The U.S. Department of Defense's most advanced research and development arm is calling upon engineers and scientists to help develop robots capable of remotely repairing satellites in space. According to the The Defense Advanced Research Projects Agency (DARPA), which is a part of the U.S. Department of Defense, the organization hopes to develop and launch the sophisticated space bots into orbit within the next five years. Currently, says DARPA, about 400 satellites owned by the government and private industry in the U.S. are orbiting Earth with some more than 20,000 miles away making it service and maintenance all but impossible. The U.S. Department of Defense is calling upon engineers and scientists to help develop robots capable of remotely repairing satellites in space. 'With no prospects for assistance once in orbit, satellites destined for [geosynchronous equatorial orbit] today are loaded with backup systems and as much fuel as can be accommodated, adding to their complexity, weight and cost,' reads a release from DARPA.

Who's flying that drone over my house, and what exactly are they looking for? Is the pilot a police officer, a search-and-rescue volunteer, or Creepy Steve from four doors down? These concerns over the origin and intention of small drones have bedeviled the drone industry for as long as it has existed. Our inability to figure out who is piloting the weird quadcopter over our neighborhoods surely has a lot to do with why so many still distrust drones. People are working on it, though.

Today, Alphabet's Wing division became the first drone delivery company to receive its Air Carrier Certification from the US Federal Aviation Administration (FAA). The certification means Wing can begin a commercial drone delivery service, and the company hopes to launch its first delivery trial later this year. Over the next several months, Wing will work with the FAA's Unmanned Aircraft System Integration Pilot Program (UAS IPP) in Southwest Virginia. It will soon begin reaching out to residents and businesses in the Blacksburg and Christiansburg, Virginia, areas to demonstrate its technology and to gather feedback. This has been years in the making.

The Wing company, a Google spinoff, has won federal approval to operate its drone delivery system as an airline in the U.S. Wing hide caption The Wing company, a Google spinoff, has won federal approval to operate its drone delivery system as an airline in the U.S. The Federal Aviation Administration has certified Alphabet's Wing Aviation to operate as an airline, in a first for U.S. drone delivery companies. Wing, which began as a Google X project, has been testing its autonomous drones in southwest Virginia and elsewhere. "Air Carrier Certification means that we can begin a commercial service delivering goods from local businesses to homes in the United States," Wing said in a statement posted to the Medium website. The company has touted many advantages of using unmanned drones to deliver packages, from reducing carbon emissions and road congestion to increasing connections between communities and local businesses. "This is an important step forward for the safe testing and integration of drones into our economy. Safety continues to be our Number One priority as this technology continues to develop and realize its full potential," Secretary of Transportation Elaine L. Chao said in a statement from the agency.

Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

The American space agency's InSight lander appears to have detected its first seismic event on Mars. The faint rumble was picked up by the probe's sensors on 6 April - the 128th Martian day, or sol, of the mission. It is the first seismic signal detected on the surface of a planetary body other than the Earth and its Moon. Scientists say the source for this "Marsquake" could either be movement in a crack inside the planet or the shaking from a meteorite impact. Nasa's InSight probe touched down on the Red Planet in November last year. It aims to identify multiple quakes, to help build a clearer picture of Mars' interior structure.

It sounds like a subway train rushing by. But it's something much more exotic: in all likelihood, the first "marsquake" ever recorded by humans. NASA's InSight mission detected the quake on April 6, four months after the lander's highly sensitive seismometer was installed on the Martian surface. The instrument had previously registered the howling winds of the red planet and the motions of the lander's robotic arm. But the shaking picked up this month is believed to be the first quake from Mars' interior.

Conditional simple temporal networks with uncertainty (CSTNUs) allow for the representation of temporal plans subject to both conditional constraints and uncertain durations. Dynamic controllability (DC) of CSTNUs ensures the existence of an execution strategy able to execute the network in real time (i.e., scheduling the time points under control) depending on how these two uncontrollable parts behave. However, CSTNUs do not deal with resources. In this paper, we define conditional simple temporal networks with uncertainty and resources (CSTNURs) by injecting resources and runtime resource constraints (RRCs) into the specification. Resources are mandatory for executing the time points and their availability is represented through temporal expressions, whereas RRCs restrict resource availability by further temporal constraints among resources. We provide a fully-automated encoding to translate any CSTNUR into an equivalent timed game automaton in polynomial time for a sound and complete DC-checking.

This paper describes a prototype system that integrates social media analysis into the European Flood Awareness System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data. Finally, we describe a method for selecting relevant and representative messages and displaying them back in the interface of EFAS.

Sometimes knowing the future given the present is not enough. Predicting possible futures given different defined scenarios can be more important. However, the workhorse for causality detection and impulse response, the Vector Autoregression (VAR), assumes linearity and has produced poor forecasts (Reis, 2018). Here, we introduce a vector autoencoder nonlinear autoregression neural network (VANAR) capable of both automatic time series feature extraction for its inputs and automatic functional form estimation. We evaluate VANAR in three ways: first in terms of pure forecast accuracy, second in terms of detecting the correct causality between variables, and lastly in terms of impulse response where we model trajectories given external shocks. These tests were performed on datasets with different underlying dynamics: a simulated nonlinear chaotic system, a simulated linear system, and an empirical system using Philippine macroeconomic data. Results show that VANAR significantly outperforms VAR in the forecast and causality tests. For the macroeconomic forecast, VANAR has consistently superior accuracy even over state of the art models such as SARIMA and TBATS (Hyndman et al., 2011). For the impulse response test, VANAR outperforms VAR in the linear system but both models fail to predict the shocked trajectories of the nonlinear chaotic system. VANAR was robust in its ability to model a wide variety of dynamics, from chaotic, high noise, and low data environments to complex macroeconomic systems, thus illustrating its potential usefulness in modeling more real world dynamical systems.