Discovery Bus: UK QSAR meeting at GSK
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The document discusses an automated system called the Discovery Bus that can derive quantitative structure-activity relationship (QSAR) models independently without user input. It explores many possible model combinations and applies various machine learning techniques and feature selection filters to build over 1,500 models. It is designed to automatically apply new data and methods. The system demonstrates models generated by the Discovery Bus that perform as well as those made by experts. Future work aims to expand the system with more data, descriptors, learning methods and other enhancements.
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Sustainable research progress in many scientific disciplines critically depends on the existence of robust specialized databases that integrate and structure all available experimental information in the respective fields. Over years a multitude of chemical formats and approaches were created to address various aspects of handling chemical information and building databases of chemical knowledge. Additional to that inconsistencies in data formatting by individual labs leads to the need to invest significant resources in data curation and interpretation by the technical staff involved in the maintenance of the centralized data collection resource. Acquisition of data from public sources is inefficient, time consuming and limited in scope. The NIH has recently posted its intention to financially support data deposition by investigators through the ‘data sharing plan' for each funded proposal. However, this plan also points to a current weakness of the centralized data sharing and acquisition as all laboratories use different data collection and formatting approaches. It would be far more efficient and useful if there were a standardized data collection and deposition template with standard key terms that could be modified to add new or important additional data or parameters for each investigator. These new features could be ultimately adopted in the classification scheme and guide the scope of the expanding database. This approach would be a win-win as it would enable structure for the investigators laboratory, consistency in data reporting and a means of transmitting data to the database in parallel to publication to eliminate the acquisition step from the process. In this talk we will outline our experience building Open Data Science Platform, a federated database system for direct acquisition, curation and management of research data with integrated Machine Learning capabilities.
Prediction of pKa from chemical structure using free and open source tools
Prediction of pKa from chemical structure using free and open source toolsUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excretion and toxicity and thus pKa is a fundamental chemical property and is used in many models of chemical toxicity.
Experimentally determining pKa is not feasible for high-throughput assays. Predicting pKa is challenging and existing models have been developed only using restricted chemical space (e.g., anilines, phenols, benzoic acids, primary amines) and lack of a generalized model impedes ADME modeling.
No free and open source models exist for heterogeneous chemical classes, however, several proprietary programs exist. In this work, pKa open data bundled with DataWarrior (http://www.openmolecules.org/) were used to develop predictive models for pKa. After data cleaning, there were ~3100 and ~3900 monoprotic chemicals with an acidic or basic pKa, respectively. 1D and 2D chemical descriptors (AlogP, Topological polar surface area, etc) in addition to 12 fingerprints (presence or absence of a chemical group) were generated using PaDEL software. Three datasets were used: acidic, basic and acidic and basic combined.
13 datasets were examined, the 1D/2D descriptors and 12 fingerprints. Using the Extreme Gradient Boosting algorithm showed that the MACCS and Substructure Count fingerprints yielded the best results, with models showing an R-Squared of ~0.78 and a RMSE of 1.42.
Recently, Deep Learning models have showed remarkable progress in image recognition and natural language processing. To determine if the Deep Learning algorithms would increase model performance we examined the datasets and found that the Deep Learning models were somewhat superior than Extreme Gradient Boosting with an R-Squared of ~0.80 and an RMSE of ~1.38.
This work does not reflect U.S. EPA policy.
Forecast of long term wind speed based on optimized support vector regression...
Forecast of long term wind speed based on optimized support vector regression...Aboul Ella Hassanien
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo UniversityAlgorithm Selection for Preferred Extensions Enumeration
The document discusses algorithms for enumerating preferred extensions in abstract argumentation frameworks. It compares the performance of four algorithms: AspartixM, NAD-Alg, PrefSAT, and SCC-P. It finds that algorithm selection based on graph features can accurately predict runtime, with up to 80% accuracy in classification, and improves performance over a single best solver by 2-3 times. Key discriminating features include density, number of arguments, number of strongly connected components, and features related to computing graph properties.
Translating data to predictive models
Biological, chemical and physical properties of molecules are encoded in their molecular structure. The challenge lies in discovering the relationships between the molecular graphs and the measured activity. Where data is measured, collected and curated for a series of compounds there is an opportunity to find the hidden relationships.
