A brief talk on reservoir computing from the perspective of dynamical system. Mostly based on these 2 papers:
1. Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018). Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.
2. A Parsimonious Dynamical Model for Structural Learning in the Human Brain. arXiv preprint arXiv:1807.05214.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Survey of Attention mechanism & Use in Computer VisionSwatiNarkhede1
This presentation contains the overview of Attention models. It also has information of the stand alone self attention model used for Computer Vision tasks.
In this talk we walk the audience through how to marry correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges one may encounter based on production data. We also showcase how deep learning can be leveraged to learn nonlinear correlation, which in turn can be used to further contain the false positive rate of an anomaly detection system. Further, we provide an overview of how correlation can be leveraged for common representation learning.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Deep Learning Explained: The future of Artificial Intelligence and Smart Netw...Melanie Swan
This talk provides an overview of an important emerging artificial intelligence technology, deep learning neural networks. Deep learning is a branch of computer science focused on machine learning algorithms that model and make predictions about data. A key distinction is that deep learning is not merely a software program, but a new class of information technology that is changing the concept of the modern technology project by replacing hard-coded software with a capacity to learn and execute tasks. In the future, deep learning smart networks might comprise a global computational infrastructure tackling real-time data science problems such as global health monitoring, energy storage and transmission, and financial risk assessment.
Survey of Attention mechanism & Use in Computer VisionSwatiNarkhede1
This presentation contains the overview of Attention models. It also has information of the stand alone self attention model used for Computer Vision tasks.
In this talk we walk the audience through how to marry correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges one may encounter based on production data. We also showcase how deep learning can be leveraged to learn nonlinear correlation, which in turn can be used to further contain the false positive rate of an anomaly detection system. Further, we provide an overview of how correlation can be leveraged for common representation learning.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
발표자: 이인웅 (연세대 박사과정)
발표일: 2017.12.
개요:
영상에서 사람의 행동을 인식하는 방법은 크게 영상에서 직접적으로 행동 라벨을 추출하는 것과 자세 정보를 기반으로 행동 라벨을 추출하는 것으로 나뉠 수 있습니다.
본 발표는 행동 인식에 대한 전반적인 개요를 설명하고 그 중에서도 사람의 자세 정보를 기반으로 하는 행동 인식 기술에 초점을 두고 최근 ICCV 2017 학회에서 발표된 Temporal Sliding LSTM 네트워크를 활용한 행동 인식 기술을 중점적으로 설명합니다. 구체적으로, 스켈레톤 기반 행동 인식 이슈, 제안하는 방법과 실험 결과들이 소개되고 앞으로 나아갈 만한 새로운 연구 이슈들도 추가적으로 설명합니다.
A short overview of current quantum computing hardware and some of the machine learning algorithms that have been developed on these systems for machine learning tasks.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Global Chaos Synchronization of Hyperchaotic Pang and Hyperchaotic Wang Syste...CSEIJJournal
This paper investigates the global chaos synchronization of identical hyperchaotic Wang systems, identical
hyperchaotic Pang systems, and non-identical hyperchaotic Wang and hyperchaotic Pang systems via
adaptive control method. Hyperchaotic Pang system (Pang and Liu, 2011) and hyperchaotic Wang system
(Wang and Liu, 2006) are recently discovered hyperchaotic systems. Adaptive control method is deployed
in this paper for the general case when the system parameters are unknown. Sufficient conditions for global
chaos synchronization of identical hyperchaotic Pang systems, identical hyperchaotic Wang systems and
non-identical hyperchaotic Pang and Wang systems are derived via adaptive control theory and Lyapunov
stability theory. Since the Lyapunov exponents are not required for these calculations, the adaptive control
method is very convenient for the global chaos synchronization of the hyperchaotic systems discussed in
this paper. Numerical simulations are presented to validate and demonstrate the effectiveness of the
proposed synchronization schemes.
THE ACTIVE CONTROLLER DESIGN FOR ACHIEVING GENERALIZED PROJECTIVE SYNCHRONIZA...ijait
This paper discusses the design of active controllers for achieving generalized projective synchronization (GPS) of identical hyperchaotic Lü systems (Chen, Lu, Lü and Yu, 2006), identical hyperchaotic Cai systems (Wang and Cai, 2009) and non-identical hyperchaotic Lü and hyperchaotic Cai systems. The synchronization results (GPS) for the hyperchaotic systems have been derived using active control method and established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active control method is very effective and convenient for achieving the GPS of the
hyperchaotic systems addressed in this paper. Numerical simulations are provided to illustrate the effectiveness of the GPS synchronization results derived in this paper.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
발표자: 이인웅 (연세대 박사과정)
발표일: 2017.12.
개요:
영상에서 사람의 행동을 인식하는 방법은 크게 영상에서 직접적으로 행동 라벨을 추출하는 것과 자세 정보를 기반으로 행동 라벨을 추출하는 것으로 나뉠 수 있습니다.
본 발표는 행동 인식에 대한 전반적인 개요를 설명하고 그 중에서도 사람의 자세 정보를 기반으로 하는 행동 인식 기술에 초점을 두고 최근 ICCV 2017 학회에서 발표된 Temporal Sliding LSTM 네트워크를 활용한 행동 인식 기술을 중점적으로 설명합니다. 구체적으로, 스켈레톤 기반 행동 인식 이슈, 제안하는 방법과 실험 결과들이 소개되고 앞으로 나아갈 만한 새로운 연구 이슈들도 추가적으로 설명합니다.
A short overview of current quantum computing hardware and some of the machine learning algorithms that have been developed on these systems for machine learning tasks.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Global Chaos Synchronization of Hyperchaotic Pang and Hyperchaotic Wang Syste...CSEIJJournal
This paper investigates the global chaos synchronization of identical hyperchaotic Wang systems, identical
hyperchaotic Pang systems, and non-identical hyperchaotic Wang and hyperchaotic Pang systems via
adaptive control method. Hyperchaotic Pang system (Pang and Liu, 2011) and hyperchaotic Wang system
(Wang and Liu, 2006) are recently discovered hyperchaotic systems. Adaptive control method is deployed
in this paper for the general case when the system parameters are unknown. Sufficient conditions for global
chaos synchronization of identical hyperchaotic Pang systems, identical hyperchaotic Wang systems and
non-identical hyperchaotic Pang and Wang systems are derived via adaptive control theory and Lyapunov
stability theory. Since the Lyapunov exponents are not required for these calculations, the adaptive control
method is very convenient for the global chaos synchronization of the hyperchaotic systems discussed in
this paper. Numerical simulations are presented to validate and demonstrate the effectiveness of the
proposed synchronization schemes.
THE ACTIVE CONTROLLER DESIGN FOR ACHIEVING GENERALIZED PROJECTIVE SYNCHRONIZA...ijait
This paper discusses the design of active controllers for achieving generalized projective synchronization (GPS) of identical hyperchaotic Lü systems (Chen, Lu, Lü and Yu, 2006), identical hyperchaotic Cai systems (Wang and Cai, 2009) and non-identical hyperchaotic Lü and hyperchaotic Cai systems. The synchronization results (GPS) for the hyperchaotic systems have been derived using active control method and established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active control method is very effective and convenient for achieving the GPS of the
hyperchaotic systems addressed in this paper. Numerical simulations are provided to illustrate the effectiveness of the GPS synchronization results derived in this paper.
Deterministic Stabilization of a Dynamical System using a Computational ApproachIJAEMSJORNAL
The qualitative behavior of a multi-parameter dynamical system has been investigated. It is shown that changes in the initial data of a dynamical system will affect the stabilization of the steady-state solution which is originally unstable. It is further shown that the stabilization of a five-dimensional dynamical system can be used as an alternative method of verifying qualitatively the concept of the stability of a unique positive steady-state solution. These novel contributions have not been seen elsewhere; these are presented and discussed in this paper.
