Newtown marks 6 months since school massacre

Carlee Soto, front, and Jillian Soto, back, sisters of slain teacher Victoria Soto embrace during a ceremony on the six-month anniversary honoring the 20 children and six adults gunned down at Sandy Hook Elementary School on Dec. 14, 2012 in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. (AP Photo/Jessica Hill)

Carlee Soto, front, and Jillian Soto, back, sisters of slain teacher Victoria Soto embrace during a ceremony on the six-month anniversary honoring the 20 children and six adults gunned down at Sandy Hook Elementary School on Dec. 14, 2012 in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. (AP Photo/Jessica Hill)

People gather during a ceremony on the six-month anniversary honoring the 20 children and six adults gunned down at Sandy Hook Elementary school on Dec. 14, 2012 at Edmond Town Hall in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. (AP Photo/Jessica Hill)

A teddy bear, flowers and a candle are the only items left at the entrance to Sandy Hook Elementary School on the six-month anniversary of the Dec. 14 shooting in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. (AP Photo/Jessica Hill)

Ruth Goodsnyder of Sandy Hook, Conn., reacts during a ceremony on the six-month anniversary honoring the 20 children and six adults gunned down at Sandy Hook Elementary school on Dec. 14 in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. Goodsnyder's granddaughter is student at the elementary school. (AP Photo/Jessica Hill)

Carlee Soto, left, and Jillian Soto, sisters of slain teacher Victoria Soto, hold hands during a 26-second moment of silence at Edmond Town Hall honoring the 20 children and six adults gunned down at Sandy Hook Elementary School on Dec. 14, 2012 in Newtown, Conn., Friday, June 14, 2013. Newtown held a moment of silence Friday for the victims of the massacre at Sandy Hook Elementary School at a remembrance event that doubled as a call to action on gun control, with the reading of names of thousands of victims of gun violence. (AP Photo/Jessica Hill)

NEWTOWN, Conn. (AP) ? The town where 20 children and six educators were massacred in December went silent for a moment Friday, six months later, at a remembrance event that doubled as a call to action on weapons control, with the reading of names of thousands of victims of gun violence.

The mood of the six-month marker was decidedly more political than private, with a group called Mayors Against Illegal Guns holding events in 10 states calling for lawmakers to expand background checks and urging senators who opposed the bill to reconsider.

Two sisters of slain teacher Victoria Soto addressed a crowd gathered at Edmond Town Hall in Newtown for a 26-second moment of silence, honoring the 20 children and six adults gunned down at the school on Dec. 14.

"This pain is excruciating and unbearable, but thanks to people like you, that come out and support us, we are able to get through this," said Carlee Soto, who hugged and held hands with her sister Jillian before taking the stage.

The event then transitioned to the reading of the names of more than 5,000 Americans killed with guns since the tragedy in Newtown. The reading of names was expected to take 12 hours.

Mayors Against Illegal Guns, which organized the event in Newtown, also launched a bus tour that will travel to 25 states over 100 days to build support for legislation to expand background checks for gun buyers. Such legislation failed in the Senate in April, and there are no indications it has gained traction over concerns about protecting gun rights.

The gunman in Newtown killed his mother and the 26 people at Sandy Hook Elementary School with a semiautomatic rifle, then committed suicide as police arrived. The shooting led some relatives of victims to campaign for tougher gun laws, including some who were in Washington this week lobbying lawmakers for action.

Jillian and Carlee Soto met with President Barack Obama as they campaigned for gun control.

"He just told us to have faith," said Jillian Soto, 24. "It isn't something that happens overnight. It's something that you have to continue to fight for. Within good time we will have this passed and we will have change."

Carlee Soto, who is 20, said they got back from Washington at 2 a.m. She said that the president and vice president spoke of waging a long battle and that she plans to continue her efforts, as well.

"It's a very tough battle to fight," she said. "It's very frustrating, but knowing I'm doing this for my sister and the other 25 and everyone else that's been affected by gun violence, it's worth it."

