Search results “Biodata mining and visualization tools”
How to Make Money Selling Analytics Services
You know GA, GTM and more Analytics? Turn your investment into skills profitable and start making money. How? In this video I’m going to show you 5 services you could be offering with your analytics skill set. They are.. Audits Implementation Analysis and Reporting Training Action #MakeMoneyOnline #AnalyticsServices #Measure 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool LinkedIn: https://www.linkedin.com/company/measureschool
Views: 10496 Measureschool
What is BIOCURATOR? What does BIOCURATOR mean? BIOCURATOR meaning, definition & explanation
What is BIOCURATOR? What does BIOCURATOR mean? BIOCURATOR meaning - BIOCURATOR pronunciation - BIOCURATOR definition - BIOCURATOR explanation - How to pronounce BIOCURATOR? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A biocurator is a professional scientist who curates, collects, annotates, and validates information that is disseminated by biological and model Organism Databases. The role of a biocurator encompasses quality control of primary biological research data intended for publication, extracting and organizing data from original scientific literature, and describing the data with standard annotation protocols and vocabularies that enable powerful queries and biological database inter-operability. Biocurators communicate with researchers to ensure the accuracy of curated information and to foster data exchanges with research laboratories. Biocurators (also called scientific curators, data curators or annotators) have been recognized as the "museum catalogers of the Internet age". In genome annotation for example, biocurators commonly employ—and take part in the creation and development of—shared biomedical ontologies: structured, controlled vocabularies that encompass many biological and medical knowledge domains, such as the Open Biomedical Ontologies found in the OBO Foundry. These domains include genomics and proteomics, anatomy, animal and plant development, biochemistry, metabolic pathways, taxonomic classification, and mutant phenotypes. Biocurators enforce the consistent use of gene nomenclature guidelines and participate in the genetic nomenclature committees of various model organisms, often in collaboration with the HUGO Gene Nomenclature Committee (HGNC). They also enforce other nomenclature guidelines like those provided by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB), one example of which is the Enzyme Commission EC number. There has been also recent interest in exploring the use of natural-language processing and text mining technologies to enable a more systematic extraction of candidate information for manual literature curation. Therefore, the definition of the main literature curation stages of a 'canonical' biocuration workflow has been examined. The use of text mining techniques for these various stages, from the initial detection of curation-relevant articles (triage) to the extraction of annotations and entity relationships has been attempted by various specialized systems. Traditionally, biological knowledge has been aggregated through expert curation, conducted manually by dedicated experts. However, with the burgeoning volume of biological data and increasingly diverse densely informative published literatures, expert curation becomes more and more laborious and time-consuming, increasingly lagging behind knowledge creation. Community Curation harnesses community intelligence in knowledge curation, bears great promise in dealing with the flood of biological knowledge. To exploit the full potential of the scientific community for knowledge curation, multiple biological wikis (bio-wikis) have been built to date. To increase community curation in bio-wikis, AuthorReward, an extension to MediaWiki, is developed for rewarding community-curated efforts in knowledge curation. AuthorReward provides bio-wikis with an authorship metric; it quantifies researchers' contributions by properly factoring both edit quantity and quality and yields automated explicit authorship according to their quantitative contributions. Another community based approach to analyze biological data is called Systems Biology Verification (SBV) IMPROVER. Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. SBV IMPROVER presents a crowd-verification approach for the visualization and expansion of biological networks.
Views: 186 The Audiopedia
Mirco Musolesi: Mining and Understanding Big (and Small) Mobile Data
This talk was recorded at Europe's first Computational Social Science conference at the University of Warwick in June 2014, hosted by the Data Science Lab at Warwick Business School (http://www.datasciencelab.co.uk). ABSTRACT | Mobile phones are increasingly equipped with sensors, such as accelerometers, GPS receivers, proximity sensors and cameras, which, together with social media infromation can be used to sense and interpret people behaviour in real-time. Novel user-centered sensing applications can be built by exploiting the availability of these technologies. Moreover, data extracted from the sensors can also be used to model and predict people behaviour and movement patterns, providing a very rich set of multi-dimensional and linked data, which can be extremely useful, for instance, for marketing applications, real-time support for policy-makers and health interventions. In this talk I will discuss some recent projects in the area of large-scale scale data mining and modelling of mobile data, with a focus on human mobility prediction and epidemic spreading containment. I will also overview other possible practical applications of this work, in particular with respect to the emerging area of anticipatory computing and the challenges ahead for the research community. BIOGRAPHY | Dr. Mirco Musolesi is a Reader in Networked Systems and Data Science at the School of Computer Science at the University of Birmingham. He received a PhD in Computer Science from University College London in 2007. Before joining Birmingham, he held research positions at Dartmouth College and Cambridge and a Lectureship at the University of St Andrews. His research interests lie at the interface of different areas, namely ubiquitous computing, large-scale data mining, and network science.
Views: 304 Data Science Lab
New AI-driven analytics and data storytelling in Power BI
On this episode, Will Thompson from the Power BI team joins Jeremy Chapman to take a hands-on look at updates to Power BI, including new capabilities to help you to bookmark and spotlight your data during presentations, enhanced AI for Quick insights and Q&A, options for bi-directional integration between Power BI and other apps like Visio, new metrics to see how your reports are being consumed, and how you can to securely share your reports externally.
Views: 48460 Microsoft Mechanics
Unlock Knowledge Mining on your domain: build custom skills to tailor content - BRK3001
From accounting to retail, from healthcare to legal, industries and businesses have specific ways they need to interpret their data in order to unlock value. Come learn how to apply knowledge to your specific business scenarios, domain or industry with custom skills on Azure Search. We'll show how to create custom skills using tools like Azure Functions and Containers and connect them to intelligent knowledge mining processes. Real examples included.
Views: 117 Microsoft Developer
Barbara Plank - Keynote Q&A
Barbara Plank is tenured Assistant Professor in Natural Language Processing at the University of Groningen, The Netherlands. Her research focuses on cross-domain and cross-language NLP. She is interested in robust language technology, learning under sample selection bias (domain adaptation, transfer learning), annotation bias (embracing annotator disagreements in learning), and generally, semi-supervised and weakly-supervised machine learning for a variety of NLP tasks and applications, including syntactic processing, opinion mining, information and relation extraction and personality prediction. Natural Language Processing: Challenges and Next Frontiers Despite many advances of Natural Language Processing (NLP) in recent years, largely due to the advent of deep learning approaches, there are still many challenges ahead to build successful NLP models. In this talk I will outline what makes NLP so challenging. Besides ambiguity, one major challenges is variability. In NLP, we typically deal with data from a variety of sources, like data from different domains, languages and media, while assuming that our models work well on a range of tasks, from classification to structured prediction. Data variability is an issue that affects all NLP models. I will then delineate one possible way to go about it, by combining recent success in deep multi-task learning with fortuitous data sources, which allows learning from distinct views and distinct sources. This will be one step towards one of the next frontiers: learning under limited (or absence) of annotated resources, for a variety of NLP tasks. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 210 PyData
Top 10 Myths Regarding Data Scientists Roles in India | Data Science Myths vs Reality | Edureka
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification *** This video debunks the Myths about Data Scientists Roles in India and Abroad. Data Science Being a new field has gained quite a good momentum and there are a few misconceptions about Data Scientists in People's mind which has been cleared in this video. Data Science Podcast: https://castbox.fm/channel/id1832236 Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ #edureka #edurekadatascience #datascientist #datascientistsmyths #top10datascientistmyths - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands-on expertise. By the end of the program, you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 3250 edureka!
