μInformatics curates and transforms large collections of materials science information using efficient workflows to extract knowledge of bi-directional structure-property/processing connections for most material classes. In this talk, a few early case studies in data-driven methods to solve materials science problems will be explored. Emerging spatial statistics tools will be explored that enable an objective comparison of static and evolving 3-D material volumes from molecular dynamics simulation, micro-CT, and Scanning Electron Microscopy. Also, the statistics will provide a foundation to create improved bottom-up homogenization relationships in fuel cell materials. Lastly, applications of the Materials Knowledge System, a data-driven meta-model to create top-down localization relationships will be explored for phase field model and finite element model information.

Spatially resolved pair correlation functions for point cloud data

Tony Fast

Exploring Architected Materials Using Machine Learning

Advanced-Concepts-Team

The promise of computer aided manufacturing is to make materializable structures that could not be fabricated using traditional methods. An example is 3D printed lattices, where variation in the lattice geometry and print media can define a vast spectrum of resulting material behaviour, ranging from fully flexible forms to completely stiff examples with high strength. While these “architected materials” offer huge promise for industrial applications, in practice they are difficult to generate and explore digitally, and even harder to simulate for mechanical testing. In this talk I will outline a range of approaches to the study of architected materials using machine learning. I will describe several projects using graph neural networks (GNNs) to model lattice geometry, and report on a few recent works that construct inverse models. These approaches are progress toward better methods for approximation of the material behaviour of the space of all lattice geometries, offering potential for real-time material feedback at the design stage, and a streamlined selection process for architected materials.

Iaetsd latent fingerprint recognition and matching

Iaetsd Iaetsd

The document discusses latent fingerprint recognition and matching using statistical texture analysis. It proposes extracting three statistical features from fingerprints - entropy coefficient from intensity histogram, correlation coefficient using Wiener filter, and wavelet energy coefficient from 5-level wavelet decomposition. These features are used to represent fingerprints mathematically and provide efficient fingerprint recognition. Existing fingerprint recognition methods are also discussed, including those based on minutiae matching and dealing with nonlinear distortions. However, these do not fully address the problem. The proposed statistical analysis approach can provide more accurate recognition results.

Object recognition

saniacorreya

This document discusses object recognition by computers. It notes that while object recognition is easy for humans, it is difficult for computers because they cannot rely on appearance alone. Key challenges for computers include variations in scale, shape, occlusion, lighting and background clutter. The document then discusses techniques used for object recognition, including feature detection methods like SIFT and SURF that extract keypoints, descriptors that describe regions around keypoints, and feature matching to identify corresponding regions between images. It also covers bag-of-words models, visual vocabularies and inverted indexing to allow large scale image retrieval. Finally, it lists applications of object recognition like digital watermarking, face detection and robot navigation.

self operating maps

AltafSMT

This document is the presentation slides for a technical seminar on Self Organizing Maps. It introduces self-organizing maps as an unsupervised neural network technique. The presentation covers topics such as statistical pattern recognition, unsupervised pattern classification, advantages and disadvantages of self-organizing maps, and concludes that self-organizing maps provide a structural representation of input data.

Introduction to 3D Computer Vision and Differentiable Rendering

Preferred Networks

Modern perception pipelines in autonomous driving (AD) systems are based on Deep Neural Networks (DNNs) which utilize multiple hyper-parameter configurations and training strategies. Data augmentations is now a well-established training strategy to improve the generalization of DNNs, especially in a low dataset regime. Self-supervised learning and semi-supervised methods depend heavily on data augmentation strategies. In this study we view generalization due to data augmentations training DNNs since they implicitly model the geometric, viewpoint based transformations present on images/pointclouds due to noise, perspective, motion of the ego-vehicle. We shortly review current data augmentation strategies for perception tasks in AD, and recent developments on understanding its effects on model generalization. In the talk we shall review data augmentation strategies through two case studies: - Improving model performance of monocular 3D object detection model by using geometry preserving data augmentations on images - Understand the role of data augmentation in reducing data redundancy and improving label efficiency within an active learning pipeline

