Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
SOLVING BVPs OF SINGULARLY PERTURBED DISCRETE SYSTEMSTahia ZERIZER
In this article, we study boundary value problems of a large
class of non-linear discrete systems at two-time-scales. Algorithms are given to implement asymptotic solutions for any order of approximation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
My PhD talk "Application of H-matrices for computing partial inverse"Alexander Litvinenko
Sometimes you need not the whole solution of a partial differential equation, but only a part (e.g. in boundary layer). How to compute not the whole inverse matrix, but only a part or it (which can nevertheless provide you the solution in a subdomain)?
SOLVING BVPs OF SINGULARLY PERTURBED DISCRETE SYSTEMSTahia ZERIZER
In this article, we study boundary value problems of a large
class of non-linear discrete systems at two-time-scales. Algorithms are given to implement asymptotic solutions for any order of approximation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
My PhD talk "Application of H-matrices for computing partial inverse"Alexander Litvinenko
Sometimes you need not the whole solution of a partial differential equation, but only a part (e.g. in boundary layer). How to compute not the whole inverse matrix, but only a part or it (which can nevertheless provide you the solution in a subdomain)?
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022anasKhalaf4
طبعة جديدة ومنقحة
حل تمارين الكتاب
شرح المواضيع الرياضية بالتفصيل وبأسلوب واضح ومفهوم لجميع المستويات
حلول الاسألة الوزارية
اعداد الدكتور أنس ذياب خلف
email: anasdhyiab@gmail.com
Reinforcement learning: hidden theory, and new super-fast algorithms
Lecture presented at the Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering,
February 21, 2018
Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. The first half of this lecture will provide an overview of stochastic approximation, with a focus on optimizing the rate of convergence. A new approach to optimize the rate of convergence leads to the new Zap Q-learning algorithm. Analysis suggests that its transient behavior is a close match to a deterministic Newton-Raphson implementation, and numerical experiments confirm super fast convergence.
Based on
@article{devmey17a,
Title = {Fastest Convergence for {Q-learning}},
Author = {Devraj, Adithya M. and Meyn, Sean P.},
Journal = {NIPS 2017 and ArXiv e-prints},
Year = 2017}
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsSean Meyn
A tutorial, and very new algorithms -- more details on arXiv and at NIPS 2017 https://arxiv.org/abs/1707.03770
Part of the Data Science Summer School at École Polytechnique: http://www.ds3-datascience-polytechnique.fr/program/
---------
2018 Updates:
See Zap slides from ISMP 2018 for new inverse-free optimal algorithms
Simons tutorial, March 2018 [one month before most discoveries announced at ISMP]
Part I (Basics, with focus on variance of algorithms)
https://www.youtube.com/watch?v=dhEF5pfYmvc
Part II (Zap Q-learning)
https://www.youtube.com/watch?v=Y3w8f1xIb6s
Big 2017 survey on variance in SA:
Fastest convergence for Q-learning
https://arxiv.org/abs/1707.03770
You will find the infinite-variance Q result there.
Our NIPS 2017 paper is distilled from this.
Application H-matrices for solving PDEs with multi-scale coefficients, jumpin...Alexander Litvinenko
We develop hierarchical domain decomposition method to compute a part of the solution, a part of the inverse operator with O(n log n) storage and computing cost.
On maximal and variational Fourier restrictionVjekoslavKovac1
Workshop talk slides, Follow-up workshop to trimester program "Harmonic Analysis and Partial Differential Equations", Hausdorff Institute, Bonn, May 2019.
Theta θ(g,x) and pi π(g,x) polynomials of hexagonal trapezoid system tb,aijcsa
A counting polynomial, called Omega Ω(G,x), was proposed by Diudea. It is defined on the ground of
“opposite edge strips” ops. Theta Θ(G,x) and Pi Π(G,x) polynomials can also be calculated by ops
counting. In this paper we compute these counting polynomials for a family of Benzenoid graphs that called
Hexagonal trapezoid system Tb,a.
2017-07, Research Seminar at Keio University, Metric Perspective of Stochasti...asahiushio1
In this talk, I explain several major stochastic optimizers from the perspective of the metric, that is the definition of the parameter space of the model.
