The quality of area covering 3D point clouds as captured by aerial and mobile mapping platforms still experiences a considerable boost due to the ongoing advancements in LiDAR technology and Multi-View-Stereo-Matching (MVS). In addition to further enhancement of the respective accuracy, density and reliability the semantic segmentation of these point clouds come more and more into focus. Also triggered by the astonishing improvements in the field of pattern recognition and machine learning, automatic interpretation of area covering point clouds including tasks like object detection and classification or object-dependent filtering and smoothing is moving rapidly to a mature state. In view of these developments, the meeting brings together experts from industry, academia and national mapping agencies to present and discuss the processing and evaluation of point clouds focusing on mapping purposes. The program will provide a mix of invited speakers from industry, academia and governmental organizations as well as presentations selected on an abstract based review process. Prospective presenters may send a 1000 word abstract to pcp2019@ifp.uni-stuttgart.de

DUBAI, UNITED ARAB EMIRATES – A weekend drone attack on Saudi Arabia that cut into global energy supplies and halved the kingdom's oil production threatened Sunday to fuel a regional crisis, as Iran denied U.S. allegations it launched the assault and tensions remained high over Tehran's collapsing nuclear deal. Iran called the U.S. claims "maximum lies," while a commander in its paramilitary Revolutionary Guard reiterated its forces could strike U.S. military bases across the Mideast with their arsenal of ballistic missiles. A prominent U.S. senator suggested striking Iranian oil refineries in response to the assault, claimed by Yemen's Iran-backed Houthi rebels, on Saudi Arabia's largest oil processing facility. "Because of the tension and sensitive situation, our region is like a powder keg," warned Guard Brig. "When these contacts come too close, when forces come into contact with one another, it is possible a conflict happens because of a misunderstanding."

Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case? Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in?

Digital agriculture increasingly relies on the generation of large quantity of images. These images are processed with machine learning techniques to speed up the identification of objects, their classification, visualization, and interpretation. However, images must comply with the FAIR principles to facilitate their access, reuse, and interoperability. As stated in recent paper authored by the Planteome team (Trigkakis et al, 2018), "Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy." In this third Ontologies Community of Practice webinar, Justin Preece, Senior Faculty Research Assistant Oregon State University, presents the module developed by the Planteome project using the Bio-Image Semantic Query User Environment (BISQUE), an online image analysis and storage platform of Cyverse.

With more than 2 billion X-Ray exams done annually, X-Ray is often the hospital's first impression of a patient. Just like first impressions with people, the first image taken helps set the path going forward. "We are getting portable X-Rays all the time for our patients," said Dr. Rachael Callcut, Associate Professor of Surgery at the University of California, San Francisco (UCSF) Medical Center and Director of Data Science for the Center for Digital Health Innovation. "When an X-Ray is taken on a patient, especially a patient who's suffering from an emergent condition or a potentially life-threatening condition, the time that it takes to process, have someone read that and have the image actually come into a queue is a really important time period where minutes and hours matter. For example, a collapsed lung, known as a pneumothorax, is a condition which strikes nearly 74,000 Americans each year[1] and can be deadly if not diagnosed quickly and accurately[2]. A pneumothorax occurs when air leaks into the space between the lung and chest wall. This air pushes on the outside of the lung and makes it collapse. It can be caused by trauma, cigarette smoking, drug abuse, certain lung diseases or be caused by complications from surgery. Today, patients who present with symptoms associated with this condition receive a chest X-Ray, which can take anywhere between two to eight hours to read[3]. Tension pneumothorax or an enlarging pneumothorax can develop as a result of delayed treatment[4], potentially leading to fatal consequences if not treated quickly. This is an example of what may be designated as a "STAT" chest X-Ray, which is supposed to be reserved for potentially life-threatening circumstances. It is a designation on the exam placed at the time of order entry and refers to the ordering provider's determination that the results require immediate interpretation and follow-up. STAT portable chest X-Rays can attribute to more than 60 percent of a radiology center's mobile chest X-ray volume, almost double that of routine exams3. "There's no universally accepted definition of what constitutes a STAT exam," said Dr. Karl Yaeger, a diagnostic radiologist at St. Luke's University Health Network in Bethlehem, Pennsylvania. "Is it STAT because the patient is medically unstable?

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Artificial intelligence (AI) can transform the productivity and GDP potential of the UK landscape. But, we need to invest in the different types of AI technology to make that happen. Our research shows that the main contributor to the UK's economic gains between 2017 and 2030 will come from consumer product enhancements stimulating consumer demand (8.4%). This is because AI will drive a greater choice of products, with increased personalisation and make those products more affordable over time. Labour productivity improvements will also drive GDP gains as firms seek to "augment" the productivity of their labour force with AI technologies and to automate some tasks and roles.

The prediction of molecular reactions triggered by light is to date extremely time-consuming and therefore costly. A team led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna has now presented a method using artificial neural networks that drastically accelerates the simulation of light-induced processes. The method provides new possibilities for a better understanding of biological processes such as the first steps of carcinogenesis or ageing processes of matter. The study appeared in the current issue of the journal "Chemical Science", also including an accompanying illustration on one of its covers. Machine learning plays an increasingly important role in chemical research, e.g. in the discovery and development of new molecules and materials.