Adaptive Controller and Synchronizer Design for Hyperchaotic Zhou System with...Zac Darcy
In this paper, we establish new results for the adaptive controller and synchronizer design for the
hyperchaotic Zhou system (2009), when the parameters of the system are unknown. Using adaptive control theory and Lyapunov stability theory, we first design an adaptive controller to stabilize the hyperchaotic Zhou system to its unstable equilibrium at the origin. Next, using adaptive control theory and Lyapunov stability theory, we design an adaptive controller to achieve global chaos synchronization
of the identical hyperchaotic Zhou systems with unknown parameters. Simulations have been provided for adaptive controller and synchronizer designs to validate and illustrate the effectiveness of the schemes.
Foundation and Synchronization of the Dynamic Output Dual Systemsijtsrd
In this paper, the synchronization problem of the dynamic output dual systems is firstly introduced and investigated. Based on the time domain approach, the state variables synchronization of such dual systems can be verified. Meanwhile, the guaranteed exponential convergence rate can be accurately estimated. Finally, some numerical simulations are provided to illustrate the feasibility and effectiveness of the obtained result. Yeong-Jeu Sun "Foundation and Synchronization of the Dynamic Output Dual Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29256.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/29256/foundation-and-synchronization-of-the-dynamic-output-dual-systems/yeong-jeu-sun
ADAPTIVE STABILIZATION AND SYNCHRONIZATION OF LÜ-LIKE ATTRACTORijcseit
This paper derives new results for the adaptive chaos stabilization and synchronization of Lü-like attractor
with unknown parameters. The Lü-like attractor is one of the recently discovered 3-scroll chaotic systems,
which was proposed by D. Li (2007). First, adaptive control laws are determined to stabilize the Lü-like
attractor to its unstable equilibrium at the origin. These adaptive laws are established using Lyapunov
stability theory. Then adaptive synchronization laws are determined so as to achieve global chaos
synchronization of identical Lü-like attractors with unknown parameters. Numerical simulations are
presented to validate and demonstrate the effectiveness of the proposed adaptive control and
synchronization schemes for the Lü-like attractor.
ADAPTIVESYNCHRONIZER DESIGN FOR THE HYBRID SYNCHRONIZATION OF HYPERCHAOTIC ZH...ijitcs
This paper derives new adaptive synchronizers for the hybrid synchronization of hyperchaotic Zheng
systems (2010) and hyperchaotic Yu systems (2012). In the hybrid synchronization design of master and
slave systems, one part of the systems, viz. their odd states, are completely synchronized (CS), while the
other part, viz. their even states, are completely anti-synchronized (AS) so that CS and AS co-exist in the
process of synchronization. The research problem gets even more complicated, when the parameters of the
hyperchaotic systems are not known and we handle this complicate problem using adaptive control. The
main results of this research work are established via adaptive control theory andLyapunov stability
theory. MATLAB plotsusing classical fourth-order Runge-Kutta method have been depictedfor the new
adaptive hybrid synchronization results for the hyperchaotic Zheng and hyperchaotic Yu systems.
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
This paper derives new results for the global chaos synchronization of identical hyperchaotic Qi systems (2008), identical hyperchaotic Jha systems (2007) and non-identical hyperchaotic Qi and Jha systems. Active nonlinear control is the method adopted to achieve the complete synchronization of the identical and different hyperchaotic Qi and Jha systems. Our stability results derived in this paper are established using Lyapunov stability theory. Numerical simulations are shown to validate and illustrate the effectiveness of the synchronization results derived in this paper.