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022anasKhalaf4
طبعة جديدة ومنقحة
حل تمارين الكتاب
شرح المواضيع الرياضية بالتفصيل وبأسلوب واضح ومفهوم لجميع المستويات
حلول الاسألة الوزارية
اعداد الدكتور أنس ذياب خلف
email: anasdhyiab@gmail.com
Reinforcement learning: hidden theory, and new super-fast algorithms
Lecture presented at the Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering,
February 21, 2018
Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. The first half of this lecture will provide an overview of stochastic approximation, with a focus on optimizing the rate of convergence. A new approach to optimize the rate of convergence leads to the new Zap Q-learning algorithm. Analysis suggests that its transient behavior is a close match to a deterministic Newton-Raphson implementation, and numerical experiments confirm super fast convergence.
Based on
@article{devmey17a,
Title = {Fastest Convergence for {Q-learning}},
Author = {Devraj, Adithya M. and Meyn, Sean P.},
Journal = {NIPS 2017 and ArXiv e-prints},
Year = 2017}
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsSean Meyn
A tutorial, and very new algorithms -- more details on arXiv and at NIPS 2017 https://arxiv.org/abs/1707.03770
Part of the Data Science Summer School at École Polytechnique: http://www.ds3-datascience-polytechnique.fr/program/
---------
2018 Updates:
See Zap slides from ISMP 2018 for new inverse-free optimal algorithms
Simons tutorial, March 2018 [one month before most discoveries announced at ISMP]
Part I (Basics, with focus on variance of algorithms)
https://www.youtube.com/watch?v=dhEF5pfYmvc
Part II (Zap Q-learning)
https://www.youtube.com/watch?v=Y3w8f1xIb6s
Big 2017 survey on variance in SA:
Fastest convergence for Q-learning
https://arxiv.org/abs/1707.03770
You will find the infinite-variance Q result there.
Our NIPS 2017 paper is distilled from this.
Application H-matrices for solving PDEs with multi-scale coefficients, jumpin...Alexander Litvinenko
We develop hierarchical domain decomposition method to compute a part of the solution, a part of the inverse operator with O(n log n) storage and computing cost.
On maximal and variational Fourier restrictionVjekoslavKovac1
Workshop talk slides, Follow-up workshop to trimester program "Harmonic Analysis and Partial Differential Equations", Hausdorff Institute, Bonn, May 2019.
Theta θ(g,x) and pi π(g,x) polynomials of hexagonal trapezoid system tb,aijcsa
A counting polynomial, called Omega Ω(G,x), was proposed by Diudea. It is defined on the ground of
“opposite edge strips” ops. Theta Θ(G,x) and Pi Π(G,x) polynomials can also be calculated by ops
counting. In this paper we compute these counting polynomials for a family of Benzenoid graphs that called
Hexagonal trapezoid system Tb,a.
2017-07, Research Seminar at Keio University, Metric Perspective of Stochasti...asahiushio1
In this talk, I explain several major stochastic optimizers from the perspective of the metric, that is the definition of the parameter space of the model.
I am Ben R. I am a Statistics Assignment Expert at statisticshomeworkhelper.com. I hold a Ph.D. in Statistics, from University of Denver, USA. I have been helping students with their homework for the past 5 years. I solve assignments related to Statistics.
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ATT00001
ATT00002
ATT00003
ATT00004
ATT00005
CARD.DTA
Card_1995_geo_var_schooling.pdf
Exam2_2014.pdf
ADVANCED ECONOMETRICS
Midterm 2 (Take Home)
Due: Dec.25, 2014
Answer all questions. You should not discuss solutions with your peers but me. Good luck!
Prof. Dr. H. Taştan ,
First Name:...................................................
Last Name:................................................
No:...................................................
1 (20) In class we have shown that when the number of instrumental variables is larger than the number
of endogenous variables the generalized IV estimator (or 2SLS) can be written as
β̂IV =
(
X>PzX
)−1
X>Pzy
where Pz = Z(Z
>Z)−1Z>. In this formulation X is n×k and Z is n× l, l > k.