Software, It's a Thing
Matthew G. Kirschenbaum discusses software at the 2014 annual meeting of the National Digital Information Infrastructure and Preservation Program. Speaker Biography: Matthew G. Kirschenbaum is associate professor of English at the University of Maryland and associate director of the Maryland Institute for Technology in the Humanities (MITH), an applied thinktank for the digital humanities. He is also an affiliated faculty member with the College of Information Studies at Maryland, and a member of the teaching faculty at the University of Virginia's Rare Book School. For transcript, captions, and more information, visit http://www.loc.gov/today/cyberlc/feature_wdesc.php?rec=6378
Views: 541 LibraryOfCongress
V. PASCUCCI Extreme Data Management Analysis and Visualization for Exascale Supercomputers
Valerio Pascucci Director, Center for Extreme Data Management, Analysis and Visualization DOE Laboratory Fellow at the Pacific Northwest National Laboratory Professor, Scientific Computing and Imaging Institute and School of Computing, University of Utah Laboratory Fellow, Pacific Northwest National Laboratory USA Title Extreme Data Management Analysis and Visualization for Exascale Supercomputers Abstract Effective use of data management techniques for analysis and visualization of massive scientific data is a crucial ingredient for the success of any supercomputing center and cyberinfrastructure for data-intensive scientific investigation. In the progress towards exascale computing, the data movement challenges have fostered innovation leading to complex streaming workflows that take advantage of any data processing opportunity arising while the data is in motion. In this talk I will present a number of techniques developed at the Center for Extreme Data Management Analysis and Visualization (CEDMAV) that allow to build a scalable data movement infrastructure for fast I/O while organizing the data in a way that makes it immediately accessible for analytics and visualization. In addition, I will present a topological analytics framework that allows processing data in-situ and achieve massive data reductions while maintaining the ability to explore the full parameter space for feature selection. Overall, this leads to a flexible data streaming workflow that allows working with massive simulation models without compromising the interactive nature of the exploratory process that is characteristic of the most effective data analytics and visualization environment. Biography Valerio Pascucci is the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV) of the University of Utah. Valerio is also a Faculty of the Scientific Computing and Imaging Institute, a Professor of the School of Computing, University of Utah, and a Laboratory Fellow, of PNNL and a visiting professor in KAUST. Before joining the University of Utah, Valerio was the Data Analysis Group Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory, and an Adjunct Professor of Computer Science at the University of California Davis. Valerio's research interests include Big Data management and analytics, progressive multi-resolution techniques in scientific visualization, discrete topology, geometric compression, computer graphics, computational geometry, geometric programming, and solid modeling. Valerio is the coauthor of more than two hundred refereed journal and conference papers and is an Associate Editor of the IEEE Transactions on Visualization and Computer Graphics. http://www.tophpc.com/2017/
Views: 35 TopHPC Office
People, Data and Analysis
Speaker/Performer: Pat Hanrahan, Computer Graphics Laboratory, Stanford University Sponsor: CITRIS (Ctr for Info Technology Research in the Interest of Society) Abstract: Big data is a hot topic in computing. Most research has focused on automatic methods of data processing such as machine learning and natural language processing. Another important direction of research is how to build systems that can store and process massive data sets. Unfortunately, what has been lost in the discussion is how people should use data to perform analysis and make decisions. It is unlikely that people will be replaced completely by automated decision making systems in the near future. Hence, an important question to ask is what should people do and what should computers do? In this talk, I will discuss promising approaches for building interactive tools that allow people to perform data analysis more easily and effectively. Biography: Pat Hanrahan is a computer graphics researcher, the Canon USA Professor of Computer Science and Electrical Engineering in the Computer Graphics Laboratory at Stanford University. His research focuses on rendering algorithms, graphics processing units, as well as scientific illustration and visualization. As a founding employee at Pixar Animation Studios in the 1980s, Hanrahan was part of the design of the RenderMan Interface Specification and the RenderMan Shading Language. More recently, Hanrahan has served as a co-founder and Chief Scientist of Tableau Software. He has been involved with several Pixar productions, including Tin Toy, The Magic Egg, and Toy Story. In 2005, Stanford University was named the first Regional Visualization and Analytics Center (RVAC), where Hanrahan assembled a multidisciplinary team of researchers, focused on broad-ranging problems in information visualization and visual analytics.
Views: 463 CITRIS
Records Management in the Legal Environment
Podcast Link: http://amazon.sjsu.edu/slisPod/colloquia/sp18/maraHarringtonSP18.mp4 Abstract Every industry presents its own unique properties and challenges in the management of physical and electronic records. In this presentation, you will be introduced to the ever expanding and important world of records management within the legal environment of a law firm setting. Biography Kay Harrington, CRM, has worked as a records manager in the field of records management in the legal environment for 30 years at both the local and national level. At the national level, she oversaw seven branch law offices and developed a nationwide records program that encompassed paper and electronic files and brought the backlog of closed files for all of the offices into compliance with the firm’s retention policy. Currently, Harrington serves as president of the ARMA Mile High Denver Chapter.
SookYoung Son  | South Korea | Big Data Analysis and Data Mining  2015 | Conference Series LLC
2nd International Conference on Big Data Analysis and Data Mining November 30-December 01, 2015 San Antonio, USA Scientific Talk On: A case study on the application of process mining techniques in offshore plant construction process analysis Click here for Abstract and Biography: http://datamining.conferenceseries.com/speaker/2015/sookyoung-son-hyundai-heavy-industries-south-korea Conferenceseries LLC : http://www.conferenceseries.com Omics International : http://www.omicsonline.org/
How to Remember What You Read
Forget something? Check out these resources for strengthening your memory Unlimited Memory: How to Use Learning Strategies to Learn Faster: http://amzn.to/1ZhQd79 The Memory Book: The Guide to Improving Your Memory at Work: http://amzn.to/1GyJ4DK Moonwalking with Einstein: The Art and Science of Remembering: http://amzn.to/1R1vC0U The Memory Jogger 2: Tools for Continuous Improvement: http://amzn.to/1Oo5pMt Memory Improvement: How To Improve Your Memory In Just 30 Days: http://amzn.to/1Oo5pMt Watch more How to Improve Your Reading videos: You can recall what you read if you decide it matters enough to know and remember. Find the motivation to absorb information and improve retention. Step 1: Skim info Skim information slowly at first to remember what you read. Never use this as a primary means of absorbing information, but always as a preparation for gathering key points. Tip Machines that train the eyes to scan and select meaning and context rather than picking out the smaller letter arrangements help speed reading and deepen concentration. Step 2: Read word groups Read word groups rather than single words by developing the habit of snapping the eyes across or down the page. Step 3: Categorize and associate Categorize and associate information you read with other knowledge in your life, to establish familiar cues for retrieving facts. Step 4: Take notes Take succinct notes on significant facts and information when reading books or studying for exams. Say the words aloud to hear yourself and help commit it to memory. Repeat the process. Step 5: Study with purpose Study with purpose and confidence, interacting with the material. Compare and contrast what is being memorized and paraphrase for simplified understanding. Grill yourself with questions to reinforce the lesson. Tip Avoid lazily highlighting everything. Highlight key passages, words, or phrases. Step 6: Visualize to recall Visualize faces with names that have to be remembered. Link important dates in your assignment mentally by picturing significant calendar events, birthdays, or holidays near the newly learned dates. Step 7: Keep single focus Concentrate with purpose on one thing at a time -- a paragraph, a sentence, or a word. Disallow any distractions, focusing on the meaning, and test to make sure you have it before moving on. Step 8: Pick the time of day Pay attention to the times of day when you're most alert and schedule your study time accordingly. Work in short bursts at first to expand attention and grasp. Did You Know? More than 300,000 iPads were sold on the first day they hit the market.
Views: 3109240 Howcast
Life Of A Trillionaire - Montage
My First Ever Luxury Lifestyle Video! Comment on What You Want to See Next! Yachts? Skiing? Cars? Skydiving? Action? Gold? Celebs?
Views: 10558 WorldLuxury
ArcGIS User Seminar – Time-Enabled 3D Spatial Analysis using a Space Time Cube in ArcGIS Pro (Elec)
This example uses ArcGIS Pro and time-enabled 3D spatial analysis, with space time cube and hot spot analysis, to identify areas for proactive tree trimming to reduce electrical outages. Shown: ArcGIS Pro Space Time Pattern Mining Toolbox - http://pro.arcgis.com/en/pro-app/tool-reference/space-time-pattern-mining/an-overview-of-the-space-time-pattern-mining-toolbox.htm Space Time Cube Explorer Add-In - http://www.arcgis.com/home/item.html?id=5c85bf58f8584d2faa5b1b76a2807dca#overview Contact Esri for questions and support at http://www.esri.com/about-esri/contact.