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS ...ijistjournal
This paper derives new results for the global chaos synchronization of identical hyperchaotic Qi systems (2008), identical hyperchaotic Jha systems (2007) and non-identical hyperchaotic Qi and Jha systems. Active nonlinear control is the method adopted to achieve the complete synchronization of the identical and different hyperchaotic Qi and Jha systems. Our stability results derived in this paper are established using Lyapunov stability theory. Numerical simulations are shown to validate and illustrate the effectiveness of the synchronization results derived in this paper.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
THE DESIGN OF ADAPTIVE CONTROLLER AND SYNCHRONIZER FOR QI-CHEN SYSTEM WITH UN...IJCSEA Journal
This paper investigates the design problem of adaptive controller and synchronizer for the Qi-Chen system (2005), when the system parameters are unknown. First, we build an adaptive controller to stabilize the QiChen chaotic system to its unstable equilibrium at the origin. Then we build an adaptive synchronizer to achieve global chaos synchronization of the identical Qi-Chen chaotic systems with unknown parameters. The results derived for adaptive stabilization and adaptive synchronization for the Qi-Chen chaotic system are established using adaptive control theory and Lyapunov stability theory. Numerical simulations have been shown to demonstrate the effectiveness of the adaptive control and synchronization schemes derived in this paper for the Qi-Chen chaotic system.
Adaptive Projective Lag Synchronization of T and Lu Chaotic Systems IJECEIAES
In this paper, the synchronization problem of T chaotic system and Lu chaotic system is studied. The parameter of the drive T chaotic system is considered unknown. An adaptive projective lag control method and also parameter estimation law are designed to achieve chaos synchronization problem between two chaotic systems. Then Lyapunov stability theorem is utilized to prove the validity of the proposed control method. After that, some numerical simulations are performed to assess the performance of the proposed method. The results show high accuracy of the proposed method in control and synchronization of chaotic systems.
In this paper we consider the initial-boundary value problem for a nonlinear equation induced with respect to the mathematical models in mass production process with the one sided spring boundary condition by boundary feedback control. We establish the asymptotic behavior of solutions to this problem in time, and give an example and simulation to illustrate our results. Results of this paper are able to apply industrial parts such as a typical model widely used to represent threads, wires, magnetic tapes, belts, band saws, and so on.
We consider the problem of model estimation in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts generated from a small number of latent states. We are interested in estimating the latent state decoding function (the mapping from the observations to latent states) based on data generated under a fixed behavior policy. We derive an information-theoretical lower bound on the error rate for estimating this function and present an algorithm approaching this fundamental limit. In turn, our algorithm also provides estimates of all the components of the MDP.
We apply our results to the problem of learning near-optimal policies in the reward-free setting. Based on our efficient model estimation algorithm, we show that we can infer a policy converging (as the number of collected samples grows large) to the optimal policy at the best possible asymptotic rate. Our analysis provides necessary and sufficient conditions under which exploiting the block structure yields improvements in the sample complexity for identifying near-optimal policies. When these conditions are met, the sample complexity in the minimax reward-free setting is improved by a multiplicative factor $n$, where $n$ is the number of contexts.
ANTI-SYNCHRONIZATION OF HYPERCHAOTIC BAO AND HYPERCHAOTIC XU SYSTEMS VIA ACTI...IJCSEIT Journal
This paper investigates the anti-synchronization of identical hyperchaotic Bao systems (Bao and Liu,
2008), identical hyperchaotic Xu systems (Xu, Cai and Zheng, 2009) and non-identical hyperchaotic Bao
and hyperchaotic Xu systems. Active nonlinear control has been deployed for the anti- synchronization of
the hyperchaotic systems addressed in this paper and the main results have been established using
Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active
nonlinear control method is very effective and convenient to achieve anti-synchronization of identical and
non-identical hyperchaotic Bao and hyperchaotic Xu systems. Numerical simulations have been provided to
validate and demonstrate the effectiveness of the anti-synchronization results for the hyperchaotic Cao and
hyperchaotic Xu systems.