Teresa Rousseau, whose daughter Lauren was among the six educators killed at Sandy Hook, also met with the president this week. She said at first she wondered how she would survive, and now she knows she can and feels empowered as she campaigns for tougher gun laws.

"I think it's time the average American gets a little louder in what he has to say," Rousseau said.

Laura Miller was among many in the crowd wearing the school's green and white colors. She said that her son, a kindergartner, was unharmed but that his teacher was shot in the foot.

"I'm here for the people who were less fortunate than me," she said. "I think they're the bravest people in the world to be able to come out here and fight for change, and that's what we need to do. If more people come out, that's the only way anything is ever going to change."

The mayors group also held events in 10 states calling for lawmakers to expand background checks and urging senators who opposed the bill to reconsider. Those events, which include gun violence survivors and gun owners, were being in Arkansas, Arizona, Florida, Georgia, Indiana, Montana, North Carolina, New Hampshire, Ohio and Pennsylvania.

Suzanne Conway, 37, was among a handful of people who attended a rally in a Charlotte, N.C., park. The mother of four young children said the shooting compelled her to start a chapter of an anti-gun group, Moms Demand Action.

"Newtown hit me hard. I had to do something about it," she said. "People are not going to stop fighting. This is a very important issue."

In Indiana, about two dozen protesters gathered on the Monon Trail in Indianapolis and talked about pressing Indiana's congressional delegation to support background check legislation. Sen. Joe Donnelly, a Democrat, supports the checks, but Sen. Dan Coats, a Republican, does not.

The protest, held in a liberal swath of Republican Rep. Susan Brooks' district, should be about laying continued pressure on lawmakers, said Peter Luster, Indiana state director of Mayors Against Illegal Guns. Lawmakers have daily calls with their staff to check in on what constituents are talking about, and they should hear about background checks constantly, he said.

New York Mayor Michael Bloomberg, who co-founded the mayors group, this week sent a letter asking donors not to support Democratic senators who opposed the bill to expand background checks.

On the other side of the debate, the National Rifle Association is focusing on Sen. Joe Manchin, D-W.Va., who co-sponsored the bill to expand background checks, with a TV ad urging viewers to phone Manchin's office and tell him "to honor his commitment to the 2nd Amendment." The NRA plans to spend $100,000 airing the ad in West Virginia markets over the next two weeks.

___

Associated Press writers Mitch Weiss in North Carolina and Tom LoBianco in Indiana contributed to this report.

Associated Press

Source: http://hosted2.ap.org/APDEFAULT/386c25518f464186bf7a2ac026580ce7/Article_2013-06-14-Connecticut%20School%20Shooting/id-7ee848810c934f55855d81c12a178d48

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What do you think about the new look for iOS 7? [Poll]

The iOS 7 announcement at WWDC 2013 left us with a lot to talk about, but the most immediately noticeable difference is the appearance. Since Jony Ive took charge of the design, we had heard countless rumors about all the skeuomorphic elements being stripped out leaving us with a much simpler, flatter design. And that's pretty much what we got -- though if you look carefully at the press shots of Notes and Reminders, there's still a hint of paper in there. It's different, a whole lot different, but we want to know what you think of it.

The green felt has gone, the stitched leather has gone, the icons have been dramatically changed. iOS 7 is bright, colorful in places but reserved and simple in others. Billed as the biggest change to iOS since the introduction of the iPhone, there is no doubt that visually at least, those words are correct. But we want to hear your thoughts on it. Is it too flat? Too bright? Do you love it, hate it, or fall somewhere in between? Drop your vote in the poll up top and leave us your thoughts in the comments below.

Source: http://feedproxy.google.com/~r/TheIphoneBlog/~3/ObIR4Eq0oGs/story01.htm

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How Archaea might find their food: Sensor protein characterized

June 10, 2013 ? The microorganism Methanosarcina acetivorans lives off everything it can metabolise into methane. How it finds its sources of energy, is not yet clear. Scientists at the Ruhr-Universit?t Bochum together with colleagues from Dresden, Frankfurt, Muelheim and the USA have identified a protein that might act as a "food sensor." They characterised the molecule in detail and found both similarities and differences to the system that is responsible for the search for food in bacteria.