(a) Show that β̂IV can be obtained as a solution to the following minimization problem
min
β
Q(β) = (y−Xβ)>Pz (y−Xβ)
(b) Show that when k = l the generalized IV estimator reduces to the simple IV estimator:
β̂IV =
(
Z>X
)−1
Z>y
2 (20) Consider the following simple consumption model as a function of permanent income
ci = β1 + β2y
∗
i + ui, ui ∼ iid (0,σ
2
u)
where ci is the logarithm of consumption by household i, and y
∗
i is the permanent income of household
i which is not observed. Instead we observe current income, yi
yi = y
∗
i + vi, vi ∼ iid (0,σ
2
v)
where vi is assumed to be uncorrelated with y
∗
i and ηi. We run the following regression
ci = β1 + β2yi + ηi
(a) Show that yi is negatively correlated with ηi. You can assume β2 > 0.
(b) Evaluate the plim of the OLS estimator β̂2:
β̂2 =
∑n
i=1(yi − ȳ)ci∑n
i=1(yi − ȳ)2
In particular, show that this plim is less than the true β2.
1
3 (30) Use card.dta to answer the following questions. Also read Card (1993), “Using Geographic
Variation in College Proximity to Estimate the Return to Schooling”, NBER Working Paper.
(a) Run the OLS regression of log(wage) on educ, exper, exper2, black, smsa, south, smsa66, reg662
to reg669. Comment on the coefficient estimate of educ.
(b) Estimate the same model by 2SLS using nearc4 as an instrument for educ. Compare the OLS
and IV coefficient estimates on educ. (Note that we partly did this in class). Carry out the
Hausman test.
(c) Use both nearc2 and nearc4 as instruments for educ. Run the reduced form model for educ.
Compare 2SLS estimates to the results obtained in the previous section. Carry out the OID
test.
(d) Discuss the plausibility of Card (1993)’s econometric methodology and empirical findings. Do
you agree with his conclusions?
4 (30) A continuous time model for short term interest rates may be written as a stochastic differential
equation
dr = (α + βr)dt + σrγ�
√
dt
where r is the short term interest rate, � is standard normal random variable, dt is a short time
interval and α,β,γ,σ are parameters. Discrete time approximation is given as
rt+1 − rt = α + βrt + �t+1
with
E(�t+1) = 0, E(�
2
t+1) = σ
2 ...
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...Tomonari Masada
A derivation of the sampling formulas for An Entity-Topic Model for
Entity Linking [Han+ EMNLP-CoNLL12]
and
A Context-Aware Topic Model for Statistical Machine Translation [Su+ ACL15]
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning.
Our project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.
Through this project, the research team will leverage the computing resources and expertise at UT to develop a “data discovery environment” for transportation data to aid decision-making. Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The team will start by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project will focus on creating more meaning from existing data sources, and as the project progresses, it will grow to include more novel data sources and methods. The data platform will be web-based and part of the research includes not only building the tool but developing appropriate protocols for access and governance.
With changing transportation paradigms, there is significant potential for a shift in the balance between the overall population use of, and reliance on, ridesharing services versus traditional transportation options such as personal car ownership or transit use. This shift could lead to a realignment of the bulk of the responsibility for mobility to private entities and away from individual citizens and public entities. Today, as supplemental to the multitude of transportation options that are available, the availability, or lack thereof, of ridesharing services produces low to minimal risk to the traveling public. However, in a future in which ridesharing is optimally (widely) employed, the current independent nature of ridesharing services will influence wider community transit services. This problem statement explores the effects of new types of transportation on transit through the creation of several plausible future scenarios, and what policy decisions could potentially be made to ensure that transit is optimally employed.
Advanced driver assistance systems (ADAS) are a key technology for improving road safety. But both current and proposed ADAS are limited in important ways. Vision- and lidar-based ADAS performs poorly in heavy rain, snow, or fog. Lack of vehicle situational awareness due to these sensing limitations will unfortunately be the cause of many accidents, including fatalities, for connected and automated vehicles in the years to come. The goal of this research is to develop and test a sensing strategy with robust perception: No blind spots, applicable to all driveable environments, and available in all weather conditions. We believe there are three key requirements for collaborative all-weather sensing:
– Precise vehicle positioning within a common reference frame
– Decimeter-accurate vision and radar mapping
– A means of quantifying the benefits of collaborative sensing
Vehicular radar and communication are the two primary means of using radio frequency (RF) signals in transportation systems. Automotive radars provide high-resolution sensing using proprietary waveforms in millimeter wave (mmWave) bands and vehicular communications allow vehicles to exchange safety messages or raw sensor data. Both the techniques can be used for applications such as forward collision warning, cooperative adaptive cruise control, and pre-crash applications.
Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate).
There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference — namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.
Investigation and findings on reservation-based intersections and managed lanes
Real-Time Signal Control and Traffic Stability
Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance.
Improved Models for Managed Lane Operations
Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
UiPath Test Automation using UiPath Test Suite series, part 5
Statistical Inference Using Stochastic Gradient Descent
1. Statistical inference using
stochastic gradient descent
Constantine Caramanis1
Liu Liu1 Anastasios (Tasos) Kyrillidis2 Tianyang Li1
1The University of Texas at Austin
2IBM T.J. Watson Research Center, Yorktown Heights → Rice University
2. Statistical inference is important
Quantifying uncertainty
Signal? Noise?
Skill? Luck?
Frequentist inference
confidence interval
hypothesis testing
3. Statistical inference is important
Quantifying uncertainty
Signal? Noise?
Skill? Luck?
Frequentist inference
confidence interval
hypothesis testing
Confidence intervals can be used to detect adversarial
attacks.
4. Outline of This Work
(a) Large Scale Problems: Point Estimates computed via SGD
(b) Confidence Intervals computed by Boostrap: too expensive.
(c) This talk: we can compute using SGD.
(d) Application to adversarial attacks: implicitly learning the
manifold.
5. SGD in ERM – mini batch SGD
To solve empirical risk minimization (ERM)
f (θ) =
1
n
n
i=1
fi (θ),
where fi (θ) = θ(Zi ).
At each step:
Draw S i.i.d. uniformly random indices It from [n] (with
replacement)
Compute stochastic gradient gs(θt) = 1
S i∈It
fi (θt)
θt+1 = θt − ηgs(θt)
6. Asymptotic normality – classical results
M-estimator – statistics
When number of samples n → ∞,
√
n(θ − θ∗
) N(0, H∗−1
G∗
H∗−1
),
where G∗ = EZ [ θ θ∗ (Z) θ θ∗ (Z) ] and H∗ = EZ [ 2
θ θ∗ (Z)].
Stochastic approximation – optimization
When number of steps t → ∞,
√
t
1
t
t
i=1
θt − θ N(0, H−1
GH−1
),
where G = E[gs(θ)gs(θ) |= θ] and H = 2f (θ).
7. Asymptotic normality – classical results
M-estimator – statistics
When number of samples n → ∞,
√
n(θ − θ∗
) N(0, H∗−1
G∗
H∗−1
),
where G∗ = EZ [ θ θ∗ (Z) θ θ∗ (Z) ] and H∗ = EZ [ 2
θ θ∗ (Z)].
Stochastic approximation – optimization
When number of steps t → ∞,
√
t
1
t
t
i=1
θt − θ N(0, H−1
GH−1
),
where G = E[gs(θ)gs(θ) |= θ] and H = 2f (θ).
SGD not only useful for optimization,
but also useful for statistical inference!
8. Statistical inference using mini batch SGD
burn in
θ−b, θ−b+1, · · · θ−1, θ0,
¯θ
(i)
t =1
t
t
j=1 θ
(i)
j
θ
(1)
1 , θ
(1)
2 , · · · , θ
(1)
t
discarded
θ
(1)
t+1, θ
(1)
t+2, · · · , θ
(1)
t+d
θ
(2)
1 , θ
(2)
2 , · · · , θ
(2)
t θ
(2)
t+1, θ
(2)
t+2, · · · , θ
(2)
t+d
...
θ
(R)
1 , θ
(R)
2 , · · · , θ
(R)
t θ
(R)
t+1, θ
(R)
t+2, · · · , θ
(R)
t+d
At each step:
Draw S i.i.d. uniformly random
indices It from [n] (with replacement)
Compute stochastic gradient
gs(θt) = 1
S i∈It
fi (θt)
θt+1 = θt − ηgs(θt)
Use an ensemble of i = 1, 2, . . . , R estima-
tors for statistical inference:
θ(i)
= θ +
√
S
√
t
√
n
(¯θ
(i)
t − θ).
