This document provides a course calendar and lecture plans for topics related to Bayesian estimation methods. The course calendar lists 12 class dates from September to December covering topics like Bayes estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms. One lecture plan provides details on the hidden Markov model, including the introduction, definition of HMMs, and problems of evaluation, decoding, and learning. Another lecture plan covers particle filters, including the sequential importance sampling algorithm, choice of proposal density, and the particle filter algorithm of sampling, weight update, resampling, and state estimation.
Random Matrix Theory and Machine Learning - Part 3Fabian Pedregosa
ICML 2021 tutorial on random matrix theory and machine learning.
Part 3 covers: 1. Motivation: Average-case versus worst-case in high dimensions 2. Algorithm halting times (runtimes) 3. Outlook
Random Matrix Theory and Machine Learning - Part 1Fabian Pedregosa
ICML 2021 tutorial on random matrix theory and machine learning. Part 1 covers: 1. A brief history of Random Matrix Theory, 2. Classical Random Matrix Ensembles (basic building blocks)
Random Matrix Theory and Machine Learning - Part 3Fabian Pedregosa
ICML 2021 tutorial on random matrix theory and machine learning.
Part 3 covers: 1. Motivation: Average-case versus worst-case in high dimensions 2. Algorithm halting times (runtimes) 3. Outlook
Random Matrix Theory and Machine Learning - Part 1Fabian Pedregosa
ICML 2021 tutorial on random matrix theory and machine learning. Part 1 covers: 1. A brief history of Random Matrix Theory, 2. Classical Random Matrix Ensembles (basic building blocks)
Nonlinear Stochastic Programming by the Monte-Carlo methodSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 4.
More info at http://summerschool.ssa.org.ua
Computational Motor Control: Optimal Control for Deterministic Systems (JAIST...hirokazutanaka
This is lecure 2 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=lNH1q4y1m-U
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Applied Digital Signal Processing 1st Edition Manolakis Solutions Manualtowojixi
Full download http://alibabadownload.com/product/applied-digital-signal-processing-1st-edition-manolakis-solutions-manual/
Applied Digital Signal Processing 1st Edition Manolakis Solutions Manual
Molecular Solutions For The Set-Partition Problem On Dna-Based Computingijcsit
Consider that the every element in a finite set S having q elements is a positive integer. The set-partition
problem is to determine whether there is a subset T Í S such that ,
Î Î
=
x T x T
x x where T = {x| x Î S and
x Ï T}. This research demonstrates that molecular operations can be applied to solve the set-partition
problem. In order to perform this goal, we offer two DNA-based algorithms, an unsigned parallel adder
and a parallel Exclusive-OR (XOR) operation, that formally demonstrate our designed molecular solutions
for solving the set-partition problem.
Ch 05 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 5 of the book entitled "MATLAB Applications in Chemical Engineering": Numerical Solution of Partial Differential Equations. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
In this talk we consider the question of how to use QMC with an empirical dataset, such as a set of points generated by MCMC. Using ideas from partitioning for parallel computing, we apply recursive bisection to reorder the points, and then interleave the bits of the QMC coordinates to select the appropriate point from the dataset. Numerical tests show that in the case of known distributions this is almost as effective as applying QMC directly to the original distribution. The same recursive bisection can also be used to thin the dataset, by recursively bisecting down to many small subsets of points, and then randomly selecting one point from each subset. This makes it possible to reduce the size of the dataset greatly without significantly increasing the overall error. Co-author: Fei Xie
A lambda calculus for density matrices with classical and probabilistic controlsAlejandro Díaz-Caro
Slides of my presentation at APLAS'17 (Suzhou, China, December 2017).
Publication: LNCS 10695:448-467, 2017 (http://dx.doi.org/10.1007/978-3-319-71237-6_22)
ArXiv'd at https://arxiv.org/abs/1705.00097
Nonlinear Stochastic Programming by the Monte-Carlo methodSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 4.