Views: 2409 ArcGIS
Dataiku introduces DSS
Dataiku, the software developer behind DSS, is disrupting the predictive analytics market with an all-in-one predictive analytics development platform that gives data professionals the power to build and run highly specific services that transform raw data into business impacting predictions. Learn more on about DSS on our website: http://www.dataiku.com/dss/
Views: 14392 Dataiku
PLOTCON 2017: Matthew Dillon, Interactive web-based visualization for microbiome science
Interactive web-based visualization for microbiome science with QIIME 2 QIIME 2 is a decentralized, extensible microbiome bioinformatics framework that enables reproducible data science by automatically tracking data provenance. Output visualizations make use of modern web-based technologies, enabling rich, interactive explorations of data, without requiring developers to use any particular visualization framework or tool. Visualizations are shareable via a standard zip container format, and can be viewed by anyone with a modern web browser, using a novel Service Worker-based client-side server. Biography Matthew Dillon is a Research Software Engineer in the Caporaso Lab, a working group of the Pathogen and Microbiome Institute at Northern Arizona University (Flagstaff, AZ). He is a core developer of QIIME 2 - an NSF-funded project that will revolutionize microbiome bioinformatics.
Views: 419 Plotly
GEKKO Optimization Suite Overview
Resources for GEKKO Presentation and Example Files Shown During Webinar: https://github.com/loganbeal/CAST_GEKKO_webinar Starter Guide: https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization Documentation: https://gekko.readthedocs.io/en/latest/ GEKKO is an optimization suite for Python [1]. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predictive control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This presentation introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems enabled by the GEKKO library. Biography: Logan Beal is a PhD candidate at Brigham Young University in the Process Research and Intelligent System Modeling (PRISM) group. His research interests are in the areas of nonlinear predictive control, nonlinear programming solver development, process simulation, real-time numerical methods, and moving horizon estimation. He led the development of combined scheduling and control for the NSF EAGER project (#1547110): Cyber-Manufacturing with Multi-echelon Control and Scheduling. He is joining ExxonMobil as an Application Engineer. [1] Beal, L.D.R., Hill, D., Martin, R.A., and Hedengren, J. D., GEKKO Optimization Suite, Processes, Volume 6, Number 8, 2018, doi: 10.3390/pr6080106. Article: http://www.mdpi.com/2227-9717/6/8/106
Views: 664 APMonitor.com
Multiplex graph analysis with GraphBLAS
by Gabor Szarnyas At: FOSDEM 2019 https://video.fosdem.org/2019/H.1308/graph_multiplex_analysis_graphblas.webm Introduction Graph analysis workloads present resource-intensive computations that require a large amount of memory and CPU time. Consequently, there an abundance of graph processing tools which build on distributed data processing frameworks, including Spark GraphX, Flink Gelly and Giraph (which runs on Hadoop). According to a recent survey, most of these systems build on the vertex-centric programming model, originally introduced in Google’s Pregel paper. This model defines graph analytical algorithms in terms of vertices communicating with their neighbours through message passing, which allows both easy parallelization (for the systems) and intuitive formalization of the computation (for developers). While these systems indeed exhibit horizontal scalability, they introduce numerous inefficiencies requiring a large amount of resources even for moderately sized graphs. Most practical applications only use graphs up to a few hundred million vertices and edges, which can now be stored comfortably on a single machine. For such graphs, it is worth investigating techniques that allow their evaluation without the additional cost and complexity of operating a distributed cluster. GraphBLAS The GraphBLAS initiative is an effort to design a set of standard building blocks that allow users to formulate graph computations in the language of linear algebra, using operations on sparse adjacency matrices defined on custom semirings. Since its inception, GraphBLAS has been implemented for multiple languages (e.g. C, C++, and Java). Additionally, GraphBLAS is being designed in collaboration with hardware vendors (such as Intel and Nvidia) to define a standardized set of interfaces, which will allow building specialized hardware components for graph processing in the future. Multiplex graph metrics Graph analysis has a significant overlap with network science, a field that aims to uncover the hidden structural properties of graphs and determine the interplay between their vertices. Most works in network science only study homogeneous (monoplex) graphs, and do not distinguish between different types of vertices and edges. We believe this abstraction is wasteful for most real-life networks, which are heterogeneous (multiplex) and emerge by different types of interactions. To illustrate such analyses, we calculated multiplex clustering metrics on the Paradise papers data set to find interesting entities that were engaged in disproportionately high levels of activities with their interconnected neighbours. We found that even on this relatively small data set (2M vertices and 3M edges), naive implementations did not terminate in days. Hence, we adapted techniques from GraphBLAS to optimize the computations to finish in a few minutes. Outline of the talk This talk gives a brief overview of how linear algebra can be used to define graph computations on monoplex graphs, and how we applied it to speedup the calculation of multiplex graph metrics. We present the lessons learnt while experimenting with sparse matrix libraries in C, Java, and Julia. Our graph analyzer framework is available as open-source. Intended audience: users interested in applying multiplex graph analytical techniques for their problems and developers who strive to implement high-performing graph analytical computations. Speaker biography. Gabor Szarnyas is a researcher working on graph processing techniques. His core research areas are live graph pattern matching, benchmarking graph queries, and analyzing large-scale multiplex networks. His main research project is ingraph, an openCypher-compatible query engine supporting live query evaluation. His research team was the first to publish a formalisation that captures the semantics of a core subset of the openCypher language. Gabor works at the Budapest University of Technology and Economics, teaching system modelling and database theory. He conducted research visits at the University of York, McGill University and the University of Waterloo. He is a member of the openCypher Implementers Group and the LDBC Social Network Benchmark task force. He received 1st prize in the MODELS 2016 ACM Student Research Competition conference and 2nd prize at the SIGMOD 2018 competition. He is a frequent speaker at industrial conferences (FOSDEM, GraphConnect) and meetups (openCypher meetup NYC, Budapest Neo4j meetup). Room: H.1308 (Rolin) Scheduled start: 2019-02-02 14:40:00+01
Views: 120 FOSDEM
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2117679 Greatest AudioBooks
Supporting Investigative Analysis through Visual Analytics
Carsten Goerg, Professor - University of Colorado Medical School Presents... Supporting Investigative Analysis through Visual Analytics Today's analysts and researchers are faced with the daunting task of analyzing and understanding large amounts of data, often including textual documents and unstructured data. Sensemaking tasks, such as finding relevant pieces of information, formulating hypotheses, and combining facts to establish supporting or contradicting evidence, become more and more challenging as the data grow in size and complexity. Visual analytics aims at developing methods and tools that integrate computational approaches with interactive visualizations to support analysts in performing these types of sensemaking tasks. In this talk, I first briefly introduce the fields of investigative analysis and visual analytics and then discuss methods for the design, development, and evaluation of visual analytics systems in the context of the Jigsaw project. Jigsaw is a visual analytics system for exploring and understanding document collections. It represents documents and their entities visually in order to help analysts examine them more efficiently and develop theories more quickly. Jigsaw integrates computational text analyses, including document summarization, similarity, clustering, and sentiment analysis, with multiple coordinated views of documents and their entities. It has a special emphasis on visually illustrating connections between entities across the different documents. Brief biography: Carsten Grg is a faculty member in the Computational Bioscience Program and in the Pharmacology Department in the University of Colorado Medical School. He received a Ph.D. in computer science from Saarland University, Germany in 2005 and worked as a Postdoctoral Fellow in the Graphics, Visualization & Usability Center at the Georgia Institute of Technology before joining the University of Colorado. Dr. Grg's research interests include visual analytics and information visualization with a focus on designing, developing, and evaluating visual analytics tools to support the analysis of biological and biomedical datasets.
