Given that some outrage-stirring politicians have begun recounting details from action movies like "Sicario" as though they were hard facts, certain viewers may bristle at the very idea of the low-budget thriller "My Stretch of Texas Ground," which imagines a scenario where a terrorist cell sneaks an assassin across the Mexican border to take out a U.S. senator. But director Erich Kemp and screenwriter Ralph Cinque are surprisingly even-handed in their depiction of international crime and its consequences. By opening with multiple discussions of drone attacks and "enhanced interrogation," "My Stretch of Texas Ground" creates a context for its central stand-off, between a shrewd killer, Abdul (Junes Zahdi), and a wise small-town sheriff, Joe Haladin (Jeff Weber). If anything, the film's main problem is that it feels more like a debate than a cop picture, with too much of its leisurely running time set aside for airing different points of view, and too little for shootouts and chases. The other big stumbling block is that the production often borders on the amateurish, with weak acting, flat lighting and poor sound.
NASA is ready to put its drone traffic management system to the ultimate test and has chosen Nevada and Texas as its final testing sites. The agency, together with the FAA, has been developing an Unmanned aircraft Traffic Management (UTM) system over the past four years in an effort to figure out how to safely fly drones in an urban environment. Now that the project is in its last phase, it has teamed up with the Nevada Institute for Autonomous Systems in Las Vegas and the Lone Star UAS Center for Excellence & Innovation in Corpus Christi, Texas to conduct a final series of technical demonstrations. NASA and the FAA are planning to demo a big list of technologies, including their interface with vehicle-integrated detect-and-avoid capabilities, vehicle-to-vehicle communication and collision avoidance, as well as automated safe landing technologies. All those will help NASA understand the challenges of flying in an urban environment and conjure up ideas for future rules and policies.
Machine learning is everywhere in science and technology: powering facial recognition, picking your recommendations on Netflix, and controlling self-driving cars. But how reliable are machine learning techniques really? A statistician says that the answer is "not very," arguing that questions of accuracy and reproducability of machine learning have not been fully addressed. Dr Genevera Allen, associate professor of statistics, computer science, and electrical and computer engineering Rice University in Houston, Texas has discussed this topic at a press briefing and at a scientific conference, the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). She warned that researchers in the field of machine learning have spent so much time developing predictive models that they have not devoted enough attention to checking the accuracy of their models, and that the field must develop systems which can assess the accuracy of their own findings.
Smart Cities are the future. So when Houston, Texas faced rebuilding in the aftermath of Hurricane Harvey in 2017, it seized the opportunity to transform itself as a tech-centric, smart city by incorporating emerging technologies including Artificial Intelligence, IoT, Machine Learning and data analytics. Houston is being extremely planful in building multiple innovative solutions across departments at the same time that communicate with one another which is significantly increasing the positive impact it's bringing to its citizens. As a result, Houston has come to serve as a model for Smart City initiatives. We will hear from those responsible for Houston's transformation and examine what others - from policymakers to city officials to business leaders - can learn from their experience.
At his keyboard in Austin, Texas, Bryan Bishop was writing quickly. A nationally ranked speed typist, he had drafted a polite inquiry to a prominent futurist in the UK. He wanted advice on his "designer baby startup." For a few years now, Bishop, a 29-year-old programmer and Bitcoin investor, has been leaving a trail of comments about human "enhancement" on the web. He's a transhumanist, which means he thinks humans can be improved in profound ways by technology. He'd long exhorted others to do something about the human condition. Now, he had decided to do it himself.
Tensor Flow, Theano, Torch, Kaldi, etc.) - 3 years experience in commercial R&D experience relating to acoustic/speech modeling and/or audio DSP - 2 years graduate-level academic research experience in acoustic/speech modeling and/or audio DSP Suggested experience: - Statistical and Audio Digital Signal Processing (Linear Systems) - Mathematical optimization (designing cost functions, adversarial/perceptual methods to improve audio quality, etc.) - Familiarity with hardware/embedded systems- - Linguistics and speech modeling Neosensory provides a competitive compensation package, stock options, benefits, and a fun work environment. We're located within a 5 min walk from the California Ave Caltrain station in a nice area of Palo Alto. We are also about to launch a second office in Houston, Texas. Team Neosensory is made of an awesome group of intellectual individuals who value hard work and enjoy sharing a diverse set of hobbies.
A robot named Moxi, designed to help nurses, has concluded its first real-world trial in a Texas hospital. Designed by Boston-based Diligent Robotics, the trial was designed to test a collaborative automation integration in a working hospital. Robots are widely seen as one potential tool to help relieve strain on healthcare workers like nurses. According to the Bureau of Labor Statistics, demand for nurses in the U.S. is set to grow from 2.7 million in 2014 to 3.2 million in 2024, an increase of 16 percent. Much of the growth will be driven by aging baby boomers who need additional care.
Apple said it would add more than 1,000 employees apiece in San Diego, Seattle and Culver City, Calif., areas where it has been increasing staff to support its development of custom chips, machine-learning systems and Hollywood programming. It also plans hundreds of additional jobs in cities where it already has offices, including New York, Boston and Portland, Ore. The Austin campus would have the capacity to eventually accommodate 15,000 employees, Apple said, and was expected to make the company the city's largest private employer. The announcement came weeks after Amazon.com Inc. and Alphabet Inc. GOOGL -0.02% said they would expand in regions where they already have a presence.
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and managerial factors, such as climate, soil conditions, tillage, and irrigation. In this paper, we present a novel Spatial-temporal Multi-Task Learning algorithms for within-field crop yield prediction in west Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data sources to learn different features simultaneously, and to aggregate spatial-temporal features by introducing a weighted regularizer to the loss functions. Our comprehensive experimental results consistently outperform the results of other conventional methods, and suggest a promising approach, which improves the landscape of crop prediction research fields.
RaySearch exhibited its latest advances in oncology software at the 2018 American Society for Radiation Oncology (ASTRO) annual meeting, Oct. 21-24 in San Antonio, Texas, backed by machine learning and user-friendly tools to enable optimal use of clinical resources. The company showed its latest development in machine learning technology and automation in both its RayStation treatment planning system (TPS) and RayCare oncology information system (OIS). With its in-house machine learning department, RaySearch is currently developing machine learning and deep learning prototypes for areas such as treatment plan generation, organ segmentation and target volume estimation. RaySearch is also developing techniques for efficient large-scale data extraction and analysis. The first clinical applications of machine learning are automated treatment planning and automated organ segmentation and will be included in the next RayStation release in December.