Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space.

The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction include the conterminous traffic density, EV registrations, and proximity to certain types of public buildings. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.

This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and estimated observations and knowledge of the chief's position with known CR3BP dynamics to determine the deputy's initial state. Convergence is achieved through limiting particle starting positions to feasible positions based on the known chief position, and sensor constraints. Parallel and GPU processing methods are used to improve computation time and provide an accurate initial state estimate for a variety of cislunar orbit geometries.

Agricultural drones, self-driving tractors and seed-planting robots are among the innovations that could be key to future food supplies, as autonomous farming promises to produce more crops with less effort and less impact on the environment. Global farming shortages are affecting food chains globally. Last year the National Farmers' Union (NFU) in the UK wrote to Prime Minister Boris Johnson asking for the implementation of a'Covid Recovery Visa' to alleviate labour shortages across the supply chain. Seasonal worker visa scheme has been extended until end of 2024. The extension of the scheme was a key lobbying ask by the NFU There will be 30,000 visas available this year with potential to increase by 10,000 if necessary Find out more https://t.co/gsBU8Nca6W

BE PART OF BUILDING THE FUTURE. What do NASA and emerging space companies have in common with COVID vaccine R&D teams or with Roblox and the Metaverse? The answer is data, -- all fast moving, fast growing industries rely on data for a competitive edge in their industries. And the most advanced companies are realizing the full data advantage by partnering with Pure Storage. Pure's vision is to redefine the storage experience and empower innovators by simplifying how people consume and interact with data.

There is little doubt that artificial intelligence and machine learning will revolutionise decision-making. But how these new technologies make decisions is a mystery and the black art that goes on behind the scenes to deliver those decisions is based on mathematical models that cannot easily be explained. AI relies on accurate data, but data protection regulations can sometimes act as a barrier to prevent the access required to train algorithms with more diverse use cases. Without this diversity, the dataset is stymied by only including data from individuals who have opted in to sharing their personal information. Such data mining could improve the accuracy of the data models used in machine learning.

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. With revenues of $125M, and a staff of more than 630, Riverside Research provides high-end technical services, research and development, and prototype solutions to some of the country's most challenging technical problems. Riverside Research also supports advanced technical education and collaborates widely with university researchers. The company was formed from a respected research laboratory at Columbia University and has a current focus on technical areas including Radar systems, Optics and Photonics, Electromagnetics, Plasma physics, Geoint, Masint, Systems Engineering, and Modeling & Simulation. Riverside Research's open innovation R&D model encourages both internal and external collaboration to accelerate innovation, advance science, and expand market opportunities.

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

In the intensifying race for global competitiveness in artificial intelligence (AI), the United States, China and the European Union are vying to be the home of what could be the most important technological revolution of our lifetimes. AI governance proposals are also developing rapidly, with the EU proposing an aggressive regulatory approach to add to its already-onerous regulatory regime. It would be imprudent for the U.S. to adopt Europe's more top-down regulatory model, however, which already decimated digital technology innovation in the past and now will do the same for AI. The key to competitive advantage in AI will be openness to entrepreneurialism, investment and talent, plus a flexible governance framework to address risks. The International Economyjournal recently asked 11 experts from Europe and the U.S. where the EU currently stood in global tech competition.