Covid-19 has caused an unprecedented amount of uncertainty across the world, with businesses and people alike feeling the strain. However, amidst all of this uncertainty, one thing has remained constant and that is the unwavering efforts of those on the NHS frontline. While we have passed the peak of the pandemic in the UK, we must not forget the immense strain which the NHS has been put under. It is a testament to all of those working within the service that it has remained firm, saving countless lives in the process. Moving forwards, though, we must find a way to ease the burden on NHS workers.

Today the United States crumbles under the weight of two pandemics: coronavirus and police brutality. Both wreak physical and psychological violence. And both are animated by technology that we design, repurpose, and deploy--whether it's contact tracing, facial recognition, or social media. We often call on technology to help solve problems. But when society defines, frames, and represents people of color as "the problem," those solutions often do more harm than good.

Osaka – Late last month, the Diet passed a revised bill paving the way for so-called "super cities" or "smart cities." Supporters tout them as high-tech marvels where artificial intelligence and big data are to be used to provide more efficient and cost-effective solutions to social problems, especially in areas faced with aging and declining populations and a reduced tax base. Opponents warn that data leaks could lead to privacy violations and even a surveillance state. What was the purpose of the recently passed bill? In order to realize the creation of smart cities in various parts of the country, any number of basic regulations involving multiple ministries needs to be changed. The May 27 revision to a national strategic special zone law included measures the government can now take to do that more quickly and under more specific guidelines.

After a nine minute trip into orbit and a few hundred mile journey to the coast of Florida, SpaceX's Falcon 9 rocket has finally returned home. The rocket pulled into Port Canaveral aboard the firm's drone ship'Of Course I Still Love You' after launching NASA astronauts Bob Behnken and Doug Hurley toward the International Space Station (ISS) aboard a Crew Dragon capsule May 30. Falcon 9 pulled into the port as a hero, following the launch on Saturday that brought spaceflight back to US soil. NASA and Elon Musk's SpaceX made history with their'Launch America' mission on May 30 that launched Behnken and Hurley from Kennedy Space Center in Cape Canaveral, Florida to the International Space Station - the first time in nine years an American crew has launched from US soil. Falcon 9 pulled into Port Canaveral aboard the firm's drone ship'Of Course I Still Love You' after launching NASA astronauts Bob Behnken and Doug Hurley toward the International Space Station aboard a Crew Dragon capsule May 30 The launch was initially set to take place May 27 but was scrubbed with 16 minutes and 54 seconds left on the countdown clock due to poor weather.

While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more pervasive, but one of its key limitations lies in its inconsistent reliability stemming from the data contributed by unreliable human sensors. In this paper, we present DASC, a road Damage-Aware Social-media-driven Car sensing framework that exploits the collective power of social sensing and VSNs for reliable disaster response applications. However, integrating VSNs with social sensing introduces a new set of challenges: i) How to leverage noisy and unreliable social signals to route the vehicles to accurate regions of interest? ii) How to tackle the inconsistent availability (e.g., churns) caused by car drivers being rational actors? iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media? The DASC framework addresses the above challenges by establishing a novel hybrid social-car sensing system that employs techniques from game theory, feedback control, and Markov Decision Process (MDP). In particular, DASC distills signals emitted from social media and discovers the road damages to effectively drive cars to target areas for verifying emergency events. We implement and evaluate DASC in a reputed vehicle simulator that can emulate real-world disaster response scenarios. The results of a real-world application demonstrate the superiority of DASC over current VSNs-based solutions in detection accuracy and efficiency.

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at \url{https://github.com/PredictiveIntelligenceLab/DeepStefan}.

Hidden Markov models (HMMs) are commonly used for sequential data modeling when the true state of the system is not fully known. We formulate a special case of recurrent neural networks (RNNs), which we name hidden Markov recurrent neural networks (HMRNNs), and prove that each HMRNN has the same likelihood function as a corresponding discrete-observation HMM. We experimentally validate this theoretical result on synthetic datasets by showing that parameter estimates from HMRNNs are numerically close to those obtained from HMMs via the Baum-Welch algorithm. We demonstrate our method's utility in a case study on Alzheimer's disease progression, in which we augment HMRNNs with other predictive neural networks. The augmented HMRNN yields parameter estimates that offer a novel clinical interpretation and fit the patient data better than HMM parameter estimates from the Baum-Welch algorithm.

This has led to the development of a plethora of domain-dependent and context-specific methods for dealing with the interpretation of machine learning (ML) models and the formation of explanations for humans. Unfortunately, this trend is far from being over, with an abundance of knowledge in the field which is scattered and needs organisation. The goal of this article is to systematically review research works in the field of XAI and to try to define some boundaries in the field. From several hundreds of research articles focused on the concept of explainability, about 350 have been considered for review by using the following search methodology. In a first phase, Google Scholar was queried to find papers related to "explainable artificial intelligence", "explainable machine learning" and "interpretable machine learning". Subsequently, the bibliographic section of these articles was thoroughly examined to retrieve further relevant scientific studies. The first noticeable thing, as shown in figure 2 (a), is the distribution of the publication dates of selected research articles: sporadic in the 70s and 80s, receiving preliminary attention in the 90s, showing raising interest in 2000 and becoming a recognised body of knowledge after 2010. The first research concerned the development of an explanation-based system and its integration in a computer program designed to help doctors make diagnoses [3]. Some of the more recent papers focus on work devoted to the clustering of methods for explainability, motivating the need for organising the XAI literature [4, 5, 6].

Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.

Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and NLP) of AI are driven by Deep Learning. Deep Learning algorithms mimic human brain using artificial neural networks and progressively learn to accurately solve a given problem. But there are significant challenges in Deep Learning systems. There have been many attempts to make deep learning models imitate the biological neural network. However, many deep learning models have performed poorly in the presence of adversarial examples. Poor performance in adversarial examples leads to adversarial attacks and in turn leads to safety and security in most of the applications. In this paper we make an attempt to characterize the solution space of a deep neural network in terms of three different subsets viz. weights belonging to exact trained patterns, weights belonging to generalized pattern set and weights belonging to adversarial pattern sets. We attempt to characterize the solution space with two seemingly different learning paradigms viz. the Deep Neural Networks and the Dense Associative Memory Model, which try to achieve learning via quite different mechanisms. We also show that adversarial attacks are generally less successful against Associative Memory Models than Deep Neural Networks.