Pitt Stop Motorsports Equipment
System to Success
With RS 1 pit box walling systems you are automatically in pole position! Visually outstanding, extremely variable and above all easy, it results in extremely low transport costs, particularly for overseas shipments. These are the decisive advantages of our pit box RS1 wall systems. Additional accessories such as cross bars, lighting, TFT’s, trays, barrier stands, flight cases, etc also belong to our product portfolio.
Pitt Stop Motorsports Equipment
System to Success
With RS 1 pit box walling systems you are automatically in pole position! Visually outstanding, extremely variable and above all easy, it results in extremely low transport costs, particularly for overseas shipments. These are the decisive advantages of our pit box RS1 wall systems. Additional accessories such as cross bars, lighting, TFT’s, trays, barrier stands, flight cases, etc also belong to our product portfolio.
% plot sin cos and tan
d=0:1:360;
r=(pi/180)*d;
s1=sin(r);
c1=cos(r);
t1=tan(r);
grid on
plot(r,s1,'-')
hold on
xlim([0,2*pi])
ylim([-2,2])
plot(r,c1,'r-')
plot(r,t1,'b--')
title("Graph of Sin(x) Cos(x) & Tan(x)")
xlabel("Angles")
ylabel("Values")
grid on
grid minor
legend("sin(x)","cos(x)","tan(x)")
AfterGlow is a script that assists with the visualization of log data. It reads CSV files and converts them into a Graph description. Check out http://afterglow.sf.net for more information also.
This short presentation gives an overview of AfterGlow and outlines the features and capabilities of the tool. It discusses some of the harder to understand features by showing some configuration examples that can be used as a starting point for some more sophisticated setups.
AftterGlow is one the most downloaded security visualization tools with over 17,000 downloads.
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build
and run applications that work with highly connected datasets. The core of Neptune is a purpose-built,
high-performance graph database engine. This engine is optimized for storing billions of relationships
and querying the graph with milliseconds latency. Neptune supports the popular graph query languages
Apache TinkerPop Gremlin, the W3C’s SPARQL, and Neo4j's openCypher, enabling you to build
queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as
recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security Neptune is highly available, with read replicas, point-in-time recovery, continuous backup to Amazon
S3, and replication across Availability Zones. Neptune provides data security features, with support
for encryption at rest and in transit. Neptune is fully managed, so you no longer need to worry about
database management tasks like hardware provisioning, software patching, setup, configuration, or
backups
% plot sin cos and tan
d=0:1:360;
r=(pi/180)*d;
s1=sin(r);
c1=cos(r);
t1=tan(r);
grid on
plot(r,s1,'-')
hold on
xlim([0,2*pi])
ylim([-2,2])
plot(r,c1,'r-')
plot(r,t1,'b--')
title("Graph of Sin(x) Cos(x) & Tan(x)")
xlabel("Angles")
ylabel("Values")
grid on
grid minor
legend("sin(x)","cos(x)","tan(x)")
AfterGlow is a script that assists with the visualization of log data. It reads CSV files and converts them into a Graph description. Check out http://afterglow.sf.net for more information also.
This short presentation gives an overview of AfterGlow and outlines the features and capabilities of the tool. It discusses some of the harder to understand features by showing some configuration examples that can be used as a starting point for some more sophisticated setups.
AftterGlow is one the most downloaded security visualization tools with over 17,000 downloads.
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build
and run applications that work with highly connected datasets. The core of Neptune is a purpose-built,
high-performance graph database engine. This engine is optimized for storing billions of relationships
and querying the graph with milliseconds latency. Neptune supports the popular graph query languages
Apache TinkerPop Gremlin, the W3C’s SPARQL, and Neo4j's openCypher, enabling you to build
queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as
recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security Neptune is highly available, with read replicas, point-in-time recovery, continuous backup to Amazon
S3, and replication across Availability Zones. Neptune provides data security features, with support
for encryption at rest and in transit. Neptune is fully managed, so you no longer need to worry about
database management tasks like hardware provisioning, software patching, setup, configuration, or
backups
New language from Google, static safe compiler, with GC and as fast as C++ or Java, syntax simpler then Python - 2 hour-long tutorial and you can start code.
