Source: http://www.thehollywoodgossip.com/2012/10/teresa-palmer-and-scott-speedman-its-over/
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Monday, October 8, 2012
DIY Water Reservoir Planter [Gardening]
DIY weblog Dabbletree shares a design for the reservoir noting that you just need to cut the bottom from the plastic bottle and remove the cap. Partially fill the planter with soil and add the bottle. Continue to add soil to the outside of the bottle until you reach an inch or two below the top of the planter and add new plants in the round shape surrounding the bottle.
Since we're now in Fall you won't need to add much water to the reservoir but when hot weather comes again you fill the bottle and water will slowly trickle down into the plant roots.
Planter DIY Water Reservoir | Dabbletree
Source: http://feeds.gawker.com/~r/lifehacker/full/~3/8bA_Fysoc-8/diy-water-reservoir-planter
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Libyan assembly passes vote of no confidence dismissing prime minister
TRIPOLI (Reuters) - Libya's national congress dismissed the newly elected prime minister on Sunday in a vote of no confidence which underscored the difficulties of forming a government which can unite the country's different factions and regions.
The vote came minutes after prime minister Mustafa Abushagur named 10 new ministers - his second and ultimately unsuccessful attempt to form a government - after he was forced to withdraw his previous cabinet in the face of protests.
The national congress will now need to choose a new prime minister who will have to try again to form a viable government as Libya, a major oil and gas exporter, seeks to emerge from the civil war that toppled Muammar Gaddafi last year.
Abushagur was elected by the assembly on Sept 12 and last week announced a government which included 29 ministries.
He withdrew the list after protests from the national congress and the public saying it was not representative of the country. Between 100 and 150 demonstrators from the western town of Zawiyah stormed the national congress on Thursday.
The original list had many unknown figures, and was believed to include several members of the political arm of the Muslim Brotherhood. But there were no candidates from the National Forces Alliance, Libya's leading liberal coalition.
After withdrawing his initial choice, Abushagur was then given 72 hours to name a new government acceptable to the national congress or face a vote of no confidence.
He said he had come under pressure by political parties demanding roles in certain ministries.
"The first list was not successful, it had some mistakes, and I was prepared to fix it," he told the national congress on Sunday. "Some political entities that demanded certain positions began to discuss a vote of no-confidence. I would not bow down to the pressure of political entities."
Libya desperately needs a viable government so that it can focus on reconstruction and on healing regional divisions opened up by the war which toppled Gaddafi.
Hamuda Syala, spokesman for the National Forces Alliance led by wartime rebel prime minister Mohammed Jibril, said the coalition had been let down by Abushagur's original list of ministerial appointments.
But he said the National Forces Alliance was in negotiations with the Muslim Brotherhood's political arm, the Justice and Development Party, to find "a suitable leader who will be representative of Libya's choice".
Abushagur appeared on state television late at night to offer his thanks to those who helped him.
"I call on the revolutionaries to take care of this country and not let anyone take advantage of it," he said. "I am ready to be a servant to this country and it will be a big honor to keep serving it."
(Reporting and writing By Hadeel Al-Shalchi; Editing by Myra MacDonald)
Source: http://news.yahoo.com/libya-pm-names-cabinet-protests-171259959.html
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Thursday, October 4, 2012
NSF announces interagency progress on administration's Big Data initiative
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Public release date: 3-Oct-2012[ | E-mail |
Share ] Contact: Lisa-Joy Zgorski
lisajoy@nsf.gov
703-292-8311
National Science Foundation
NSF invests nearly $15 million in new Big Data research projects, and the start of an idea-generating challenge
The National Science Foundation (NSF), with support from the National Institutes of Health (NIH), today announced nearly $15 million in new Big Data fundamental research projects. These awards aim to develop new tools and methods to extract and use knowledge from collections of large data sets to accelerate progress in science and engineering research and innovation.
These grants were made in response to a joint NSF-NIH call for proposals issued in conjunction with the March 2012 Big Data Research and Development Initiative launch: NSF Leads Federal Efforts in Big Data.
"I am delighted to provide such a positive progress report just six months after fellow federal agency heads joined the White House in launching the Big Data Initiative," said NSF Director Subra Suresh. "By funding the fundamental research to enable new types of collaborations--multi-disciplinary teams and communities--and with the start of an exciting competition, today we are realizing plans to advance the foundational science and engineering of Big Data, fortifying U.S. competitiveness for decades to come."
"To get the most value from the massive biological data sets we are now able to collect, we need better ways of managing and analyzing the information they contain," said NIH Director Francis S. Collins. "The new awards that NIH is funding will help address these technological challenges--and ultimately help accelerate research to improve health--by developing methods for extracting important, biomedically relevant information from large amounts of complex data."
