Three Southland Greats Selected for Conference’s Hall of Honor

first_imgA native of Beaumont, Simmons earned music degrees from Memphis State, Houston and McNeese State before returning to Beaumont in 1970, joining the Lamar faculty as an instructor and director of the marching band. He rose through the ranks, later being named chair of the Department of Music and Theatre and dean of the College of Fine Arts, and assumed the school’s presidency on September 1, 1999.Throughout his educational career, Simmons has attracted acclaim as a performer on clarinet, saxophone and piano. He continues to maintain a performance schedule. FRISCO, Texas – The Southland Conference revealed its Hall of Honor Class of 2018 featuring a former university president, a longtime volleyball official and administrator, and a former standout student-athlete, league Commissioner Tom Burnett announced Thursday.  The honorees will bring the total number of inductees to 50 dating back to the Southland’s first Hall of Honor class in 1999. Southland Hall of Honor “The Southland Conference membership is honored to induct these three exceptionally worthy individuals, each with remarkable records of sustained success,” said Burnett.  “Dr. Jimmy Simmons provided more than 40 years of service to Lamar and is regarded as one of the most impactful presidents in school history.  He served Lamar athletics tremendously throughout his tenure, and was a strong presidential voice in Southland matters.” Rhonda Rube-Baird, Northwestern State softballRhonda Rube-Baird, a Baton Rouge native, was the first All-American in Northwestern State softball history. The catcher was a two-time Southland Conference Softball Player of the Year and earned All-Conference honors in each of her four years in Natchitoches (1989-92). The Class of 2018 features the induction of former Lamar University president Dr. Jimmy Simmons, legendary league coordinator of volleyball officials Linda Fletcher, and former Northwestern State softball student-athlete Rhonda Rube-Baird.   The inductees, selected by the Conference’s Awards Committee, are slated for induction May 22 at the annual Southland Honors Dinner and Ceremony at the Westin Stonebriar Hotel in Frisco.   Fletcher will be honored posthumously.   His tenure was marked with numerous capital campaigns, reaching over $130 million raised by the time he retired, including development efforts to provide funding for scholarship programs, academic offerings, and construction projects.   During Simmons’ years in office, several programs were added at Lamar including five new bachelor’s degrees, three new master’s degrees, and three doctoral degrees and the first Ph.D. program at the university. Dr. James “Jimmy” Simmons, President, LamarDr. Jimmy Simmons was president of Lamar for 14 years — from 1999 until 2014 – and he worked at the university for more than four decades.  He also served as the chairman of the Southland Conference Board of Directors from 2001-2004. Fletcher began officiating in 1967 as a State Volleyball referee for the University Interscholastic League (Texas high schools). She went on to help found the Southwest Volleyball Officials’ Association, later renamed the Texas Association of Sports Officials. The longtime Austin, Texas, resident was a charter member of the Austin High School Volleyball Chapter of the organization. Former Southland Conference volleyball referee and Fletcher’s longtime friend Gloria Cox will accept the award at the Honors Dinner. Linda Fletcher, Former Southland Conference Coordinator of Volleyball OfficialsVolleyball pioneer Linda Fletcher was the conference’s coordinator of officials for nearly 30 years. Fletcher began as the league’s coordinator of officials in 1989, serving in the role until she passed away unexpectedly in October 2017.  Fletcher served in a similar capacity for the Southwest Conference. A 2000 inductee in the Professional Association of Volleyball Officials Hall of Fame, Fletcher was an advocate for teaching and coaching, mentoring hundreds of referees during her half century in the sport. “Rhonda Rube-Baird was a dominant performer in Southland softball from 1989-92, as evidenced by her All-American honors and placement on the Southland Softball All-Time Team,” Burnett said. “Ultimately, she was a true student-athlete, receiving Southland All-Academic recognition in addition to her on-field accomplishments.” Fletcher held bachelor’s and master’s degrees from the University of Texas. Athletics at Lamar blossomed under Simmons’ direction, as the Cardinals’ varsity football program was brought back to competition after a 25-year absence.  Additionally, Lamar also brought back varsity softball and added women’s soccer during his tenure.   “Linda Fletcher started with the Southland when volleyball was brand new to the league,” Burnett added.  “And she went on to spend 29 years performing wonderful work as an administrator and ambassador for the game that she loved deeply. It’s impossible to overstate her passion and how beloved she was within the volleyball community.” In conjunction with the Southland’s 50th anniversary in 2013, Baird was named to the conference’s Softball All-Time Team.   She finished her career with a .327 career batting average, departing with the Southland record for career doubles with 45 (10th best all-time in the NCAA at the time). Defensively, she cut down 32 percent (48-148) of runners who attempted to steal on her and picked off 13 baserunners from behind the plate. Other highlights include her team’s first Southland championship in 1991, a season in which she hit .341 and boasted a .992 fielding percentage. As a senior, Baird earned Southland All-Academic honors. She went on to secure two bachelor’s degrees from Northwestern State.  She is a registered nurse in Baton Rouge.last_img read more