MsmS has a different function to that thought

The protein MsmS has so far only been studied from a bioinformatics point of view. Computer analyses of its gene sequence had predicted that it might be a phytochrome, i.e. a red light sensor. Using spectroscopic methods, the research team of the current study have refuted this theory. MsmS has a heme cofactor, like haemoglobin in red blood cells, and can, among other things, bind the substance dimethyl sulphide. This is one of the energy sources of Methanosarcina acetivorans. MsmS might thus serve the microorganism as a sensor to directly or indirectly detect this energy source. In genetic studies, the scientists also found evidence that MsmS regulates systems which are important for the exploitation of dimethyl sulphide.

Archaea: flexible "eaters"

Methanosarcina acetivorans belongs to the Archaea which constitute the third domain of life, alongside Bacteria and Eukarya; the term Eukarya comprising all living organisms with a cell nucleus. Many of them are adapted to extreme conditions or are able to use unusual energy sources. Among the organisms that live from methane production, the so-called methanogenic organisms, M. acetivorans is one of the most flexible when it comes to the choice of food sources. It converts many different molecules into methane, and thus produces energy. How M. acetivorans detects the different food sources, is still largely unknown.

In Archaea, unlike bacteria

For this purpose, bacteria use the so-called two-component system: when a sensor protein comes in contact with the food source, the protein modifies itself; it attaches a phosphate group to a certain amino acid residue, the histidine. The phosphate group is then transferred to a second protein. In methanogenic organisms such a process could trigger cellular processes that activate the methane production. Archaea might also use comparable sensor proteins in a way similar to bacteria. MsmS would be a candidate for such a task, because the analyses of the research team showed that it is able to transfer a phosphate residue to an amino acid. The target site of this phosphorylation is, however, probably not histidine. "So there could be differences between the signal transduction systems of Archaea and Bacteria" speculates Prof. Dr. Nicole Frankenberg-Dinkel from the work group Physiology of Microorganisms. "It is also interesting that the heme cofactor is covalently bound, i.e. linked with the protein by an electron-pair bond. This is very uncommon for sensor proteins which are present in the cell fluid."

Source: http://feeds.sciencedaily.com/~r/sciencedaily/top_news/top_environment/~3/x9JPmr4yXJg/130610095030.htm

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Finest news source, as always (Unqualified Offerings)

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When will my computer understand me?

Public release date: 10-Jun-2013
[ | E-mail | Share ]

Contact: Faith Singer-Villalobos
faith@tacc.utexas.edu
512-232-5771
University of Texas at Austin, Texas Advanced Computing Center

Linguists, computer scientists use supercomputers to improve natural language processing

It's not hard to tell the difference between the "charge" of a battery and criminal "charges." But for computers, distinguishing between the various meanings of a word is difficult.

For more than 50 years, linguists and computer scientists have tried to get computers to understand human language by programming semantics as software. Driven initially by efforts to translate Russian scientific texts during the Cold War (and more recently by the value of information retrieval and data analysis tools), these efforts have met with mixed success. IBM's Jeopardy-winning Watson system and Google Translate are high profile, successful applications of language technologies, but the humorous answers and mistranslations they sometimes produce are evidence of the continuing difficulty of the problem.

Our ability to easily distinguish between multiple word meanings is rooted in a lifetime of experience. Using the context in which a word is used, an intrinsic understanding of syntax and logic, and a sense of the speaker's intention, we intuit what another person is telling us.

"In the past, people have tried to hand-code all of this knowledge," explained Katrin Erk, a professor of linguistics at The University of Texas at Austin focusing on lexical semantics. "I think it's fair to say that this hasn't been successful. There are just too many little things that humans know."

Other efforts have tried to use dictionary meanings to train computers to better understand language, but these attempts have also faced obstacles. Dictionaries have their own sense distinctions, which are crystal clear to the dictionary-maker but murky to the dictionary reader. Moreover, no two dictionaries provide the same set of meanings frustrating, right?