The eight projects announced today run the gamut of scientific techniques for big data management, new data analytic approaches, and e-science collaboration environments with possible future applications in a variety of fields, such as physics, economics and medicine.
"Data represents a transformative new currency for science, engineering, and education," said Farnam Jahanian, assistant director for NSF's Directorate for Computer and Information Science and Engineering. "By advancing the techniques and technologies for data management and knowledge extraction, these new research awards help to realize the enormous opportunity to capitalize on the transformative potential of data."
NSF, along with NASA and the Department of Energy, also announced the start of an idea-generating challenge series, opening additional avenues for innovation in seizing the opportunities afforded by Big Data science and engineering. The competition will be run by the NASA Tournament Lab (NTL), a collaboration between Harvard University and TopCoder, a competitive community of digital creators.
The NTL platform and process allows U.S. government agencies to conduct high risk/high reward challenges in an open and transparent environment with predictable cost, measurable outcomes-based results and the potential to move quickly into unanticipated directions and new areas of software technology. Registration is open through Oct. 13, 2012, for the first of four idea generation competitions in the series. Full competition details and registration information is available at the Ideation Challenge Phase website.
"Big Data is characterized not only by the enormous volume or the velocity of its generation, but also by the heterogeneity, diversity, and complexity of the data," said Suzi Iacono, co-chair of the interagency Big Data Senior Steering Group, a part of the Networking and Information Technology Research and Development program and senior science advisor at NSF. "There are enormous opportunities to extract knowledge from these large-scale, diverse data sets, and to provide powerful new approaches to drive discovery and decision-making, and to make increasingly accurate predictions. We're excited about the awards we are making today and to see what the idea generation competition will yield."
Today at a Tech America event on Capitol Hill, Iacono announced the award recipients. They are listed below.
Big Data Awards
BIGDATA: Mid-Scale: DCM: Collaborative Research: Eliminating the Data Ingestion Bottleneck in Big-Data Applications
Rutgers University, Martin Farach-Colton
Stony Brook University, Michael Bender
Big-data practice suggests that there is a tradeoff between the speed of data ingestion, the ability to answer queries quickly (e.g., via indexing), and the freshness of data. This tradeoff has manifestations in the design of all types of storage systems. In this project the principal investigators show that this is not a fundamental tradeoff, but rather a tradeoff imposed by the choice of data structure. They depart from the use of traditional indexing methodologies to build storage systems that maintain indexing 200 times faster in databases with billions of entries.
BIGDATA: Mid-Scale: ESCE: DCM: Collaborative Research: DataBridge - A Sociometric System for Long-Tail Science Data Collections
University of North Carolina at Chapel Hill, Arcot Rajasekar
Harvard University, Gary King
North Carolina Agriculture & Technical State University, Justin Zhan
The sheer volume and diversity of data present a new set of challenges in locating all of the data relevant to a particular line of scientific research. Taking full advantage of the unique data in the "long-tail of science" requires new tools specifically created to assist scientists in their search for relevant data sets. DataBridge supports advances in science and engineering by directly enabling and improving discovery of relevant scientific data across large, distributed and diverse collections using socio-metric networks. The system will also provide an easy means of publishing data through the DataBridge, and incentivize data producers to do so by enhancing collaboration and data-oriented networking.
BIGDATA: Mid-Scale: DCM: A Formal Foundation for Big Data Management
University of Washington, Dan Suciu
This project explores the foundations of big data management with the ultimate goal of significantly improving the productivity in Big Data analytics by accelerating data exploration. It will develop open source software to express and optimize ad hoc data analytics. The results of this project will make it easier for domain experts to conduct complex data analysis on Big Data and on large computer clusters.
BIGDATA: Mid-Scale: DA: Analytical Approaches to Massive Data Computation with Applications to Genomics
Brown University, Eli Upfal
The goal of this project is to design and test mathematically well-founded algorithmic and statistical techniques for analyzing large scale, heterogeneous and so called noisy data. This project is motivated by the challenges in analyzing molecular biology data. The work will be tested on extensive cancer genome data, contributing to better health and new health information technologies, areas of national priority.
BIGDATA: Mid-Scale: DA: Distribution-based Machine Learning for High-dimensional Datasets
Carnegie Mellon University, Aarti Singh
The project aims to develop new statistical and algorithmic approaches to natural generalizations of a class of standard machine learning problems. The resulting novel machine learning approaches are expected to benefit other scientific fields in which data points can be naturally modeled by sets of distributions, such as physics, psychology, economics, epidemiology, medicine and social network-analysis.
BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
Iowa State University, Srinivas Aluru
Stanford University, Oyekunie Olukotun
Virginia Polytechnic University, Wuchun Feng
The goal of the project is to develop core techniques and software libraries to enable scalable, efficient, high-performance computing solutions for high-throughput DNA sequencing, also known as next-generation sequencing. The research will be conducted in the context of challenging problems in human genetics and metagenomics, in collaboration with domain specialists.
BIGDATA: Mid-Scale: DA: Collaborative Research: Big Tensor Mining: Theory, Scalable Algorithms and Applications
Carnegie Mellon University, Christos Faloutsos
University of Minnesota, Twin Cities, Nikolaos Sidiropoulos
The objective of this project is to develop theory and algorithms to tackle the complexity of language processing, and to develop methods that approximate how the human brain works in processing language. The research also promises better algorithms for search engines, new approaches to understanding brain activity, and better recommendation systems for retailers.
BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature
Rutgers University, Paul Kantor
Cornell University, Thorsten Joachims
Princeton University, David Biei
This project will focus on the problem of bringing massive amounts of data down to the human scale by investigating the individual and social patterns that relate to how text repositories are actually accessed and used. It will improve the accuracy and relevance of complex scientific literature searches.
###
[ | E-mail | Share ] ?
AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.
Public release date: 3-Oct-2012[ | E-mail |
Share ] Contact: Lisa-Joy Zgorski
lisajoy@nsf.gov
703-292-8311
National Science Foundation
NSF invests nearly $15 million in new Big Data research projects, and the start of an idea-generating challenge
The National Science Foundation (NSF), with support from the National Institutes of Health (NIH), today announced nearly $15 million in new Big Data fundamental research projects. These awards aim to develop new tools and methods to extract and use knowledge from collections of large data sets to accelerate progress in science and engineering research and innovation.
These grants were made in response to a joint NSF-NIH call for proposals issued in conjunction with the March 2012 Big Data Research and Development Initiative launch: NSF Leads Federal Efforts in Big Data.
"I am delighted to provide such a positive progress report just six months after fellow federal agency heads joined the White House in launching the Big Data Initiative," said NSF Director Subra Suresh. "By funding the fundamental research to enable new types of collaborations--multi-disciplinary teams and communities--and with the start of an exciting competition, today we are realizing plans to advance the foundational science and engineering of Big Data, fortifying U.S. competitiveness for decades to come."
"To get the most value from the massive biological data sets we are now able to collect, we need better ways of managing and analyzing the information they contain," said NIH Director Francis S. Collins. "The new awards that NIH is funding will help address these technological challenges--and ultimately help accelerate research to improve health--by developing methods for extracting important, biomedically relevant information from large amounts of complex data."
The eight projects announced today run the gamut of scientific techniques for big data management, new data analytic approaches, and e-science collaboration environments with possible future applications in a variety of fields, such as physics, economics and medicine.
"Data represents a transformative new currency for science, engineering, and education," said Farnam Jahanian, assistant director for NSF's Directorate for Computer and Information Science and Engineering. "By advancing the techniques and technologies for data management and knowledge extraction, these new research awards help to realize the enormous opportunity to capitalize on the transformative potential of data."
NSF, along with NASA and the Department of Energy, also announced the start of an idea-generating challenge series, opening additional avenues for innovation in seizing the opportunities afforded by Big Data science and engineering. The competition will be run by the NASA Tournament Lab (NTL), a collaboration between Harvard University and TopCoder, a competitive community of digital creators.
The NTL platform and process allows U.S. government agencies to conduct high risk/high reward challenges in an open and transparent environment with predictable cost, measurable outcomes-based results and the potential to move quickly into unanticipated directions and new areas of software technology. Registration is open through Oct. 13, 2012, for the first of four idea generation competitions in the series. Full competition details and registration information is available at the Ideation Challenge Phase website.
"Big Data is characterized not only by the enormous volume or the velocity of its generation, but also by the heterogeneity, diversity, and complexity of the data," said Suzi Iacono, co-chair of the interagency Big Data Senior Steering Group, a part of the Networking and Information Technology Research and Development program and senior science advisor at NSF. "There are enormous opportunities to extract knowledge from these large-scale, diverse data sets, and to provide powerful new approaches to drive discovery and decision-making, and to make increasingly accurate predictions. We're excited about the awards we are making today and to see what the idea generation competition will yield."
Today at a Tech America event on Capitol Hill, Iacono announced the award recipients. They are listed below.