Views: 1574 SCIInstitute
Big Data for Official Statistics
Smart phones, satellite imagery, social media, and the internet of things. Data are everywhere all the time. These technologies continuously generate data —Big Data—faster and more detailed than ever before, … which offers new measurement opportunities and challenges for national statistical systems around the world. For more information, visit: http://unstats.un.org/
Views: 3417 UN DESA
Vince McCoy, "Essential Methodologies in Visualization"
Vince McCoy, CBIP, Principal BI Consultant, WIT Presentation from the 2014 Great Lakes Business Intelligence & Big Data Summit Topic: Essential Methodologies in Visualization You've staged the servers. You've purchased the software. Now what? How do you begin the implementation of the latest visualization tools and actually begin to realize the benefits you anticipated? Regardless of the tool selection, you need a plan -- an implementation method -- an orderly process to guide your organization to the insights everyone's been anticipating. This session will outline an agile and iterative approach to delivery of visualization projects, and provide you a template to deliver implementation results that yield predictable, repeatable, and sustainable outcomes. Taking examples from multiple industry sectors, the session will address essential techniques in data modeling and design for visual projects. We will outline the Five Stages of Visualization, and point out variations in project planning using leading technologies like QlikView and Tableau. We'll also discuss the impact that visualization projects may have on resource provision, project staffing, and skill set provision for IT personnel. http://www.greatlakesbisummit.com/ March 13, 2014 Marriott Hotel Troy, MI About WIT WIT Inc., is a Business Intelligence ("BI") consulting firm, headquartered in Troy, Michigan. Founded in 1996, WIT's mission is to help its customers "Harness the Power of BI" through elite software and professional services solutions. WIT specializes in traditional data warehouse, reporting and dashboard development projects, aligned with more contemporary subject areas like Big Data, Data Discovery, Predictive Analytics, and Unstructured Data. Clients range from small businesses to Fortune 500 companies across all major industries. For more information, please visit www.witinc.com.
Views: 182 WIT
Lev Manovich. Methods of Cultural Analytics. 2010
http://www.egs.edu/ Lev Manovich, Russian-American artist and theorist, lectures on the different methods of visualization, and exploratory analysis. In this lecture, Lev Manovich, discusses the field of cultural analytics, which takes cultural data sets, and through a given method of visualization and automatic image analysis, provides a new type of interface for media exploration. Manovich argues that, these new, digital modes of cultural analytics allow us to deal with the exponential growth of culture and its products. Moreover, it allows us both to re-categorize and mine phenomena of all kinds, and to give sets of data visual representations which reveal large-scale patterns for massive amounts of data, both today and in the past. In this lecture Manovich discusses Mark Rothco, Piet Modriane, Bruno Latour, Time Magazine covers, Dziga Vertov's The Eleventh Year (1928), video games, the difference between statistical society and data mining, the question of reduction, pattern recognition, and the shift from objects to patterns in cultural studies. At the end of the lecture, Manovich answers students' questions. Public lecture for the students and faculty of the European Graduate School EGS Media and Communication department program Saas-Fee Switzerland Europe 2010 Lev Manovich. Biography Paragraphs: Lev Manovich (b. 1960) is a Russian-American artist and theorist. He studied computer science and architecture in Moscow, after which he earned an M.A. in experimental psychology, from the University of New York in 1988. Following this, Lev earned a PhD in visual and cultural studied, from the University of Rochester, in 1993. His PhD work traced the relation between computers and the avant-garde movements of the early 20th century. Currently, Lev Manovich is a professor in the Department of Visual Arts at the University of California, San Diego. His art has been displayed in countless major international exhibitions, including a retrospective by the ICA, London entitled: Lev Manovich: Adventures of digital cinema (2002). Manovich is also the director of Software Studies Initiative, a research lab at the University of California, San Diego. Besides his tenure at UCSD, he is a visiting professor at Goldsmiths College, London, De Monfort University, Leicester, University of South Wales, Sydney, and the Donau-Universität Krems, Austria. He has been a visiting professor at numerous international institutes, including: UCLA, the University of Amsterdam, Stockholm University, Cologne University, and the Hong Kong Arts Center. Manovich has also worked as a designer, programmer and computer animator, and created the first digital film project designed for the web, Freud Lissitzky Navigator, in 1994. Lev Manovich has published over 90 influential articles on media aesthetics, and a series of fundamental books in the area, including: Tekstura: Russian Essays on Visual Culture (1993), Metamediji (2001), Black Box -- White Cube (2005), Soft Cinema DVD (2005), and Software Takes Command (2008). His most influential work, however, is undoubtedly The Language of New Media (2001). In this work, Manovich develops his first systematic theory of 'new media', and places it in the context and historical development of other areas of culture, including: painting, cinema, television, photography and etc. The aim of the book was to explain the origins or genealogy of 'new media' by finding its origins in other areas of culture, and types of media. The book's other primary focus is to investigate the effects and consequences of the digital revolution on visual culture at large; to this end, Manovich at times relies on the theory and history of cinema as his conceptual lens. The Language of New Media, has been recognized by many as the work which placed 'new media' "within the most suggestive and broad ranging media history since Marshall McLuhan" (Telepolis).
What happens when one of the biggest touring artists in the world is also a massive geek? We find out in Deadmau5's INCREDIBLE house/studio... Buy Nvidia video cards Amazon: http://geni.us/HB0U Newegg: http://geni.us/Z2pqlN Discuss on the forum: https://linustechtips.com/main/topic/803638-exposing-deadmau5s-studio-spoiler-hes-a-huge-geek/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 4235280 Linus Tech Tips
Space Engineers - Announcing the Video Competition!
Announcing the Space Engineers Video Competition! :) For more information, please see this link: https://steamcommunity.com/games/244850/announcements/detail/3464866264414850063 Please like, share, subscribe and click the bell below, so you receive notifications about new Space Engineers content! http://www.SpaceEngineersGame.com/ Like us on https://www.facebook.com/SpaceEngineers Follow us on http://twitter.com/SpaceEngineersG Join our Discord: https://discord.gg/keenswh Space Engineers Merchandise: https://www.zazzle.com/keenswh
Views: 12423 Space Engineers
World's biggest economies throughout history | The Economist
Time is money: An animated infographic showing the top three economies throughout history. Does China have the world's largest economy? Is China's economy bigger than America's? Time is money–the world's largest economies throughout history. At the start of the Common Era, India was the world’s largest economy, followed by China. The far-flung Roman Empire came a distant third. A thousand years later, it looked almost the same. But third place shifted to Byzantium, in modern-day Turkey. Five hundred years after that, Italy returned, rich from renaissance trade. Over several centuries, other European powers vied for third: initially France, and then Britain. China and India swapped places. After the industrial revolution, the top three economies accounted for less than half of global output. In the 20th century, America dominated. China temporarily fell away. Russia made the top three. As did Japan. Britain dropped down. Now the modern world resembles the distant past: China and India are back, along with a single Western economy. And America’s preeminence is over. China overtakes US as the world's largest economy. For more multimedia content from The Economist visit our website: http://econ.st/1sWSMMP
Views: 148702 The Economist
New methods for understanding complex data, from storytelling to machine learning - Mauro Martino
Abstract: The aesthetics of science is changing, the diffusion of data visualization tools is enabling a revival of beauty in scientific research. More and more papers are presented with seductive images, convincing videos, and sharp interactive tools. Scientific storytelling will be discussed with 2 case studies: "Charting Culture, 2014", and "Rise of partisanship, 2015". In the second part of the talk we explore the connection between Machine Learning & Data Visualization. We will see together 3 projects: News Explorer - exploration of real-time news, Ted Watson - exploration of a large corpus of videos, and Watson 500 - the analysis of relationships between entities and topics in a specific corpus of date. We encourage the public to use these tools before the talk: http://news-explorer.mybluemix.net/ http://watson.ted.com/ http://watson500.mybluemix.net/ Biography: Mauro Martino is an Italian expert in data visualization based in Boston. He created and leads the Cognitive Visualization Lab at IBM Watson in Cambridge, Massachusetts, USA. Martino’s data visualizations have been published in the scientific journals Nature, Science, and the Proceedings of the National Academy of Sciences. His projects have been shown at international festivals including Ars Electronica, and art galleries including the Serpentine Gallery, UK, GAFTA, USA, and the Lincoln Center, USA. Jointly organized by the Data Science program and the Cyberinfrastructure for Network Science Center, this talk is partially supported by Indiana University’s Consortium for the Study of Religion, Ethics and Society, a consortium sponsored by the Vice President for Research Office Talk details can be found at http://cns.iu.edu/cnstalks. All talks will take place in the new Social Science Research Commons, Woodburn Hall 200 (unless otherwise noted). Official Page: http://www.mamartino.com/ LinkedIn: https://www.linkedin.com/in/mauromartino Twitter: https://twitter.com/martino_design Google Scholar Citations: https://scholar.google.com/citations?user=c9gkCgIAAAAJ&hl=en
Views: 100 IU Data Science
Data Mining Project - 2014 Baltimore City Citation Leads
This video was created to show my virtual assistant how to complete a 18,000+ record data mining project. Daniel DiGiacomo WE BUY HOUSES www.baltimorewholesaleproperty.com www.sellyourhousetodan.com
Views: 85 Daniel DiGiacomo
Space-time cube: Introduction