In this talk Serhii will talk about Go, also known as Golang – an open source language developed at Google and used in production by companies such as Docker, Dropbox, Facebook and Google itself. Go is now heavily used as a general-purpose programming language that’s a pleasure to use and maintain. This introductory talk contains many live demos of basic language concepts, concurrency model, simple HTTP-based endpoint implementation and, of course, tests using build-in framework. This presentation will be interesting for backend engineers and DevOps to understand why Go had become so popular and how it might help to build robust and maintanable services.
Agenda of the presentation:
1. Go is not C, not Java, not anything
2. Rob Pike argument
3. Main ideas and basics
4. Concurrency model
5. Tools
6. Issues
Webinar slides: MORE secrets of ClickHouse Query Performance. By Robert Hodge...Altinity Ltd
Webinar May 27, 2020
ClickHouse is famously fast, but a small amount of extra work makes it much faster. Join us for the latest version of our popular talk on single-node ClickHouse performance. We start by examining the system log to see what ClickHouse queries are doing. Then we introduce standard tricks to increase speed: adding CPUs, reducing I/O with filters, restructuring joins, adding indexes, and using materialized views, plus many more. In each case we show how to measure the results of your work. There will as usual be time for questions as well at the end. Sign up now to polish your ClickHouse performance skills!
Loom & Functional Graphs in Clojure @ LambdaConf 2015Aysylu Greenberg
Graphs are ubiquitous data structures, and the algorithms for analyzing them are fascinating. Loom is an open-source Clojure library that provides many graph algorithms and visualizations. We will discuss how graphs are represented in a functional world, bridge the gap between procedural description of algorithms and their functional implementation, and learn about the way Loom integrates with other graph representations.
Graph algorithms are cool and fascinating. We'll look at a graph algorithms and visualization library, Loom, which is written in Clojure. We will discuss the graph API, look at implementation of the algorithms and learn about the integration of Loom with Titanium, which allows us to run the algorithms on and visualize data in graph databases.
Webinar: Secrets of ClickHouse Query Performance, by Robert HodgesAltinity Ltd
From webinars September 11 and September 17, 2019
ClickHouse is famous for speed. That said, you can almost always make it faster! This webinar uses examples to teach you how to deduce what queries are actually doing by reading the system log and system tables. We'll then explore standard ways to increase query speed: data types and encodings, filtering, join reordering, skip indexes, materialized views, session parameters, to name just a few. In each case we'll circle back to query plans and system metrics to demonstrate changes in ClickHouse behavior that explain the boost in performance. We hope you'll enjoy the first step to becoming a ClickHouse performance guru!
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
Flink Forward San Francisco 2019: Streaming your Lyft Ride Prices - Thomas We...Flink Forward
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). To accomplish this, our system consumes a massive amount of events from different sources.
The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python/Tensorflow and Apache Flink as the streaming engine. Enablement of data science tools for machine learning and a process that allows for faster deployment is of growing importance for the business. Topics covered in this talk include:
* Examples for dynamic pricing based on real-time event streams, including location of driver, ride requests, user session event and based on machines learning models
* Comparison of legacy system and new streaming platform for dynamic pricing
* Processing live events in realtime to generate features for machine learning models
* Overview of streaming platform architecture and technology stack
* Apache Beam portability framework as bridge to distributed execution without code rewrite for JVM based streaming engine
* Lessons learned
Streaming your Lyft Ride Prices - Flink Forward SF 2019Thomas Weise
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python and Apache Flink as the streaming engine.
https://sf-2019.flink-forward.org/conference-program#streaming-your-lyft-ride-prices
Flink Forward San Francisco 2019: Streaming your Lyft Ride Prices - Thomas We...Flink Forward
Streaming your Lyft Ride Prices
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). To accomplish this, our system consumes a massive amount of events from different sources.
