Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
論文紹介:Transferable Decoding with Visual Entities for Zero-Shot Image CaptioningToru Tamaki
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
Junjie Fei, Teng Wang, Jinrui Zhang, Zhenyu He, Chengjie Wang, Feng Zheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3136-3146
https://openaccess.thecvf.com/content/ICCV2023/html/Fei_Transferable_Decoding_with_Visual_Entities_for_Zero-Shot_Image_Captioning_ICCV_2023_paper.html
【DL輪読会】Pervasive Label Errors in Test Sets Destabilize Machine Learning Bench...Deep Learning JP
The document summarizes a research paper that investigated the impact of label errors in test sets on machine learning benchmarks. Some key points:
1. The researchers estimated label error rates in 10 popular datasets, finding an average of 3.6% errors. They corrected labels in CIFAR10 and ImageNet.
2. Experiments showed that while test label errors did not change the relative performance of models on the full test set, models could achieve higher accuracy on the errors compared to corrected labels.
3. This suggests models may overfit to common label errors in both train and test sets, undermining the reliability of benchmarks in selecting the best model. The importance of ensuring test label accuracy is emphasized.
論文紹介:Transferable Decoding with Visual Entities for Zero-Shot Image CaptioningToru Tamaki
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
Junjie Fei, Teng Wang, Jinrui Zhang, Zhenyu He, Chengjie Wang, Feng Zheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3136-3146
https://openaccess.thecvf.com/content/ICCV2023/html/Fei_Transferable_Decoding_with_Visual_Entities_for_Zero-Shot_Image_Captioning_ICCV_2023_paper.html
【DL輪読会】Pervasive Label Errors in Test Sets Destabilize Machine Learning Bench...Deep Learning JP
The document summarizes a research paper that investigated the impact of label errors in test sets on machine learning benchmarks. Some key points:
1. The researchers estimated label error rates in 10 popular datasets, finding an average of 3.6% errors. They corrected labels in CIFAR10 and ImageNet.
2. Experiments showed that while test label errors did not change the relative performance of models on the full test set, models could achieve higher accuracy on the errors compared to corrected labels.
3. This suggests models may overfit to common label errors in both train and test sets, undermining the reliability of benchmarks in selecting the best model. The importance of ensuring test label accuracy is emphasized.
The document summarizes the LabPQR color space model proposed by researchers at Rochester Institute of Technology. The model uses a transformation from tristimulus values and a set of basis vectors derived from principal component analysis to represent color spectra in a lower dimensional space. This representation allows spectral data to be compressed while maintaining accuracy for applications like multi-spectral color reproduction. The model builds on prior work using matrix algebra to decompose color stimuli into fundamental and residue components.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
文献紹介:X3D: Expanding Architectures for Efficient Video RecognitionToru Tamaki
Christoph Feichtenhofer; X3D: Expanding Architectures for Efficient Video Recognition , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 203-213
https://openaccess.thecvf.com/content_CVPR_2020/html/Feichtenhofer_X3D_Expanding_Architectures_for_Efficient_Video_Recognition_CVPR_2020_paper.html
This document provides an overview of MOS transistors and CMOS technology. It discusses the basic MOSFET structure and its IV characteristics. It introduces surface mobility and how it affects transistor current. Higher mobility is desirable for faster circuit speeds. The document also covers CMOS technology, including how NMOS and PMOS transistors combine in complementary fashion to enable low-power logic circuits. It provides examples of a CMOS inverter structure and layout. Key innovations in MOSFET fabrication processes are also summarized.