Dear participant, Thank you in advance for your cooperation. The purpose of this survey is to test a visualization type which is much discussed in literature as a potential suitable visualization for animal tracking data analyses: the space-time cube. Together with time geography the space-time cube was introduced by Hägerstrand in 1970 and it uses the third dimension to visualize time. The combination of the 2D map and time on the z-axis create a cube in which paths can be visualized. In this way spatiotemporal data is visualized without animations but with time fully integrated, where both discrete and continuous time can be visualized (Li, 2005). The idea of the conceptual framework of time geography is that every population consists of "socially and geographically interrelated individuals and not as indivisible masses" (Vrotsou et al., 2010, p. 264). The space-time cube was born out of this concept and is useful for visualizing individuals' space-time paths. By combining multiple space-time paths within a cube one can discover bundles or meetings among individuals (figure 1) and by doing so study human behaviour. Since humans are part of the animal kingdom, it is a small step from time-geography to ecology. This, together with the complex nature of time-track datasets insinuates that the space-time cube has potential in ecology. Especially because animal tracking devices become more and more advanced. However, little or no research is done concerning the use of the space-time cube in ecology. Up till now... Hereby I like to present a space-time cube of three African buffalo (Syncerus caffer) in Kruger NP. Each animal's space-time path is represented with a different colour. The small points do not only represent how far the nearest water source is, but also indicate each time the GPS-collar saved every location. The survey consists out of three parts. Firstly, three personal questions are asked. In the second part 7 specific questions about this particular cube are asked and finally seven general questions about the space-time cube as a concept are asked. Additional suggestions and comments are appreciated. The test will take around half an hour. Literature: Hägerstrand, T., 1970. What about people in regional science? Papers of the Regional Science Association 24: 7-24. Li, X., 2005. New Methods of Visualization of Multivariable Spatio-temporal Data: PCP-Time-Cube and Multivariable-Time-Cube.(Master's Thesis), International Institute For Geo-Information Science and Earth Observation. Enschede, The Netherlands. Vrotsou, K., Forsell, C. and Cooper, M., 2010. 2D and 3D representations for feature recognition in time geographical diary data. Information Visualization 9: 263-276.
Views: 3405 Maarten Baas
SDS|2015: Rosaria Silipo on Big Data with KNIME
"Re-engineering IoT Legacy Analytics with Big Data " Abstract: In this project, we rescued a few legacy IoT solutions and made them faster by exploiting speed and performance of a big data platform based execution. In our analytics solution park, a number of workflows are dedicated to Internet of Things applications, and particularly to the analysis of energy usage time series. One solution, in particular, predicts the amount of electrical energy usage for clusters of smart meter IDs in Ireland. The bottle neck of this solution, however, lies in the first ETL process, which takes a very long time to execute. This made the solution difficult to use in production, and challenging for re-training. Recently, we decided to re-engineer this legacy solution and to run it on a Big Data platform (Cloudera Impala, Apache Hive, ParStream). Within a KNIME analytics workflow, the user can connect to any big data platform using dedicated or generic connector nodes. Dedicated connector nodes have been designed for specific big data platforms, hard-coding and hiding the most complex configuration details, and therefore simplifying the overall connection process. After the connection has been established, a SQL query is implemented to retrieve the final data. As complex as the SQL query and as exotic as the SQL dialect can be, a deep knowledge of the SQL syntax is not necessary. The user can rely on a number of SQL manipulation nodes to build complex SQL queries hiding the SQL code. Relying on KNIME big data access and manipulation nodes, we transformed all ETL processes of our legacy solution into In-Database ETL processing nodes. A complex and specific SQL query, implementing all necessary conversions, joins, and aggregations, was built and executed on Hadoop clusters. The (smaller) resulting data set was then pulled back into the KNIME analytics platform to proceed with the analytics and build the time series prediction model. The execution of this re-engineered ETL process allows for significant speed-ups and for more frequent model re-trainings! During this talk we will guide the user step-by-step to connect to and run a series of ETL operations on any big data platform from the comfort of a visual analytics workbench, such as the KNIME Analytics Platform. Speaker Biography: Dr Rosaria Silipo has been mining data, big and small, since her master degree in 1992. She kept mining data throughout all her doctoral program, her postdoctoral program, and most of her following job positions. She has many years of experience in data analytics, data visualization, data manipulation, reporting, business intelligence, training, and writing. In the last few years she has been using KNIME for all her projects, becoming an expert KNIME user and a KNIME certified trainer. She is also the author of more than 50 scientific publications and 3 books for data analysis practitioners. For more information visit http://dlab.zhaw.ch/sds2015
Views: 500 ZHAW Datalab
The Story of Stuff
From its extraction through sale, use and disposal, all the stuff in our lives affects communities at home and abroad, yet most of this is hidden from view. The Story of Stuff is a 20-minute, fast-paced, fact-filled look at the underside of our production and consumption patterns. The Story of Stuff exposes the connections between a huge number of environmental and social issues, and calls us together to create a more sustainable and just world. It'll teach you something, it'll make you laugh, and it just may change the way you look at all the stuff in your life forever. http://storyofstuff.org And for all you fact checkers out there: http://storyofstuff.org/movies/story-of-stuff/ GET INVOLVED: http://action.storyofstuff.org/sign/social-action/ FOLLOW US: Facebook: https://www.facebook.com/storyofstuff/ Twitter: https://twitter.com/storyofstuff Instagram: https://www.instagram.com/storyofstuff/ SUPPORT THE PROJECT: https://action.storyofstuff.org/donate/social_donations/ Help us caption & translate this video! http://amara.org/v/BKO/
Healthcare leaders on unlocking the key to cancer with the Canadian Partnership for Tomorrow Project
Healthcare leaders lend their thoughts at the launch of the Canadian Partnership for Tomorrow Project (CPTP) data portal, and the potential to unlock the key to cancer. Follow CPAC: Twitter: @cancer_strategy Facebook: www.facebook.com/CanadianPartnershipAgainstCancer TRANSCRIPT: Dr. Heather Bryant, Vice President Cancer Control, Canadian Partnership Against Cancer (CPAC): "Cancer is the number one killer in Canada. In every single province and territory, it’s the number one killer – and we know also, that because the population is getting older and entering high-risk age groups for cancer, that we’re going to see a 40% increase in the number of cancer cases over the next decade or two." What is CPTP? Chris Power, Chair, Canadian Partnership Against Cancer (CPAC): "This project was designed to meet the need of creating a database to help us understand what causes cancer and how we can prevent it." Shelly Jamieson, Chief Executive Officer, CPAC: "300,000 Canadians have signed up to be followed through their adult lives to track their lifestyles, their behaviours, there are some genetic bio-samples material and they’re going to be available for researchers to actually figure out what causes cancer. Why do some people get cancer, and other people don’t?" What makes the CPTP dataset so unique? Dr. Heather Bryant: "We have done studies before where you collect information from people who’ve developed a particular type of cancer, and then you ask them questions and you ask people who didn’t develop the cancer about what was going on in their lives ten and twenty years ago – and that, while you can do it with fewer people, we know that people can’t remember well what their lifestyles were like 10 or 20 years ago." Dr. William Ghali, Scientific Director, O'Brien Institute for Public Health: "I think the combination of clinical information about people – the health behaviours, but then also a lot of biological specimens that allow for the study of environmental exposures, genetic factors that cause disease – is really quite a powerful combination of information that you don’t usually have in one place." How will using this research portal help researchers? "Dr. Jacques Magnan, Senior Scientific Leader, CPAC: It’s not a single question project; it’s a platform and like most platforms it helps take you off the ground. So at the end of the day, what we have done is created an open access platform all researchers, all qualified researchers, will be able to have access to the data." Shelly Jamieson: "When a research project begins, the first thing people have to do is recruit people to be part of their study. What we’re doing, is we are saying we have already recruited the people. So we’re accelerating the number of questions, the turn around on research questions. It’s very difficult, if I was interested in studying the factors that cause cancer on my own, to pull together the resources that are needed to create a big data set that we can follow patients with over time." Dr. Jacques Magnan: "What we know already is that researchers collaborate on a global basis. If you have a rare cancer, they’ll go wherever they can get the information from, they’ll go wherever they can get the collaborations from. So from that perspective, it puts us in a good position to be able to collaborate internationally in the fight against cancer." Dr. Wiliam Ghali: "The key now is to get the word out about this portal now that’s been created for researchers to access data." Why do you think Canadians have been inspired to participate in CPTP? Mary O'Neill, CPTP Participant, Board Member, CPAC : "The experience that I’ve had with family and friends who have had cancer, and always as many others do, ask the question, why? But for me, the questions was always why them and not me? I have encouraged as best I can everybody I know, and don’t know, to join because I think participating is the answer." Chris Power: "I think this is so impressive that people have come together because they want to make a difference for cancer and for hundreds of thousands of Canadians – to say it’s not okay that so many people are suffering from cancer or haven’t even been diagnosed yet, but we know will be diagnosed, to say we want to make a difference, we want to step-up, we’re in this together."