ADAPTIVE STABILIZATION AND SYNCHRONIZATION OF HYPERCHAOTIC QI SYSTEM cseij
The hyperchaotic Qi system (Chen, Yang, Qi and Yuan, 2007) is one of the important models of fourdimensional hyperchaotic systems. This paper investigates the adaptive stabilization and synchronization of hyperchaotic Qi system with unknown parameters. First, adaptive control laws are designed to stabilize the hyperchaotic Qi system to its equilibrium point at the origin based on the adaptive control theory and Lyapunov stability theory. Then adaptive control laws are derived to achieve global chaos synchronization of identical hyperchaotic Qi systems with unknown parameters. Numerical simulations are shown to demonstrate the effectiveness of the proposed adaptive stabilization and synchronization schemes.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
20191018 reservoir computing
1. Introduction to Reservoir Computing
From a Dynamical System Perspective
Chia-Hsiang Kao
Oct. 19, 2019 @Mozilla Community SpaceTaipei
2. Outline
• Introduction to the configuration of reservoir
• Introduction to chaotic system
• Prediction of chaotic system using reservoir computing
• Mechanism of reservoir computing
2
4. Main Reference
• Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018). Model-free
prediction of large spatiotemporally chaotic systems from data: A
reservoir computing approach. Physical review letters, 120(2), 024102.
• Pathak, J., Lu, Z., Hunt, B. R., Girvan, M., & Ott, E. (2017). Using machine
learning to replicate chaotic attractors and calculate Lyapunov
exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear
Science, 27(12), 121102.
• Jaideep Pathak. MACHINE LEARNING FOR ANALYSIS OF HIGH-
DIMENSIONAL CHAOTIC SPATIOTEMPORAL DYNAMICAL SYSTEMS.
Princeton Plasma Physics Laboratory Theory Semina. (6/12/18)
4
5. Reservoir computing = a RNN with
𝑊 and 𝑈 fixed
Figure adopted from https://bit.ly/2J0mXjU5
6. Configuration of the Reservoir
Input: 𝑢(𝑡)
Output: u(𝑡)
State of reservoir: r(𝑡)
𝒖(𝒕)
v(𝒕)
𝒓(𝒕)
𝑾 𝒓𝒓
𝑊𝑖𝑛 and 𝑊𝑟𝑟 are fixed.
𝑊𝑜𝑢𝑡 is trainable!
Wrr: large, low-degree, directed,
random adjacent matrix
Update 𝑟(𝑡):
r t + Δt = tanh[𝐖𝐫𝐫r t + 𝐖𝐢𝐧u(t)] ,r(t)我會稱之為「reservoir的狀態」
Refer to Skibinsky-Gitlin et al. (2018, June). Cyclic Reservoir Computing with FPGA Devices for Efficient Channel
Equalization. In InternationalConference on Artificial Intelligence andSoftComputing (pp. 226-234). Springer, Cham.6
7. Hardware implementation using a
variety of physical systems
Figure adopted fromTanaka, G.,Yamane,T., Héroux, J. B., Nakane, R., Kanazawa, N.,
Takeda, S., ... & Hirose, A. (2019). Recent advances in physical reservoir computing:A
review. Neural Networks.
7
9. Blue point: (0,1,0)
Red point: (0,1.001,0)
Movie retrieved fromhttps://www.youtube.com/watch?v=8z_tSVeEFTA9
10. Motivation of this paper
An existing but unavailable
dynamical system
Short-term
forecasting &
long-term
dynamics
Reasonably accurate and
complete observational
data can be obtained
Figure adopted from Lu, Z., & Bassett, D. S. (2018). A Parsimonious Dynamical Model
for Structural Learning in the Human Brain. arXiv preprint arXiv:1807.05214.10
11. ~𝒙′(𝒕)
In this paper, a chaotic dynamical
system is concerned.
• We say a dynamical system is chaotic if two nearby
trajectories diverge exponentially.
• Consider separation 𝛿(𝑡) = 𝑥′
(𝑡) − 𝑥(𝑡).
• 𝛿 𝑡 ~𝑒 𝝀𝑡
𝛿 0
• Lyapunov exponent 𝝀 indicates predictability for a
dynamical system.
• 𝜆<0: distance decreases
• 𝜆>0: deviation grows exponentially
Figure adopted from https://bit.ly/35LZlJH11
12. In this paper, a chaotic dynamical
system is concerned.
• We say a dynamical system is chaotic if two nearby
trajectories diverge exponentially.
• Consider separation 𝛿(𝑡) = 𝑥′
(𝑡) − 𝑥(𝑡).
• 𝛿 𝑡 ~𝑒 𝝀𝑡
𝛿 0
• Lyapunov exponent 𝝀 indicates predictability for a
dynamical system.
• 𝜆<0: distance decreases
• 𝜆>0: deviation grows exponentially
• A dynamical system usually contains multiple
Lyapunov exponents.
Figure adopted fromJaideep Pathak. MACHINE LEARNING FORANALYSISOF HIGH-DIMENSIONALCHAOTIC
SPATIOTEMPORAL DYNAMICAL SYSTEMS. Princeton Plasma Physics LaboratoryTheory Semina. (6/12/18)12
13. Blue point: (0,1,0)
Red point: (0,1.001,0)
Movie retrieved fromhttps://www.youtube.com/watch?v=8z_tSVeEFTA13
14. Q: Can a traditional RNN or LSTM
learn to predict the future state of a
Lorenz system?
• RNN and LSTM can of course forecast the behavior of the Lorenz
system in short-term.
• Why or How?
• How about reservoir computing?
• Why or How?
14
16. In this paper, the reservoir is built to
forecast the behavior of Kuramoto-
Sivashinsky Equation
• 𝑦𝑡 = −𝑦𝑦𝑡 − 𝑦𝑥𝑥 − 𝑦𝑥𝑥𝑥𝑥
gif retrieved from https://zhuanlan.zhihu.com/p/37730449
𝑥
16
17. In this paper, we want the reservoir
forecast the behavior of Kuramoto-
Sivashinsky Equation
• 𝑦𝑡 = −𝑦𝑦𝑡 − 𝑦𝑥𝑥 − 𝑦𝑥𝑥𝑥𝑥
• 𝑥 ∈ [0, 𝐿)
Figure adopted from Pathak, J. et al. (2017). Using machine learning to replicate chaotic attractors and calculate
Lyapunov exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(12), 121102.17
18. In this paper, we want the reservoir
forecast the behavior of Kuramoto-
Sivashinsky Equation
• 𝑦𝑡 = −𝑦𝑦𝑡 − 𝑦𝑥𝑥 − 𝑦𝑥𝑥𝑥𝑥
• 𝐱 ∈ [𝟎, 𝐋)
L
12 13.5
36 100
Figure adopted from Edson, R. A., Bunder, J. E., Mattner,T.W., & Roberts, A. J. (2019). Lyapunov
exponents of the Kuramoto–Sivashinsky PDE.TheANZIAM Journal, 61(3), 270-285.18
19. In this paper, we want the reservoir
forecast the behavior of Kuramoto-
Sivashinsky Equation
• 𝑦𝑡 = −𝑦𝑦𝑡 − 𝑦𝑥𝑥 − 𝑦𝑥𝑥𝑥𝑥
• 𝑥 ∈ [0, 𝐿)
• 𝐲 𝐱 + 𝑳 = 𝐲(𝐱)
Figure adopted from Pathak, J. et al. (2017). Using machine learning to replicate chaotic attractors and calculate
Lyapunov exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(12), 121102.19
20. Predict the Behavior of
Kuramoto-Sivashinsky Equation (4)
• 𝑦𝑡 = −𝑦𝑦𝑡 − 𝑦𝑥𝑥 − 𝑦𝑥𝑥𝑥𝑥
• 𝑥 ∈ [0, 𝐿)
• 𝑦 𝑥 + 𝐿 = 𝑦(𝑥)
• Divided [0,L) into Q parts such that