Big Data Awards
BIGDATA: Mid-Scale: DCM: Collaborative Research: Eliminating the Data Ingestion Bottleneck in Big-Data Applications
Rutgers University, Martin Farach-Colton
Stony Brook University, Michael Bender
Big-data practice suggests that there is a tradeoff between the speed of data ingestion, the ability to answer queries quickly (e.g., via indexing), and the freshness of data. This tradeoff has manifestations in the design of all types of storage systems. In this project the principal investigators show that this is not a fundamental tradeoff, but rather a tradeoff imposed by the choice of data structure. They depart from the use of traditional indexing methodologies to build storage systems that maintain indexing 200 times faster in databases with billions of entries.
BIGDATA: Mid-Scale: ESCE: DCM: Collaborative Research: DataBridge - A Sociometric System for Long-Tail Science Data Collections
University of North Carolina at Chapel Hill, Arcot Rajasekar
Harvard University, Gary King
North Carolina Agriculture & Technical State University, Justin Zhan
The sheer volume and diversity of data present a new set of challenges in locating all of the data relevant to a particular line of scientific research. Taking full advantage of the unique data in the "long-tail of science" requires new tools specifically created to assist scientists in their search for relevant data sets. DataBridge supports advances in science and engineering by directly enabling and improving discovery of relevant scientific data across large, distributed and diverse collections using socio-metric networks. The system will also provide an easy means of publishing data through the DataBridge, and incentivize data producers to do so by enhancing collaboration and data-oriented networking.
BIGDATA: Mid-Scale: DCM: A Formal Foundation for Big Data Management
University of Washington, Dan Suciu
This project explores the foundations of big data management with the ultimate goal of significantly improving the productivity in Big Data analytics by accelerating data exploration. It will develop open source software to express and optimize ad hoc data analytics. The results of this project will make it easier for domain experts to conduct complex data analysis on Big Data and on large computer clusters.
BIGDATA: Mid-Scale: DA: Analytical Approaches to Massive Data Computation with Applications to Genomics
Brown University, Eli Upfal
The goal of this project is to design and test mathematically well-founded algorithmic and statistical techniques for analyzing large scale, heterogeneous and so called noisy data. This project is motivated by the challenges in analyzing molecular biology data. The work will be tested on extensive cancer genome data, contributing to better health and new health information technologies, areas of national priority.
BIGDATA: Mid-Scale: DA: Distribution-based Machine Learning for High-dimensional Datasets
Carnegie Mellon University, Aarti Singh
The project aims to develop new statistical and algorithmic approaches to natural generalizations of a class of standard machine learning problems. The resulting novel machine learning approaches are expected to benefit other scientific fields in which data points can be naturally modeled by sets of distributions, such as physics, psychology, economics, epidemiology, medicine and social network-analysis.
BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
Iowa State University, Srinivas Aluru
Stanford University, Oyekunie Olukotun
Virginia Polytechnic University, Wuchun Feng
The goal of the project is to develop core techniques and software libraries to enable scalable, efficient, high-performance computing solutions for high-throughput DNA sequencing, also known as next-generation sequencing. The research will be conducted in the context of challenging problems in human genetics and metagenomics, in collaboration with domain specialists.
BIGDATA: Mid-Scale: DA: Collaborative Research: Big Tensor Mining: Theory, Scalable Algorithms and Applications
Carnegie Mellon University, Christos Faloutsos
University of Minnesota, Twin Cities, Nikolaos Sidiropoulos
The objective of this project is to develop theory and algorithms to tackle the complexity of language processing, and to develop methods that approximate how the human brain works in processing language. The research also promises better algorithms for search engines, new approaches to understanding brain activity, and better recommendation systems for retailers.
BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature
Rutgers University, Paul Kantor
Cornell University, Thorsten Joachims
Princeton University, David Biei
This project will focus on the problem of bringing massive amounts of data down to the human scale by investigating the individual and social patterns that relate to how text repositories are actually accessed and used. It will improve the accuracy and relevance of complex scientific literature searches.
###
[ | E-mail | Share ] ?
AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.
Source: http://www.eurekalert.org/pub_releases/2012-10/nsf-nai100312.php
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Lee Schrager on SoBeWFF, Mixology and Where He Eats in Miami ...
One of the hottest things right now, according to Lee Schrager, is mixology -- the skill of blending cocktails with a focus on quality ingredients and innovative flavor combinations. "There's so much emphasis on cocktails right now," he says.
To reflect what he calls the "big explosion of mixology," the South Beach Wine & Food Festival will stage a cocktail event at the Miami Beach Botanical Garden featuring My Ceviche -- Sam Gorenstein's small joint for fresh local seafood -- and Bar Lab, a beverage-consulting service founded by bar chefs Gabriel Orta and Elad Zvi, who catapulted into Miami mixology stardom with their popular pop-up bar the Broken Shaker. This fall, the bar will resurface permanently at the Freehand Miami Beach with an adjacent full-service restaurant.
