Présentation PowerPoint - Diapositive 1

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The document summarizes research on ranking the susceptibility of electrical feeders in the New York City power grid to failure using machine learning techniques. It finds that Rankboost and SVM performed comparably well in predicting failures over 7-day periods, while MartiRank performed slightly worse. All algorithms detected periods of concept drift where less historical data was optimal for prediction. Future work could integrate analysis of feeder subcomponents, better handle concept drift, and estimate time to failure.

Ranking Electrical Feeders of the New York CityPower Grid Phil Gross AnsafSalleb-AbouissiHaimontiDutta Albert Boulanger Center for Computational Learning Systems, Columbia University Experiment Compare the effectiveness of several machine learning techniques for generating feeder rankings ,[object Object]

This document discusses ontology-based top-k continuous query answering over streaming data from multiple heterogeneous sources. It aims to investigate how ontologies and top-k queries can improve continuous query processing by exploiting ordering. The research will analyze state of the art solutions, define an evaluation framework, and assess the effects on correctness and performance of techniques that integrate stream reasoning and top-k queries. Preliminary results include an extension of an RDF stream processor testbench and a case study on real-time social media analytics.

Uj 1

ujwalamishra908

The document summarizes a life cycle analysis of advanced fossil fuel power generation technologies that are being developed to meet stricter environmental standards and improve efficiency. The analysis developed a consistent methodology to assess the environmental performance of these new technologies across all phases of fossil energy production and use. It applied this methodology which included system scoping, emissions inventory assessment, and impact assessment to gasification-based power systems. The results provided a quantification of the environmental advantages of these advanced technologies compared to conventional technologies.

environmental scivis via dynamic and thematc mapping

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FDL 2018 Virtual Briefing 1

Leonard Silverberg

The document discusses the Frontier Development Lab (FDL) program, which applies artificial intelligence to challenges in space science. It provides examples of FDL's work over the past 3 years on challenges like planetary defense, space weather forecasting, and lunar mapping. FDL is seeking input to help define challenges for 2018 within areas like lunar resources, astrobiology, space weather, and near-Earth objects. Suggestions are requested for challenge questions, opportunities for AI, relevant data sources, potential mentors, researchers, and partners.

The GEM database of seismic hazard models

Global Earthquake Model Foundation

The document describes the GEM Foundation's efforts to create a centralized database of global seismic hazard models using common data formats and open-source software. This will allow models to be more easily compared, reproduced and inspected. It will also facilitate combining models and generating new data. Currently the database includes major models from regions around the world. Quality assurance testing has revealed some differences between models when reproduced, calling for further investigation.

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GreenLabAtDI

This document discusses detecting anomalous energy consumption in Android applications. It introduces the Android Power Tutor consumption model for modeling app energy usage. The talk will cover instrumenting app source code to collect energy data, classifying source code methods by their energy impact, and GreenDroid, an Android framework for energy profiling. Open research questions are discussed, such as when optimizations like faster code can be greener, how to adapt spectrum-based fault localization for energy issues, and green-aware software product lines.

BIG Terrain Data by Morten Revsbæk, Co-founder and CEO, SCALGO

Tata Consultancy Services

ChemSpider reactions – delivering a free community resource of chemical synth...

US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure

Presentation delivered by Colin Batchelor from the RSC eScience team at ACS New Orleans Spring Meeting April 2013. There are dozens of public compound databases now available online, some of these providing access to tens of millions of chemical compounds. However, very little effort has been put into the delivery of databases of chemical reactions with the majority of large resources being commercial in nature. In our five years of delivering chemical based data resources to the chemistry community one of the primary requests has been that chemists want to know how to synthesize many of the chemicals they are researching. This presentation will provide an overview of our concerted efforts to enhance access to freely available chemistry data and will discuss the ChemSpider Reactions as an integrating hub of content including data extracted from US patents, from RSC Journals and databases and from our micro-publishing platform ChemSpider Synthetic Pages (CSSP).

This document describes a meta-analysis study that assesses whether male or female farmers are more technically efficient. The study collected data on regression coefficients from previous empirical studies that examined the effect of gender on technical efficiency in farming. A meta-regression model was developed to analyze the gender coefficient data and identify factors that help explain differences in technical efficiency between males and females. The results are presented in tables showing the meta-regression coefficients and their statistical significance. The study aims to provide a clear answer on whether males or females are generally more technically efficient and insights for agricultural policymaking.

