This is a presentation I recently gave on classification of EEG P300 ERPs using Riemannian Geometry and Quantum Computing. It is a quick introduction to both of them. It describes how to prepare your data using Riemannian Geometry and how to build a Quantum Kernel using a Quantum Feature Map.
Fast R-CNN is a method that improves object detection speed and accuracy over previous methods like R-CNN and SPPnet. It uses a region of interest pooling layer and multi-task loss to jointly train a convolutional neural network for classification and bounding box regression in a single stage of training. This allows the entire network to be fine-tuned end-to-end for object detection, resulting in faster training and testing compared to previous methods while achieving state-of-the-art accuracy on standard datasets. Specifically, Fast R-CNN trains 9x faster than R-CNN and runs 200x faster at test time.
Quantum Computing: The next new technology in computingData Con LA
Data Con LA 2020
Description
Quantum computing is rapidly becoming commercially feasible. Many tech giants - Google, IBM, Honeywell, and Microsoft - are spending billions to far outpace Moore's Law. Last year achieved the major milestone of Quantum Supremacy where it was shown that a quantum computer could greatly outperform a classical computer. Quantum computing offers the promise of solving problems which would be impossible for a classical computer including optimization, anomaly detection, and material design. It also allows unhackable communication.
In this presentation I will summarize what quantum computing is and why it is so important. I will sketch the landscape of the field including the hardware, software, and major customers at present. The tool most critical for data analysis - quantum machine learning - will be explained, along with the type of applications it is best suited for. Finally I will explain how you can take the first steps into leveraging quantum computing for your enterprise's benefit.
* What is quantum computing
* Who are the major players in the field
* What is quantum machine learning and what types of problems can it address
* How your company can take advantage of this
Speaker
Mark Jackson, Cambridge Quantum Computing, Scientific Lead of Business Development
This document introduces quantum programming and the IBM quantum computer. It discusses why quantum computation is useful for solving complex problems that are intractable on classical computers. It then provides an overview of the IBM quantum computer, including qubit implementations and coherence times. Common quantum algorithms like Deutsch's algorithm, Shor's algorithm, and Grover's algorithm are summarized. The document explains how to program a quantum computer using quantum circuits and gates. It introduces tools for quantum programming like the IBM Quantum Experience, QISKit, and OpenQASM.
Performance is a key component of usability and crucial for the user experience, especially in today's modern user interfaces where graphical elements are being animated and transitioned. Bringing Qt Everywhere means a significant need for speed across desktop and embedded platforms. This presentation will give you a brief overview of performance improvements done in Qt, and will be highly interactive with hands-on sessions on how to squeeze every last drop of performance out of your Qt application.
Presentation by Bjørn Erik Nilsen held during Qt Developer Days 2009.
http://qt.nokia.com/whatsnew
Operationalizing Machine Learning Using GPU-accelerated, In-database AnalyticsKinetica
Mate Radalj's presentation on how to operationalize machine learning using GPU-accelerated, in-database analytics, given at the Bay Area GPU-Accelerated Computing Meetup on October 19, 2017. Presentation includes use cases and links to demos.
Quantum computing startup IQM aims to come up with more efficient battery and material designs. This is the 20-slide pitch deck that landed it $128 million in funding.
Plus Slide Backup I: Dilution Refrigerator from Maybell Quantum and Backup II: IQM technical slide
This document provides an introduction to quantum programming languages. It begins with basic concepts in quantum mechanics like state superposition and entanglement. It then discusses popular quantum algorithms like Deutsch, Shor, and Grover algorithms. The document reviews several quantum programming languages including quantum pseudocode, Quipper which is embedded in Haskell, and the Python toolbox QuTiP. It also mentions Mathematica packages for quantum computation. Finally, it introduces the IBM Quantum Experience platform for designing and running quantum circuits in a quantum processor or simulator.
Python Raster Function - Esri Developer Conference - 2015akferoz07
This document discusses Python raster functions in ArcGIS, which allow users to implement raster analysis and processing algorithms from Python. Key points covered include:
- Raster functions transform one raster dataset into another on-the-fly through a chain of functions.
- Python raster functions are implemented as Python modules that participate in the raster function chain via an ArcGIS adapter.
- Examples demonstrate how to perform tasks like linear spectral unmixing from Python.
- The API for creating Python raster functions and templates is explained.
- Considerations for publishing and optimizing Python raster functions are provided.
Fast R-CNN is a method that improves object detection speed and accuracy over previous methods like R-CNN and SPPnet. It uses a region of interest pooling layer and multi-task loss to jointly train a convolutional neural network for classification and bounding box regression in a single stage of training. This allows the entire network to be fine-tuned end-to-end for object detection, resulting in faster training and testing compared to previous methods while achieving state-of-the-art accuracy on standard datasets. Specifically, Fast R-CNN trains 9x faster than R-CNN and runs 200x faster at test time.
Quantum Computing: The next new technology in computingData Con LA
Data Con LA 2020
Description
Quantum computing is rapidly becoming commercially feasible. Many tech giants - Google, IBM, Honeywell, and Microsoft - are spending billions to far outpace Moore's Law. Last year achieved the major milestone of Quantum Supremacy where it was shown that a quantum computer could greatly outperform a classical computer. Quantum computing offers the promise of solving problems which would be impossible for a classical computer including optimization, anomaly detection, and material design. It also allows unhackable communication.
In this presentation I will summarize what quantum computing is and why it is so important. I will sketch the landscape of the field including the hardware, software, and major customers at present. The tool most critical for data analysis - quantum machine learning - will be explained, along with the type of applications it is best suited for. Finally I will explain how you can take the first steps into leveraging quantum computing for your enterprise's benefit.
* What is quantum computing
* Who are the major players in the field
* What is quantum machine learning and what types of problems can it address
* How your company can take advantage of this
Speaker
Mark Jackson, Cambridge Quantum Computing, Scientific Lead of Business Development
This document introduces quantum programming and the IBM quantum computer. It discusses why quantum computation is useful for solving complex problems that are intractable on classical computers. It then provides an overview of the IBM quantum computer, including qubit implementations and coherence times. Common quantum algorithms like Deutsch's algorithm, Shor's algorithm, and Grover's algorithm are summarized. The document explains how to program a quantum computer using quantum circuits and gates. It introduces tools for quantum programming like the IBM Quantum Experience, QISKit, and OpenQASM.
Performance is a key component of usability and crucial for the user experience, especially in today's modern user interfaces where graphical elements are being animated and transitioned. Bringing Qt Everywhere means a significant need for speed across desktop and embedded platforms. This presentation will give you a brief overview of performance improvements done in Qt, and will be highly interactive with hands-on sessions on how to squeeze every last drop of performance out of your Qt application.
Presentation by Bjørn Erik Nilsen held during Qt Developer Days 2009.
http://qt.nokia.com/whatsnew
Operationalizing Machine Learning Using GPU-accelerated, In-database AnalyticsKinetica
Mate Radalj's presentation on how to operationalize machine learning using GPU-accelerated, in-database analytics, given at the Bay Area GPU-Accelerated Computing Meetup on October 19, 2017. Presentation includes use cases and links to demos.
Quantum computing startup IQM aims to come up with more efficient battery and material designs. This is the 20-slide pitch deck that landed it $128 million in funding.
Plus Slide Backup I: Dilution Refrigerator from Maybell Quantum and Backup II: IQM technical slide
This document provides an introduction to quantum programming languages. It begins with basic concepts in quantum mechanics like state superposition and entanglement. It then discusses popular quantum algorithms like Deutsch, Shor, and Grover algorithms. The document reviews several quantum programming languages including quantum pseudocode, Quipper which is embedded in Haskell, and the Python toolbox QuTiP. It also mentions Mathematica packages for quantum computation. Finally, it introduces the IBM Quantum Experience platform for designing and running quantum circuits in a quantum processor or simulator.
Python Raster Function - Esri Developer Conference - 2015akferoz07
This document discusses Python raster functions in ArcGIS, which allow users to implement raster analysis and processing algorithms from Python. Key points covered include:
- Raster functions transform one raster dataset into another on-the-fly through a chain of functions.
- Python raster functions are implemented as Python modules that participate in the raster function chain via an ArcGIS adapter.
- Examples demonstrate how to perform tasks like linear spectral unmixing from Python.
- The API for creating Python raster functions and templates is explained.
- Considerations for publishing and optimizing Python raster functions are provided.
Quantum machine learning is one of the promising application domains of quantum computing, which is expected to improve and accelerate the most resource-intensive machine learning calculations. This talk will explain how we implement quantum machine learning algorithms, what are the limits and challenges, and how these challenges can be addressed.
This document summarizes a presentation on writing image processing algorithms using Python raster functions in ArcGIS. It introduces the concept of raster functions and raster models as a way to chain raster functions together. It then discusses how to build raster functions using Python by implementing various callback methods to interact with raster data. The presentation demonstrates applying a Compound Topographic Index (CTI) raster function and a site suitability analysis raster model to image layers in ArcGIS. It also provides additional considerations for optimizing performance, publishing functions, and collaborating on GitHub.
Strengths and limitations of quantum computingVinayak Sharma
Quantum computing as a research field has been around for about 30 years. It seems like a way to overcome the challenges that classical (boolean based) computers are facing due to “quantum tunneling” effect. Although, there are various theoretical and practical challenges that are needed to be dealt with if we want quantum computes to perform better that classical computers (i.e achieving “quantum supremacy”). This seminar will aim to shed light on basics of quantum computing and its strengths and weaknesses.
Video Links
Part 1: https://www.youtube.com/watch?v=-WLD_HnUvy0
Part 2: https://www.youtube.com/watch?v=xXzUmpk8ztU
The document discusses quantum computing and IBM's efforts in the field. It provides an overview of quantum computing concepts like superposition and entanglement. It describes IBM's superconducting qubit technology and how qubits can be controlled and entangled. The document outlines IBM's quantum computing platforms including the IBM Quantum Experience for experimenting with quantum circuits in the cloud. It encourages users to get involved with the Qiskit open source framework and global quantum computing community.
This document summarizes and compares two deep learning frameworks: Microsoft Cognitive Toolkit (CNTK) and PyTorch. CNTK is an open-source toolkit created by Microsoft for deep learning. It is production-ready and optimized for performance and scalability. It uses symbolic computational graphs and supports distributed training techniques like 1-bit SGD. PyTorch is a Python-based framework developed by Facebook for rapid prototyping. It uses dynamic computational graphs and supports automatic differentiation via its autograd package. Both frameworks express neural networks as compositions of basic building blocks and allow defining and training custom models.
Expanding HPCC Systems Deep Neural Network CapabilitiesHPCC Systems
The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities.
Quantum Computing with Amazon Braket
In this talk, I describe some fundamental principles of quantum computing including qu-bits, superposition, and entanglement. I will demonstrate how to perform secure quantum computing tasks across many Quantum Processing Units (QPUs) using Amazon Braket, IAM, and S3.
AI and Machine Learning, Quantum Computing, Amazon Braket, QPU
A framework and approaches to develop an in-house CAT with freeware and open ...Tetsuo Kimura
The document outlines a symposium on developing in-house computerized adaptive tests (CATs) using free and open-source software. It discusses three stages of CAT development: pretesting and item analysis using the R package ltm to construct an item bank, simulating CATs with the existing item bank using the R package SimulCAT to determine specifications, and implementing CATs by publishing them on the Moodle platform using the Moodle UCAT program. The symposium will introduce various free and open-source tools that can be used at each stage of CAT development, including Exametrika, ltm, SimulCAT, catR, Moodle UCAT, and Concerto.
Object Detection Beyond Mask R-CNN and RetinaNet IWanjin Yu
This document summarizes a tutorial on object detection beyond RetinaNet and Mask R-CNN. It discusses challenges in object detection including the backbone network, detection head, pretraining, handling scale variations, large batch sizes, detecting objects in crowds, and neural architecture search. It also introduces recent works that aim to address these challenges, such as DetNet, Light Head R-CNN, Objects365 pretraining, SFace for scale, MegDet for batch size, CrowdHuman benchmark for crowds, and NAS approaches. The document concludes that further improving object detection requires focusing on details and that continued progress will significantly benefit computer vision applications.
Python in the real world : from everyday applications to advanced roboticsJivitesh Dhaliwal
The use of Python in Robotics. A presentation at PyCon India 2011. To see the video, please visit http://urtalk.kpoint.com/kapsule/gcc-ce0164df-0518-447c-9ade-a9ec8dd931de
The slides of the first Meetup of the Quantum Technology community in Paris ! Hosted on 10/16/2018 at WeWork Lafayette
Contribtutors
- Chris Erven CEO of KETS Quantum Security
- Michael Marthaler CEO of Heisenberg Quantum Simulations
- Wojciech Burkot CPO of Beit, on quantum optimization
- Christophe Jurczak CEO of Quantonation, on VC funding
Performance Analysis of Lattice QCD with APGAS Programming ModelKoichi Shirahata
The document analyzes the performance of a lattice quantum chromodynamics (QCD) application implemented using the Asynchronous Partitioned Global Address Space (APGAS) programming model in X10. It finds that the X10 implementation achieves a 102.8x speedup in strong scaling up to 256 places. However, the MPI implementation outperforms X10, being 2.26-2.58x faster due to more optimized communication overlapping in MPI. Analysis shows the X10 implementation suffers overhead from thread activation and synchronization. Communication using one-sided operations also outperforms two-sided in X10. The work contributes an X10 implementation of lattice QCD and evaluates its scalability and performance compared to MPI.
Overview of quantum computing and it's application in artificial intelligenceBincySam2
This document discusses the basic building blocks of quantum computing, including qubits, superposition, entanglement, and qubit gates. It explains how quantum computing differs from classical computing and reviews some applications in artificial intelligence like quantum principal component analysis and quantum support vector machines. While quantum computing could enable solving problems more efficiently, challenges remain in areas like algorithm creation, low temperature requirements, and building large-scale quantum computers.
TROSGi is a framework that enhances OSGi with real-time Java support through three integration levels (L0, L1, L2). L0 provides minimal access to real-time Java. L1 adds a real-time characterization service. L2 introduces admission control, fault tolerance, and composition services. The services were implemented and tested on a real-time Java prototype, showing improved performance over non-real-time approaches. Ongoing work focuses on further implementation improvements and integrating other languages.
This document summarizes a presentation on the design and implementation of a low-cost articulated robot with an automatic tool changer (ATC) and computer vision (CV). The presentation was given by three students to review their project, which focuses on developing a robotic arm with a coupling mechanism to change tools in the end effector. Work done so far includes confirming the coupling mechanism, finalizing the architecture and components. Upcoming work includes building the mechanical structure, implementing the circuitry and coupling mechanism, and developing the kinematic movements and software. The project aims to optimize performance and versatility in tool swapping tasks.
Quantum computing for CS students: open source softwareBruno Fedrici, PhD
1. The document discusses quantum computing software for CS students, including open source options like IBM QX and QISKit.
2. It describes different models of quantum computation like the quantum circuit model and adiabatic quantum computation. Main components include defining the problem, creating a quantum algorithm and circuit, compiling for the quantum processor, and simulating results.
3. IBM QX is highlighted as an example - it includes a circuit composer GUI to design circuits and run them on simulators or quantum processors, and QISKit is the Python version. Results from experiments are probabilistic due to the nature of quantum theory.
This document provides an overview of quantum computing presented by Dr Marcus Doherty. It discusses how quantum computing works, important concepts in quantum physics, applications of quantum computing, and opportunities for software professionals. Some key points include:
- Quantum computing offers potential solutions to problems that are intractable for classical computers by exploiting properties of quantum mechanics like superposition and entanglement.
- It works by initializing qubit states, applying quantum gates to encode data and algorithms, then reading out and repeating to build statistics.
- Challenges include errors, limited connectivity, and lack of a quantum random access memory. Software plays a key role in error correction, compilation, and developing applications.
- Potential applications include optimization, machine learning
"The Arrival of Quantum Computing" by Will ZengImpact.Tech
Slides from the Impact.tech seminar about quantum computing, given by Will Zeng. The presentation addresses the technologies, the actors, and the market around quantum computing.
If there is a sense of reality, there must be a sense of possibility.
Impact.tech Launch Seminars are meant to give entrepreneurs and investors a launch into a topic where they can apply their skills to make a major positive impact for humanity and the world.
Quantum machine learning is one of the promising application domains of quantum computing, which is expected to improve and accelerate the most resource-intensive machine learning calculations. This talk will explain how we implement quantum machine learning algorithms, what are the limits and challenges, and how these challenges can be addressed.
This document summarizes a presentation on writing image processing algorithms using Python raster functions in ArcGIS. It introduces the concept of raster functions and raster models as a way to chain raster functions together. It then discusses how to build raster functions using Python by implementing various callback methods to interact with raster data. The presentation demonstrates applying a Compound Topographic Index (CTI) raster function and a site suitability analysis raster model to image layers in ArcGIS. It also provides additional considerations for optimizing performance, publishing functions, and collaborating on GitHub.
Strengths and limitations of quantum computingVinayak Sharma
Quantum computing as a research field has been around for about 30 years. It seems like a way to overcome the challenges that classical (boolean based) computers are facing due to “quantum tunneling” effect. Although, there are various theoretical and practical challenges that are needed to be dealt with if we want quantum computes to perform better that classical computers (i.e achieving “quantum supremacy”). This seminar will aim to shed light on basics of quantum computing and its strengths and weaknesses.
Video Links
Part 1: https://www.youtube.com/watch?v=-WLD_HnUvy0
Part 2: https://www.youtube.com/watch?v=xXzUmpk8ztU
The document discusses quantum computing and IBM's efforts in the field. It provides an overview of quantum computing concepts like superposition and entanglement. It describes IBM's superconducting qubit technology and how qubits can be controlled and entangled. The document outlines IBM's quantum computing platforms including the IBM Quantum Experience for experimenting with quantum circuits in the cloud. It encourages users to get involved with the Qiskit open source framework and global quantum computing community.
This document summarizes and compares two deep learning frameworks: Microsoft Cognitive Toolkit (CNTK) and PyTorch. CNTK is an open-source toolkit created by Microsoft for deep learning. It is production-ready and optimized for performance and scalability. It uses symbolic computational graphs and supports distributed training techniques like 1-bit SGD. PyTorch is a Python-based framework developed by Facebook for rapid prototyping. It uses dynamic computational graphs and supports automatic differentiation via its autograd package. Both frameworks express neural networks as compositions of basic building blocks and allow defining and training custom models.
Expanding HPCC Systems Deep Neural Network CapabilitiesHPCC Systems
The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities.
Quantum Computing with Amazon Braket
In this talk, I describe some fundamental principles of quantum computing including qu-bits, superposition, and entanglement. I will demonstrate how to perform secure quantum computing tasks across many Quantum Processing Units (QPUs) using Amazon Braket, IAM, and S3.
AI and Machine Learning, Quantum Computing, Amazon Braket, QPU
A framework and approaches to develop an in-house CAT with freeware and open ...Tetsuo Kimura
The document outlines a symposium on developing in-house computerized adaptive tests (CATs) using free and open-source software. It discusses three stages of CAT development: pretesting and item analysis using the R package ltm to construct an item bank, simulating CATs with the existing item bank using the R package SimulCAT to determine specifications, and implementing CATs by publishing them on the Moodle platform using the Moodle UCAT program. The symposium will introduce various free and open-source tools that can be used at each stage of CAT development, including Exametrika, ltm, SimulCAT, catR, Moodle UCAT, and Concerto.
Object Detection Beyond Mask R-CNN and RetinaNet IWanjin Yu
This document summarizes a tutorial on object detection beyond RetinaNet and Mask R-CNN. It discusses challenges in object detection including the backbone network, detection head, pretraining, handling scale variations, large batch sizes, detecting objects in crowds, and neural architecture search. It also introduces recent works that aim to address these challenges, such as DetNet, Light Head R-CNN, Objects365 pretraining, SFace for scale, MegDet for batch size, CrowdHuman benchmark for crowds, and NAS approaches. The document concludes that further improving object detection requires focusing on details and that continued progress will significantly benefit computer vision applications.
Python in the real world : from everyday applications to advanced roboticsJivitesh Dhaliwal
The use of Python in Robotics. A presentation at PyCon India 2011. To see the video, please visit http://urtalk.kpoint.com/kapsule/gcc-ce0164df-0518-447c-9ade-a9ec8dd931de
The slides of the first Meetup of the Quantum Technology community in Paris ! Hosted on 10/16/2018 at WeWork Lafayette
Contribtutors
- Chris Erven CEO of KETS Quantum Security
- Michael Marthaler CEO of Heisenberg Quantum Simulations
- Wojciech Burkot CPO of Beit, on quantum optimization
- Christophe Jurczak CEO of Quantonation, on VC funding
Performance Analysis of Lattice QCD with APGAS Programming ModelKoichi Shirahata
The document analyzes the performance of a lattice quantum chromodynamics (QCD) application implemented using the Asynchronous Partitioned Global Address Space (APGAS) programming model in X10. It finds that the X10 implementation achieves a 102.8x speedup in strong scaling up to 256 places. However, the MPI implementation outperforms X10, being 2.26-2.58x faster due to more optimized communication overlapping in MPI. Analysis shows the X10 implementation suffers overhead from thread activation and synchronization. Communication using one-sided operations also outperforms two-sided in X10. The work contributes an X10 implementation of lattice QCD and evaluates its scalability and performance compared to MPI.
Overview of quantum computing and it's application in artificial intelligenceBincySam2
This document discusses the basic building blocks of quantum computing, including qubits, superposition, entanglement, and qubit gates. It explains how quantum computing differs from classical computing and reviews some applications in artificial intelligence like quantum principal component analysis and quantum support vector machines. While quantum computing could enable solving problems more efficiently, challenges remain in areas like algorithm creation, low temperature requirements, and building large-scale quantum computers.
TROSGi is a framework that enhances OSGi with real-time Java support through three integration levels (L0, L1, L2). L0 provides minimal access to real-time Java. L1 adds a real-time characterization service. L2 introduces admission control, fault tolerance, and composition services. The services were implemented and tested on a real-time Java prototype, showing improved performance over non-real-time approaches. Ongoing work focuses on further implementation improvements and integrating other languages.
This document summarizes a presentation on the design and implementation of a low-cost articulated robot with an automatic tool changer (ATC) and computer vision (CV). The presentation was given by three students to review their project, which focuses on developing a robotic arm with a coupling mechanism to change tools in the end effector. Work done so far includes confirming the coupling mechanism, finalizing the architecture and components. Upcoming work includes building the mechanical structure, implementing the circuitry and coupling mechanism, and developing the kinematic movements and software. The project aims to optimize performance and versatility in tool swapping tasks.