Tissue engineering aims to augment, improve, treat or replace tissues using cells, biomaterials, and physiochemical factors. It requires appropriate cells, a scaffold for structure, and growth factors. Recent examples include artificial skin, muscle for meat production, implanted bladders, and cartilage for knee repair. Scaffolds must allow cell attachment, migration, and diffusion while providing mechanical support and inducing physiological changes in seeded cells. Bone contains osteoblasts that form bone and osteoclasts that resorb it. Its extracellular matrix includes hydroxyapatite and collagen fibers. Potential artificial bone materials include metals, ceramics, polymers, and coral hydroxyapatite. Osteoconduction provides a scaffold for bone formation, while osteo
How useful is self-supervised pretraining for Visual tasks?Seunghyun Hwang
Review : How useful is self-supervised pretraining for Visual tasks?
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
The document summarizes the LabPQR color space model proposed by researchers at Rochester Institute of Technology. The model uses a transformation from tristimulus values and a set of basis vectors derived from principal component analysis to represent color spectra in a lower dimensional space. This representation allows spectral data to be compressed while maintaining accuracy for applications like multi-spectral color reproduction. The model builds on prior work using matrix algebra to decompose color stimuli into fundamental and residue components.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
文献紹介:X3D: Expanding Architectures for Efficient Video RecognitionToru Tamaki
Christoph Feichtenhofer; X3D: Expanding Architectures for Efficient Video Recognition , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 203-213
https://openaccess.thecvf.com/content_CVPR_2020/html/Feichtenhofer_X3D_Expanding_Architectures_for_Efficient_Video_Recognition_CVPR_2020_paper.html
This document provides an overview of MOS transistors and CMOS technology. It discusses the basic MOSFET structure and its IV characteristics. It introduces surface mobility and how it affects transistor current. Higher mobility is desirable for faster circuit speeds. The document also covers CMOS technology, including how NMOS and PMOS transistors combine in complementary fashion to enable low-power logic circuits. It provides examples of a CMOS inverter structure and layout. Key innovations in MOSFET fabrication processes are also summarized.
Tissue engineering aims to augment, improve, treat or replace tissues using cells, biomaterials, and physiochemical factors. It requires appropriate cells, a scaffold for structure, and growth factors. Recent examples include artificial skin, muscle for meat production, implanted bladders, and cartilage for knee repair. Scaffolds must allow cell attachment, migration, and diffusion while providing mechanical support and inducing physiological changes in seeded cells. Bone contains osteoblasts that form bone and osteoclasts that resorb it. Its extracellular matrix includes hydroxyapatite and collagen fibers. Potential artificial bone materials include metals, ceramics, polymers, and coral hydroxyapatite. Osteoconduction provides a scaffold for bone formation, while osteo
How useful is self-supervised pretraining for Visual tasks?Seunghyun Hwang
Review : How useful is self-supervised pretraining for Visual tasks?
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stoch...Seunghyun Hwang
FickleNet is a method for weakly and semi-supervised semantic image segmentation that generates multiple localization maps from a single image using random combinations of hidden units. It aggregates these maps to discover relationships between object locations. This allows it to expand activated regions beyond just discriminative parts. Experiments on PASCAL VOC 2012 show it achieves state-of-the-art performance in both weakly and semi-supervised settings. Key techniques include feature map expansion for efficient inference and center-preserving dropout to relate kernel centers to other locations.
May 2015 talk to SW Data Meetup by Professor Hendrik Blockeel from KU Leuven & Leiden University.
With increasing amounts of ever more complex forms of digital data becoming available, the methods for analyzing these data have also become more diverse and sophisticated. With this comes an increased risk of incorrect use of these methods, and a greater burden on the user to be knowledgeable about their assumptions. In addition, the user needs to know about a wide variety of methods to be able to apply the most suitable one to a particular problem. This combination of broad and deep knowledge is not sustainable.
The idea behind declarative data analysis is that the burden of choosing the right statistical methodology for answering a research question should no longer lie with the user, but with the system. The user should be able to simply describe the problem, formulate a question, and let the system take it from there. To achieve this, we need to find answers to questions such as: what languages are suitable for formulating these questions, and what execution mechanisms can we develop for them? In this talk, I will discuss recent and ongoing research in this direction. The talk will touch upon query languages for data mining and for statistical inference, declarative modeling for data mining, meta-learning, and constraint-based data mining. What connects these research threads is that they all strive to put intelligence about data analysis into the system, instead of assuming it resides in the user.