Watching annotators struggle to make sense of conflicting definitions led Erk to try a different tactic. Instead of hard-coding human logic or deciphering dictionaries, why not mine a vast body of texts (which are a reflection of human knowledge) and use the implicit connections between the words to create a weighted map of relationships a dictionary without a dictionary?

"An intuition for me was that you could visualize the different meanings of a word as points in space," she said. "You could think of them as sometimes far apart, like a battery charge and criminal charges, and sometimes close together, like criminal charges and accusations ("the newspaper published charges..."). The meaning of a word in a particular context is a point in this space. Then we don't have to say how many senses a word has. Instead we say: 'This use of the word is close to this usage in another sentence, but far away from the third use.'"

To create a model that can accurately recreate the intuitive ability to distinguish word meaning requires a lot of text and a lot of analytical horsepower.

"The lower end for this kind of a research is a text collection of 100 million words," she explained. "If you can give me a few billion words, I'd be much happier. But how can we process all of that information? That's where supercomputers and Hadoop come in."

Applying Computational Horsepower

Erk initially conducted her research on desktop computers, but around 2009, she began using the parallel computing systems at the Texas Advanced Computing Center (TACC). Access to a special Hadoop-optimized subsystem on TACC's Longhorn supercomputer allowed Erk and her collaborators to expand the scope of their research. Hadoop is a software architecture well suited to text analysis and the data mining of unstructured data that can also take advantage of large computer clusters. Computational models that take weeks to run on a desktop computer can run in hours on Longhorn. This opened up new possibilities.

"In a simple case we count how often a word occurs in close proximity to other words. If you're doing this with one billion words, do you have a couple of days to wait to do the computation? It's no fun," Erk said. "With Hadoop on Longhorn, we could get the kind of data that we need to do language processing much faster. That enabled us to use larger amounts of data and develop better models."

Treating words in a relational, non-fixed way corresponds to emerging psychological notions of how the mind deals with language and concepts in general, according to Erk. Instead of rigid definitions, concepts have "fuzzy boundaries" where the meaning, value and limits of the idea can vary considerably according to the context or conditions. Erk takes this idea of language and recreates a model of it from hundreds of thousands of documents.

Say That Another Way

So how can we describe word meanings without a dictionary? One way is to use paraphrases. A good paraphrase is one that is "close to" the word meaning in that high-dimensional space that Erk described.

"We use a gigantic 10,000-dimentional space with all these different points for each word to predict paraphrases," Erk explained. "If I give you a sentence such as, 'This is a bright child,' the model can tell you automatically what are good paraphrases ('an intelligent child') and what are bad paraphrases ('a glaring child'). This is quite useful in language technology."

Language technology already helps millions of people perform practical and valuable tasks every day via web searches and question-answer systems, but it is poised for even more widespread applications.

Automatic information extraction is an application where Erk's paraphrasing research may be critical. Say, for instance, you want to extract a list of diseases, their causes, symptoms and cures from millions of pages of medical information on the web.

"Researchers use slightly different formulations when they talk about diseases, so knowing good paraphrases would help," Erk said.

In a paper to appear in ACM Transactions on Intelligent Systems and Technology, Erk and her collaborators illustrated they could achieve state-of-the-art results with their automatic paraphrasing approach.

Recently, Erk and Ray Mooney, a computer science professor also at The University of Texas at Austin, were awarded a grant from the Defense Advanced Research Projects Agency to combine Erk's distributional, high dimensional space representation of word meanings with a method of determining the structure of sentences based on Markov logic networks.

"Language is messy," said Mooney. "There is almost nothing that is true all the time. "When we ask, 'How similar is this sentence to another sentence?' our system turns that question into a probabilistic theorem-proving task and that task can be very computationally complex."

In their paper, "Montague Meets Markov: Deep Semantics with Probabilistic Logical Form," presented at the Second Joint Conference on Lexical and Computational Semantics (STARSEM2013) in June, Erk, Mooney and colleagues announced their results on a number of challenge problems from the field of artificial intelligence.

In one problem, Longhorn was given a sentence and had to infer whether another sentence was true based on the first. Using an ensemble of different sentence parsers, word meaning models and Markov logic implementations, Mooney and Erk's system predicted the correct answer with 85% accuracy. This is near the top results in this challenge. They continue to work to improve the system.