• 𝑢 𝑡 = 𝑦 Δ𝑥, 𝑡 , 𝑦 2Δ𝑥, 𝑡 , … , 𝑦(QΔ𝑥, 𝑡) 𝑇
• 𝑸 =
𝑳
𝚫𝒙
is the input size of reservoir
Δ𝑥
t
Figure adopted from Pathak, J. et al. (2017). Using machine learning to replicate chaotic attractors and calculate
Lyapunov exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(12), 121102.20
21. Configuration of the Reservoir in the Paper
𝐫 𝒕
r t + Δt = tanh[𝐖𝐫𝐫 ⋅ r t + 𝐖𝐢𝐧 ⋅ u(t)] ,
v t = 𝐖𝐨𝐮𝐭 ⋅ r t
𝐖𝐢𝐧
𝐖𝒐𝒖𝒕𝐖𝐫𝐫
v 𝑡
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.21
22. Experiment
Training Prediction
−𝑇 ≤ 𝑡 ≤ 0 0 < 𝑡
→Adjust P so that 𝑣(𝑡) approximate 𝑢(𝑡 + Δ𝑡)
→ 𝐖𝒐𝒖𝒕(𝒓 𝒕 + 𝚫𝐭 , 𝑷)=𝑃1 𝑟(𝑡 + Δ𝑡) +
𝑃2 𝑟(𝑡 + Δ𝑡)2
→ Replace 𝑢(𝑡 + Δ𝑡) with 𝑣(𝑡)
→ r(t) is not reset
0-T
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.22
23. Experiment
Training Prediction
−𝑇 ≤ 𝑡 ≤ 0 0 < 𝑡
→ Adjust 𝐖𝒐𝒖𝒕 so that 𝒗(𝒕) approximate 𝒖(𝒕 +
𝚫𝒕)
→ Replace 𝑢(𝑡 + Δ𝑡) with 𝑣(𝑡)
→ r(t) is not reset
0-T
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.23
24. Outcomes &
Model ParametersTop:True state of the standard KS equation
Middle: Reservoir Prediction
Bottom: Difference (by subtraction )
Paramete
r
Exp1 Exp2
Q - 64
L 60 22
𝑫 𝑹 9000 5000
T 20000 -
Δ𝑡 0.25 0.25
𝜇 0 0
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.24
26. Outpu
t
Extension – Parallelized Reservoir
Scheme
• 𝑅𝑖 has its own 𝐴𝑖 (adjacency matrix), 𝑟𝑖 (internal state) and 𝑊𝑖𝑛,𝑖 (input weights).
• 𝑅𝑖 receives additional input from continuous variables. ⇒ ℎ𝑖
• Input 𝑢(𝑡) is split into 𝑔 group, each group consisting of 𝑞 variables. ⇒
𝑄 = 𝑔 ⋅ 𝑞
Input
𝑔𝑖−1 𝑔𝑖+1
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.26
27. Parallelized Reservoir Scheme
- Performance increased when # of reservoir↑ and size↓
L/g held fixed L=200, Q=512
Figure adopted from Pathak, J. et al. (2018). Model-free prediction of large spatiotemporally chaotic
systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.27
30. Explanation 2 (1-1)
-The Dynamical Structure of Input is
Learned
Figure adopted from Lu, Z., & Bassett, D. S. (2018). A Parsimonious Dynamical Model
for Structural Learning in the Human Brain. arXiv preprint arXiv:1807.05214.32
31. Explanation 2 (1-2)
-The Dynamical Structure of Input is
Learned
How, when and where is the structure learned?
Especially when 𝑾 𝒓𝒓 and 𝑾𝒊𝒏 are fixed.