More info at http://summerschool.ssa.org.ua
Computational Motor Control: Optimal Control for Deterministic Systems (JAIST...hirokazutanaka
This is lecure 2 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=lNH1q4y1m-U
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Applied Digital Signal Processing 1st Edition Manolakis Solutions Manualtowojixi
Full download http://alibabadownload.com/product/applied-digital-signal-processing-1st-edition-manolakis-solutions-manual/
Applied Digital Signal Processing 1st Edition Manolakis Solutions Manual
Molecular Solutions For The Set-Partition Problem On Dna-Based Computingijcsit
Consider that the every element in a finite set S having q elements is a positive integer. The set-partition
problem is to determine whether there is a subset T Í S such that ,
Î Î
=
x T x T
x x where T = {x| x Î S and
x Ï T}. This research demonstrates that molecular operations can be applied to solve the set-partition
problem. In order to perform this goal, we offer two DNA-based algorithms, an unsigned parallel adder
and a parallel Exclusive-OR (XOR) operation, that formally demonstrate our designed molecular solutions
for solving the set-partition problem.
Ch 05 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 5 of the book entitled "MATLAB Applications in Chemical Engineering": Numerical Solution of Partial Differential Equations. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
In this talk we consider the question of how to use QMC with an empirical dataset, such as a set of points generated by MCMC. Using ideas from partitioning for parallel computing, we apply recursive bisection to reorder the points, and then interleave the bits of the QMC coordinates to select the appropriate point from the dataset. Numerical tests show that in the case of known distributions this is almost as effective as applying QMC directly to the original distribution. The same recursive bisection can also be used to thin the dataset, by recursively bisecting down to many small subsets of points, and then randomly selecting one point from each subset. This makes it possible to reduce the size of the dataset greatly without significantly increasing the overall error. Co-author: Fei Xie
A lambda calculus for density matrices with classical and probabilistic controlsAlejandro Díaz-Caro
Slides of my presentation at APLAS'17 (Suzhou, China, December 2017).
Publication: LNCS 10695:448-467, 2017 (http://dx.doi.org/10.1007/978-3-319-71237-6_22)
ArXiv'd at https://arxiv.org/abs/1705.00097
Stochastic reaction networks (SRNs) are a particular class of continuous-time Markov chains used to model a wide range of phenomena, including biological/chemical reactions, epidemics, risk theory, queuing, and supply chain/social/multi-agents networks. In this context, we explore the efficient estimation of statistical quantities, particularly rare event probabilities, and propose two alternative importance sampling (IS) approaches [1,2] to improve the Monte Carlo (MC) estimator efficiency. The key challenge in the IS framework is to choose an appropriate change of probability measure to achieve substantial variance reduction, which often requires insights into the underlying problem. Therefore, we propose an automated approach to obtain a highly efficient path-dependent measure change based on an original connection between finding optimal IS parameters and solving a variance minimization problem via a stochastic optimal control formulation. We pursue two alternative approaches to mitigate the curse of dimensionality when solving the resulting dynamic programming problem. In the first approach [1], we propose a learning-based method to approximate the value function using a neural network, where the parameters are determined via a stochastic optimization algorithm. As an alternative, we present in [2] a dimension reduction method, based on mapping the problem to a significantly lower dimensional space via the Markovian projection (MP) idea. The output of this model reduction technique is a low dimensional SRN (potentially one dimension) that preserves the marginal distribution of the original high-dimensional SRN system. The dynamics of the projected process are obtained via a discrete $L^2$ regression. By solving a resulting projected Hamilton-Jacobi-Bellman (HJB) equation for the reduced-dimensional SRN, we get projected IS parameters, which are then mapped back to the original full-dimensional SRN system, and result in an efficient IS-MC estimator of the full-dimensional SRN. Our analysis and numerical experiments verify that both proposed IS (learning based and MP-HJB-IS) approaches substantially reduce the MC estimator’s variance, resulting in a lower computational complexity in the rare event regime than standard MC estimators. [1] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. Learning-based importance sampling via stochastic optimal control for stochastic reaction net-works. Statistics and Computing 33, no. 3 (2023): 58. [2] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. (2023). Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach. To appear soon.