Technology And Mechanisation At VV Minerals, India
Technology and mechanization is an important aspect of heavy mineral mining. Watch the video to find out how this has helped VV Minerals, Vaikundarajan in making this company the top mineral manufacturers and exporters of India.
Views: 204 VV Mineral Mining
Engage your customer using cognitive analytics in IBM Watson Explorer
Rakesh Ranjan, Travis Cook, sharan kadagad, Sachet Hegde Talk about 'Engage your customer using cognitive analytics in IBM Watson Explorer' at https://SiliconValley-CodeCamp.com in San Jose Hosted by PayPal Come, learn and expand your knowledge in the growing field of cognitive content analytics. As an attendee you will be downloading and installing the latest Watson explorer product on your laptop and create a content mining application. Session Details: https://SiliconValley-CodeCamp.com/Session/2017/engage-your-customer-using-cognitive-analytics-in-ibm-watson-explorer Silicon Valley Code Camp site: https://SiliconValley-CodeCamp.com Subscribe to the Silicon Valley Code Camp Youtube Channel https://www.youtube.com/c/SiliconValleyCodeCampVideos Follow Silicon Valley Code Camp on Twitter: https://twitter.com/sv_code_camp Join the Silicon Valley Code Camp G+ community: https://plus.google.com/110656351842726857531 Engage your customer using cognitive analytics in IBM Watson Explorer at Silicon Valley Code Camp 2017 Follow Rakesh Ranjan on Twitter: https://twitter.com/ranjans Speaker Biography for Rakesh Ranjan Rakesh Ranjan is a Program Director and Architect of Cloud Data Services at IBM Silicon Valley Lab in California. He has designed and developed several data and analytics services in Bluemix that powers next gen cognitive applications. He also teaches a graduate level software engineering program at local San Jose State University. Follow Travis Cook on Twitter: https://twitter.com/ Speaker Biography for Travis Cook Experienced Software Engineer with a demonstrated history of working in the information technology and services industry. Skilled in Communication, Node.js, Mac, .NET Framework, and HTML. Strong engineering professional with a B.S. in Computer Science from University of Houston-Downtown. Follow sharan kadagad on Twitter: https://twitter.com/https://twitter.com/sharanrk10 Speaker Biography for sharan kadagad Dedicated and self-motivated software engineer with 3 years of experience in varied technologies. I have worked as a web developer, Desktop application developer. I have always been open to exploring work in new technologies and challenging situations. Follow Sachet Hegde on Twitter: https://twitter.com/sachethegde Speaker Biography for Sachet Hegde Aiming to build a career in computer science and research and succeed in an environment of growth and excellence by applying my skills and knowledge. I have designed and created "Download and Go" version of an IBM software which helped customers running IBM Information Governance Catalog with a delightful experience doing it. I have created self served apps for Mac and Windows (Linux coming soon) that orchestrated deployment of the IGC software using docker container on customer's laptop.
PromCon 2017: Analyze Prometheus Metrics like a Data Scientist - Georg Öttl
* Abstract: Gathering software metrics with Prometheus is great and easy. However, at some point there are too many timeseries to craft handwritten rule based alert systems. In this talk I will show how to export data from the Prometheus HTTP API, show how and what to analyze with open-source tools like R, Python SciPi and describe why DevOps and Whitebox Monitoring fits so great here. As an outlook I will show how to integrate/export timeseries to machine learning services. * Speaker biography: Georg Öttl is an IT professional with an agile software development portfolio and 15+ years of professional experience. His focus so far was on Software Development, Knowledge Discovery/Data Science Services and IT-Security. He is a Full Stack developer, DevOps enthusiast and Continuous Delivery specialist. * Slides: * PromCon website: https://promcon.io/
Views: 2536 Prometheus Monitoring
Dr. Pieter Dorrestein talks about challenges of big data for scientists
Q: What are some of the challenges and opportunities of big data for the 21st century life scientists? Dr. Pieter Dorrestein delivered a lecture at NIH in April 2015 on “Social Networks For Molecular Analysis.” Dr. Dorrestein is professor at the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California at San Diego. Watch his lecture here: http://videocast.nih.gov/Summary.asp?file=18943&bhcp=1
Views: 367 NCCIH
MIT Enterprise Forum of NYC: Fireside Chat with MIT Prof Sandy Pentland and author Stephen Baker
Digital social networks seem to be taking over our world, from news -to management - to government. But can they really make us smarter or even more efficient? The Arab Spring, the Twitter crash on Wall Street and the polarization of politics are cautionary notes - and then there is the Snowdon affair and cyberattack to consider. Professor Pentland will describe a series of groundbreaking studies that show when social networks make us smarter and when they don`t, and how this new understanding of social networks can be used to promote greater privacy and security. Moderator: - Stephen L. Baker, American Journalist, Author and Blogger Fireside Chat Biography: - Alex "Sandy" Pentland, Toshiba Professor of Media Arts and Sciences, Director, Media Lab Entrepreneurship Program, Director, Media Lab Human Dynamics Laboratory Digital social networks seem to be taking over our world, from news -to management - to government. But can they really make us smarter or even more efficient? The Arab Spring, the Twitter crash on Wall Street and the polarization of politics are cautionary notes - and then there is the Snowdon affair and cyberattack to consider. Professor Pentland will describe a series of groundbreaking studies that show when social networks make us smarter and when they don`t, and how this new understanding of social networks can be used to promote greater privacy and security. Moderator: - Stephen L. Baker, American Journalist, Author and Blogger Fireside Chat Biography: - Alex "Sandy" Pentland, Toshiba Professor of Media Arts and Sciences, Director, Media Lab Entrepreneurship Program, Director, Media Lab Human Dynamics Laboratory
Views: 84 mitefnyc
Webinar: The Essential Role of Mentoring
ELA / XSEDE webinar Presenter: Dr. Carlos Monroy Title: From Richard Tapia to the Turings and Fields Medalists: What I Have Learned About the Essential Role of Mentoring Date: November 20, 2014 Abstract: Mentoring is an essential activity to ensure that people not only succeed in different undertakings but also excel in them. This is even more critical in education, specifically with students from traditionally under represented minorities. As Richard Tapia states: “Underrepresentation endangers the health of the nation, and not the health of the discipline.” In this talk I will start by sharing the experiences with my mentor as a doctoral student in computer science; followed by the story of my first encounter with Dr. Tapia and how that changed my life and appreciation for mentoring; and conclude with lessons learned from my role as mentor with the Richard Tapia Center for Excellence and Equity, in what I call: “The Inverse Mentoring Coefficient Effect.” In this journey, I will share stories and anecdotes from my recent participation in the Heidelberg Laureate Forum in Germany, where I personally met a group of Abel, Fields and Turing award recipients such as Michael Atiyah, Stephen Cook, Vinton Cerf, Manuel Blum and others. This forum was created by Dr. Klaus Tschira, founder of the software giant SAP, with the goal of “passing the torch” from the old to the new generation of scientists. What I found during this event was a one-week intensive mentoring program with certain similarities to what Dr. Tapia has been doing for many years. Short Biography: Dr. Carlos Monroy is a Data Scientist with the Rice Center for Digital Learning and Scholarship where he works in learning analytics and big data. His areas of interest are data mining, information retrieval and visualization, digital humanities and multidisciplinary collaboration. For more than fifteen years, Dr. Monroy’s work and research have enabled numerous interdisciplinary collaborations with domain experts in education, linguistics, art history and nautical archaeology. He received his Ph.D. from Texas A&M University in Computer Science. Dr. Monroy has experienced the critical role mentoring played and continues to play in his academic career, and is committed to mentoring students, presently serving as mentor with the Richard Tapia Center for Excellence and Equity. Throughout his career,Dr. Monroy has received numerous awards and recognitions such as: Young Researcher Award – Heidelberg Laureate Forum (Germany); Outstanding Publication Award – American Educational Research Association (USA); and National Outstanding Undergraduate Thesis (Guatemala). He is a member of the International Network of Guatemalan Scientists, the Association for Computing Machinery, the Association for Linguistic and Literary Computing and the IEEE Computer Society.