There is a common saying in the machine-learning world that goes: "There's no data like more data." While more data helps, taking advantage of that data is key.

"We want to get to a point where we don't have to learn a computer language to communicate with a computer. We'll just tell it what to do in natural language," Mooney said. "We're still a long way from having a computer that can understand language as well as a human being does, but we've made definite progress toward that goal."

###

---

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?

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: 10-Jun-2013
[ | E-mail | Share ]

Contact: Faith Singer-Villalobos
faith@tacc.utexas.edu
512-232-5771
University of Texas at Austin, Texas Advanced Computing Center

Linguists, computer scientists use supercomputers to improve natural language processing

It's not hard to tell the difference between the "charge" of a battery and criminal "charges." But for computers, distinguishing between the various meanings of a word is difficult.

For more than 50 years, linguists and computer scientists have tried to get computers to understand human language by programming semantics as software. Driven initially by efforts to translate Russian scientific texts during the Cold War (and more recently by the value of information retrieval and data analysis tools), these efforts have met with mixed success. IBM's Jeopardy-winning Watson system and Google Translate are high profile, successful applications of language technologies, but the humorous answers and mistranslations they sometimes produce are evidence of the continuing difficulty of the problem.

Our ability to easily distinguish between multiple word meanings is rooted in a lifetime of experience. Using the context in which a word is used, an intrinsic understanding of syntax and logic, and a sense of the speaker's intention, we intuit what another person is telling us.

"In the past, people have tried to hand-code all of this knowledge," explained Katrin Erk, a professor of linguistics at The University of Texas at Austin focusing on lexical semantics. "I think it's fair to say that this hasn't been successful. There are just too many little things that humans know."

Other efforts have tried to use dictionary meanings to train computers to better understand language, but these attempts have also faced obstacles. Dictionaries have their own sense distinctions, which are crystal clear to the dictionary-maker but murky to the dictionary reader. Moreover, no two dictionaries provide the same set of meanings frustrating, right?

Watching annotators struggle to make sense of conflicting definitions led Erk to try a different tactic. Instead of hard-coding human logic or deciphering dictionaries, why not mine a vast body of texts (which are a reflection of human knowledge) and use the implicit connections between the words to create a weighted map of relationships a dictionary without a dictionary?

"An intuition for me was that you could visualize the different meanings of a word as points in space," she said. "You could think of them as sometimes far apart, like a battery charge and criminal charges, and sometimes close together, like criminal charges and accusations ("the newspaper published charges..."). The meaning of a word in a particular context is a point in this space. Then we don't have to say how many senses a word has. Instead we say: 'This use of the word is close to this usage in another sentence, but far away from the third use.'"

To create a model that can accurately recreate the intuitive ability to distinguish word meaning requires a lot of text and a lot of analytical horsepower.

"The lower end for this kind of a research is a text collection of 100 million words," she explained. "If you can give me a few billion words, I'd be much happier. But how can we process all of that information? That's where supercomputers and Hadoop come in."

Applying Computational Horsepower

Erk initially conducted her research on desktop computers, but around 2009, she began using the parallel computing systems at the Texas Advanced Computing Center (TACC). Access to a special Hadoop-optimized subsystem on TACC's Longhorn supercomputer allowed Erk and her collaborators to expand the scope of their research. Hadoop is a software architecture well suited to text analysis and the data mining of unstructured data that can also take advantage of large computer clusters. Computational models that take weeks to run on a desktop computer can run in hours on Longhorn. This opened up new possibilities.

"In a simple case we count how often a word occurs in close proximity to other words. If you're doing this with one billion words, do you have a couple of days to wait to do the computation? It's no fun," Erk said. "With Hadoop on Longhorn, we could get the kind of data that we need to do language processing much faster. That enabled us to use larger amounts of data and develop better models."