Chemical structures come in various shapes and sizes, depending on the scientists or even algorithms that create them. Though variability may sometimes seem subtle to a trained chemist’s eyes, these can introduce inconsistencies that impair chemical search algorithms or model building. Structure normalization is a key component of any cheminformatics workflow with an often underestimated significance. Finding relationships between chemical structures and their measured properties primarily relies on the representation of the chemical matter. Variability of the calculated features and descriptors for these representations can influence data analysis and accuracy of the predictions. During the first part of the presentation we will present the effect of chemical normalization on investigating correlations and building predictive models.
The second part of the talk will incorporate the results of model building on 163 ChEMBL targets extracted from the bioactivity benchmark set1. Results with different descriptor generation methods including ECFP fingerprints, MACCS key, structural properties, geometry properties and phy-chem properties will be discussed in detail. This part focuses on summarizing the results of more than 3000 Random Forest models.
Finally model development for ADMET targets will be highlighted including hERG cardiotoxicity prediction, permeability and blood brain barrier penetration. We will describe how these models can be built, analyzed, optimized and deployed using our new machine learning platform.
Automated parameter optimization should be included in future
defect predict...
Automated parameter optimization should be included in future
defect predict...Chakkrit (Kla) Tantithamthavorn
Automated parameter optimization techniques like Caret can substantially improve the performance of defect prediction models over using default parameter settings. When applied to 18 datasets using 26 classification techniques, Caret optimized models improved average AUC performance by up to 40 percentage points for some techniques. Caret optimized models also tended to be more stable than default models, with the stability ratio being lower than 1 for 35% of techniques studied. Overall, automated parameter optimization can significantly enhance both the performance and stability of defect prediction models.Svd filtered temporal usage clustering
This document discusses using SVD (singular value decomposition) as a filtering technique prior to clustering temporal usage data. It describes applying SVD to filter out noise and high dimensionality before performing k-means clustering. SVD is used to decompose the data matrix and filter out components associated with the smallest singular values. Then k-means clustering is applied to the correlation between observations and the remaining right eigenvectors. This approach provides a robust way to cluster high-dimensional temporal data and identify distinct customer usage patterns over time.
Machine learning methods for chemical properties and toxicity based endpoints
In the last decade there is an increasing interest in using in silico tools for potential risk assessment of newly released chemicals due to the large number of chemicals enter the market yearly and the big uncertainty on their possible hazardous effects. Different tools and methods based on machine learning techniques already exist and were used in a wide range of applications starting from quantitative structure-property relationships and expanding into predictive toxicology. There is a lot of historical data accumulated across multiple databases which is publicly available and can be used with novel machine learning methods. Unfortunately, due to different datasets, metrics and validation strategies, the significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. This work is an attempt to develop a multitask system which can serve as searchable curated collections of multiple chemical datasets and ready to use novel machine learning methods solely built using open source frameworks and libraries. We have implemented a set of self-tuned, using grid search and k-fold validation, traditional machine learning methods (shallow methods) such as Naïve Bayes, k-Nearest Neighbors, Random Forest, Boosted Decision Trees, Regularized Logistic Regression, and Support Vector Machines base on open source Scikitlearn (http://scikit-learn.org/stable/). The novel Deep Neural Networks models of different complexity have been also implemented using Keras (https://keras.io/), a deep learning open library, and a Tensorflow (www.tensorflow.org) as a backend. The machine learning models were trained and evaluated to predict measures of toxicity from the physical characteristics of the structure of chemicals using the same datasets as in the Toxicity Estimation Software Tool (https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test). The Deep Learning models showed very good performance evaluation characteristics and were found to be useful in predicting of toxicological and physicochemical parameter endpoints. The results of this work support an optimistic view that some of current obstacles in cheminformatics can be overcome by using Deep Learning methods.
Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery
This document presents a system for human detection in UAV imagery using HOG descriptors calculated for superpixels extracted via SLIC clustering. The system extracts superpixels, calculates HOG descriptors for each, and classifies the descriptors using an AdaBoost classifier. Evaluation on two datasets shows the superpixel-based approach achieves 3% higher accuracy than the original HOG method while requiring more computational time for training.