See Also:
- Lee Schrager on 2013's South Beach Wine & Food Festival: Hip and Hot, Part One
The festival isn't solely focused on the latest trends, though. In fact, the tribute dinner is dedicated to honoring some of the most respected figures in the industry. For 2013, the annual event will honor celebrity chef and restaurateur Nobu Matsuhisa. Previous honorees have included Daniel Boulud and Alain Ducasse.
As for the possibility of a second volume of the festival's cookbook, which was released in 2010, Schrager says, "Once was enough. It was an incredible amount of work and incredibly rewarding, but maybe again in the 20th year."
Still, he has favorites among the 100 recipes: Pierre Herm?'s chocolate coconut cake and Martha Stewart's lobster roll.
But Schrager doesn't do much cooking at home in Miami. Instead, he typically grills and bakes during his summers on Long Island -- where he gets a kick out of visiting farm stands for local produce.
When in Miami, the festival's director enjoys celebrating the area he has called home for more than 18 years, the Design District. "I love what's happening here," he says. He then follows with a list of his top picks in the area: Michael's Genuine Food & Drink, Michy's, Red Light Little River, and Sugarcane Raw Bar Grill. A list like that shows he has much more than vision. Schrager has taste too.
Follow Short Order on Facebook and on Twitter @Short_Order.
Source: http://blogs.miaminewtimes.com/shortorder/2012/10/hip_and_hot_lee_schrager_on_20_1.php
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Climate Change Could Delay Fall Foliage Colors [Video]
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Source: http://news.yahoo.com/climate-change-could-delay-fall-foliage-colors-video-154500554.html
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Peanut butter recall includes major retailers
FILE - In this Feb. 11, 2008 file photo, a customer departs Trader Joe's in Los Angeles. The grocery store chain Trader Joe's is recalling peanut butter that has been linked to 29 salmonella illnesses in 18 states, Saturday, Sept. 22, 2012 (AP Photos/Ric Francis, File)
FILE - In this Feb. 11, 2008 file photo, a customer departs Trader Joe's in Los Angeles. The grocery store chain Trader Joe's is recalling peanut butter that has been linked to 29 salmonella illnesses in 18 states, Saturday, Sept. 22, 2012 (AP Photos/Ric Francis, File)
WASHINGTON (AP) ? A recall of peanut butter and other nut products has some of the country's largest grocery stores pulling store-brand products off their shelves.
New Mexico-based Sunland Inc. has expanded its recall of peanut butter and almond butter to include cashew butters, tahini and blanched and roasted peanut products. The company, which sells its nuts and nut butters to large groceries and other food distributors around the country, recalled products under multiple brand names last month after salmonella illnesses were linked to Trader Joe's Creamy Salted Valencia Peanut Butter, one of the brands it manufactures.
In addition to Trader Joe's, the recall over the past week has included some nut butters and nut products sold at Whole Foods Market, Target, Safeway, Fresh & Easy, Harry and David, Sprouts, Heinen's, Stop & Shop Supermarket Company, Giant Food of Landover, Md., and several other stores. Some of those retailers used Sunland ingredients in items they prepared and packaged themselves.
The federal Centers for Disease Control and Prevention says there have been 30 salmonella illnesses in 19 states that can be traced to the Trader Joe's peanut butter. No other foods have been linked to the illnesses, but Sunland recalled other products manufactured on the same equipment as the Trader Joe's product.
Some of the brand names included in the recall are Target's Archer Farms, Safeway's Open Nature, Earth Balance, Fresh & Easy, Late July, Heinen's, Joseph's, Natural Value, Naturally More, Peanut Power Butter, Serious Food, Snaclite Power, Sprouts Farmers Market, Sprouts, Sunland and Dogsbutter.
Sunland's recall includes 101 products, and several retailers have issued additional recalls including items made with Sunland ingredients.
Those sickened reported becoming ill between June 11 and Sept. 11, according to the CDC. Almost two-thirds of those who became ill were children under the age of 10. No deaths have been reported.
Salmonella can cause diarrhea, fever and abdominal cramps 12 hours to 72 hours after infection. It is most dangerous to children, the elderly and others with weak immune systems.
___
Find Mary Clare Jalonick on Twitter at http://twitter.com/mcjalonick
___
Online:
Full list of recalled items: http://www.fda.gov/Food/FoodSafety/CORENetwork/ucm320413.htm
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