Skytree big data london meetup - may 2013

bigdatalondon

Skytree focuses on production grade machine learning using algorithms that reduce computational complexity from quadratic and cubic to linear or logarithmic. This allows machine learning to be applied to large datasets. Skytree's products include Skytree Adviser for desktop machine learning and Skytree Server for enterprise machine learning applications such as prediction, detection, finding trends and patterns, and identifying outliers. The company was founded by experts in machine learning, algorithms, and distributed systems.

Calin Constantinov - Neo4j - Bucharest Big Data Week Meetup - Bucharest 2018

Calin Constantinov

Face Recognition System using Self Organizing Feature Map and Appearance Base...

ijtsrd

Face Recognition has develop one of the most effective presentations of image analysis. This area of research is important not only for the applications in human computer interaction, biometric and security but also in other pattern classification problem. To improve face recognition in this system, two methods are used PCA Principal component analysis and SOM Self organizing feature Map .PCA is a subspace projection method is used compress the input face image. SOM method is used to classify DCT based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. The aim of this system is that input image has to compare with stored images in the database using PCA and SOM method. An image database of 100 face images is evaluated containing 10 subjects and each subject having 10 images with different facial expression. This system is evaluated by measuring the accuracy of recognition rate. This system has been implemented by MATLAB programming. Thaung Yin | Khin Moh Moh Than | Win Tun "Face Recognition System using Self-Organizing Feature Map and Appearance-Based Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26691.pdfPaper URL: https://www.ijtsrd.com/computer-science/cognitive-science/26691/face-recognition-system-using-self-organizing-feature-map-and-appearance-based-approach/thaung-yin

Burnaev and Notchenko. Skoltech. Bridging gap between 2D and 3D with Deep Lea...

Skolkovo Robotics Center

Bridging the gap between 2D and 3D with Deep Learning discusses the increasing popularity of 3D deep learning and representations of 3D data. It summarizes recent developments in 3D deep learning for tasks like shape retrieval, scene understanding, and robotic vision. Representations like voxels and point clouds are discussed alongside challenges like scaling to large datasets and encoding texture. The need for joint learning across deep learning, geometry, and probabilistic approaches is highlighted for holistic scene understanding.

Detection of Dense, Overlapping, Geometric Objects

gerogepatton

Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting thermophysical property data of metal systems, whose plot points are represented primarily by circles, triangles, and squares. We built a highly accurate single class U-Net convolutional neural network model to identify 97 % of image objects in a defined set of test images, locating the centers of the objects to within a few pixels of the correct locations. We found an optimal way in which to mark our training data masks to achieve this level of accuracy. The optimal markings for object classification, however, required more information in the masks to identify particular types of geometries. We show a range of different patterns used to mark the training data masks, and how they help or hurt our dual goals of location and classification. Altering the annotations in the segmentation masks can increase both the accuracy of object classification and localization on the plots, more than other factors such as adding loss terms to the network calculations. However, localization of the plot points and classification of the geometric objects require different optimal training data.

DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTS

gerogepatton

DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTS

ijaia

High-Performance Graph Analysis and Modeling

Nesreen K. Ahmed

This document discusses knowledge discovery and machine learning on graph data. It makes three main observations: 1) Graphs are typically constructed from input data rather than given directly, as relationships must be inferred. 2) Graph data management is challenging due to issues like large size, dynamic nature, heterogeneity and attribution. 3) Useful insights and accurate modeling depend on the representation of the data as a graph, such as through decomposition, feature learning or other techniques.

Segmentation and recognition of handwritten digit numeral string using a mult...

ijfcstjournal

In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also presents a new technique to remove slope and slant from handwritten numeral string and to normalize the size of text images and classify with supervised learning methods. Experimental results on a database of 102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained on independent digits contained in the numeral string of digits includes both the skewed and slant data.

Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET Transform

IRJET Journal

This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.

V.karthikeyan published article

KARTHIKEYAN V

1) The document presents a Tamil character recognition system using Hilditch's algorithm and structural characteristics of characters. 2) It describes the image preprocessing steps of binarization, segmentation, bounding box detection, normalization and skeletonization. 3) Hilditch's algorithm is applied to obtain thin, connected skeletons of characters. 4) Moments-based features are extracted from skeletons for character recognition using a neural network classifier. 5) The system achieved a recognition accuracy of 99% for identified Tamil characters.

Partial Object Detection in Inclined Weather Conditions

IRJET Journal

This document provides a comprehensive analysis of imbalance problems in object detection. It presents a taxonomy to classify different types of imbalances and discusses solutions proposed in literature. The analysis highlights significant gaps including existing imbalances that require further attention, as well as entirely new imbalances that have never been addressed before. A survey of imbalance problems caused by weather conditions and common object imbalances is conducted. Methods for addressing imbalances include data augmentation using GANs and balancing training based on class performance.

User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...

Aalto University

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...

Chris Rackauckas

This document discusses scientific machine learning and differentiable simulation. It begins by explaining that scientific machine learning uses both data and physical knowledge to make accurate predictions with less data. It then discusses differentiable simulation and how universal differential equations can be used to replace unknown portions of models with neural networks while preserving known physical structure. Several examples are provided of applications in various domains like epidemiology, black hole detection, earthquake engineering, and chemistry. The document emphasizes that understanding the engineering principles and numerical properties of the domain is important for applying these methods stably and efficiently.

Stereo Matching by Deep Learning

Yu Huang

Python for data science

botsplash.com

IRJET - Object Detection using Deep Learning with OpenCV and Python

IRJET Journal

This document summarizes research on object detection techniques using deep learning. It discusses using the YOLO algorithm to identify objects in images using a single neural network that predicts bounding boxes and class probabilities. The document reviews prior research on algorithms like R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. It then describes the YOLO loss function and methodology for finding bounding boxes of objects in an image. The document concludes that YOLO is well-suited for real-time object detection applications due to its advantages over other algorithms.

Matlab project

shahu2212

Github for Research Science

Tony Fast

The Materials Data Scientist

Tony Fast

A talk for the Institute of Data Analytics and High Performance Computing Chalk and Talk lunch series on Thursday April 25, 2014. This high level talk discusses materials science on the grounds of the information that drive new discoveries in materials science. Understanding the nature of the data that encompasses the landscape of materials science is important for the next generation workforce and the emerging discipline of Materials Data Scientist

Similar to Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013

IEEE Pattern analysis and machine intelligence 2016 Title and Abstract

tsysglobalsolutions

AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...

Ravi Kiran B.

Skytree big data london meetup - may 2013

bigdatalondon

Calin Constantinov - Neo4j - Bucharest Big Data Week Meetup - Bucharest 2018

Calin Constantinov

Face Recognition System using Self Organizing Feature Map and Appearance Base...

ijtsrd

Burnaev and Notchenko. Skoltech. Bridging gap between 2D and 3D with Deep Lea...

Skolkovo Robotics Center

Detection of Dense, Overlapping, Geometric Objects

gerogepatton

DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTS

gerogepatton

DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTS

ijaia

High-Performance Graph Analysis and Modeling

Nesreen K. Ahmed

Segmentation and recognition of handwritten digit numeral string using a mult...

ijfcstjournal

Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET Transform

IRJET Journal

V.karthikeyan published article

KARTHIKEYAN V

Partial Object Detection in Inclined Weather Conditions

IRJET Journal

User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...

Aalto University

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...

Chris Rackauckas

Stereo Matching by Deep Learning

Yu Huang

Python for data science

botsplash.com

IRJET - Object Detection using Deep Learning with OpenCV and Python

IRJET Journal

Matlab project

shahu2212

Similar to Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013 (20)

IEEE Pattern analysis and machine intelligence 2016 Title and Abstract

AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...