Hendrik Blockeel is a professor of computer science at KU Leuven, Belgium, and part-time associate professor at Leiden University, The Netherlands. His research interests lie mostly in machine learning and data mining. He has made a variety of research contributions in these fields, including work on decision tree learning, inductive logic programming, predictive clustering, probabilistic-logical models, inductive databases, constraint-based data mining, and declarative data analysis. He is an action editor for Machine Learning and serves on the editorial board of several other journals. He has chaired or organized multiple conferences, workshops, and summer schools, including ILP, ECMLPKDD, IDA and ACAI, and he has been vice-chair, area chair, or senior PC member for ECAI, IJCAI, ICML, KDD, ICDM. He was a member of the board of the European Coordinating Committee for Artificial Intelligence from 2004 to 2010, and currently serves as publications chair for the ECMLPKDD steering committee.
Your Classifier is Secretly an Energy based model and you should treat it lik...Seunghyun Hwang
Review : Your Classifier is Secretly an Energy based model and you should treat it like one
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Deep Generative model-based quality control for cardiac MRI segmentation Seunghyun Hwang
Review : Deep Generative model-based quality control for cardiac MRI segmentation
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
The document summarizes Md Abul Hayat's research on image segmentation using deep neural networks. It discusses using various CNN architectures like autoencoders, fully convolutional networks, U-Net, ResNet, and DenseNet for segmenting OCT images of skin. It presents experimental results comparing the DCU-Net and U-Net models on fingertip and palm image datasets, finding that DCU-Net achieved better performance for segmentation and potential for transfer learning across datasets. Future work could include training on larger datasets, accounting for temporal variations, generalizing to other body parts, using 3D models, and collecting more annotations.
This document proposes ResNeSt, a split-attention network that divides feature maps into groups and applies attention mechanisms across groups. It outperforms ResNet variants on image classification, object detection, semantic segmentation, and instance segmentation while maintaining the same computational efficiency. The paper introduces ResNeSt's split attention block, training strategies including large batches, data augmentation, and regularization methods. Evaluation shows ResNeSt achieves state-of-the-art accuracy on ImageNet and downstream tasks using less computation than NAS models.
MEME – An Integrated Tool For Advanced Computational ExperimentsGIScRG
The document describes MEME, an integrated tool for advanced computational experiments. MEME allows users to efficiently explore model responses through parameter sweeps and design of experiments. It supports running simulations in parallel on local clusters and grids. MEME collects, analyzes, and visualizes results. It implements intelligent "IntelliSweep" methods like iterative uniform interpolation and genetic algorithms to refine parameter space exploration.
Iwsm2014 cosmic approximate sizing using a fuzzy logic approach (alain abran)Nesma
This document describes a case study comparing two approaches to approximate sizing of software projects using the COSMIC functional size measurement (FSM) method: the Equal Size Bands approach and a fuzzy logic model called EPCU. Participants assigned size estimates for use cases of an example system using both approaches. The fuzzy logic model produced more accurate estimates, with mean errors 45% versus 63% for the bands approach. The study suggests fuzzy logic may be preferable for early sizing when information is limited but more research is needed with varied case studies and participants.
Prototype-based classifiers and their applications in the life sciencesUniversity of Groningen
This document contains frequently asked questions about LVQ (Learning Vector Quantization) and relevance learning techniques. It discusses issues like overfitting, determining good distance measures, and the uniqueness of relevance matrices. It also provides examples of applying LVQ and generalized matrix LVQ to classify adrenal tumors using urinary steroid profiles, achieving good performance with an adaptive distance measure parameterized by a relevance matrix.
