To be an international trade or development hub, the WHO should take seriously this opportunity. We should not treat other countries as an obstacle to globalisation. A globalisation that requires the development of high-quality, effective and sustainable healthcare would also need to address the real needs of the non-human animals. It must be seen in that the current outbreak in Wuhan's Zhuhai Province in China is one of the most complex challenges to social control of the animal population in Wuhan, the number of infected animals at the time are estimated to be about 200,000, and the number of non-human animals is estimated to be about 400,000. All these numbers are based on the assumption that the animal rights of non-human animals are not strictly based on the rights and behaviour of non-human animals but only on the rights and behaviour of them.

In a move that caused a ripple effect across the Middle East, Iranian General Qassem Soleimani was killed in a US drone strike near Baghdad's international airport on January 3. On that day, the Pentagon announced the attack was carried out "at the direction of the president". In a new report examining the legality of armed drones and the Soleimani killing in particular, Agnes Callamard, UN special rapporteur on extrajudicial and arbitrary killings, said the US raid that killed Soleimani was "unlawful". Callamard presented her report at the Human Rights Council in Geneva on Thursday. The United States, which is not a member after quitting the council in 2018, rejected the report saying it gave "a pass to terrorists". In Callamard's view, the consequences of targeted killings by armed drones have been neglected by states.

In mathematics, a type of variation called direct variation describes'a simple relationship between two variables -- such that we observe an increase of one variable when the other increases, or a decrease of the same when the other reduces'. This law works in business as well, wherein the population of the workforce determines the productivity of the company. However, recent development in artificial intelligence defies this law. In 2020, a business could have only a team of essential workers with a couple of well-utilized computers and do more than a larger company which has the help of its thousands of workers but without digital intervention or its innovative implementation whatsoever. The usefulness of computer technology in business has been paced recently in such a way that most businesses will not need to hire more workers in the future.

Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions. However, learning an accurate Markov Decision Process (MDP) over high-dimensional states (e.g., raw pixels) is extremely challenging because it requires function approximation, which leads to compounding errors. Instead, to avoid compounding errors, we propose learning an abstract MDP over abstract states: low-dimensional coarse representations of the state (e.g., capturing agent position, ignoring other objects). We assume access to an abstraction function that maps the concrete states to abstract states. In our approach, we construct an abstract MDP, which grows through strategic exploration via planning. Similar to hierarchical RL approaches, the abstract actions of the abstract MDP are backed by learned subpolicies that navigate between abstract states. Our approach achieves strong results on three of the hardest Arcade Learning Environment games (Montezuma's Revenge, Pitfall!, and Private Eye), including superhuman performance on Pitfall! without demonstrations. After training on one task, we can reuse the learned abstract MDP for new reward functions, achieving higher reward in 1000x fewer samples than model-free methods trained from scratch.

Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with massive pedestrians. We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability, and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: 1) varying pedestrian distribution ratios, 2) varying exit width ratios, and 3) varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to the high efficiency of exit utilization.

Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative models, while introducing a randomized predictor that samples a structure from a randomly perturbed score function. In this work, we learn the variance of these randomized structured predictors and show that it balances better between the learned score function and the randomized noise in structured prediction. We demonstrate empirically the effectiveness of learning the balance between the signal and the random noise in structured discrete spaces.

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Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another. Our observations about the challenges of human translation of tense and mood have important implications for multilingual NLP. Of particular importance is the challenge of modeling tense and mood in rule-based, phrase-based statistical and neural machine translation.

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers. In this paper, we present an interaction scenario adapted to ASD teenagers, propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection, and present the results of our basic testing of the scenario with human caregivers. These results are preliminary due to the number of session (1) and participants (4). They include a technical assessment of the performance of Openpose, as well as a preliminary user study to confirm our game scenario could elicit the expected response from subjects.