Data fusion is the process of combining data from different sources to enhance the utility of the combined product. In remote sensing, input data sources are typically massive, noisy, and have different spatial supports and sampling characteristics. We take an inferential approach to this data fusion problem: we seek to infer a true but not directly observed spatial (or spatio-temporal) field from heterogeneous inputs. We use a statistical model to make these inferences, but like all models it is at least somewhat uncertain. In this talk, we will discuss our experiences with the impacts of these uncertainties and some potential ways addressing them.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The issues about maneuvering target track prediction were discussed in this paper. Firstly, using Kalman filter which based on current statistical model describes the state of maneuvering target motion, thereby analyzing time range of the target maneuvering occurred. Then, predict the target trajectory in real time by the improved gray prediction model. Finally, residual test and posterior variance test model accuracy, model accuracy is accurate.
Bayesian inference for mixed-effects models driven by SDEs and other stochast...Umberto Picchini
An important, and well studied, class of stochastic models is given by stochastic differential equations (SDEs). In this talk, we consider Bayesian inference based on measurements from several individuals, to provide inference at the "population level" using mixed-effects modelling. We consider the case where dynamics are expressed via SDEs or other stochastic (Markovian) models. Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that account for (i) the intrinsic random variability in the latent states dynamics, as well as (ii) the variability between individuals, and also (iii) account for measurement error. This flexibility gives rise to methodological and computational difficulties.
Fully Bayesian inference for nonlinear SDEMEMs is complicated by the typical intractability of the observed data likelihood which motivates the use of sampling-based approaches such as Markov chain Monte Carlo. A Gibbs sampler is proposed to target the marginal posterior of all parameters of interest. The algorithm is made computationally efficient through careful use of blocking strategies, particle filters (sequential Monte Carlo) and correlated pseudo-marginal approaches. The resulting methodology is is flexible, general and is able to deal with a large class of nonlinear SDEMEMs [1]. In a more recent work [2], we also explored ways to make inference even more scalable to an increasing number of individuals, while also dealing with state-space models driven by other stochastic dynamic models than SDEs, eg Markov jump processes and nonlinear solvers typically used in systems biology.
[1] S. Wiqvist, A. Golightly, AT McLean, U. Picchini (2020). Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms, CSDA, https://doi.org/10.1016/j.csda.2020.107151
[2] S. Persson, N. Welkenhuysen, S. Shashkova, S. Wiqvist, P. Reith, G. W. Schmidt, U. Picchini, M. Cvijovic (2021). PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models, bioRxiv doi:10.1101/2021.07.01.450748.
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...Chiheb Ben Hammouda
In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics are dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of Stochastic Reaction Networks (SRNs). In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. In this talk, we propose a novel implicit scheme, split-step implicit tau-leap (SSI-TL), to improve numerical stability and provide efficient simulation algorithms for those systems. Furthermore, to estimate statistical quantities related to SRNs, we propose a novel hybrid Multilevel Monte Carlo (MLMC) estimator in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This estimator uses the SSI-TL scheme at levels where the explicit-TL method is not applicable due to numerical stability issues, and then, starting from a certain interface level, it switches to the explicit scheme. We present numerical examples that illustrate the achieved gains of our proposed approach in this context.
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the
modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral
measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to
estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we
propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial
kernel to build a periodogram which we then smooth by two spectral windows taking into account the
width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing
often encountered in the case of estimation from discrete observations of a continuous time process.
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the
modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral
measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to
estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we
propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial
kernel to build a periodogram which we then smooth by two spectral windows taking into account the
width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing
often encountered in the case of estimation from discrete observations of a continuous time process.
MIXED SPECTRA FOR STABLE SIGNALS FROM DISCRETE OBSERVATIONSsipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the
modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral
measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to
estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we
propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial
kernel to build a periodogram which we then smooth by two spectral windows taking into account the
width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing
often encountered in the case of estimation from discrete observations of a continuous time process.