Learning Sparse Networks using Targeted DropoutSeunghyun Hwang
Targeted dropout is a technique that applies dropout primarily to network units and weights that are believed to be less useful based on their magnitudes. This makes networks robust to post-hoc pruning while achieving high sparsity. Experiments on ResNet, Wide ResNet and Transformer models on image and text tasks achieved up to 99% sparsity with less than 4% accuracy drop. Scheduling the targeting proportion and dropout rates over time was found to improve results compared to random pruning before training. Targeted dropout is an effective regularization method for training networks that can be heavily pruned after training.
This document provides an introduction to simulation and modeling. It discusses how simulation can be used to study systems that cannot be easily experimented on in reality. Simulation involves developing a model of a system's behavior and performing experiments on the model. The document outlines when simulation is an appropriate approach and its advantages and disadvantages. It also presents examples of different types of systems that can be modeled and simulated, including stores, networks, and mobility models. Discrete event simulation is introduced as a method for simulating discrete systems.
The document discusses different techniques for cross-validation in machine learning. It defines cross-validation as a technique for validating model efficiency by training on a subset of data and testing on an unseen subset. It then describes various cross-validation methods like hold out validation, k-fold cross-validation, leave one out cross-validation, and their implementation in scikit-learn.
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
The document discusses network design and training issues for artificial neural networks. It covers architecture of the network including number of layers and nodes, learning rules, and ensuring optimal training. It also discusses data preparation including consolidation, selection, preprocessing, transformation and encoding of data before training the network.
An annotation sparsification strategy for 3D medical image segmentation via r...Seunghyun Hwang
Review : An annotation sparsification strategy for 3D medical image segmentation via representative selection and self-training (University of Notre Dame , AAAI 2020)
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Do wide and deep networks learn the same things? Uncovering how neural networ...Seunghyun Hwang
Review : Do wide and deep networks learn the same things? Uncovering how neural network representations vary with width and depth (Google Research, arxiv preprint)
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Deep Learning-based Fully Automated Detection and Quantification of Acute Inf...Seunghyun Hwang
Presented work is accepted at RSNA 2020, Scientific Section.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Diagnosis of Maxillary Sinusitis in Water’s view based on Deep learning model Seunghyun Hwang
Presented work is accepted at Korean domestic conference for Medical AI, Korean Society of Artificial Intelligence in Medicine (KOSAIM) 2020.
Special Thanks to Dongmin Choi, the first author and presenter of this work.
(Link to Dongmin Choi Bio: https://www.slideshare.net/DongminChoi6/)
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Energy-based Model for Out-of-Distribution Detection in Deep Medical Image Se...Seunghyun Hwang
Presented work is accepted in Korean domestic conference, Korean Society of Artificial Intelligence in Medicine (KOSAIM) 2020, as a poster session.
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Segmenting Medical MRI via Recurrent Decoding CellSeunghyun Hwang
Review : Segmenting Medical MRI via Recurrent Decoding Cell
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Progressive learning and Disentanglement of hierarchical representationsSeunghyun Hwang
Review : Progressive learning and Disentanglement of hierarchical representations
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
A Simple Framework for Contrastive Learning of Visual RepresentationsSeunghyun Hwang
Review : A Simple Framework for Contrastive Learning of Visual Representat
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Large Scale GAN Training for High Fidelity Natural Image SynthesisSeunghyun Hwang
Review : Large Scale GAN Training for High Fidelity Natural Image Synthesis
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 6
A Probabilistic U-Net for Segmentation of Ambiguous Images
1. A Probabilistic U-Net for Segmentation of
Ambiguous Images
Hwang seung hyun
Yonsei University Severance Hospital CCIDS
DeepMind, Division of Medical Image Computing, German
Cancer Research Center, Germany | NIPS 2018
2020.04.19
2. Introduction Related Work Methods and
Experiments
01 02 03
Conclusion
04
Yonsei Unversity Severance Hospital CCIDS
Contents
3. Probabilistic Unet
Introduction – Limitations of prior methods
• There exist ambiguities in segmentation task, especially in medical imaging applications
• A lesion might be clearly visible, but ground truth labels can vary depending on
radiologists.