Skytree big data london meetup - may 2013

Calin Constantinov - Neo4j - Bucharest Big Data Week Meetup - Bucharest 2018

Face Recognition System using Self Organizing Feature Map and Appearance Base...

Burnaev and Notchenko. Skoltech. Bridging gap between 2D and 3D with Deep Lea...

Detection of Dense, Overlapping, Geometric Objects

DETECTION OF DENSE, OVERLAPPING, GEOMETRIC OBJECTS

High-Performance Graph Analysis and Modeling

Segmentation and recognition of handwritten digit numeral string using a mult...

Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET Transform

V.karthikeyan published article

Partial Object Detection in Inclined Weather Conditions

User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...

Stereo Matching by Deep Learning

Python for data science

IRJET - Object Detection using Deep Learning with OpenCV and Python

Matlab project

More from Tony Fast

Github for Research Science

Tony Fast

The Materials Data Scientist

Tony Fast

An Slight Overview of the Critical Elements of Spatial Statistics

Tony Fast

The document discusses using spatial statistics and signal processing techniques to better analyze and describe materials from multidimensional datasets. It focuses on advanced statistical descriptors like pair correlation functions and vector-resolved spatial statistics that can provide more information about the distributions of material features. The goal is to develop scalable methods for extracting structure-property relationships from increasingly large and complex materials data.

Materials Informatics Overview

Tony Fast

The document provides an overview of materials informatics and the Materials Genome Initiative. It discusses how materials informatics uses data-driven approaches and techniques from fields like signal processing, machine learning and statistics to generate structure-property-processing linkages from materials science data and improve understanding of materials behavior. This includes extracting features from materials microstructure, using statistical analysis and data mining to discover relationships and create predictive models, and evaluating how knowledge has improved.

Information sciences to fuel the data age of materials science

Tony Fast

Microstructure Informatics

Tony Fast

Higher-Order Localization Relationships Using the MKS Approach

Tony Fast

Higher-Order Microstructure Statistics for Next Generation Materials Taxonomy

Tony Fast

Novel and Enhanced Structure-Property-Processing Relationships with Microstru...

Tony Fast

Tony Fast presented on novel microstructure informatics approaches using statistical distributions and data mining techniques to establish structure-property and structure-processing relationships from large datasets in materials science. The μInformatics framework uses modular workflows involving microstructure representation, dimensionality reduction, classification, and regression to distill rich physical data into forms that facilitate comparisons and linkages between material structure and performance. Examples were given applying the framework to problems in metals, composites, and organic materials.

More from Tony Fast (9)

Github for Research Science

The Materials Data Scientist

An Slight Overview of the Critical Elements of Spatial Statistics

Materials Informatics Overview

Information sciences to fuel the data age of materials science

Microstructure Informatics

Higher-Order Localization Relationships Using the MKS Approach

Higher-Order Microstructure Statistics for Next Generation Materials Taxonomy

Novel and Enhanced Structure-Property-Processing Relationships with Microstru...

Recently uploaded

Best 20 SEO Techniques To Improve Website Visibility In SERP

Pixlogix Infotech

HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU

panagenda

Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/ DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen! Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell. Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten. Diese Themen werden behandelt - Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten - Wie funktionieren CCB- und CCX-Lizenzen wirklich? - Verstehen des DLAU-Tools und wie man es am besten nutzt - Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw. - Praxisbeispiele und Best Practices zum sofortigen Umsetzen

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Tosin Akinosho

Monitoring and Managing Anomaly Detection on OpenShift Overview Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices. Key Topics Covered 1. Introduction to Anomaly Detection - Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems. 2. Understanding Edge (IoT) - Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source. 3. What is ArgoCD? - Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices. 4. Deployment Using ArgoCD for Edge Devices - Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD. 5. Introduction to Apache Kafka and S3 - Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions. 6. Viewing Kafka Messages in the Data Lake - Learn how to view and analyze Kafka messages stored in a data lake for better insights. 7. What is Prometheus? - Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices. 8. Monitoring Application Metrics with Prometheus - Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system. 9. What is Camel K? - Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes. 10. Configuring Camel K Integrations for Data Pipelines - Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow. 11. What is a Jupyter Notebook? - Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text. 12. Jupyter Notebooks with Code Examples - Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.