Common Fixed Theorems Using Random Implicit Iterative Schemesinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Discretization of a Mathematical Model for Tumor-Immune System Interaction wi...mathsjournal
The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor and immune cells. The model consists of differential equations with piecewise constant arguments and based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is obtained a system of difference equations from the system of differential equations. In order to get local and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a consequence of Neimark-Sacker bifurcation.
DISCRETIZATION OF A MATHEMATICAL MODEL FOR TUMOR-IMMUNE SYSTEM INTERACTION WI...mathsjournal
The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor
and immune cells. The model consists of differential equations with piecewise constant arguments and
based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is
obtained a system of difference equations from the system of differential equations. In order to get local
and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion
and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a
consequence of Neimark-Sacker bifurcation.
DISCRETIZATION OF A MATHEMATICAL MODEL FOR TUMOR-IMMUNE SYSTEM INTERACTION WI...mathsjournal
The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor
and immune cells. The model consists of differential equations with piecewise constant arguments and
based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is
obtained a system of difference equations from the system of differential equations. In order to get local
and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion
and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a
consequence of Neimark-Sacker bifurcation.
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
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.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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.
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.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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
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.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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2012 mdsp pr05 particle filter
1. Course Calendar
Class DATE Contents
1 Sep. 26 Course information & Course overview
2 Oct. 4 Bayes Estimation
3 〃 11 Classical Bayes Estimation - Kalman Filter -
4 〃 18 Simulation-based Bayesian Methods
5 〃 25 Modern Bayesian Estimation :Particle Filter
6 Nov. 1 HMM(Hidden Markov Model)
Nov. 8 No Class
7 〃 15 Supervised Learning
8 〃 29 Bayesian Decision
9 Dec. 6 PCA(Principal Component Analysis)
10 〃 13 ICA(Independent Component Analysis)
11 〃 20 Applications of PCA and ICA
12 〃 27 Clustering, k-means et al.
13 Jan. 17 Other Topics 1 Kernel machine.
14 〃 22(Tue) Other Topics 2
2. Lecture Plan
Hidden Markov Model
1. Introduction
2. Hidden Markov Model (HMM)
Discrete-time Markov Chain & HMM
3. Evaluation Problem
4. Decoding Problem
5. Learning Problem
3. 1. Introduction
3
1.1 Discrete-time hidden Markov model (HMM)
The HMM is a stochastic model of a process that can be used for
modeling and estimation. Its distinguishing feature is the probabilistic
model which is driven by internal probability distribution for both
states and measurements. The internal states are usually not observed
directly therefore, are hidden.
1.2 Applications
Discrete representation of stochastic processes:
Speech recognition, Communications, Economics,
Biomedical (DNA analysis), Computer vision (Gesture recognition)
4. 2. HMM
4
2.1 Discrete-time Markov Chains
1, 2,
At each time step t, the state variables are defined by
state space
where, .....,
Pr : the probability that at time t the state i is occupied.
1st-order Markovian:
Pr
xN
i
n
x t
x t
x t
X
X X X X
1 , 2 ,...., 0 Pr 1
Define a :=Pr 1 (time-stationary)
m r l n m
mn n m
x t x t x x t x t
x t x t
5.
a set of noisy measurements up to , i.e.,
: 0,1,
, the MAP or Minimum Mean Square Error (MMSE)
estimate of
(Assume the probability densities are represented by continu
t
t
Y y t
x t
Given
find
ous
PDF such as )tp x t Y
State estimation problem:
6. 1.2 Basic Bayesian Approach (Review)
The state estimation problem in the context of Bayesian approach is
to provide the following relation from Bayes’ rule.