Treating words in a relational, non-fixed way corresponds to emerging psychological notions of how the mind deals with language and concepts in general, according to Erk. Instead of rigid definitions, concepts have "fuzzy boundaries" where the meaning, value and limits of the idea can vary considerably according to the context or conditions. Erk takes this idea of language and recreates a model of it from hundreds of thousands of documents.

Say That Another Way

So how can we describe word meanings without a dictionary? One way is to use paraphrases. A good paraphrase is one that is "close to" the word meaning in that high-dimensional space that Erk described.

"We use a gigantic 10,000-dimentional space with all these different points for each word to predict paraphrases," Erk explained. "If I give you a sentence such as, 'This is a bright child,' the model can tell you automatically what are good paraphrases ('an intelligent child') and what are bad paraphrases ('a glaring child'). This is quite useful in language technology."

Language technology already helps millions of people perform practical and valuable tasks every day via web searches and question-answer systems, but it is poised for even more widespread applications.

Automatic information extraction is an application where Erk's paraphrasing research may be critical. Say, for instance, you want to extract a list of diseases, their causes, symptoms and cures from millions of pages of medical information on the web.

"Researchers use slightly different formulations when they talk about diseases, so knowing good paraphrases would help," Erk said.

In a paper to appear in ACM Transactions on Intelligent Systems and Technology, Erk and her collaborators illustrated they could achieve state-of-the-art results with their automatic paraphrasing approach.

Recently, Erk and Ray Mooney, a computer science professor also at The University of Texas at Austin, were awarded a grant from the Defense Advanced Research Projects Agency to combine Erk's distributional, high dimensional space representation of word meanings with a method of determining the structure of sentences based on Markov logic networks.

"Language is messy," said Mooney. "There is almost nothing that is true all the time. "When we ask, 'How similar is this sentence to another sentence?' our system turns that question into a probabilistic theorem-proving task and that task can be very computationally complex."

In their paper, "Montague Meets Markov: Deep Semantics with Probabilistic Logical Form," presented at the Second Joint Conference on Lexical and Computational Semantics (STARSEM2013) in June, Erk, Mooney and colleagues announced their results on a number of challenge problems from the field of artificial intelligence.

In one problem, Longhorn was given a sentence and had to infer whether another sentence was true based on the first. Using an ensemble of different sentence parsers, word meaning models and Markov logic implementations, Mooney and Erk's system predicted the correct answer with 85% accuracy. This is near the top results in this challenge. They continue to work to improve the system.

There is a common saying in the machine-learning world that goes: "There's no data like more data." While more data helps, taking advantage of that data is key.

"We want to get to a point where we don't have to learn a computer language to communicate with a computer. We'll just tell it what to do in natural language," Mooney said. "We're still a long way from having a computer that can understand language as well as a human being does, but we've made definite progress toward that goal."

###

---

[ | 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/2013-06/uota-wwm061013.php

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Jury selection begins in George Zimmerman trial

George Zimmerman, accused in the Trayvon Martin shooting, in Seminole Circuit Court during his pretrial hearing??

Lawyers on both sides of the George Zimmerman trial today will begin what is expected to be a weekslong process of selecting a 12-member jury in the incendiary case in Sanford, Florida.

Zimmerman's attorneys declined to ask for a change of venue in the case, suggesting they are confident they can find impartial jurors in the area despite the wall-to-wall media coverage that 17-year-old Trayvon Martin's killing attracted last year. Zimmerman, out on a $1 million bond and in hiding for much of the past year, is charged with second-degree murder for killing the unarmed teen in a confrontation in his gated community, where Zimmerman acted as a volunteer watchman.

Prosecutors argue that Zimmerman racially profiled, followed and then shot Martin. Zimmerman's lawyers counter that their client was attacked by Martin and that he acted in self-defense.

Defense lawyer Jose Baez, who represented Casey Anthony in her high-profile trial in Orlando in 2011, said jury selection in this trial will be especially complicated because of the case's racial overtones. (Zimmerman is Hispanic; Martin was black.)