Similar to Discovery Bus: UK QSAR meeting at GSK (20)
Unsupervised selection of mother wavelets and parameter optimization
Unsupervised selection of mother wavelets and parameter optimization
The caret Package: A Unified Interface for Predictive Models
The caret Package: A Unified Interface for Predictive Models
OPERA: A free and open source QSAR tool for predicting physicochemical proper...
OPERA: A free and open source QSAR tool for predicting physicochemical proper...
Lab 2: Classification and Regression Prediction Models, training and testing ...
Lab 2: Classification and Regression Prediction Models, training and testing ...
Introduction to Genetic algorithm and its significance in VLSI design and aut...
Introduction to Genetic algorithm and its significance in VLSI design and aut...
Blanka Láng, László Kovács and László Mohácsi: Linear regression model select...
Blanka Láng, László Kovács and László Mohácsi: Linear regression model select...
Prediction of pKa from chemical structure using free and open source tools
Prediction of pKa from chemical structure using free and open source tools
Forecast of long term wind speed based on optimized support vector regression...
Forecast of long term wind speed based on optimized support vector regression...
Algorithm Selection for Preferred Extensions Enumeration
Algorithm Selection for Preferred Extensions Enumeration
Automated parameter optimization should be included in future
defect predict...
Automated parameter optimization should be included in future
defect predict...
Machine learning methods for chemical properties and toxicity based endpoints
Machine learning methods for chemical properties and toxicity based endpoints
Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery
Using HOG Descriptors on Superpixels for Human Detection of UAV Imagery
More from David Leahy
AI is the Future of Drug Discovery
Artificial intelligence (AI) has the potential to transform drug discovery through the use of semantic networks to represent biomedical knowledge as facts and infer new insights, probabilistic rules to evaluate beliefs about candidate drugs, and optimization and simulation techniques to iteratively improve outcomes. By continuously learning from diverse sources of data, AI systems could automate and enhance the processes of target identification, compound screening and testing in ways that accelerate research and development compared to traditional analytical tools.
Most Drug Discovery Scientists could be replaced by Software Systems
The document discusses how drug discovery scientists could potentially be replaced by software systems in the future. It argues that drug discovery has become a mature field with established methodologies and best practices. It is presented as a multi-objective optimization problem that considers many potential drug targets, compounds, and goals. The document proposes that human understanding is no longer essential in drug discovery and that systems could select which compound to synthesize next through computational models and rules. It then provides examples of how expert strategies, workflows, and a "panel of experts" approach could be modeled computationally through packages, rules, and optimization engines to enable more automated "declarative drug design".
From Hammett to the Semantic Web
The document discusses several topics related to quantitative structure-activity relationship (QSAR) modeling including:
1) A chemical reaction converting pyrylium to pyridinium salt.
2) Hansch analysis, which is not a linear free energy relationship, and includes terms for various molecular properties.
3) A diagram depicting the workflow for QSAR modeling which includes steps for calculating descriptors, filtering features, building models, and making predictions.
4) The concept of a "Meta QSAR" system for automating decision making in QSAR modeling through various software agents.
InkSpot Science presentation at Open Science Meeting
InkSpot Science is an on-demand computing platform that provides scientists with hosting, content management, data visualization, and collaboration tools as an alternative to desktop applications. It offers secure hosting of various data types including tables, graphs, equations, blogs and wikis. Users can design workflows, access specialized services for tasks like drug discovery, and take advantage of social networking features to share information.
PBPK simulation as an alternative to animal testing
Generic physiologically based pharmacokinetic (PBPK) models for rats and humans were developed using in vitro absorption, distribution, metabolism, and excretion (ADME) data. The models accurately predicted plasma concentrations of drugs in independent test sets, performing equally to or better than traditional allometric scaling methods. However, PBPK models have advantages over scaling in accounting for differences in drug chemistry, metabolism, and administration routes between species.
Forager Poster
Forager is a multi-objective reverse QSAR search agent that was developed by researchers from Newcastle University and the Research Centre for Cheminformatics in Belgrade. The agent uses multiple objectives to search chemical structure databases for compounds with desired properties based on quantitative structure-activity relationship models. Forager allows for the balancing of multiple desired properties during the reverse search.
Colonist
Genetic algorithms use genetic operators like mutation and crossover to evolve molecules. The researchers developed new programming concepts to apply genetic operators to reduced molecule representations to enable powerful molecular evolution. They introduced genetic operators that produce variation in reduced representations as a fundamental process for molecular evolution.