Figure adopted from Lu, Z., & Bassett, D. S. (2018). A Parsimonious Dynamical Model
for Structural Learning in the Human Brain. arXiv preprint arXiv:1807.05214.33
32. Explanation 2 (1-2)
-The Dynamical Structure of Input is
Learned
(x,y,z) (x,y’,z’)
𝑥 = 𝜎(𝑦 − 𝑧)
𝑦 = −𝑥𝑧 + 𝑟𝑥 − 𝑦 𝑦′ = −𝑥𝑧′ + 𝑟𝑥 − 𝑦′
𝑧 = 𝑥𝑦 − 𝑏𝑧 𝑧′ = 𝑥𝑦′ − 𝑏𝑧′
Figure adopted from Pecora, L. M., &Carroll, T. L. (2015). Synchronization of chaotic
systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(9), 097611.34
33. Explanation 2 (1-4)
-The Dynamical Structure of Input is
Learned - A simple and parsimonious explanation
External System
(drive)
Central System
(response)
Space ℝ 𝑛
ℝ 𝑁
Chaotic
attractor
𝐴k 𝑃k
Input
trajectory
𝑠(𝑡) 𝑥(𝑡)
𝜑(⋅)
𝜙(⋅)
35
36. Figure adopted from McClintock, P.V. (2006). Biological physics of the developing embryo.
←The way states evolve.
Input also affect states.
Ex: Divergence and
Convergence of data in
High-dimensional space.
39
37. Information is processed by extremely
complex but surprisingly stereotypic
microcircuits
Figure adopted from Mountcastle,V. B. (1997).The columnar organization of the neocortex. Brain: a
journal of neurology, 120(4), 701-722. & Habenschuss, S., Jonke, Z., & Maass,W. (2013). Stochastic
computations in cortical microcircuit models. PLoS computational biology, 9(11), e1003311.40
38. Credits and Reference
• 【Template】SlidesCarnival
• Real-Time ComputingWithout Stable States: A New Framework for Neural Computation
Based on Perturbations
• Recent Advances in Physical Reservoir Computing: A Review
• Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir
Computing Approach
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents
from data
• 【Seminar】 Machine learning for analysis of high-dimensional chaotic statiotemporal
dynamical systems
• Lyapunov exponents of the Kuramoto–Sivashinsky PDE
• 【知乎】神经网络的参数都是随机的,有的效果很好,有的效果很差,这真的不是玄
学吗?, https://www.zhihu.com/question/265476523/answer/747653415
• A Parsimonious Dynamical Model for Structural Learning in the Human Brain.
• Pecora, L. M., & Carroll,T. L. (2015). Synchronization of chaotic systems. Chaos: An
Interdisciplinary Journal of Nonlinear Science, 25(9), 097611.
41
39. Figure
• Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization.
• http://ycpcs.github.io/cs360-spring2015/lectures/lecture15.html
• https://medium.com/ai-journal/lstm-gru-recurrent-neural-networks-81fe2bcdf1f9
• https://2e.mindsmachine.com/asf05.01.html
• The columnar organization of the neocortex.
• https://juliadynamics.github.io/DynamicalSystems.jl/latest/chaos/lyapunovs/
• https://www.youtube.com/watch?v=8z_tSVeEFTA
42
Fig: Edson, R. A., Bunder, J. E., Mattner, T. W., & Roberts, A. J. (2019). Lyapunov exponents of the Kuramoto–Sivashinsky PDE. The ANZIAM Journal, 61(3), 270-285.
Pathak, J., Lu, Z., Hunt, B. R., Girvan, M., & Ott, E. (2017). Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(12), 121102.
就像是一般的RNN
Lu, Z., & Bassett, D. S. (2018). A Parsimonious Dynamical Model for Structural Learning in the Human Brain. arXiv preprint arXiv:1807.05214.
先看rossler系統,drive和response
X同時驅動了y,z和y‘,z’兩個系統
在這裡,看到yz,和y‘z’的結構一模一樣。我們稱這種同步行為為identical synchronization
而實際上,就算兩者結構有些許的布一樣,類似的同步狀態還是會誕生的。
那大家有感覺到了嗎?
Pecora, L. M., & Carroll, T. L. (2015). Synchronization of chaotic systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(9), 097611.
1. 解釋圖表
2. Declare不是所有的drive-response都會成功,但目前我不清楚怎麼樣的drive-response設定之間才會成功
Pecora, L. M., & Carroll, T. L. (2015). Synchronization of chaotic systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(9), 097611.