Views: 28 R Tap
No Time to Think
Google Tech Talks March, 5 2008 ABSTRACT Vannevar Bush's 1945 article, "As We May Think," has been much celebrated as a central inspiration for the development of hypertext and the World Wide Web. Less attention, however, has been paid to Bush's motivation for imagining a new generation of information technologies; it was his hope that more powerful tools, by automating the routine aspects of information processing, would leave researchers and other professionals more time for creative thought. But now, more than sixty years later, it seems clear that the opposite has happened, that the use of the new technologies has contributed to an accelerated mode of working and living that leaves us less to think, not more. In this talk I will explore how this state of affairs has come about and what we can do about it. Speaker: David M. Levy David Levy earned a Ph.D. in Computer Science at Stanford University in 1979 and a Diploma in Calligraphy and Bookbinding from the Roehampton Institute (London) in 1983. For more than fifteen years he was a researcher at the Xerox Palo Alto Research Center (PARC), where his work, described in "Scrolling Forward: Making Sense of Documents in the Digital Age" (Arcade, 2001), centered on exploring the transition from paper and print to digital. During the year 2005-2006, he was the holder of the Papamarkou Chair in Education and Technology at the Library of Congress. A professor at the UW Information School since 2000-2001, he has been investigating how to restore contemplative balance to a world marked by information overload, fragmented attention, extreme busyness, and the acceleration of everyday life.
Views: 157894 GoogleTechTalks
Data & Humans: A Love Story
Jer Thorp discusses the relationship between people and data at the Library's symposium, "Collections as Data: Stewardship and Use Models to Enhance Access." Speaker Biography: Jer Thorp is an artist and educator from Vancouver, Canada, currently living in New York. Coming from a background in genetics, his digital art practice explores the many-folded boundaries between science, data, art and culture. His work has been featured by The Guardian, Scientific American, the New Yorker and Popular Science. Thorp's award-winning software-based work has been exhibited in Europe, Asia, North America and South America, including in the Museum of Modern Art in Manhattan. Thorp has more than a decade of teaching experience, in New York University's ITP program, at Langara College, and as an artist-in-residence at the Emily Carr University of Art and Design. Jer is a National Geographic Fellow, a member of the World Economic Forum's Global Agenda Council on Design Innovation and a co-founder of the Office For Creative Research, a multi- disciplinary research group exploring new modes of engagement with data. From 2010-2012, Thorp was the Data Artist in Residence at the New York Times. For transcript and more information, visit http://www.loc.gov/today/cyberlc/feature_wdesc.php?rec=7624
Views: 1270 LibraryOfCongress
Accelerating Cancer Research and Clinical Innovations Using OncoLand
Presented At: LabRoots - Clinical Diagnostics Virtual Event 2018 Presented By: Bing Zhou, PhD - Sr. Scientist, Bioinformatics, QIAGEN Speaker Biography: Dr. Zhou is a professional scientist with more than 10 years of experience in oncology, cancer genetics, and preclinical drug discovery. She previously works as research associate in the Lineberger Cancer Center of University of Carolina at Chapel Hill, where her interests are centered around molecular mechanisms of breast cancer and prostate cancer using animal models. Her experiences in industry includes target discovery and validation, cell-based assay development, and small molecules screening. Currently, Bing is managing Oncoland curation team within OmicSoft, a QIAGEN company. So far, OncoLand curation team has curated thousands of projects from cancer genomics consortia as well as public data repositories. Her knowledge on cancer biology and clinical ontologies has supported the establishment of internal curation standards. Webinar: Accelerating Cancer Research and Clinical Innovations Using OncoLand Webinar Abstract: With significant decrease in the cost of sequencing in numerous commercial as well as cancer center–driven initiatives, genomic profiling is increasingly becoming routine across multiple cancer types. There is a large amount of diverse omic-data that has been generated, which are either publicly accessible through numerous repositories or managed through controlled data archives. However, the sheer volume and diversity of the data presents a significant challenge in data management and analysis. OncoLand is an integrated oncology genomics repository and visualization platform. Feature tremendous numbers of consortia data and tens of thousands of processed and carefully curated cancer genomics datasets from public domains. The OncoLand tool enables investigators to easily query and navigate a gene or sets of genes of interest in multiple tumors across data from different platforms, including DNA mutation, gene expression and fusion, copy number, protein, and methylation. The built-in Omicsoft Genome Browser (OGB) allows a detailed view of coverage, gene fusions and alternatively spliced isoforms. OncoLand contains a variety of modules for integrative analysis, comparisons, and clinical associations explorations. OncoLand has become an invaluable platform to accelerate successful cancer research and clinical innovations. Earn PACE/CME Credits: 1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-event/clinical-diagnostics-research-2018) 2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-event/clinical-diagnostics-research-2018) 3. Click Here to get your PACE (Expiration date – November 14, 2020 09:00 AM) – https://www.labroots.com/credit/pace-credits/3170/third-party LabRoots on Social: Facebook: https://www.facebook.com/LabRootsInc Twitter: https://twitter.com/LabRoots LinkedIn: https://www.linkedin.com/company/labroots Instagram: https://www.instagram.com/labrootsinc Pinterest: https://www.pinterest.com/labroots/ SnapChat: labroots_inc
Views: 72 LabRoots
A Decision Support System for Monitoring, Reporting and Forecasting Ecological Conditions of the ...