Quantum computing for CS students: open source softwareBruno Fedrici, PhD
1. The document discusses quantum computing software for CS students, including open source options like IBM QX and QISKit.
2. It describes different models of quantum computation like the quantum circuit model and adiabatic quantum computation. Main components include defining the problem, creating a quantum algorithm and circuit, compiling for the quantum processor, and simulating results.
3. IBM QX is highlighted as an example - it includes a circuit composer GUI to design circuits and run them on simulators or quantum processors, and QISKit is the Python version. Results from experiments are probabilistic due to the nature of quantum theory.
This document provides an overview of quantum computing presented by Dr Marcus Doherty. It discusses how quantum computing works, important concepts in quantum physics, applications of quantum computing, and opportunities for software professionals. Some key points include:
- Quantum computing offers potential solutions to problems that are intractable for classical computers by exploiting properties of quantum mechanics like superposition and entanglement.
- It works by initializing qubit states, applying quantum gates to encode data and algorithms, then reading out and repeating to build statistics.
- Challenges include errors, limited connectivity, and lack of a quantum random access memory. Software plays a key role in error correction, compilation, and developing applications.
- Potential applications include optimization, machine learning
"The Arrival of Quantum Computing" by Will ZengImpact.Tech
Slides from the Impact.tech seminar about quantum computing, given by Will Zeng. The presentation addresses the technologies, the actors, and the market around quantum computing.
If there is a sense of reality, there must be a sense of possibility.
Impact.tech Launch Seminars are meant to give entrepreneurs and investors a launch into a topic where they can apply their skills to make a major positive impact for humanity and the world.
Similar to Classification of EEG P300 ERPs using Riemannian Geometry and Quantum Computing (20)
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Classification of EEG P300 ERPs using Riemannian Geometry and Quantum Computing
1. Classification of EEG P300 ERPs
using quantum computation
Anton Andreev (Gipsa-lab France), Gregoire Cattan (IBM Poland)
1
2. Overview
• Quantum computing advantages
• in terms of computational time and outcomes
• for pattern recognition or when using limited training sets
• Objective: classify two states (P300 ERPs) from a BCI experiment using
a Quantum Classifier
EEG dataset
Riemannian Geometry +
Tangent Space
Quantum
classifier
2
3. Projects and Tools
• MOABB - helper project
• pyRiemann - state of the art ERP classification
• QISKIT - IBM Python wrapper around Quantum Computing
• pyRieamann-qiskit - PyRiemann + QISKIT + MOABB
3
4. MOABB
• Github project page:
https://github.com/NeuroTechX/moabb
• Reproducible research
• same performance evaluation code and datasets
• EEG/MEG datasets and specifically P300 ERPs
• Automatic download
4
5. PyRiemann (1)
• Github project page:
https://github.com/pyRiemann/pyRiemann
• Python ML framework
• Provides very good classification for BCI (P300, motor imagery)
• No preprocessing is needed
• Multi channel and Multiclass
• Works well between different sessions and subjects
• It won several BCI challenges
5
6. PyRiemann (2)
• Uses Riemannian Geometry
• Can use a matrix as in input
• Methods:
1. Covariance matrices + Tangent Space + standard classifier
2. Own MDM (Minimum Distance to Mean) classifier
• similar to K-means classifier, but supervised
• centroids are calculated for each class
• specific “distance” and “mean” used
• no manual parameter tuning
3. Other methods
6
7. Covariance matrices + Tangent
Space + standard classifier
7
P300
Epochs
Covariance
matrices
Point on the
manifold
Definition of
Riemannian
manifold
Definition of
Tangent space
Mapping to
Tangent space
Good representation
of the input data
Any Classifier
The workflow can be used by a Quantum Classifier
9. Qiskit (1)
• Github project page: https://github.com/Qiskit/qiskit
• Launched in 2017 by IBM Research
• Provides access to IBM’s cloud quantum computing service
• Where can it be used?