1
1 1 1 2
1
1 1
2 1 1
,
,
(5)
Likelihood : , (6)
Prediction : 1 1 (7)
Ig
t t
t t
t
t
t t
p x t Y p x t y t Y
p y t x t Y p x t Y N N
Dp y t Y
N p y t x t Y p y t x t
N p x t Y p x t x t p x t Y
1
noring the term
1 1 (8)t t
evicence D
p x t Y p y t x t p x t x t p x t Y
6
posterior density
at t
posterior density
at t-1
UPDATE
7. 1p x t x t
11 tp x t Y
p y t x t
1tp x t Y
tp x t Y
t-1
t
Likelihood N1
Prediction PDF N2
posterior PDF at t-1
posterior PDF at t
State transition
Figure Update Scheme
8. 2. Sequential Importance Sampling (SIS)
2.1 Importance Sampling
( ) ( )
( ) ( )
1
N particles: , , ; 1
The posterior approximated by the importance sampling
1
(9)
The weight of the i-th particle at
p
i i i
t p
N
i i
t t t
p i
s state weight x t i N
p x Y x t x t
N
t
( )
( )
( )
( ) ( ) ( ) ( )
1( )
( ) (
,
(10)
Using Beyes rule and related equalities, we have a sequential relation as
1 1
i
ti
t i
t
i i i i
ti
t i i
p x t Y
q x t Y
p y t x t p x t x t p x t Y
q x t x
) ( )
1
(11)
1 , 1i
t tt Y q x t Y
(SIS-based estimation approach called by particle filter, bootstrap filter, condensation)
9.
( ) ( ) ( )
( )
1( ) ( )
1
(12)
1 ,
i i i
i
ti i
t
p y t x t p x t x t
q x t x t Y
( ) ( )( )
1 1
:
At step , suppose the following approximation by the samples(particles)
1 1 , ; 1 (13)
After observing , we wish to approximate with a new
i ii
t pt
t -1
p x t Y x t i N
y t
Problem
( )( )
samples at
(14), ; 1 p
jj
t t t
t
p x Y x t j N
2.2 Choice of the proposal density – Convenient way -
( ) ( ) ( ) ( )
easy to evaluate
( ) ( )( )
1
1 , 1 (15)
From (12), (16)
i i i i
t
i ii
t t
q x t x t Y p x t x t
p y t x t
update weight
at t
previous weight
at t-1
Likelihood at t
10. 10
3. Particle Filter Algorithm
- Sampling Importance Resampling (SIR) Filter-
(A) Sampling (Prediction)
(A-1) Generate random values according to
1 1 1, ,
p
i
w P
N
w t p w t i N
10
1
(A-2) Compute predicted particles:
1 , 1i i i
tx t f x t w t
1
,i
pParticles x t N
11. 11
(B) Weight Update
(B-1) Compute the likelihood :
i
p y t x t
(B-2) Update the weight
(19)i i
t p y t x t
1
(B-3) Normalization
: (20)p
i
i t
t N
j
t
j
,i i
tParticles x t
12. 12
(C) Resampling for degeneracy avoiding
(C-1) Weighted samples Equal weight samples
ˆThe distribution of gives an approximation of posterior distribution
t
j
x t
p x t Y
1
Now, we have
1
ˆ ˆ
pN
j
t t
jp
p x t Y p x t Y x t x t
N
1
Using above approximation we may obtain an MMSE Estimation as
1
ˆ ˆ
pN
j
jp
x t x t
N
1
ˆParticles , ,i ji
t px t x t N
(D) State Estimation
ˆ is used for in the next step sampling
(prediction) processes, i.e. go to sampling step A).