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Computers Learn to Imagine the Future

first_imgPredicting the future position of objects comes natural for humans, but it is quite difficult for a computer. (Credit: Shutterstock)In many ways, the human brain is still the best computer around. For one, it’s highly efficient. Our largest supercomputers require millions of watts, enough to power a small town, but the human brain uses approximately the same energy as a 20-watt bulb. While teenagers may seem to take forever to learn what their parents regard as basic life skills, humans and other animals are also capable of learning very quickly. Most of all, the brain is truly great at sorting through torrents of data to find the relevant information to act on.At an early age, humans can reliably perform feats such as distinguishing an ostrich from a school bus, for instance – an achievement that seems simple, but illustrates the kind a task that even our most powerful computer vision systems can get wrong. We can also tell a moving car from the static background and predict where the car will be in the next half-second. Challenges like these, and far more complex ones, expose the limitations in our ability to make computers think like people do. But recent research at Los Alamos National Laboratory is changing all that.Brain neuroscientists and computer scientists call this field neuromimetic computing – building computers inspired by how the cerebral cortex works. The cerebral cortex relies on billions of small biological “processors” called neurons. They store and process information in densely interconnected circuits called neural networks. In Los Alamos, researchers are simulating biological neural networks on supercomputers, enabling machines to learn about their surroundings, interpret data and make predictions much the way humans do.This kind of machine learning is easy to grasp in principle, but hard to implement in a computer. Teaching neuromimetic machines to take on huge tasks like predicting weather and simulating nuclear physics is an enterprise requiring the latest in high-performance computing resources.Los Alamos has developed codes that run efficiently on supercomputers with millions of processing cores to crunch vast amounts of data and perform a mind-boggling number of calculations (over 10 quadrillion!) every second. Until recently, however, researchers attempting to simulate neural processing at anything close to the scale and complexity of the brain’s cortical circuits have been stymied by limitations on computer memory and computational power.All that has changed with the new Trinity supercomputer at Los Alamos, which became fully operational in mid-2017. The fastest computer in the United States, Trinity has unique capabilities designed for the National Nuclear Security Administration’s stockpile stewardship mission, which includes highly complex nuclear simulations in the absence of testing nuclear weapons. All this capability means Trinity allows a fundamentally different approach to large-scale cortical simulations, enabling an unprecedented leap in the ability to model neural processing.To test that capability on a limited-scale problem, computer scientists and neuroscientists at Los Alamos created a “sparse prediction machine” that executes a neural network on Trinity. A sparse prediction machine is designed to work like the brain: researchers expose it to data – in this case, thousands of video clips, each depicting a particular object, such as a horse running across a field or a car driving down a road.Cognitive psychologists tell us that by the age of six to nine months, human infants can distinguish objects from background. Apparently, human infants learn about the visual world by training their neural networks on what they see while being toted around by their parents, well before the child can walk or talk.Similarly, the neurons in a sparse prediction machine learn about the visual world simply by watching thousands of video sequences without using any of the associated human-provided labels – a major difference from other machine-learning approaches. A sparse prediction machine is simply exposed to a wide variety of video clips much the way a child accumulates visual experience.In this sequence of video frames, the first three are machine-learning data representations of scanned videos. In the fourth frame, the video predicted or “imagined” what the next frame would be, based on the data. The work was performed at Los Alamos National Laboratory on Trinity, the largest supercomputer in the United States. (Courtesy of LANL)When the sparse prediction machine on Trinity was exposed to thousands of eight-frame video sequences, each neuron eventually learned to represent a particular visual pattern. Whereas a human infant can have only a single visual experience at any given moment, the scale of Trinity meant it could train on 400 video clips simultaneously, greatly accelerating the learning process. The sparse prediction machine then uses the representations learned by the individual neurons, while at the same time developing the ability to predict the eighth frame from the preceding seven frames, for example, predicting how a car moves against a static background.The Los Alamos sparse prediction machine consists of two neural networks executed in parallel, one called the Oracle, which can see the future, and the other called the Muggle, which learns to imitate the Oracle’s representations of future video frames it can’t see directly. With Trinity’s power, the Los Alamos team more accurately simulates the way a brain handles information by using only the fewest neurons at any given moment to explain the information at hand. That’s the “sparse” part, and it makes the brain very efficient and very powerful at making inferences about the world – and, hopefully, a computer more efficient and powerful, too.After being trained in this way, the sparse prediction machine was able to create a new video frame that would naturally follow from the previous, real-world video frames. It saw seven video frames and predicted the eighth. In one example, it was able to continue the motion of car against a static background. The computer could imagine the future.This ability to predict video frames based on machine learning is a meaningful achievement in neuromimetic computing, but the field still has a long way to go. As one of the principal scientific grand challenges of this century, understanding the computational capability of the human brain will transform such wide-ranging research and practical applications as weather forecasting and fusion energy research, cancer diagnosis and the advanced numerical simulations that support the stockpile stewardship program in lieu of real-world testing.To support all those efforts, Los Alamos will continue experimenting with sparse prediction machines in neuromorphic computing, learning more about both the brain and computing, along with as-yet undiscovered applications on the wide, largely unexplored frontiers of quantum computing. We can’t predict where that exploration will lead, but like that made-up eighth video frame of the car, it’s bound to be the logical next step.[Garrett Kenyon is a computer scientist specializing in neurally inspired computing in the Information Sciences group at Los Alamos National Laboratory, where he studies the brain and models of neural networks on the Lab’s high-performance computers. Other members of the sparse prediction machine project were Boram Yoon of the Applied Computer Science group and Peter Schultz of the New Mexico Consortium.]last_img read more

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