9. Advantages of SGD inference
empirically not more expensive, uses
many fewer operations than
bootstrap
can be used when training neural
networks with SGD
easy to plug into existing SGD code
Other statistical inference
methods
directly computing inverse
Fisher information matrix
resampling:
bootstrap, subsampling
10. Advantages of SGD inference
empirically not more expensive, uses
many fewer operations than
bootstrap
can be used when training neural
networks with SGD
easy to plug into existing SGD code
Other statistical inference
methods
directly computing inverse
Fisher information matrix
resampling:
bootstrap, subsampling
Too computationally expensive,
not suited for “big data”!
11. Intuition – Ornstein-Uhlenbeck process approximation
In SGD, denote ∆t = θt − θ, and we have
∆t+1 = ∆t − ηgs(θ + ∆t).
∆t can be approximated by the Ornstein-Uhlenbeck process
d∆(T) = −H∆ dT +
√
ηG
1
2 dB(T),
where B(T) is a standard Brownian motion.
12. Intuition – Ornstein-Uhlenbeck process approximation
Denote ¯θt = 1
t
t
i=1 θt.
√
t(¯θt − θ) can be approximated as
√
t(¯θt − θ) = 1√
t
t
i=1
(θi − θ)
= 1
η
√
t
t
i=1
(θi − θ)η ≈ 1
η
√
t
tη
0
∆(T) dT,
(1)
where we use the approximation that η ≈ dT. By rearranging terms and multiplying both sides by H−1,
we can rewrite the stochastic differential equation as ∆(T) dT = −H−1 d∆(T) +
√
ηH−1G
1
2 dB(T).
Thus, we have
tη
0
∆(T) dT = −H−1
(∆(tη) − ∆(0)) +
√
ηH−1
G
1
2 B(tη). (2)
After plugging (2) into (1) we have
√
t ¯θt − θ ≈ − 1
η
√
t
H−1
(∆(tη) − ∆(0)) + 1√
tη
H−1
G
1
2 B(tη).
When ∆(0) = 0, the variance Var −1/η
√
t · H−1 (∆(tη) − ∆(0)) = O (1/tη). Since 1/√
tη ·
H−1G
1
2 B(tη) ∼ N(0, H−1GH−1), when η → 0 and ηt → ∞, we conclude that
√
t(¯θt − θ) ∼ N(0, H−1
GH−1
).
13. Theoretical guarantee
Theorem
For a differentiable convex function f (θ) = 1
n
n
i=1 fi (θ), with gradient f (θ), let θ ∈ Rp be
its minimizer, and denote its Hessian at θ by H := 2f (θ) . Assume that ∀θ ∈ Rp, f satisfies:
(F1) Weak strong convexity: (θ − θ) f (θ) ≥ α θ − θ 2
2, for constant α > 0,
(F2) Lipschitz gradient continuity: f (θ) 2 ≤ L θ − θ 2, for constant L > 0,
(F3) Bounded Taylor remainder: f (θ) − H(θ − θ) 2 ≤ E θ − θ 2
2, for constant E > 0,
(F4) Bounded Hessian spectrum at θ: 0 < λL ≤ λi (H) ≤ λU < ∞, ∀i.
Furthermore, let gs(θ) be a stochastic gradient of f , satisfying:
(G1) E [gs(θ) | θ] = f (θ),
(G2) E gs(θ) 2
2 | θ ≤ A θ − θ 2
2 + B,
(G3) E gs(θ) 4
2 | θ ≤ C θ − θ 4
2 + D,
(G4) E gs(θ)gs(θ) | θ − G 2
≤ A1 θ − θ 2 + A2 θ − θ 2
2 + A3 θ − θ 3
2 + A4 θ − θ 4
2,
for positive, data dependent constants A, B, C, D, Ai , for i = 1, . . . , 4. Assume that
θ1 − θ 2
2 = O(η); then for sufficiently small step size η > 0, the average SGD sequence
θt = 1
t
n
i=1 θi satisfies:
tE[(¯θt − θ)(¯θt − θ) ] − H−1
GH−1
2
√
η + 1
tη + tη2,
where G = E[gs(θ)gs(θ) | θ].