More from David Leahy (7)
Most Drug Discovery Scientists could be replaced by Software Systems
Most Drug Discovery Scientists could be replaced by Software Systems
InkSpot Science presentation at Open Science Meeting
InkSpot Science presentation at Open Science Meeting
PBPK simulation as an alternative to animal testing
PBPK simulation as an alternative to animal testing
Recently uploaded
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Programming Foundation Models with DSPy - Meetup Slides
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.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
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Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
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 integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Azure API Management to expose backend services securely
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Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
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
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Dandelion Hashtable: beyond billion requests per second on a commodity server
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Programming Foundation Models with DSPy - Meetup Slides
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Nordic Marketo Engage User Group_June 13_ 2024.pptx
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Discovery Bus: UK QSAR meeting at GSK
- 1. Automated QSAR Modelling David E Leahy Newcastle University, UK & Damjan Krstajic Research Centre for Cheminformatics, Serbia
- 4. Chemical structure & response data Transform response 1/X logX X class Split and stratify ? Calculate descriptors D E H L R A Combine descriptors Filter features A&D A&L L&H&R A&E E&D A&D&R ... no filter cfs1 cfs2 cfs4 cfs5 cfs3 Cross validate Build models Test model Rnnet Rrpart Rlin Rpls GARMLR NetlabNN GUIDE GAWRMLR 4 x 8 x 6 x 8 = 1536 models ?&? ? new ff New method? 4 x 8 = 32 filter feature requests 32 filter feature requests x 8 = 256 models 10%
- 5. Solubility
- 6. Solubility Results Learner Filter Reduction Types Linear Fit Training (1167) Test (130) Filter Learner Rel.MSE r 2, Rel.MSE r 2, GUIDE H 1990 -> 558 -> 54 R,D 1.46 0.11 0.89 0.12 0.89 H 170 -> 26 -> 14 A,E,H,D 0.13 0.11 0.89 0.13 0.88 H 80 -> 16 -> 12 A,H,D 0.14 0.11 0.88 0.12 0.87 C 250 -> 2 -> 2 A,R 0.18 0.13 0.87 0.16 0.84 C 8 -> 2 -> 2 A,L 0.16 0.13 0.87 0.16 0.86 GA1 H 80 -> 16 -> 16 A,H,D 0.14 0.14 0.86 0.18 0.83 C 8 -> 2 -> 2 A,L 0.16 0.17 0.84 0.17 0.83 NN1 H 250 -> 54 -> 54 A,R 0.12 0.09 0.91 0.08 0.92 H 80 -> 16 -> 16 A,H,D 0.14 0.10 0.90 0.12 0.88 H 326 -> 46 -> 46 H,R,D 0.18 0.10 0.90 0.12 0.89
- 7. HSA Binding
- 8. HSA Binding Learner Filter Reduction Types Linear Fit Training (82) Test (9) Filter Learner Rel.MSE r 2 Rel.MSE r 2 Guide Hh2 332 -> 39 -> 8 A,E,R 0.92 0.40 0.62 0.25 0.81 H 250 -> 59 -> 12 A,R 1.62 0.47 0.56 0.30 0.76 Hh4 382 -> 20 -> 1 A 0.25 0.50 0.50 0.57 0.49 GA1 Hh2 1998 -> 39 -> 26 A,R,D 0.42 0.23 0.77 0.20 0.85 Hh4 344 -> 20 -> 19 H,R,D 0.42 0.26 0.74 0.28 0.78 Hh10 302 -> 9 -> 9 H,R 0.27 0.27 0.73 0.40 0.64 NN1 H 8 -> 5 -> 5 A,L 0.37 0.17 0.83 0.15 0.87 Hh10 346 -> 8 -> 8 A,R,D 0.30 0.30 0.70 0.16 0.84 H 302 -> 19 -> 19 H,R 0.27 0.32 0.70 0.39 0.71
- 9. P-Glycoprotein Technique % Correctly Classified Training Set % Correctly Classified Test Set Neural Net Classifier 95.6 69.7 R Part 90.4 81.0
- 15. Forager Optimisation Thanks to Tudor Oprea for a copy of Wombat
- 16. Colonist
- 17. Acknowledgements