• Qiskit Finance
• Qiskit Optimisation
• Qiskit Nature
• Qiskit Machine Learning (<- we are here)
• Emulated vs Real Quantum computer
9
10. Qiskit (2)
• Quantum circuit
• Quantum bits
• Operations
• Registers:
• Quantum (q1,q2)
• Classical (c1)
• Code generated:
• Python
• QASM
• Result is the
measurement of q1
available c1 https://quantum-computing.ibm.com
10
11. Quantum-enhanced Support Vector Machine (QSVM)
• Quantum Kernel
• Generated based on the input data and the Quantum Feature Map (QFM)
• QFM
• Maps classical data to Quantum states in Quantum Hilbert space
• Implemented as Quantum Circuit
• Can be used in SVM, Kernel Spectral Clustering or Kernel Ridge Regression
• QSVM
• QSVM inherits from ScikitLearn.svm.SVC (conventional SVM)
• Uses the Quantum Kernel
• Size of input vectors = n of qubits
11
12. Diagram with real Quantum Computer
ZZFeatureMap
Input data
Precomputed Quantum
Kernel Matrix
Scikit Learn
SVC
Quantum
Computer
12
13. PyRiemann-qiskit
• Github project page: https://github.com/pyRiemann/pyRiemann-qiskit
• Facilitates the classification of ERP signals with Quantum Computing
• Trying to get the best of both PyRiemann and Qiskit
13
14. Classification
• Dataset: bi2012 – P300, Gipsa-lab BCI game Brain Invaders, MOABB
• Two ML pipelines:
• Both using Riemannian Geometry and Tangent Space
• Quantum bits = PCA components
• But the final classifier is different
• Evaluation
• Provided by MOABB
• k-fold cross-validation
• Per session
• Experience on a real IBM quantum computer
14
RG + Tangent
Space
LDA
classifier
RG + Tangent
Space
QSVM
classifier
15. Evaluation using Quantum Emulation
Dataset: bi2012
Subjects: 25
Classes: 2
Accuracy:
- RG+LDA: 0.88
- RG+QuantumSVM: 0.72
Quantum emulation is
3 times slower
15
16. Experience on real IBM Quantum computer
• Training is slow due to usage restrictions
• Many quantum jobs are needed
• Each quantum job is queued
• Classification score can be low due to:
• Low number of “shots”
• Too small feature vector (depends on available quantum bits)
• Noise in quantum computers
• Not sufficient data
16
17. Resources
• [1] A. Blance and M. Spannowsky, ‘Quantum machine learning for particle physics
using a variational quantum classifier’, J. High Energ. Phys., vol. 2021, no. 2, p.
212, Feb. 2021, doi: 10.1007/JHEP02(2021)212.
• [2] P. Rebentrost, M. Mohseni, and S. Lloyd, ‘Quantum Support Vector Machine
for Big Data Classification’, Phys. Rev. Lett., vol. 113, no. 13, p. 130503, Sep. 2014,
doi: 10.1103/PhysRevLett.113.130503.
• [3] ‘Classification of covariance matrices using a Riemannian-based kernel for BCI
applications’ Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian
Jutten
• [4] ‘Riemannian geometry for EEG-based brain-computer interfaces; a primer and
a review’ Marco Congedo, Alexandre Barachant, Rajendra Bhatia
• [5] ‘Supervised learning with quantum enhanced feature spaces’ Vojtech
Havlicek1 , ∗ Antonio D. C´orcoles1 , Kristan Temme1 , Aram W. Harrow2 ,
Abhinav Kandala1 , Jerry M. Chow1 , and Jay M. Gambetta1
17
Literature on quantum computing suggests it may offer an advantage as compared with classical computing in terms of computational time and outcomes, such as for pattern recognition or when using limited training sets.
pyRieamann-qiskit combines the 3 projects above,so that we can easily apply Quantum computing on EP EEG signals.
PyRiemann provides state of the art classification of ERP signals.
K-means is unsupervised in general, but here in MDM we use labeled data to build the two clusters for the classes: with or without P300 ERP.
We need to properly define the distance and mean we are going to use. “distance” as defined using Riemannian Geometry.
Inherited from the geometric distance, the geometric mean is an appropriate descriptor of the central tendency (expected value) for the variance, while, inherited from the Euclidean distance, the arithmetic mean is not.
From each P300 epoch we calculate a “covariance matrix”. We want to preserve the spatial information in the covariance matrix. Usually the covariance matrix is vectorized (all rows of the matrix are put one after another) to form a single input vector to be used by any classifier, but here this is avoided. The spatial covariance matrix Cp is calculated using the single trail Xp (channels x samples): Cp = 1 / (T-1) * Xp * Xp(transposed).
Cp is symmetric by definition
With sufficient data Cp becomes positive definitive, otherwise it must be regularized (there are known algorithms for that)
Next the space of SPDs creates a manifold (based on the training data)
This manifold becomes Riemannian manifold M by adopting an Affine Invariant Metric
Now we can apply Riemannian Geometry for M
Each Cp can be represented in M
For each point on the manifold M a tangent space T can be defined
We need to select a reference point C as to where to construct the tangent space T. Usually this reference point is the Geometric Mean. The Geometric Mean might not be the best reference point, but usually is a very good choice. The Geometric mean is calculated iteratively
Each point on the manifold (each covariance matrix) can be projected on T using log mapping (and back using exponential mapping). A kind of optimization is C to become the identity matrix and to use parallel transport (parallel translation)
So the Riemannian distance in M is well approximated by Euclidean distance in T – so T (which is 2 dimensional) acts as a very good approxomation of M
Any classifier can be used in T
By using PyRiemann we get a good a feature vector that can be used for Classical or Quantum Classification.
