Complex Models for Big Data

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Max Welling (http://www.ics.uci.edu/~welling/) describes the how big data, massive simulation and advanced models go together to help us start solving challenging problems. He also describes his links to other computer science disciplines within the DSRC.

Technology Education

DS
RC

Data Science
Research Center

Complex Models
for
Big Data

Max Welling
UvA

DS
RC

The Four Paradigms

We have added big data to
computer simulation, experiment
and theory.

Not replaced it…

DS
RC

Big Simulation

Computer simulations have
become increasingly complex
(e.g. weather, earthquake models)

The Computational Wall: If a model has hundreds of parameters, how can we:

1) Find the parameter values that match the observations best?
2) Determine if we underfit (model too simple) or overfit (model too complex)?
3) Compare two models?

DS
RC

Parameter Inference
Parameter Update

Parameters

Simulation

Observations

DS
RC

Challenge I

The “posterior probability”
in closed form.

can not be computed

Solution: Markov Chain Monte Carlo Sampling (MCMC)

DS
RC

Challenge II

We cannot run MCMC because the likelihood
is not given in closed form (but rather as a simulation)

Solution: Likelihood Free MCMC (or Approximate Bayesian Computation)

Run many simulations
and compare samples
With observations.
Source: Csillery, Katalin, et al.
"Approximate Bayesian
computation (ABC)
in practice."Trends in
ecology & evolution 25.7
(2010): 410-418.

DS
RC

Challenge III

We need thousands of simulations to infer the posterior
(infeasible if every simulation takes a day or so)
Ted Meeds

If surrogate ~ log(P) with high
confidence then use surrogate to draw sample.
If not: simulate until enough confidence.

Surrogate of log(P)

Solution: Learn log(P) using Gaussian Process Surrogate functions (GPS)

D S Two Kinds of Complex Model
RC

Machine
Learning

Computational
Science
Model Capacity

“Let the model speak”

“Let the data speak”

DS
RC

3x Exponential Growth
in Machine Learning

Computer Power

Data Volume

Model Capacity

D S Growth in Model Capacity
RC
2020-2050 Human Brain
(N=+/- 100T)

?

Model Capacity over Time

2009: Hinton’s Deep Belief Net
(+/- N=10M)

2013: Google/Y!
(N=+/- 10B)

1943: First NN
(+/- N=10)

1988: NetTalk
(+/- N=20K)

D S Deep Learning: Neural Nets Strike
R C Back(again)
1970: NN discredited
(Minsky & Papert)

2 layers
1943: NN invented
(McCulloch & Pitts)

-Model Size: 10B parameters
-Used by: Yahoo!, Google,
Microsoft, Baidu,
IBM, Scyfer 

1986: Backpropagation
(Rumelhart, Hinton & Williams )

1995: SVM
(Vapnik)

3 layers

2009: Deep Learning
(Hinton)

many
layers

DS
RC

Paradox
Why does model capacity grow exponentially?
Raw Information: O(N)

Predictive Information: log(N)

Noise
?

DS
RC

Big Challenges from Industry
Scyfer connects industry to academia:
-inspire academia w/ relevant problems
-deliver ML products to industry
-host student projects
-provide employment for our students
= VALORISATION

What industry needs.

What academics are
interested in.

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Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!

Christine's Supplier Sourcing Presentaion.pptx

christinelarrosa

Taking AI to the Next Level in Manufacturing.pdf

ssuserfac0301

Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as: 1. How quickly AI is being implemented in manufacturing. 2. Which barriers stand in the way of AI adoption. 3. How data quality and governance form the backbone of AI. 4. Organizational processes and structures that may inhibit effective AI adoption. 6. Ideas and approaches to help build your organization's AI strategy.

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Tosin Akinosho

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

9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...

saastr

Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...

Pitangent Analytics & Technology Solutions Pvt. Ltd

Dandelion Hashtable: beyond billion requests per second on a commodity server

Antonios Katsarakis

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Jason Yip

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

Fwdays

At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

akankshawande

Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

ScyllaDB

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

LizaNolte

HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable. In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed: Key Takeaways: Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement. Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers. Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Alex Pruden

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security. Paper Link: https://eprint.iacr.org/2024/257

GNSS spoofing via SDR (Criptored Talks 2024)

Javier Junquera

In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security. This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing. The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Neo4j

Main news related to the CCS TSI 2023 (2023/1695)

Jakub Marek

An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers. The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 . The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .

Mutation Testing for Task-Oriented Chatbots

Pablo Gómez Abajo

Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots. To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

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