ij
x t x t
14. Revised figure in [ P. M. Djuric et al. Particle Filtering IEEE SP magazine 2003 ]
t tp y x
1 1t tp x Y
ˆ t tp x Y
t
t+1
Likelihood
Prediction Density
State transition
(A) Sampling
(A) Sampling
1 1t tp y x
(C) Resampling
(B) Weight Update
1
ˆ t tp x Y
Likelihood
(D) State Estimate
(B) Weight Update
15. -Visual tracking object for mobile systems- (moving camera and no static background)
K. Nummiaro et al. “” An adaptive color-based particle filter” Image and Vision
Computing XX (2001) 1-12
Object state model
The state of a particle is modeled as
2
: , , , , , ,
, :position of the tracked object by the particle
, : velocity of the tracked object (motion displacement)
:scale change
1
1
1 0,
T
x z x z
x z
x
z
x x Hx
z
x z H H v v a
x z
v v
a
x t x t v t
z t z t v t
H t H t N
H t
Motion model :
x
2
1 0,z HzH t N
,x z
,x zv v
xH
zH
4. Application example of particle filter
16. A distribution p(x) of the state of the object is approximated by
a set of weighted particles,
( ) ( ) ( )
: , 1
, : importance weight
j
t t
j j j j
t t t
S s j J
s x t
Update process: from one frame to the next frrame
1) motion model
2) new frame gives likelihood of the observation
( ) ( )j j
t p y t x t
( )
1j j
p x t x t
By integrating the J particles the state of the tracked object is
estimated as
( ) ( ) ( ) ( ) ( ) ( )
, , , , , , , ,
(weighted average)
TT j j j j j j
x z t x zx z H H a x z H H a
17.
( )
1
1,....,
Evaluation of the likelihood of the particle, for a new
obsevation :
Color-based similarity measure between color histograms
,
Histogram of target model: ,
Histog
j
m
u u
u
u
u m
p y t x t
y t
p q p q
p p
1,....,
ram of the image of a particle
The distance between two distributions :
: 1 , Bhattacharyya distance
u
u m
q q
d p q
2
2( ) 2
Weight of particle based on color distribution likelihood
1
2
d
j
t e
18. Tracking results by mean shift (left), Kalman+mean shift (middle), Particle filter (right)
20. 20
References:
[1] J. Candy, “ Bayesian Signal Processing Classical, Modern, and Particle Filtering
Methods”, John Wiley/IEEE Press, 2009
[2] B. Ristic, et al. , “Beyond the Kalman Filter”, Artech house Publishers, 2004
[3] F. Asano(浅野太), “Array signal processing for acoustics (音のアレイ信号処理)”,
CORONA publishing Co., LTD, 2011 (Japanese)
[4] M. Isard and A. Blake, “CONDENSATION- Conditioned Density Propagation for
Visual Tracking”, International J. of Computer Vision 29(1), 5-28 (1998)
[5] K. Nummiaro et al. “” An adaptive color-based particle filter”, Image and Vision
Computing XX (2001) 1-12
[6] D. A. Klein et al., Adaptive Real-time video-tracking for arbitrary objects,
Intelligent robotics and …, 2010
21. In practice, it is difficult to generate the samples directly from the
density . Instead, the samples can be generated by , referred
to as importance distribution or proposal distribution, whose PDF is
similar as .
i
x
p x q x
Similarity of means:
0 0
In terms of , we have
where : is referred to as the .
q x
p x q x for all x
q x
I f x x q x dx
p x
x importance weght
q x
Appendix Importance sampling
- partially revised Section 3.2 in the Lecture 4 slides -
p x
22.
The importance sampling approximation of can be written by
N
( i ) ( i )
i
I
ˆ ˆI I f x x q x dx x f x
N
1
1
( ) ( )
1
can be evaluated up to a normalization constant, i.e.
( is unknown), q ( is unknown)
Then,
=
1
,
p q
p q
q
p
N
q i i
p i
p x
p x q x
p x Z x Z
Z Z
Z p x
I f x p x dx f x q x dx
Z q x
Z
x f x
Z N
Case :
( )
( )
( )
i
i
i
p x
x
q x
23.
( )
( )
1
( )
1
Consider the case ( ), the ratio can be evaluated by using the sample
set as follows.
1
1
1
Thus,
1
i
N
q i
p i
q
N
p i
i
f x
x
Z
p x dx x
Z N
Z
Z
w x
N
( )
( ) ( ) ( )
1 1
( )
( )
( )
1
Therefore,
where :
iN N
i i i
pi i
q
i
i
N
j
j
x
I f x x f x
Z
Z
x
x
x