Several public security systems depend directly on human action in numerous stages of its operation. The monitoring of public areas, for instance, is usually done with the use of cameras spread over the busiest places in large urban centers. In general, these systems depend on an operator to pay attention to the images so that the agencies responsible for security can be activated when events such as thefts, vandalism, and traffic accidents are observed. Considering the amount of information to which the operator is exposed, there is a high probability that surveillance failures will occur, even if the patrol center has a large team [1]. Although the operators are attentive at all times, this type of monitoring has some disadvantages: the images are limited to the direction in which the camera points and have low visibility at dusk and in cases of rain or bright light. Besides, events such as gunshots, alarms, distress calls, among others, are much more noticeable in the auditory field than in the visual [2, 3]. In this sense, the monitoring of risk areas could be done through the use of audio processing techniques, reducing the need for human participation in the surveillance process, and making public security systems more efficient [4]. To support this argument, it is worth recalling two very favorable characteristics concerning these signals: initially, the sound consumes less bandwidth in the transmission of information, reducing the need for high transmission rates, as in the case of high definition images; in addition, sound processing techniques require, in general, less computational power than techniques for video processing and analysis, which would enable the implementation of simpler and therefore less costly embedded systems [3, 5].

Elham Mansoori, member of Afghan Dreamers, an all-girls robotics team in Afghanistan, works on their prototype of a ventilator. In Afghanistan, a group of teenage girls are trying to build a mechanized, hand-operated ventilator for coronavirus patients, using a design from M.I.T. and parts from old Toyota Corollas. It sounds like an impossible dream, but then again, the all-girls robotics team in question is called the "Afghan Dreamers." Living a country where two-thirds of adolescent girls cannot read or write, they're used to overcoming challenges. The team of some dozen girls aged 15 to 17 was formed three years ago by Roya Mahboob, an Afghan tech entrepreneur who heads the Digital Citizen Fund, a group that runs classes for girls in STEM and robotics and oversees and funds the Afghan Dreamers.

UK prime minister Boris Johnson told MPs today that he is confident that the government will have recruited 25,000 coronavirus contact tracers by the start of June, which he says will provide the capacity to trace the contacts of 10,000 new coronavirus cases per day. Johnson said 24,000 contact tracers have already been recruited. In April, health secretary Matt Hancock said the government hoped to recruit 18,000 contact tracers by mid-May, to coincide with the planned release of the NHS covid-19 contact tracing app. But the widespread release of the app, currently being trialled on the Isle of Wight, has now been delayed until June. There are also ongoing concerns about privacy. In a recent report, security researchers wrote that there should be a legal requirement that all data collected by the app is deleted at the end of the coronavirus crisis, rather than being anonymised or repurposed.

Artificial intelligence (AI) may be an aid to interpreting ECG results, helping healthcare staff to diagnose diseases that affect the heart. Researchers at Uppsala University and heart specialists in Brazil have developed an AI that automatically diagnoses atrial fibrillation and five other common ECG abnormalities just as well as a cardiologist. The study has been published in Nature Communications. An electrocardiogram (ECG) is a simple test that can be used to check the heart's rhythm and electrical activity. The results are shown on a graph that can reveal various conditions that affect the heart.

From May 22, Didi's ride-hailing drivers in Latin America will need to take a selfie with mask on to pass the AI verification, and from June they will need to report their body temperature to the phone app and upload photos of daily vehicle disinfection works to the phone application.

Zebra Medical Vision, the deep-learning medical imaging analytics company, announced on Monday that it secured its 5th FDA clearance, this time for an AI solution that identifies findings suggestive of compression fractures in scans. The Israeli firm said the FDA gave 510(k) clearance for its Vertebral Compression Fractures (VCF) product that enables clinicians to place patients at risk of osteoporosis "in treatment pathways to prevent potentially life-changing fractures," Zebra Medical said in a statement. The solution can be applied to abdominal or chest CT scan performed for any clinical indication, the company says. Founded in 2014 by Eyal Toledano, Eyal Gura, and Elad Benjamin, Zebra uses AI to read medical scans and automatically detect anomalies. Through its development and use of different algorithms, Zebra Medical has been able to identify visual symptoms for diseases such as breast cancer, osteoporosis, and fatty liver, as well as conditions such as aneurysms and brain bleeds.

Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and emulation. They are widely used because they are simple, flexible, and provide accurate estimates. GPs are based on a Bayesian statistical framework which provides a posterior probability function for each estimation. Therefore, besides the usual prediction (given in this case by the mean function), GPs come equipped with the possibility to obtain a predictive variance (i.e., error bars, confidence intervals) for each prediction. Unfortunately, the GP formulation usually assumes that there is no noise in the inputs, only in the observations. However, this is often not the case in earth observation problems where an accurate assessment of the measuring instrument error is typically available, and where there is huge interest in characterizing the error propagation through the processing pipeline. In this letter, we demonstrate how one can account for input noise estimates using a GP model formulation which propagates the error terms using the derivative of the predictive mean function. We analyze the resulting predictive variance term and show how they more accurately represent the model error in a temperature prediction problem from infrared sounding data.

If science was a journey, then its destination would be the discovery of simple explanations to complex phenomena. There was a time when the existence of tides, the planet's orbit around the sun, and the observation that "things fall down" were all largely considered to be independent phenomena--until 1687, when Isaac Newton formulated his law of gravitation that provided an elegantly simple explanation to all of these (and many more). Physics has made tremendous progress over the last few centuries, but the thriving field of deep learning is still very much at the beginning of its journey--often lacking a detailed understanding of the underlying principles. For some time, the tremendous success of deep learning has perhaps overshadowed the need to thoroughly understand the behaviour of Deep Neural Networks (DNNs). In an ever-increasing pace, DNNs were reported as having achieved human-level object classification performance [1], beating world-class human Go, Poker, and Starcraft players [2, 3], detecting cancer from X-ray scans [4], translating text across languages [5], helping combat climate change [6], and accelerating the pace of scientific progress itself [7]. Because of these successes, deep learning has gained a strong influence on our lives and society.

Many objectives can be achieved (or may be achieved more effectively) only by a group of agents executing a team plan. If a team plan fails, it is often of interest to determine what caused the failure, the degree of responsibility of each agent for the failure, and the degree of blame attached to each agent. We show how team plans can be represented in terms of structural equations, and then apply the definitions of causality introduced by Halpern [2015] and degree of responsibility and blame introduced by Chockler and Halpern [2004] to determine the agent(s) who caused the failure and what their degree of responsibility/blame is. We also prove new results on the complexity of computing causality and degree of responsibility and blame, showing that they can be determined in polynomial time for many team plans of interest.

In this paper, we propose the use of traditional animations, heuristic behavior and reinforcement learning in the creation of intelligent characters for computational media. The traditional animation and heuristic gives artistic control over the behavior while the reinforcement learning adds generalization. The use case presented is a dog character with a high-level controller in a 3D environment which is built around the desired behaviors to be learned, such as fetching an item. As the development of the environment is the key for learning, further analysis is conducted of how to build those learning environments, the effects of environment and agent modeling choices, training procedures and generalization of the learned behavior. This analysis builds insight of the aforementioned factors and may serve as guide in the development of environments in general.