World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (part of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and Matrix Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing entries of a partially unknown I/O matrix. Through simulations based on synthetic matrices we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input-Output Database (WIOD) tables - which are an example of industry-by-industry I/O tables - is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.

Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to jointly achieve optimal outcomes. In this paper, we introduce a new general framework for improving coordination and performance of multi-agent reinforcement learners (MARL). Our framework, named Learnable Intrinsic-Reward Generation Selection algorithm (LIGS) introduces an adaptive learner, Generator that observes the agents and learns to construct intrinsic rewards online that coordinate the agents' joint exploration and joint behaviour. Using a novel combination of MARL and switching controls, LIGS determines the best states to learn to add intrinsic rewards which leads to a highly efficient learning process. LIGS can subdivide complex tasks making them easier to solve and enables systems of MARL agents to quickly solve environments with sparse rewards. LIGS can seamlessly adopt existing MARL algorithms and, our theory shows that it ensures convergence to policies that deliver higher system performance. We demonstrate its superior performance in challenging tasks in Foraging and StarCraft II.

Stanley Black & Decker is best known as the manufacturer of tools for home improvement projects, but it also makes products the average consumer seldom notices, like fasteners to keep car parts secure and the electronic doors typically used at retail stores. Me, Myself, and AI podcast hosts Sam Ransbotham and Shervin Khodabandeh sat down with Mark Maybury, the company's first chief technology officer, to learn how artificial intelligence factors into this 179-year-old brand's story. As Stanley Black & Decker's CTO, Mark Maybury manages a team across the company's businesses and functions, advises on technological threats and opportunities, and provides access to all elements of the global technology ecosystem. Previously, Maybury spent 27 years at The Mitre Corporation, where he held a variety of strategic technology roles, including vice president of intelligence portfolios and chief security officer. Before joining Mitre, he was an officer in the U.S. Air Force, where he also served as chief scientist from 2010 to 2013. Maybury is on the Defense Science Board and the Connecticut Science Center Board and served on the Air Force Scientific Advisory Board and the Homeland Security Science and Technology Advisory Committee for several years. He is a fellow in IEEE and the Association for the Advancement of Artificial Intelligence. Maybury has a doctorate degree in AI from Cambridge University. During their conversation, Mark described how categorizing the technology-infused innovation projects he leads across the company into six levels, ranging from incremental improvements to radical innovations, helps Stanley Black & Decker balance its product development portfolio. He also shared some insights for organizations thinking about responsible AI guidelines and discussed how Stanley Black & Decker is increasing its focus on sustainability. If you're enjoying the Me, Myself, and AI podcast, continue the conversation with us on LinkedIn.

A year ago, the economic situation in LatAm seemed dire. But in the second half of 2021 and well into 2022, the business and economic outlook for Latin America has not only bounced back, but in many areas is surging forward. The picture, of course, is uneven. Exciting new technologies are being adapted swiftly, while sharp, sudden increases in some consumer prices reflect now-familiar supply chain constraints. Meanwhile, the region's rich natural resources and the upside ripple effects of high energy prices and commodity exports – thanks to some nearshoring efforts – are enabling continuity and growth, offsetting some of the inflationary pressures we are seeing elsewhere.

'The Five' weighs in on Russia asking China for military and economic aid during the Russia-Ukraine war. This is a rush transcript from "The Five," March 14, 2022. This copy may not be in its final form and may be updated. It's five o'clock in New York City, and this is THE FIVE. Brand-new drone footage showing the devastation in Mariupol where Russia is ramping up more reckless attacks shelling residential buildings injuring and killing civilians. For more let's go to Trey Yingst in Kyiv, Ukraine. We are learning more about the Russian advance on the Ukrainian capital of Kyiv. Each hour they are shelling the outskirts of the city, they are also hitting it from the air using fighter jets. It's part of the reason we've seen so much air defense in the Ukrainian arsenal here trying to shoot those planes out of the sky, but you can see from this video when a Russian shell does get through it can cause immense damage. At least two people were killed at this first location this morning when a shell slammed into an apartment building. A Russian missile hit the sidewalk earlier today killing at least one person and injuring a handful more. The scenes of devastation that we've seen across this country now starting to take place in the Ukrainian capital. In the southern city of Mariupol this drone footage shows black smoke rising up from the horizon and it shows the level of devastation in residential areas. Analysts are warning that President Putin of Russia maybe planning to surround this city, and yet you can see similar damage taking place here, but again, there are civilians all around this area and it makes it extremely difficult to find safety as Russian forces attacked from the air and ground. The city is bracing for what could be a very bloody days ahead. JESSICA TARLOV, FOX NEWS CONTRIBUTOR: Thank you. WATTERS: I'm sure you have a lot to say about what you've been seeing on television over the last couple of weeks. This is definitely a heavy news day to come back. I'm very thankful to be here with you guys, you've done an amazing job covering the story. What I was struck by from at least today's news was that now Putin is 12, 13 miles from the NATO border. And there can be something intentional or unintentional that suddenly something happened in Poland or another NATO country, and then, what, we haven't really.

We propose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Starting from a collision-free initial trajectory, our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation throughout optimization, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for time-optimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights an order of magnitude's speedup, while generating trajectories of comparable qualities as state-of-the-art P-IPM solver.

We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed-occupancy detection is necessary for automatic long-term cough monitoring, since the time which the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture was demonstrated to achieve an AUC of 0.94. When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days. As the colony forming unit (CFU) counts decreased and the time to positivity (TPP) increased, the measured cough rate decreased, indicating effective TB treatment. This provides a first indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring long-term recovery of TB patients.

Nvidia is a technology company that designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on chip units (SoCs) for the mobile computing and automotive market. With the company's intelligent video analytics (IVA), AI-powered inventory management, and customer & store analytics, organizations are able to deliver better customer experiences. NVIDIA accelerated data science solution with RAPIDS enables enterprises to tap into GPU-accelerated machine learning (ML) with faster model iteration, better prediction accuracy, and lowest data science total cost of ownership (TCO). With its subsidiaries and strong distribution network, the company has its presence across North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The major subsidiaries of the company include PGI Compilers & Tools, Icera, Uli Electronics Inc, ModViz, Inc., and others.

COVID-19 posed plenty of challenges for folks around the world, but parents of school-aged children had a particularly hard time. As millions of students were forced to attend virtual school, many parents were too busy with their own WFH woes to keep constant tabs on what their kids were doing. Many schools, likewise, were ill-prepared for the realities of digital learning, and keeping kids engaged in virtual lessons was difficult. It came as no surprise, then, when declines in learning relative to previous years were observed in students. In June 2021, market research firm Ipsos surveyed US parents with school-aged kids on the future of education.

Change.org is searching for a Data Engineer to help in the development of our next-generation ML platform to support all of our internal machine learning operational needs at large scale. We're a social impact business (a public benefit company), and the largest tech platform focused on civic action in the world with 80m monthly users, 50,000 campaigns launched on the site every month, 150 staff, and a new revenue model that has grown by 500% in 2 years. We're growing quickly, and our users win campaigns for change once every hour. From strengthening hate crime legislation in South Africa; fighting corruption in Indonesia, Italy, and Brazil; to fighting violence against women in India. We are looking for a Data Engineer who has a passion to learn and build ML workflow orchestration & distributed data processing at scale.