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This document describes Siddharth Chaudhary's MSc research project on forecasting solar electricity generation using time series models. The research aims to 1) forecast solar generation in Delhi and Jodhpur, India, 2) evaluate the performance of forecasting models, and 3) compare potential solar generation between the two cities. Four time series models - TBATS, ARIMA, simple exponential smoothing, and Holt's method - are applied to solar radiation data from each city and their accuracy is assessed.

Distributed Meta-Analysis System

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This document introduces an application architecture for a tool to facilitate distributed meta-analysis. The tool would create an intelligent database of empirical results from multiple studies that could then be statistically combined and compared using Bayesian methods. Users could find existing meta-analyses on topics, define new models, and perform meta-analyses to generate pooled estimates or hierarchical Bayesian estimates by merging multiple models. The goal is to make meta-analysis more efficient, less biased, and able to answer more targeted questions.

Open Risk Analysis Software - Data And Methodologies

Christakis Mina, PhD, ACIArb

- The document discusses open risk analysis software, data, and methodologies. It introduces the Alliance for Global Open Risk Analysis (AGORA), which aims to promote open-source risk analysis tools and collaboration. - AGORA includes universities and organizations that work to develop open-source risk software, share data and methodologies, and facilitate information exchange. - An example project described is MIRISK, which intends to create a user-friendly tool using open-source software to help decision-makers assess natural hazard risks and vulnerabilities.

jStanley: Placing a Green Thumb on Java Collections

Jácome Cunha

Software developers are more and more eager to understand their code’s energy performance. However, even with such knowledge it is di cult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption pro le of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically nds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley.

International Journal on Computational Science & Applications (IJCSA)

ijcsa

International Journal on Computational Science & Applications (IJCSA) is a Bi-monthly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Computational Science & Applications. Computational science is interdisciplinary fields in which mathematical models are combined with scientific computing methods to study of a wide range of problems in science and engineering. Modeling and simulation tools find increasing applications not only in fundamental research, but also in real-world design and industry applications.

International Journal on Computational Science & Applications (IJCSA)

ijcsa

Automated Software Enging, Fall 2015, NCSU

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This document discusses automated software engineering and the use of models in software engineering. It notes that models are now central tools in scientific research across many fields for simulating complex systems. Examples of areas where models are used include physics, biology, emergency response systems, border security, and analyzing stock market crashes. The document then discusses how optimization techniques like genetic algorithms and tabu search are used in automated software engineering to help find near-optimal or good-enough solutions for challenging problems due to computational complexity. Examples where optimization has been applied in software engineering include requirements engineering, program repair, and software product lines.

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This document provides guidance for developing a project-based volunteer opportunity. It defines a project-based volunteer as a temporary volunteer who completes a specific project within a defined time period using professional skills. It discusses brainstorming the goal and required skills for the project, considering timelines and resource constraints, and the process for recruiting a volunteer, overseeing the project, and providing feedback upon completion.

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Este documento lista y describe diferentes tipos de casos que pueden ser analizados, incluyendo casos de valores, incidentes, soluciones razonadas, mentalización, búsqueda real y temáticos. Los casos de soluciones razonadas buscan resolver situaciones sin información adicional, mientras que los casos de mentalización fomentan la comprensión de causas y consecuencias. Los casos temáticos se centran en el tema subyacente más que en el caso específico.

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The document summarizes a questionnaire completed by a fan of Professor Green after attending one of his concerts. The fan was disappointed that the concert was shorter than expected and focused more on the opening act than Professor Green's performance. While the fan enjoyed Professor Green's rap style and storytelling lyrics, the concert experience did not meet expectations likely due to insufficient advertising and Professor Green's late stage appearance.

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The document proposes streaming algorithms for performing Pearson's chi-square goodness-of-fit test in a streaming setting with minimal assumptions. It presents algorithms for the one-sample and two-sample continuous chi-square tests that use O(K^2log(N)√N) space, where K is the number of bins and N is the stream length. It also shows that no sublinear solution exists for the categorical chi-square test and provides a heuristic algorithm. The algorithms are validated on real and synthetic data and can detect deviations from distributions or differences between streams with low memory requirements.

10 Algorithms in data mining

George Ang

The document summarizes 10 influential data mining algorithms: 1. C4.5 decision tree algorithm and its successor C5.0, which can construct classifiers as decision trees or rulesets. 2. K-means clustering algorithm, an iterative algorithm that partitions data into k clusters based on minimizing distances between data points and cluster centers. 3. Additional algorithms covered include SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These algorithms cover important data mining tasks such as classification, clustering, association analysis, and link mining.

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