14. Theoretical guarantee
Theorem
For a differentiable convex function f (θ) = 1
n
n
i=1 fi (θ), with gradient f (θ), let θ ∈ Rp be
its minimizer, and denote its Hessian at θ by H := 2f (θ) . Assume that ∀θ ∈ Rp, f satisfies:
(F1) Weak strong convexity: (θ − θ) f (θ) ≥ α θ − θ 2
2, for constant α > 0,
(F2) Lipschitz gradient continuity: f (θ) 2 ≤ L θ − θ 2, for constant L > 0,
(F3) Bounded Taylor remainder: f (θ) − H(θ − θ) 2 ≤ E θ − θ 2
2, for constant E > 0,
(F4) Bounded Hessian spectrum at θ: 0 < λL ≤ λi (H) ≤ λU < ∞, ∀i.
Furthermore, let gs(θ) be a stochastic gradient of f , satisfying:
(G1) E [gs(θ) | θ] = f (θ),
(G2) E gs(θ) 2
2 | θ ≤ A θ − θ 2
2 + B,
(G3) E gs(θ) 4
2 | θ ≤ C θ − θ 4
2 + D,
(G4) E gs(θ)gs(θ) | θ − G 2
≤ A1 θ − θ 2 + A2 θ − θ 2
2 + A3 θ − θ 3
2 + A4 θ − θ 4
2,
for positive, data dependent constants A, B, C, D, Ai , for i = 1, . . . , 4. Assume that
θ1 − θ 2
2 = O(η); then for sufficiently small step size η > 0, the average SGD sequence
θt = 1
t
n
i=1 θi satisfies:
tE[(¯θt − θ)(¯θt − θ) ] − H−1
GH−1
2
√
η + 1
tη + tη2,
where G = E[gs(θ)gs(θ) | θ].
Proof idea: H−1 = η i≥0(I − ηH)i
16. 95% confidence interval coverage simulation
η t = 100 t = 500 t = 2500
0.1 (0.957, 4.41) (0.955, 4.51) (0.960, 4.53)
0.02 (0.869, 3.30) (0.923, 3.77) (0.918, 3.87)
0.004 (0.634, 2.01) (0.862, 3.20) (0.916, 3.70)
(a) Bootstrap (0.941, 4.14), normal approximation (0.928, 3.87)
η t = 100 t = 500 t = 2500
0.1 (0.949, 4.74) (0.962, 4.91) (0.963, 4.94)
0.02 (0.845, 3.37) (0.916, 4.01) (0.927, 4.17)
0.004 (0.616, 2.00) (0.832, 3.30) (0.897, 3.93)
(b) Bootstrap (0.938, 4.47), normal approximation (0.925, 4.18)
Table 1: Linear regression: dimension = 10, 100 samples. (a) diagonal
covariance (b) non-diagonal covariance
η t = 100 t = 500 t = 2500
0.1 (0.872, 0.204) (0.937, 0.249) (0.939, 0.258)
0.02 (0.610, 0.112) (0.871, 0.196) (0.926, 0.237)
0.004 (0.312, 0.051) (0.596, 0.111) (0.86, 0.194)
(a) Bootstrap (0.932, 0.253), normal approximation (0.957, 0.264)
η t = 100 t = 500 t = 2500
0.1 (0.859, 0.206) (0.931, 0.255) (0.947, 0.266)
0.02 (0.600, 0.112) (0.847, 0.197) (0.931, 0.244)
0.004 (0.302, 0.051) (0.583, 0.111) (0.851, 0.195)
(b) Bootstrap (0.932, 0.245), normal approximation (0.954, 0.256)
Table 2: Logistic regression: dimension = 10, 1000 samples. (a) diagonal
covariance (b) non-diagonal covariance
Better when
each replicate’s average uses a longer consecutive sequence
larger step size
(coverage probability, confidence interval width)
17. Adversarial Attacks
Neural network classifiers with very high accuracy on test sets are
extremely susceptible to nearly imperceptible adversarial attacks.