In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.

COVID-19 has created a global pandemic with high morbidity and mortality in 2020. Novel coronavirus (nCoV), also known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), is responsible for this deadly disease. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to SARS-CoV epidemic in 2003 (89% similarity). Limited number of clinically validated Human-nCoV protein interaction data is available in the literature. With this hypothesis, the present work focuses on developing a computational model for nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered as potential human targets for nCoV bait proteins. A gene-ontology based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at 99.98% specificity threshold. This also identifies the level-1 human spreaders for COVID-19 in human protein-interaction network. Level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using 7 potential FDA listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.

The growth of machine learning and its ability to provide deep insights using big data continues to be a hot topic. Many C-level executives are developing deliberate ML initiatives to see how their companies can benefit, and cybersecurity is no exception. Most information security vendors have adopted some form of ML, however it's clear that it isn't the silver bullet some have made it out to be. While ML solutions for cybersecurity can and will provide a significant return on investment, they do face some challenges today. Organizations should be aware of a few potential setbacks and set realistic goals to realize ML's full potential.

In 2004, the U.S. Department of Defense issued a challenge: $1 million to the first team of engineers to develop an autonomous vehicle to race across the Mojave Desert. Though the prize went unclaimed, the challenge publicized an idea that once belonged to science fiction -- the driverless car. It caught the attention of Google co-founders Sergey Brin and Larry Page, who convened a team of engineers to buy cars from dealership lots and retrofit them with off-the-shelf sensors. But making the cars drive on their own wasn't a simple task. At the time, the technology was new, leaving designers for Google's Self-Driving Car Project without a lot of direction. YooJung Ahn, who joined the project in 2012, says it was a challenge to know where to start.

About 110 U.S. service members were diagnosed with traumatic brain injuries after the Iranian ballistic missile attack at al-Asad Air Base in Iraq on Jan. 8. More than a dozen missiles struck the base in an attack that Iran carried out as retaliation for a U.S. drone strike in Baghdad that killed Tehran's most powerful general, Qassem Soleimani, on Jan. 3. Troops at al-Asad were warned of an incoming attack, and most were in bunkers scattered around the base.

Last summer it was reported that patents had been filed in the USA and Europe listing an artificial intelligence system as the inventor. The patents in question were for a food container and a warning light and were filed by Stephen Thaler on behalf of DABUS (an AI system). Those applications have been considered, and on 22 April the US patent and trademark office (USPTO) reached the same verdict as the UK and European offices, denying the patents. In his application Thaler asserted that the inventions were generated by DABUS (which he dubs a "creativity machine"), and that the system was not created to solve any particular problem. He claims it was, therefore, the machine, not a person, that recognised the novelty of the invention. The USPTO ruled that applications require the inventor to be a "natural person", and denied the patents on that basis.

This is one of many questions that has plagued philosophers for thousands of years. In his 2003 paper Are You Living in a Computer Simulation?, the Swedish philosopher, futurist, Oxford professor and Director of the Future of Humanity Institute and Governance of AI Program Nick Bostrom covers several topics that underlay the possibility that life as we experience may indeed be "fake news": Substrate-Independence -- Consciousness is not necessarily a property born of biology and could be formed from other materials or even energy. Technological Limits of Computation -- Given our current rate of progress in computational power, memory storage and AI, it may be only a matter of decades before true artificial consciousness is created, leading to the era of "posthumanity". Therefore, when it saw that a human was about to make an observation of the microscopic world, it could fill in sufficient detail in the simulation in the appropriate domain on an as‐needed basis. If there were a substantial chance that our civilization will ever get to the posthuman stage and run many ancestor‐simulations, then how come you are not living in such a simulation?

What great data analysts do--and why every organization needs them.

The coronavirus pandemic has revealed that "just-in-time" supply lines don't always operate as they should. Fortune 500 companies use predictive analytics to improve their ability to deal with the unexpected -- and now so do planners with U.S. Northern Command. The Joint Artificial Intelligence Center, or JAIC, has built a prototype AI tool that uses a wide variety of data streams to predict COVID-19 hotspots and related logistics and supply-chain problems. "You have to be looking a little in the future," said Nand Mulchandani, chief technical officer at the JAIC. Dubbed Salus, for the Roman goddess of health and well-being, the tool can work on a scale as wide as the entire nation but can also drill down on specific zip codes and, in some cases, individual stores, said Mulchandani.