We are in the exploration phase of Quantum computers. But the fact that anyone can access a real quantum computer is impressive and will help progress the domain.
Qiskit framework can be used on two levels: at a low level for Quantum models (as shown on the next slide) or at a higher level where a Quantum classifier is already provided (and we can focus on the hyper parameters).
Qiskit has 3 ML algorithms: QSVM (Quantum-enhanced Support Vector Machine) and VQC (Variational Quantum Classifier) and QGAN (Quantum Generative Adversarial Network).
With Qiskit we are doing Quantum Programming.
Qiskit compiles an algorithm to a quantum circuit.
Quantum bits can be manipulated in ways that can only be described by quantum physics.
Each quantum bit can seen as a “quantum register”. Classical bits form “classical register”.
Quantum circuit is a combination of operations on these registers. Here the quantum circuit is comprised from the quantum bits q0, q1, q2 and a normal bit c1.
Quantum operations include quantum gates, such as the Hadamard gate, as well as operations that are not quantum gates, such as the measurement operation.
From the Web interface you can generate either Qiskit python code or QASM (Open Quantum Assembly Language).
Qubits are always initialized to give the output 0.
Quantum operations:
https://qiskit.org/textbook/ch-states/atoms-computation.html
H – is a Hadamard gate
+ - is a CX (CNOT) gate on control qubit 0 and target qubit 1 (a classical XOR gate). It looks at its two input bits to see whether they are the same or different. Next, it overwrites the target qubit with the answer. The target becomes 0 if they are the same, and 1 if they are different. Another way of explaining the CNOT is to say that it does a NOT on the target if the control (the first quantum bit) is 1, and does nothing otherwise.
X – is a NOT gate (circle with + inside, exactly after Hadamard gate)
Textbook on Qiskit: https://qiskit.org/textbook/preface.html
Going to the higher dimensional space called “Feature Space” is done using a “kernel”. The job of the kernel is to transform linearly inseparable data to linearly separable space.
A Quantum Feature Map V(Φ(𝑥⃗)) converts classical data to quantum data. It maps to quantum Hilbert space with specific property of the dot product. The reason of choosing a QFM is to get the quantum advantage.
Based on the QFM we construct a quantum kernel (during the training phase).
The QFM is modeled using a quantum circuit and quantum operations as shown in the previous slide.
A different QFM will produce a different kernel. The default Quantum Feature Map (QFM) is ZZFeatureMap: https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html
Links and extras:
The Quantum Kernel python class in Qiskit is: qiskit_machine_learning.kernels.quantum_kernel
Check also on IBM web-site: qiskit-tutorials/qiskit-machine-learning/03_quantum_kernel.ipynb
QSVC code: https://github.com/Qiskit/qiskit-machine-learning/blob/1f01764186ad5e6fd4b88deb4dfec1e5cbb05d03/qiskit_machine_learning/algorithms/classifiers/qsvc.py
Article Quantum Feature Map: https://shubham-agnihotri.medium.com/quantum-machine-learning-102-qsvm-using-qiskit-731956231a54
The algorithm starts on your laptop, then the model representing the kernel and the data are sent to a real quantum computer. The result is a kernel that is compatible with Scikitlearn’s implementation of Support Vector Machines.
The following pipeline “Xdawn spatial filter, Riemannian Geometry, Tangent Space, PCA” is used to generate the same feature vectors before applying a simple LDA or QSVM.
One subject can have several recorded sessions playing the BCI game Brain Invaders.
“Within-session” evaluation uses k-fold cross-validation to determine train and test sets for each separate session for each subject http://moabb.neurotechx.com/docs/generated/moabb.evaluations.WithinSessionEvaluation.html
Evaluation of the two pipelines.
Both finish in 5 minutes on a regular laptop.
Training is slow because many users are “fighting” for the same “quantum computer”. Building the Quantum Feature Map is done by performing many “Quantum jobs” (a unit of work executed on a Quantum computer).
“Shots” – the number of times the circuit is executed. More shots gives more confidence in the result, but it also takes much longer to process.
The size of the generated feature vector (based on RG + Tangent Space) is limited to the number of available quantum bits.
A real quantum computer should perform less accurately than an emulated one. Although an emulated quantum computer can include emulation of quantum noise.
Adding more data (P300 ERP epochs) means more quantum jobs and more time to complete. But limiting the input data however means less training of the QSVM.
[1,2] Advantages of Quantum Computing
[3.4] Classification with the state of art method using Riemannian geometry
[5] Quantum Support Vector Classifier