Webinar: Designing a schema for a Data Warehouse

Federico Razzoli

Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you. A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services. But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts. We will discuss these topics: - How to gather information about a business; - Understanding dictionaries and how to identify business entities; - Dimensions and facts; - Setting a table granularity; - Types of facts; - Types of dimensions; - Snowflakes and how to avoid them; - Expanding existing dimensions and facts.

Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack

shyamraj55

AI 101: An Introduction to the Basics and Impact of Artificial Intelligence

IndexBug

Choosing The Best AWS Service For Your Website + API.pptx

Brandon Minnick, MBA

Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API? Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose? Which one is cheapest? Which one is fastest? Which one will scale to meet our needs? Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!

Ocean lotus Threat actors project by John Sitima 2024 (1).pptx

SitimaJohn

Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.

UI5 Controls simplified - UI5con2024 presentation

Wouter Lemaire

Mariano G Tinti - Decoding SpaceX

Mariano Tinti

June Patch Tuesday

Ivanti

Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

Recommendation System using RAG Architecture

fredae14

How to use Firebase Data Connect For Flutter

Daiki Mogmet Ito

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Safe Software

Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency. During the hour, we’ll take you through: Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board. Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes. Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI. We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI. This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!

Introduction of Cybersecurity with OSS at Code Europe 2024

Hiroshi SHIBATA

I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems. The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS. Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application. I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.

How to Get CNIC Information System with Paksim Ga.pptx

danishmna97

Presentation of the OECD Artificial Intelligence Review of Germany

innovationoecd

National Security Agency - NSA mobile device best practices

Quotidiano Piemontese

GenAI Pilot Implementation in the organizations

kumardaparthi1024

Fueling AI with Great Data with Airbyte Webinar

Zilliz

Recently uploaded (20)

Best 20 SEO Techniques To Improve Website Visibility In SERP

HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Webinar: Designing a schema for a Data Warehouse

Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack

AI 101: An Introduction to the Basics and Impact of Artificial Intelligence

Choosing The Best AWS Service For Your Website + API.pptx

Ocean lotus Threat actors project by John Sitima 2024 (1).pptx

UI5 Controls simplified - UI5con2024 presentation

Mariano G Tinti - Decoding SpaceX

June Patch Tuesday

Recommendation System using RAG Architecture

How to use Firebase Data Connect For Flutter

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Introduction of Cybersecurity with OSS at Code Europe 2024

How to Get CNIC Information System with Paksim Ga.pptx

Presentation of the OECD Artificial Intelligence Review of Germany

National Security Agency - NSA mobile device best practices

GenAI Pilot Implementation in the organizations

Fueling AI with Great Data with Airbyte Webinar

Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013

1. Spatially Resolved Pair Correlation Functions for Structure-Processing Taxonomies DATASOURCES Chandler Becker Al Molecular Dynamics Peter Voorhees, John Gibbs X-CT Al-Cu Solidification Karl Jacobs, Xin Dong Polymer MD MAT SCI DATA SCI Tony Fast Materials Data Analyst

2. μInformatics is material AGNOSTIC statistical framework aimed to distill rich physical data into tractable forms that facilitate structural taxonomies and bi-directional structure-property/processing homogenization and localization relationships. It provides a foundation for rigorous microstructure sensitive materials design. 3 Statistical Modules 5 Value Assessment 4 Data-Mining Modules 2 μS Signal Processing Modules Experiment & Simulation Objective & Subjective μS metrics DSP and image segmentation “HUGE influence on μI” 1 Physical Models DSP Spatial Statistics MKS Dimension Reduction MICROSTRUCTURE INFORMATICS (μI)

3. Hey, I don’t know what direction to hold this microscope image so I’m going home! MATERIAL / population RVE / sample Materials science Statistics ? ? ? Difference Between Direct comparison of microstructures is most often impractical which demands novel statistical interpretations. Statistically speaking, you probably never will, so stay here and use some statistics!

