Convergint, a global leader in service-based systems integration, announced it has acquired MVP Tech, a leading UAE-based security and IT systems contractor serving private enterprises and government clients for the past two decades. The acquisition will add more than 200 colleagues to Convergint and expand the company's presence to countries in the Gulf Cooperation Council (GCC) and Middle East. "By joining forces with Convergint, we now have the opportunity to expand our engineering-driven philosophy and to further elevate our service capabilities, for both our local and multinational customers" Founded in 2003 and headquartered in Dubai, MVP Tech has three offices across the United Arab Emirates and Iraq with near-future expansion plans into KSA. The company's diverse and multinational colleagues are comprised of 80% technical individuals with a proven industry background, managing and delivering projects with one of the largest on-the-ground workforces in the market. MVP Tech's mission is to deliver next-generation intelligence and interconnectivity across verticals such as critical infrastructure, hospitality, luxury retail and malls, and energy infrastructure.

Faire is an online wholesale marketplace built on the belief that the future is local -- there are over 2 million independent retailers in North America and Europe doing more than $2 trillion in revenue. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town -- we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally.

Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of what they perceive to perform several tasks. They become more challenging in complex environments since there is a need to perceive the environment and act under environmental uncertainties to make a decision. In this context, a system that uses active perception can improve performance by seeking the best next view through the recognition of targets while displacement occurs. This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures to perform a dynamic landing. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties without making the use of complex Convolutional Neural Networks (CNN) or Contrastive Learning (CL).

Despite the plethora of successful Super-Resolution Reconstruction (SRR) models applied to natural images, their application to remote sensing imagery tends to produce poor results. Remote sensing imagery is often more complicated than natural images and has its peculiarities such as being of lower resolution, it contains noise, and often depicting large textured surfaces. As a result, applying non-specialized SRR models on remote sensing imagery results in artifacts and poor reconstructions. To address these problems, this paper proposes an architecture inspired by previous research work, introducing a novel approach for forcing an SRR model to output realistic remote sensing images: instead of relying on feature-space similarities as a perceptual loss, the model considers pixel-level information inferred from the normalized Digital Surface Model (nDSM) of the image. This strategy allows the application of better-informed updates during the training of the model which sources from a task (elevation map inference) that is closely related to remote sensing. Nonetheless, the nDSM auxiliary information is not required during production and thus the model infers a super-resolution image without any additional data besides its low-resolution pairs. We assess our model on two remotely sensed datasets of different spatial resolutions that also contain the DSM pairs of the images: the DFC2018 dataset and the dataset containing the national Lidar fly-by of Luxembourg. Based on visual inspection, the inferred super-resolution images exhibit particularly superior quality. In particular, the results for the high-resolution DFC2018 dataset are realistic and almost indistinguishable from the ground truth images.

Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.

The flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. On the other hand, the development of natural flavors plays a critical role in modern society. In light of this, the present work proposes a novel framework based on Scientific Machine Learning to undertake an emerging problem in flavor engineering and industry. Therefore, this work brings an innovative methodology to design new natural flavor molecules. The molecules are evaluated regarding the synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product.

Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs). This paper presents new approaches based on the state-of-the-art actor-critic algorithms to address the navigation and medium transition problems for a HUAUV. We show that a double critic Deep-RL with Recurrent Neural Networks improves the navigation performance of HUAUVs using solely range data and relative localization. Our Deep-RL approaches achieved better navigation and transitioning capabilities with a solid generalization of learning through distinct simulated scenarios, outperforming previous approaches.

The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the "big picture"--the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population. We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

Visual representations of algorithms can improve our understanding of algorithmic concepts and the nature of the underlying problems. Visualizations of the Conflict-Driven Clause Learning algorithm (CDCL) are of high interest, as this is currently the most successful algorithm for solving the propositional satisfiability problem (SAT problem). Despite the complexity of the SAT problem, implementations of CDCL in so-called SAT solvers are successfully used in industrial practice, e.g., in software verification [4], hardware verification [12], product configuration [21], or planning [18]. For satisfiable instances, SAT solvers output a variable assignment which serves as a certificate of satisfiability. Such a certificate can be checked in polynomial time and hence SAT NP.

We show how to construct the deep neural network (DNN) expert to predict quasi-optimal $hp$-refinements for a given computational problem. The main idea is to train the DNN expert during executing the self-adaptive $hp$-finite element method ($hp$-FEM) algorithm and use it later to predict further $hp$ refinements. For the training, we use a two-grid paradigm self-adaptive $hp$-FEM algorithm. It employs the fine mesh to provide the optimal $hp$ refinements for coarse mesh elements. We aim to construct the DNN expert to identify quasi-optimal $hp$ refinements of the coarse mesh elements. During the training phase, we use the direct solver to obtain the solution for the fine mesh to guide the optimal refinements over the coarse mesh element. After training, we turn off the self-adaptive $hp$-FEM algorithm and continue with quasi-optimal refinements as proposed by the DNN expert trained. We test our method on three-dimensional Fichera and two-dimensional L-shaped domain problems. We verify the convergence of the numerical accuracy with respect to the mesh size. We show that the exponential convergence delivered by the self-adaptive $hp$-FEM can be preserved if we continue refinements with a properly trained DNN expert. Thus, in this paper, we show that from the self-adaptive $hp$-FEM it is possible to train the DNN expert the location of the singularities, and continue with the selection of the quasi-optimal $hp$ refinements, preserving the exponential convergence of the method.