Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -- generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.

In the heart of Silicon Valley, Stanford clinicians and researchers are exploring whether artificial intelligence could help manage a potential surge of Covid-19 patients -- and identify patients who will need intensive care before their condition rapidly deteriorates. The challenge is not to build the algorithm -- the Stanford team simply picked an off-the-shelf tool already on the market -- but rather to determine how to carefully integrate it into already-frenzied clinical operations. "The hardest part, the most important part of this work is not the model development. But it's the workflow design, the change management, figuring out how do you develop that system the model enables," said Ron Li, a Stanford physician and clinical informaticist leading the effort. Li will present the work on Wednesday at a virtual conference hosted by Stanford's Institute for Human-Centered Artificial Intelligence.

Fox News finds the coronavirus outbreak has left San Francisco streets and tourist sites including Chinatown and Fisherman's Wharf largely deserted. Get all the latest news on coronavirus and more delivered daily to your inbox. New drone footage and other video shot by Fox News shows once-busy streets and tourist areas in Los Angeles and San Francisco eerily deserted as the coronavirus has kept people indoors. Fisherman's Wharf, one of San Francisco's busiest tourist areas, once brimming with souvenir shops and seafood stalls and situated near Ghirardelli Square, was shuttered after the city's mayor called for a shelter-in-place, restricting people from leaving their homes except for trips to the grocery store or for medical supplies. The Golden Gate Bridge, which usually has seen over 100,000 cars and other vehicles a day and Alamo Square -- which overlooks the famous "Painted Ladies" -- were surprisingly barren.

Modern capitalism has never seen anything quite like the novel coronavirus SARS-CoV-2. In a matter of months, the deadly contagious bug has spread around the world, hobbling any economy in its path. In the United States, where consumer spending accounts for more than two-thirds of economic activity, commerce has come to a standstill as people stay home to slow the virus' spread. Hotels and restaurants and airlines have taken massive hits; Delta has cut its flight capacity by 70 percent. One in five US households has already lost work.

UPS has partnered with the German tech company Wingcopter to build a fleet of rugged, high speed delivery drones. The drones will be based on a model designed by Wingcopter, which can travel at speeds of up to 150mph and has a range of 75 miles. The drones can also endure a variety of difficult weather conditions, including wind speeds of up to 45mph. The agreements marks the first external partnership for UPS's Flight Forward program, which is focused on developing a range of drone delivery options, according to a report in TechCrunch. 'Drone delivery is not a one-size-fits-all operation,' UPS's Bala Ganesh said.

The education ministry said Tuesday it has completed its first screening of new textbooks under new teaching guidelines that are planned to be fully implemented from April 2021, approving 106 textbooks in 10 subjects. The average number of pages for a batch of textbooks approved to be used by junior high school students starting in fiscal 2021 rose 7.6 percent from that for current textbooks, the ministry said. The total number of textbook pages exceeded 11,000 in A5 format at the time of applications. The new teaching guidelines place importance on active learning methods, in which students learn proactively through debates and other learning activities, in order to nurture their intellectual ability to find and resolve problems themselves. For this purpose, many of the new textbooks present learning challenges at the outset of chapters and subchapters, and encourage students to have debates in groups after the end of the sections to deepen their understanding.

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.

The emerging COVID-19 pandemic has become a once-in-a-century challenge that has impacted society profoundly and has disrupted almost every facet of life for people around the world. The demands on the infrastructure of health information technology (Health IT) are numerous, as we learn to use the tools we have created to address the issues we are facing now. In most areas where work-from-home and social distancing have become prevalent, and especially in areas where shelter-in-place orders have been issued, the nature of ambulatory care has dramatically changed. Some practices, such as non-trauma orthopedics, are unable to find operating room availability, and their practices have ground to a halt. The result of this has been a dramatic and sudden decrease in office volume – 5 or 6 virtual visits in a day, plus one or two in-person visits, in the place where a practice used to be 20 visits per provider per day, is not economically sustainable, especially for small and independent practices.

A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones. A random mode-coupling matrix model is introduced to generate a large number of training data quickly, which are used to train the U-Net. The performance of AIS recovery of the U-Net is tested in range-dependent waveguides with nonlinear internal waves (NLIWs). Although the random mode-coupling matrix model is not an accurate physical model, the test results show that the U-Net successfully recovers AISs under different signal-to-noise ratios (SNRs) and different amplitudes and widths of NLIWs for different shapes.

Consider the grocery clerks at two Safeway stores in the San Francisco Bay Area. A few weeks ago, over 200 workers who are members of the United Food and Commercial Workers Local 5 (UFCW5) union picketed a Safeway store in San Jose, Calif. to voice concerns about a push by parent company Albertsons to add more A.I to its operations. Albertsons recently partnered with the startup Takeoff Technologies to create mini warehouses where computer vision technology automatically sorts items that shoppers order online. Using A.I. reduces the need for Safeway staff to manually locate and grab items for delivery--workers now just retrieve the finalized orders from a conveyor belt and sign off on them for eventual delivery. Several grocery store chains are investing heavily in micro-fulfillment centers after Amazon helped to popularize as-fast-as-you-can deliveries, said Andrew Lipsman, a principal analyst at research firm eMarketer.