The Appalachian National Scenic Trail (A.T.) is 2,175 miles (3,500 km) long and crosses fourteen (14) states in the eastern United States while intersecting eight (8) National Forests of the USDA Forest Service (FS), six (6) units of the National Park System (NPS), more than seventy (70) State Park, Forest, and Game Management units, and 287 local jurisdictions. The A.T. and its surrounding protected lands harbor forests with some of the greatest biological diversity in the U.S., including rare, threatened, and endangered species, and diverse bird and wildlife habitats, and are the headwaters of important water resources of millions of people. The Trail’s north-south alignment represents a cross-section mega-transect of the eastern United States forests and alpine areas, and offers a setting for collecting scientifically valid and relevant data on the health of the ecosystems and the species that inhabit them. The high elevation setting of the A.T. and its protected corridor provide a barometer for early detection of undesirable changes in the environment and natural resources of the eastern United States, from development encroachment to recreational misuse, acid precipitation, invasions of exotic species, and climate change. The Appalachian Trail Decision Support System (A.T.-DSS) integrated NASA multi-platform sensor data, Terrestrial Observation and Prediction System (TOPS) models, and in situ measurements from A.T. MEGA-Transect partners to address identified natural resource priorities and improve resource management decisions. This presentation will address the scientific and management questions in 1. Development of a comprehensive set of seamless indicator datasets consistent with environmental vital signs; 2. Establishment of a ground monitoring system to complement remote sensing observations; 3. Assessment of historical and current ecosystem conditions and forecast trends under climate change effects; and 4. Development of an Internet-based implementation and dissemination system for data visualization, sharing, and management to facilitate collaboration and promote public understanding of the Appalachian Trail environment. The on-line decision support system is accessible at http://www.edc.uri.edu/atmt-dss/.  Biography Dr. Yeqiao (Y.Q.) Wang is a professor at the Department of Natural Resources Science, University of Rhode Island, where he has been on the faculty since 1999. He received his B.S. degree from the Northeast Normal University in 1982 and his M.S. degree in remote sensing and mapping from the Chinese Academy of Sciences in 1987. He received his M.S. and Ph.D. degrees in Natural Resources Management & Engineering from the University of Connecticut in 1992 and 1995, respectively. From 1995 to 1999, he held the position of Assistant Professor in the Department of Geography and Department of Anthropology, University of Illinois at Chicago. Dr. Wang’s specialties are in terrestrial remote sensing and applications in natural resources analysis and mapping. Particular areas of interests include remote sensing of dynamics of landscape and land-cover/land-use change, in order to develop scientific understanding and models necessary to simulate the processes taking place; to evaluate effects of observed and predicted changes; to understand consequences of changes on environmental goods and services; and to facilitate decision-support for management and governance of natural resources. His research projects have been funded by different agencies that supported his scientific studies in various regions of the United States, in East and West Africa, and in various regions in China. As the Editor-in-Chief, he published the “Encyclopedia of Natural Resources”, a three-volume set of Land, Air and Water, by the Taylor & Francis Group/CRS Press in 2014. He also edited and published the books of “Remote Sensing of Coastal Environments” and “Remote Sensing of Protected Lands” by the CRC Press in 2009 and 2010, respectively. Among his awards and recognitions he is a recipient of a NASA New Investigator Program Award in 1999 and a Presidential Early Career Award for Scientists and Engineers in 2000.
Views: 165 Harvard CGA
Kevin Garnett says "Accountant cost him $77 million dollars"!!!
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Leland McInnes, John Healy | Clustering: A Guide for the Perplexed
PyData DC 2016 Finding clusters is a powerful tool for understanding and exploring data. While the task sounds easy, it can be surprisingly difficult to do it well. Most standard clustering algorithms can, and do, provide very poor clustering results in many cases. We discuss how to do clustering correctly. Finding clusters is a powerful tool for understanding and exploring data. While the task sounds easy, it can be surprisingly difficult to it well. Most standard clustering algorithms can, and do, provide very poor clustering results in many cases. Our intuitions for what a cluster is are not as clear as we would like, and can easily be lead astray. We will attempt to find a definition of clustering that makes sense for most cases, and introduce an algorithm for finding such clusters, along with a high performance python implementation of the algorithm, building up more intuition for what clustering really means as we go.
Views: 3706 PyData
Expert Judgment in Risk Analysis by Mark Burgman
Plenary talk from the First Global Conference on Research Integration and Implementation: "Expert Judgment in Risk Analysis." This presentation outlines the fundamentals of risk perception and their implications for assessing and making decisions under uncertainty. It describes experiments that evaluate the relationship between an expert's status and their ability to estimate uncertain facts. It describes procedures that improve the accuracy and conditioning of expert estimates of facts. Finally, it outlines the results of a four year experiment organised by Intelligence Advanced Research Projects Activity (IARPA) in which participants predicted the outcome of various geopolitical events. The results highlight the benefits of structured question formats and structured group interactions for getting relatively high quality judgements out of experts. Speaker biography: Mark A. Burgman is Managing Director of the Centre of Excellence for Biosecurity Risk Analysis, the Adrienne Clarke Chair of Botany at the University of Melbourne and Editor-in-Chief of the journal Conservation Biology. He works on ecological modelling, conservation biology and risk assessment. His research has included models on a broad range of species and settings including marine fisheries, forestry, irrigation, electrical power utilities, mining, and national park planning. He received a BSc from the University of New South Wales (1974), an MSc from Macquarie University, Sydney (1981), and a Ph.D. from the State University of New York (1987). He worked as a consultant ecologist and research scientist in Australia, the United States and Switzerland during the 1980’s before joining the University of Melbourne in 1990. He has published over two hundred refereed papers and book chapters and seven authored books. He was elected to the Australian Academy of Science in 2006. Introduced by Howard Gadlin. The First Global Conference on Research Integration and Implementation was held in Canberra in Australia, online and at three co-conferences (Lueneburg in Germany, The Hague in the Netherlands and Montevideo in Uruguay), 8-11 September 2013.
Mining mafia King Gali Janardhan Reddy Contesting in Elections!!
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Views: 4703 News One
Symposium of Architectural History The Whiteness of 19th Century American Architecture
This symposium examines the racial discourses that subtended "American Architecture" movements during the long nineteenth century. Explore this site to learn more about the specific themes, case studies and speakers that will be featured at this event. "The Whiteness of American Architecture" is organized by Charles Davis II, UB assistant professor of architecture. About the symposium “The Whiteness of 19th Century American Architecture” is a one-day symposium in architectural history organized by the School of Architecture and Planning at the University at Buffalo. This symposium is an outgrowth of the Race + Modern Architecture Project, an interdisciplinary workshop on the racial discourses of western architectural history from the Enlightenment to the present. Participants - Professor Mabel O. Wilson, Columbia GSAPP - Dianne Harris, senior program officer at the Andrew W. Mellon Foundation - Joanna Merwood-Salisbury, architectural historian - Kathryn ‘Kate’ Holliday, architectural historian - Charles Davis, assistant professor of architectural history and criticism at the University at Buffalo Race + Modern Architecture Project Race + Modern Architecture logo The “Whiteness & American Architecture” symposium continues the research that began with the Race + Modern Architecture Project, a workshop conducted at Columbia University in 2013. The forthcoming co-edited volume, Race and Modern Architecture presents a collection of seventeen groundbreaking essays by distinguished scholars writing on the critical role of racial theory in shaping architectural discourse, from the Enlightenment to the present. The book, which grows out of a collaborative, interdisciplinary, multi-year research project, redresses longstanding neglect of racial discourses among architectural scholars. With individual essays exploring topics ranging from the role of race in eighteenth-century, Anglo-American neoclassical architecture, to 1970s radical design, the book reveals how the racial has been deployed to organize and conceptualize the spaces of modernity, from the individual building to the city to the nation to the planet. Sponsors - Temple Hoyne Buell Center for the Study of American Architecture - Columbia University - Darwin D. Martin House Complex - Buffalo, NY - School of Architecture - Victoria University of Wellington - UB Humanities Institute - University at Buffalo, SUNY - School of Architecture and Planning - University at Buffalo, SUNY Purpose and Themes Our symposium will outline a critical history of the white cultural nationalisms that have proliferated under the rubric of "American Architecture" during the long nineteenth century. This theme will be explored chronologically from the late-nineteenth to the mid-twentieth century and regionally from representative avant-garde movements on the East Coast to the regionalist architectural styles of the Midwest and West Coast. Such movements included the neoclassical revivals of the Chicago World’s Fair in 1893, the Chicago School of Architecture and the Prairie Style, the East Bay Style on the West Coast, the Arts & Crafts movement across the continent, and various interwar movements that claimed to find unique historical origins for an autochthonous American style of building. The five architectural historians in attendance have been charged with providing some preliminary answers to the central question of these proceedings: What definitions of American identity have historically influenced the most celebrated national architectural movements of the long nineteenth century, and how was this influence been manifested in the labor relations, ideological commitments and material dimensions of innovative architectural forms?