Devastated by industrial crisis, America's former "steel city" has reinvented itself as an innovation hub. But today its main challenge is to keep its "One Pittsburgh" promise by ensuring that everybody in its diverse population shares the benefits of new growth. Pittsburgh is back from the brink. A flagship of triumphant industrialisation in the early 20th century, the city has since seen its steel mills decline and then shut down. As the economy lurched from one crisis to another, Pennsylvania's rusting "steel city" became an emblem of decline, like other urban "dead stars" in the rustbelt of America's Middle West. But Pittsburgh never gave up.

'Alexa, what are the early signs of a stroke?' GPs may no longer be the first port of call for patients looking to understand their ailments. 'Dr Google' is already well established in patients' minds, and now they have a host of apps using artificial intelligence (AI), allowing them to input symptoms and receive a suggested diagnosis or advice without the need for human interaction. And policymakers are on board. Matt Hancock is the most tech-friendly health secretary ever, NHS England chief executive Simon Stevens wants England to lead the world in AI, and the prime minister last month announced £250m for a national AI lab to help cut waiting times and detect diseases earlier. Amazon even agreed a partnership with NHS England in July to allow people to access health information via its voice-activated assistant Alexa.

But the president's aspirations have long run into resistance, as his own national security officials argued that abandonment of such troubled countries could have catastrophic consequences -- such as when the United States pulled out of Iraq at the end of 2011, leaving a vacuum that fostered the rise of the Islamic State in Iraq and Syria. Mr. Trump has also repeatedly pushed to withdraw from Syria, but several hundred U.S. troops remain stationed there, partly to protect coveted oil fields held by American-backed Syrian Kurdish allies from being seized by the government of President Bashar al-Assad of Syria. The current deliberations over withdrawals would not affect those in Syria, officials said. The plan under discussion to pull out of Somalia is said to not apply to U.S. forces stationed in nearby Kenya and Djibouti, where American drones that carry out airstrikes in Somalia are based, according to officials familiar with the internal deliberations who spoke on the condition of anonymity. Keeping those air bases would mean retaining the military's ability to use drones to attack militants with the Shabab, the Qaeda-linked terrorist group -- at least those deemed to pose a threat to American interests.

As network protection turns out to be significantly more essential amid COVID-19 pandemic, man-made brainpower and AI sponsored arrangements are helping a few insurance agencies tackle these dangers, an EY report said. In an ongoing case, EY had helped an insurance agency tackle the network protection danger, and we could repeat this throughout the business. An Indian insurance agency was hoping to expand its inner security to ensure its information, frameworks, and foundation from potential network protection dangers. The organization verified that it required a 24 7 security log checking framework that could work 365 days every year alongside the capacity to direct investigation, danger profiling, connection and alarming, EY report said. The organization is presently ready to forestall dynamic dangers, yet additionally lead examination on expected weaknesses.

During a disaster event, images shared on social media helps crisis managers gain situational awareness and assess incurred damages, among other response tasks. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of damage. Despite several efforts, past works mainly suffer from limited resources (i.e., labeled images) available to train more robust deep learning models. In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment. Moreover, we relabel existing publicly available datasets for new tasks. We identify exact- and near-duplicates to form non-overlapping data splits, and finally consolidate them to create larger datasets. In our extensive experiments, we benchmark several state-of-the-art deep learning models and achieve promising results. We release our datasets and models publicly, aiming to provide proper baselines as well as to spur further research in the crisis informatics community.

Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With physical and mechanistic complexity of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood mechanism driving the reaction. As oxygen accumulated on the catalyst, a transition in the mechanism was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

The Army's plans for robotic wingmen in vehicle formations, a drone on every soldier and robotic mules carrying gear all aim to take the load off the fighter. But how will the two communicate, robot and human? Voice commands like automated assistants on smartphones are great, but not when the threat of incoming fire means the robot battle buddy needs to decipher a range of priorities that humans might take for granted. The next test will come in late 2021 and involve a company-sized maneuver at Fort Hood, Texas. Think more C3PO or R2D2 in the "Star Wars" movies than Hal in "2001: A Space Odyssey" --or better yet, a friendly cyborg from "Terminator" might be the best way to see your robot combatant squad mate of the distant future.

WASHINGTON -- The U.S. Army's heavy common ground robot has reached full-rate production, less than a year after FLIR won the contract to deliver the system, FLIR's vice president in charge of unmanned ground systems told Defense News in an interview this month. "We've progressed with the U.S. Army through all the milestones on the program and are now at full-rate production on the program. We're building systems, we're delivering them, there are systems out at Fort Leonard Wood right now going through training with troops and there are more systems in the pipeline to be delivered all the way through next year and further," Tom Frost said. "I think what's remarkable is how quickly the Army was able to run a program to find a very capable, large [explosive ordnance disposal] robot and then get it out to troops as quickly as they did," he added. The service award FLIR an Other Transaction Authority type contract in November 2019 to provide its Kobra robot to serve as its Common Robotic System-Heavy -- or CRS-H.