4. reveal Statistical correlations between random points in space/time which reveal systematic patterns in the microstructure. Contains the original μS within a translation & inversion. Difference Between MaterialInformation SpatialCorrelation Objective Comparison 𝑚 𝑠 ℎ A digital signal of the microstructure at a position maybe voxel in the volume, s, of S total positions for a channel, h, of H total channels. The channels describe material features (e.g. phase, angle, curvature) using a prescribed basis function. 𝑓𝑟 ℎℎ′ = 1 𝑆 𝑚 𝑠 ℎ 𝑚 𝑠+𝑟 ℎ′ 𝑆 𝑠=1

5. Evenly Gridded Spatial Domain & Outside Cell Inside Cell k-d tree range to find point indices in each partition 8 47 22 An Algorithm for Point Cloud Spatial Statistics Provides a look-up table for material features Build a kd-tree & partition the spatial domain Build: O(N) & Search: O(log(N))

6. ot make dumb coding mistakes I will not make dumb coding mistakes I will not ot make dumb coding mistakes I will not make dumb coding mistakes I will not ot make dumb coding mistakes I will not make dumb coding mistakes I will not ot make dumb coding mistakes I will not make dumb coding mistakes I will not ot make dumb coding mistakes I will not make dumb coding mistakes I will not ot make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not t make dumb coding mistakes I will not make dumb coding mistakes I will not Combining Domains - The μS Function 𝑚 𝑠′ ℎ is the average of the weighted average of Legendre Polynomials of the processed digital signal in each partition. 8 47 22 𝑚 𝑠′ ℎ = 𝐴𝑖 𝑚𝑖 ℎ 𝑖∈𝑃 𝐴𝑖𝑖∈𝑃 Position of the Partition(𝑠′ ) 𝑑𝑥 Material Features: Orientation, Phase, Category, Curvature, Volume Fraction,…

7. Correlation Function Visualization

8. Correlation Function Visualization

9. Correlation Function Visualization

10. HEAT TREATED α-β TITANIUMMicrostructure Taxonomies Albeit these datasets are sampled from different processing routes, the taxonomy is a structure-structure relationship that doesn’t track processing history because the images are sampled after heat treatment. Principal Components Analysis Reduces D variables to d variables. Each axis corresponds to the i-th greatest direction of variance. Kalidindi, Surya R.; Niezgoda, Stephen R.; Salem, Ayman A ,"Microstructure informatics using higher-order statistics and efficient data-mining protocols", "JOM" , 2011

11.

12. POLYMER SIMULATIONSXin Dong, Karl Jacobs,GA Tech Each point indicates the statistics, or a structure, in a simulation. Each color is a different initial structure & lines track history. Initial Stages ★Final Structure

13. Al Molecular Dynamics Chandler Becker, NIST Liquid Crystalline

14. SOLIDIFICATION OF AL-CU ALLOYS20% Vf 15% Vf Peter Voorhees, John Gibbs Northwestern University Interfacial curvatures between Al & Cu during solidification rendered from X-CT 2 different volume fractions

15. Chandler Becker Al Molecular Dynamics Peter Voorhees, John Gibbs X-CT Al-Cu Solidification Karl Jacobs, Xin Dong Polymer MD Le Fin

Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013

Recommended

Recommended

More Related Content

Similar to Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013

Similar to Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013 (20)

More from Tony Fast

More from Tony Fast (9)

Recently uploaded

Recently uploaded (20)

Spatially resolved pair correlation functions for structure processing taxonomies - ICME 2013