Generally, defense lawyers would be more likely than prosecutors to want to select minorities for a jury, since, on average, African-American and Hispanic people express more skepticism of law enforcement than white people, according to Baez. But in this case, Zimmerman's defense lawyers will want to pack the jury with white "gun-toting Republicans," who would be amenable to their argument that their client shot and killed the unarmed Martin in self-defense. These jurors might also be more convinced by the argument that local police made the right decision not to arrest or charge Zimmerman in the shooting initially.

"It's a very unique case in that respect, where the general rules that a lot of lawyers use are just going be absolutely flipped upside down," Baez said. "Because it is such a racially charged case, I think that the clear line is going to be drawn here between African-American jurors and Caucasian and Hispanic jurors."

But the defense team will have to be very careful in its quest to find conservative jurors more amenable to the self-defense argument. Circuit Judge Debra Nelson, who is presiding over the televised trial in the Seminole County Courthouse, will most likely be vigilant to make sure neither side is excluding jurors based on their race, which is illegal under a 1985 Supreme Court decision.

"I think this judge is fully aware of the racial tensions involved and is going to be on high alert," Baez said. If the defense team moves to dismiss an African-American juror during the peremptory strike phase of jury selection, for example, the judge could challenge it to provide a reason for the move. If the team can't come up with a good reason (for example, that the juror attended a rally in support of Trayvon Martin), the judge will assume the attorneys wanted to dismiss the juror for racial reasons and override their preference.

Nelson has ruled that the pool of 500 potential jurors will be kept anonymous during the selection process. It's possible she could order them sequestered during the trial, which is what happened to the jurors in the Anthony case. Nelson rejected the defense's request to sequester them during jury selection.

The pool will shrink rapidly as jurors can make the case that sitting on a jury for weeks would be a hardship. After that process winnows down the lot, the attorneys will go through and disqualify anyone with a personal connection to the case. Both sides will be on the lookout for jurors who have stated their support for either Zimmerman or Martin on social media sites, or been involved in activism around the case. The attorneys may also have potential jurors fill out questionnaires gauging their beliefs about gun control, law enforcement, race and other issues.

Source: http://news.yahoo.com/blogs/lookout/jury-selection-begins-george-zimmerman-trial-121304344.html

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Is 'The Voice' dishing out enough criticism?

TV

Brittany Frederick TODAY contributor

17 hours ago

NBC

Coaches Adam Levine, Shakira, Usher and Blake Shelton on "The Voice."

Has ?The Voice? lost its bite? Not at all, say the show?s artists.

Fans have commented that this season?s coaching panel -- Blake Shelton, Adam Levine, Shakira and Usher -- has been giving out more compliments than constructive criticism.

Even host Carson Daly noticed a shift in tone on the NBC competition. ?I think that?s a fair assessment. Not that (criticism) didn't come from this season, but I understand when people say that," he told TODAY.com. "I think through the different cycles of the show, you're going to have panels that are a little more vocal that way, and some that are a little less.?

But for the most part, the season's hopefuls are pleased with what they?ve been hearing.

?I think all the coaches are definitely doing their fair share of constructive criticism,? Team Usher?s Michelle Chamuel told TODAY.com, calling the R&B star ?a tough coach, but a great coach.?

?Adam and Usher have been giving me some great advice in the past weeks," Holly Tucker said. "I appreciate all their feedback and they're really great for that.?

?When it?s time for (Blake) to get mean, he does,? said Zach Swon of The Swon Brothers. ?He will definitely tell us if something needs to be changed.?

But one artist does want more feedback from the superstar coaches.

?They're careful with what they say. I get some constructive criticism from them, but most of the comments that I've been getting are sort of a general statement about, 'Oh, it was great,? ? Team Adam?s Judith Hill told us. She said that she?d like to have the panel ?really kind of dig into my performance more instead of just being like, 'Oh, that was great.? ?

But some of the hopefuls are already seeing changes thanks to the pointers they?ve received.

?Just having (the coaches') feedback that I really need to step up and have the confidence to take it all the way, that's been the most valuable thing for me so far,? Holly added. ?I feel like I have more confidence, more strength and that's really what I need. That's what I needed all along.?

Source: http://www.today.com/entertainment/voice-dishing-out-enough-criticism-6C10012963

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