The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python/Tensorflow and Apache Flink as the streaming engine. Enablement of data science tools for machine learning and a process that allows for faster deployment is of growing importance for the business. Topics covered in this talk include:
* Examples for dynamic pricing based on real-time event streams, including location of driver, ride requests, user session event and based on machines learning models
* Comparison of legacy system and new streaming platform for dynamic pricing
* Processing live events in realtime to generate features for machine learning models
* Overview of streaming platform architecture and technology stack
* Apache Beam portability framework as bridge to distributed execution without code rewrite for JVM based streaming engine
* Lessons learned
Airframe Meetup #3: 2019 Updates & AirSpecTaro L. Saito
Presentation slides of Airframe Meetup #3 https://airframe.connpass.com/event/148169/
- Airframe 19 Milestone
- AirSpec: A new testing library for Scala
-
REA Group's journey with Data Cataloging and Amundsenmarkgrover
REA Group's journey with Data Cataloging. Presented at Amundsen community meeting on November 5th, 2020.
Presented by Stacy Sterling, Abhinay Kathuria and Alex Kompos at REA Group.
Amundsen: From discovering to security datamarkgrover
Hear about how Lyft and Square are solving data discovery and data security challenges using a shared open source project - Amundsen.
Talk details and abstract:
https://www.datacouncil.ai/talks/amundsen-from-discovering-data-to-securing-data
Amundsen: From discovering to security datamarkgrover
Hear about how Lyft and Square are solving data discovery and data security challenges using a shared open source project - Amundsen.
Talk details and abstract:
https://www.datacouncil.ai/talks/amundsen-from-discovering-data-to-securing-data
Talk on Data Discovery and Metadata by Mark Grover from July 2019.
Goes into detail of the problem, build/buy/adopt analysis and Lyft's solution - Amundsen, along with thoughts on the future.
TensorFlow Extension (TFX) and Apache Beammarkgrover
Talk on TFX and Beam by Robert Crowe, developer advocate at Google, focussed on TensorFlow.
Learn how the TensorFlow Extended (TFX) project is utilizing Apache Beam to simplify pre- and post-processing for ML pipelines. TFX provides a framework for managing all of necessary pieces of a real-world machine learning project beyond simply training and utilizing models. Robert will provide an overview of TFX, and talk in a little more detail about the pieces of the framework (tf.Transform and tf.ModelAnalysis) which are powered by Apache Beam.
In this Strata 2018 presentation, Ted Malaska and Mark Grover discuss how to make the most of big data at speed.
https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/72396
Near real-time anomaly detection at Lyftmarkgrover
Near real-time anomaly detection at Lyft, by Mark Grover and Thomas Weise at Strata NY 2018.
https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/69155
Presentation on dogfooding data at Lyft by Mark Grover and Arup Malakar on Oct 25, 2017 at Big Analytics Meetup (https://www.meetup.com/SF-Big-Analytics/events/243896328/)
Top 5 mistakes when writing Spark applicationsmarkgrover
This is a talk given at Advanced Spark meetup in San Francisco (http://www.meetup.com/Advanced-Apache-Spark-Meetup/events/223668878/). It focusses on common mistakes when writing Spark applications and how to avoid them.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
17. Upstream Plan
TODAY
Internal refactoring
Consolidation of gremlin code into new shared
amundsen-gremlin repository. Databuilder and
metadata service will utilize the shared code.
Approx. August 17
Stabilization
Improve stability/performance of existing gremlin
code
Approx. August 7
Ship to amundsen
Clean up square-specific bits of amundsen-gremlin,
publish. Publish proxy and proxy tests utilizing
amundsen-gremlin
Approx. August 21
17
18. Thank you
Kudos to the rest of the Privacy Engineering team
at Square who worked on this - Dan Simms, Alyssa
Ransbury, Sarah Harvey, and Kat Hawthorne