• Most existing segmentation algorithms either provide only consistent hypothesis of a
pixel-wise probability(e.g. “each pixel is 50% cat, 50% dog)
• Pixel wise probabilities ignores all co-variances between the pixels.
• Existing methods are Ensemble Unet, dropout Unet, M heads model, etc.
Introduction / Related Work / Methods and Experiments / Conclusion
4. Probabilistic Unet
Introduction – Probabilistic Unet Architecture
• Probabilistic Unet provides multiple segmentation hypotheses for ambiguous images.
• Combines conditional variational auto encoder(CVAE), and U-Net
• First extract latent space and encodes the possible segmentation variants
• Random sample from the space is injected into the Unet to produce segmentation map.
Introduction / Related Work / Methods and Experiments / Conclusion
5. Probabilistic Unet
Introduction – Contributions
• Provides consistent segmentation maps instead of pixel-wise probabilities,
providing joint likelihood of modes.
• Able to learn calibrated probabilities of segmentation modes.
• Can produce diverse outputs for single image
Introduction / Related Work / Methods and Experiments / Conclusion
6. Related Work
CVAE (Conditional Variational Auto Encoder)
Introduction / Related Work / Methods and Experiments / Conclusion
• Encoder를 통해 도출된 latent coding Z를 가우시
안 분포로 나타내기 위해 분산과 평균을 이용함
• Label 정보를 추가로 넣어준다
8. Methods and Experiments
Network Architecture
Introduction / Related Work / Methods and Experiments / Conclusion
• Sampling Process • Training Process
9. Methods and Experiments
Sampling Process
Introduction / Related Work / Methods and Experiments / Conclusion
• Prior Net (Unet’s encoding phase + global average
pooling) produces Latent Space
• Each position in this space encodes a
segmentation variant
• Broadcast the sample to feature map with the
same shape as the segmentation map, and
concatenate this map to the las activation map of
U-Net
* P : prior probability distribution
* fcomb = three subsequent 1x1 convolutions
* S: segmentation map corresponding to point z in latent space
10. Methods and Experiments
Training Process
Introduction / Related Work / Methods and Experiments / Conclusion
• Introduce Posterior Net that learns to recognize a
useful segmentation variant
• Posterior Net and Prior Net are updated through the
standard training procedure for CVAE, by minimizing
variational lower bound
(Kullback-Leibler divergence)
• Cross-entropy loss penalizes differences between S
and Y
• KL loss pulls the posterior distribution and prior
distribution towards each other
• Eventually covers the space of all useful segmentation
variants for input image
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11. Methods and Experiments
Sampling Process
Introduction / Related Work / Methods and Experiments / Conclusion
Output Samples
Visualization of the Latent Space
12. Methods and Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
Performance Measures
• Generalized Energy Distance Matrix
• Not only compare deterministic prediction, but also compares
distributions of segmentations
* d: distance measure
* Y, Y’ : Independent samples from the ground truth distribution
* S, S’: independent samples from the predicted distribution
* d(x,y) = 1 - IOU(x,y)
14. Methods and Experiments
Introduction / Related Work / Methods and Experiments / Conclusion
Results
• Energy Distance decreases as more samples are drawn indicating an improved
matching of the GT distribution, as well as enhanced sample diversity.
15. Conclusion
Introduction / Related Work / Methods and Experiments / Conclusion
• Each sample produced by probabilistic Unet is consistent segmentation
result that closely match the multi-modal GT distributions
• Employed energy distance matrix measures whether the model’s
individual samples are both coherent, and whether they are produced
with expected frequencies.
• Can be used to assess annotations with model
• Probabilistic U Net can replace the currently applied deterministic U
Nets in large field of studies, especially in the medical domain
• Guide steps to resolve ambiguities