A replica of the Mars 2020 Perseverance Rover is shown during a press conference ahead of the launch of a United Launch Alliance Atlas V rocket carrying the rover, at the Kennedy Space Center in Cape Canaveral, Florida, U.S. July 29, 2020. REUTERS/Joe SkipperThe $2.4 billion mission, slated for liftoff at 7:50 a.m. ET (1150 GMT) from Florida's Cape Canaveral, is planned as the U.S. space agency's ninth trek to the Martian surface. The United Arab Emirates and China separately this month launched probes to Mars in displays of their own technological prowess and ambition. Launching atop an Atlas 5 rocket from the Boeing-Lockheed (BA.N) (LMT.N) joint venture United Launch Alliance, the car-sized Perseverance rover is expected to reach Mars next February.

NASA is set to launch an ambitious mission to Mars on Thursday with the liftoff of its next-generation Perseverance rover, a six-wheeled robot tasked with deploying a mini helicopter, testing out equipment for future human missions and searching for traces of past Martian life. The $2.4 billion mission, slated for liftoff at 7:50 a.m. from Florida's Cape Canaveral, is planned as the U.S. space agency's ninth trek to the Martian surface. The United Arab Emirates and China separately this month launched probes to Mars in displays of their own technological prowess and ambition. Launching atop an Atlas 5 rocket from the Boeing-Lockheed joint venture United Launch Alliance, the car-sized Perseverance rover is expected to reach Mars next February. It is due to land at the base of an 820-foot-deep (250 meters) crater called Jezero, a former lake from 3.5 billion years ago that scientists believe could hold traces of potential past microbial Martian life.

A professor of mathematics at the University of Oxford, doubling as a philosopher of science and religion, John Lennox has some pretty unique insights to put forward when it comes to the future of artificial intelligence. The title of his new book, ambitiously named 2084: Artificial Intelligence and the Future of Humanity, certainly suggests a post-Orwellian vision of dystopia, complete with an algorithmic Big Brother and an army of bio-engineered super-humans. And similar predictions have already been made by other influential academics, too. Yuval Noah Harari, in his bestselling book Homo Deus, for example, anticipates that technological developments will lead to humans enhancing themselves with abilities like eternal life. But far from portraying an Ex-Machina-esque scenario, in which our AI creations would take over the world and fundamentally change human nature, Lennox warns that the dangers of AI are more imminent. "If creating an AI that surpasses humans were to happen, of course it would be a threat," Lennox tells ZDNet. "But there are major dangers long before then, and these dangers are actually happening now. I think it is misleading to tell people about the problems that will come in the future – it's what's happening now that demands an ethical and moral response."

PROVIDENCE, Rhode Island – As the world races to find a vaccine and a treatment for COVID-19, there is seemingly no antidote in sight for the burgeoning outbreak of coronavirus conspiracy theories, hoaxes, anti-mask myths and sham cures. The phenomenon, unfolding largely on social media, escalated this week when U.S. President Donald Trump retweeted a false video about an anti-malaria drug being a cure for the virus and it was revealed that Russian intelligence is spreading disinformation about the crisis through English-language websites. Experts worry the torrent of bad information is dangerously undermining efforts to slow the virus, whose death toll in the U.S. hit 150,000 Wednesday, by far the highest in the world, according to the tally kept by Johns Hopkins University. Over a half-million people have died in the rest of the world. Hard-hit Florida reported 216 deaths, breaking the single-day record it set a day earlier.

The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this paper, we begin to fill this theoretical gap. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error decaying asymptotically like $O(1/\sqrt{n})$ for the number $n$ of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided $n$ is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments.

The exogeneity bias and instrument validation have always been critical topics in statistics, machine learning and biostatistics. In the era of big data, such issues typically come with dimensionality issue and, hence, require even more attention than ever. In this paper we ensemble two well-known tools from machine learning and biostatistics -- stable variable selection and random graph -- and apply them to estimating the house pricing mechanics and the follow-up socio-economic effect on the 2010 Sydney house data. The estimation is conducted on an over-200-gigabyte ultrahigh dimensional database consisting of local education data, GIS information, census data, house transaction and other socio-economic records. The technique ensemble carefully improves the variable selection sparisty, stability and robustness to high dimensionality, complicated causal structures and the consequent multicollinearity, which is ultimately helpful on the data-driven recovery of a sparse and intuitive causal structure. The new ensemble also reveals its efficiency and effectiveness on endogeneity detection, instrument validation, weak instruments pruning and selection of proper instruments. From the perspective of machine learning, the estimation result both aligns with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation result is totally consistent with and supported by the classical econometric tool like two-stage least square regression and different instrument tests (the code can be found at https://github.com/isaac2math/solar_graph_learning).

We consider structural impulse response analysis for sparse high-dimensional vector autoregressive (VAR) systems. Since standard procedures like the delta-method do not lead to valid inference in the high-dimensional set-up, we propose an alternative approach. First, we directly construct a de-sparsified version of the regularized estimators of the moving average parameters that are associated with the VAR process. Second, the obtained estimators are combined with a de-sparsified estimator of the contemporaneous impact matrix in order to estimate the structural impulse response coefficients of interest. We show that the resulting estimator of the impulse response coefficients has a Gaussian limiting distribution. Valid inference is then implemented using an appropriate bootstrap approach. Our inference procedure is illustrated by means of simulations and real data applications.

Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-based charity, in developing a data-driven approach to assess the quality of incoming alerts from members of the public aimed at connecting people sleeping rough on the streets with outreach service providers. Alerts are prioritised based on the predicted likelihood of successfully connecting with the rough sleeper, helping to address capacity limitations and to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation concludes that our approach increases the rate at which rough sleepers are found following a referral by at least 15\% based on labelled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modelling process is done with careful considerations of ethics, transparency and explainability due to the sensitive nature of the data in this context and the vulnerability of the people that are affected.

NASA astronaut Megan McArthur, whose husband Bob Behnken was one of two crew members aboard SpaceX's historic Crew Dragon mission in May, will pilot the commercial craft's second launch in the spring of 2021. McArthur and Behnken met as members of the Astronaut Class of 2000, and have a six-year-old son, Theodore. NASA on Tuesday announced the four-member crew for the second operational SpaceX Crew Dragon flight to the International Space Station, which will be commanded by NASA astronaut Shane Kimbrough. Joining the crew as specialists will be Japan Aerospace Exploration Agency astronaut Akihiko Hoshide and European Space Agency astronaut Thomas Pesquet. McArthur is seen training for the spring mission with NASA astronaut Shane Kimbrough.

Cybersecurity threats and cases are increasing manifold. A study by the University of Maryland showed that a hacker attacks every 39 seconds. Imagine the number of attacks you might face in a year. The reason why these threats are increasing at an alarming rate is the inability of the traditional systems to handle large volumes of data. We have created vast volumes of data, but we do not have proper systems to keep it safe.