In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is composed of two steps: the high-dimensional features of predictions, given by a large number of regression estimators, are randomly projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the first step, and a kernel-based consensual aggregation is implemented on the projected features in the second step. We theoretically show that the performance of the aggregation scheme is close to the performance of the aggregation implemented on the original high-dimensional features, with high probability. Moreover, we numerically illustrate that the aggregation scheme upholds its performance on very large and highly correlated features of predictions given by different types of machines. The aggregation scheme allows us to flexibly merge a large number of redundant machines, plainly constructed without model selection or cross-validation. The efficiency of the proposed method is illustrated through several experiments evaluated on different types of synthetic and real datasets.

Directed energy is "the ability to create a high amount of energy in a controlled volume at a given distance in order to trigger physical reactions to study the interaction between the energy and the matter," says Dr. Chaouki Kasmi, who is the Chief Researcher at DERC, which is part of the Abu Dhabi government's Advanced Technology Research Council. The research at DERC reflects the multitude of applications that are possible using directed energy, but the research projects have at least one thing in common: the goal of solving real-world scientific or technical challenges. For example, one of DERC's recent developments is a landmine detection system – the ground-penetrating radar - designed to help developing or previously war-torn countries detect and neutralize unexploded landmines. They have their sights set much higher and further with projects focused on using lasers for communications on land, to the moon, and even underwater--truly making the entire world a better place with directed energy technology. "The disruptive innovation that we are bringing today is how we can make it affordable for developing countries. The idea is to create a technology that could really help solve a worldwide problem at low cost. And this is very important for us as we would like to have the system deployed at scale," says Dr. Kasmi.

The rapid urban growth and development have been putting increasing pressure on the environment including urban parks and green spaces. The green spaces are essential to improve the urban areas and to provide quality life to the urban population. Green spaces generally include lawns, public parks, gardens, street landscapes, forests, etc. In this regard, technologies such as satellite imagery and AI/ML can support the urban developers as well as the land managers to monitor and support decision-making for sustainable urban development in dense urban environments and prevent flooding conditions in urban areas by gathering high-resolution details of the urban area(s). Further, satellite images can provide detailed analysis for detecting major changes in the urban land cover and land use that allows frequent coverage and overlaying of different time sequences to classify environmentally safe and sustainable areas for any proposed development area(s).

Encycle Corporation, a software technology company focused on helping commercial enterprise-level utility customers dramatically improve the efficiency of their HVAC systems using IoT-enabled services, announced that it has joined Distech Controls' Digital Partner Program (DPP). Participation in the program will help link Encycle's Swarm Logic energy-saving software with commercial and industrial customers looking to reduce HVAC-related energy consumption, costs, and emissions through trusted, best-in-class solutions. The DPP brings together a network of world-class digital companies that share their expertise, technologies, and best practices to help make buildings more efficient, connected, and attractive. Distech Controls selected Encycle as a DPP partner based on its IoT-centered technology, complementarity with other program partners, technological openness (Swarm Logic is available on Tridium's Niagara Framework), and collaborative practices. "We are proud to partner with Distech Controls to tackle difficult HVAC energy challenges and mutually deliver proven energy and decarbonization results through our patented, Energy-as-a-Service approach to energy management," stated Steve Alexander, Encycle President and CEO.

This article was originally posted on our company website. Flexday Solutions LLC is a team of thought leaders in the fields of AI, ML and cloud solutions. In recent times, the application of ML and DL techniques in the various fields of science has enabled scientists to uncover interesting and useful insights. Specifically, in the field of materials science, scientists are constantly putting effort to design new materials for various end-use applications. There are enormous amounts of data related to different variety of materials available in the public domain.

This article is part of our coverage of the latest in AI research. Reinforcement learning is one of the fascinating fields of computer science, and it has proven useful in solving some of the toughest challenges of artificial intelligence and robotics. Some scientists believe that reinforcement learning will play a key role in cracking the enigma of human-level artificial intelligence. But many hurdles stand between current reinforcement learning systems and a possible path toward more general and robust forms of AI. Many RL systems struggle with long-term planning, training-sample efficiency, transferring knowledge to new tasks, dealing with the inconsistencies of input signals and rewards, and other challenges that occur in real-world applications.

MIT researchers discovered hidden magnetic properties in multi-layered electronic material by analyzing polarized neutrons using neural networks. An MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation. Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances.

It is a popular trend to start investing in regular and popular tech stocks from leading tech companies across the world. Technology stocks ensure high profit in digital wallets owing to the constant innovations with cutting-edge technologies for all kinds of industries. Thus, tech investors can start their Q2 investment with the investment in the following tech stocks on April 4, 2022. Analytics Insight provides a list of the top five tech stocks, according to Yahoo Finance. SolarEdge Technologies, Inc. is one of the top tech stocks to buy for its optimized inverter systems for solar PV installations across the world.

We explore training Binary Neural Networks (BNNs) as a discrete variable inference problem over a factor graph. We study the behaviour of this conversion in an under-parameterized BNN setting and propose stochastic versions of Belief Propagation (BP) and Survey Propagation (SP) message passing algorithms to overcome the intractability of their current formulation. Compared to traditional gradient methods for BNNs, our results indicate that both stochastic BP and SP find better configurations of the parameters in the BNN.

With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today's chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall.