Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding. Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction.

Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.

We resort to game theory in order to formulate Data-Driven methods for solid mechanics in which stress and strain players pursue different objectives. The objective of the stress player is to minimize the discrepancy to a material data set, whereas the objective of the strain player is to ensure the admissibility of the mechanical state, in the sense of compatibility and equilibrium. We show that, unlike the cooperative Data-Driven games proposed in the past, the new non-cooperative Data-Driven games identify an effective material law from the data and reduce to conventional displacement boundary-value problems, which facilitates their practical implementation. However, unlike supervised machine learning methods, the proposed non-cooperative Data-Driven games are unsupervised, ansatz-free and parameter-free. In particular, the effective material law is learned from the data directly, without recourse to regression to a parameterized class of functions such as neural networks. We present analysis that elucidates sufficient conditions for convergence of the Data-Driven solutions with respect to the data. We also present selected examples of implementation and application that demonstrate the range and versatility of the approach.

Time-dependent Partial Differential Equations (PDEs) arise frequently in reallife applications: e.g., diffusion process of liquid flows (see, e.g., [18]), heat distribution in time (see, e.g., [21]), simulations of nonlinear wave dynamics (see, e.g., [28]), groundwater flow dynamics (see, e.g., [4]), quantum dynamics (see, e.g., [8]), computational mechanics (see, e.g., [15]), etc. These applications are very important from both theoretical and practical points of view. For instance, groundwater flow simulations can be used to predict hydro-geological risks, which are crucial for infrastructures located in seismic or unstable regions (see, e.g., [1, 14]. High precision efficient simulations and modeling in this case can be used to predict different risks arising in this field. In this case, numerical models can be used to describe fluid dynamics: e.g., diffusion equation or Burgers' equations (see, e.g., [2]). In order to solve difficult nonlinear PDEs, there exist different approaches: e.g., finite element method (FEM, see, e.g., [13]) or Isogeometric analysis (IGA,

We are looking to expand our Analytics team with a data scientist to help create data-driven internal solutions for the Illuvium DAO. Illuvium Labs is an independent game development studio based in Sydney, Australia. We have developed a strong culture of independence with our team, preferring candidates who can articulate their own vision and goals. We operate almost entirely remotely so each team member designs their own hours and work schedule. In the end all that matters is the delivered product.

Aircraft manufacturing company Boeing partners with technology giant Microsoft to accelerate its digital transformation journey. This strategic partnership with Microsoft will allow Boeing to use the Microsoft Cloud and AI capabilities to upgrade its IT infrastructure and mission-critical applications with intelligent new data-driven solutions, allowing for new ways of working, operating, and conducting business. Boeing was one of the first companies to use the Microsoft Cloud, storing multiple digital aviation apps on Microsoft Azure and leveraging artificial intelligence to improve customer outcomes and streamline operations. According to the plan, Boeing will leverage Microsoft Cloud and AI capabilities to upgrade essential infrastructure, optimize business processes, and several other tasks. Chief Information Officer and Senior Vice President of Information Technology & Data Analytics at Boeing, Susan Doniz, said, "Today's announcement represents a significant investment in Boeing's digital future. Our strategic partnership with Microsoft will help us realize our cloud strategy by removing infrastructure restraints, properly scaling to unlock innovation, and further strengthening our commitment to sustainable operations."

To address this complex issue, the most promising research directions are oriented toward the conceptualization of improved information processing paradigms and smart decision support systems aimed at enhancing standard operating procedures, based on pre-defined grid conditions and static operating thresholds, with a set of interactive information services, which could promptly provide the right information at the right moment to the right decision maker. To effectively support the deployment of these services in modern smart grids it will be incumbent upon the scientific community to develop advanced techniques and algorithms for reliable power system data acquisition and processing, which should support semantics and content-based data extraction and integration from heterogeneous sensor networks. This research topic contains four articles.The paper Optimal Balancing of Wind Parks with Virtual Power Plants by Vadim Omelčenko and Valery Manokhin addresses data-driven solutions in the context of optimization of virtual power plants. This work proposes the use of machine learning to process available data measurements. The goal is to balance the power production and at the same time maximize the revenue of a portfolio of power plants with different technologies (biogas, wind, batteries, etc.) considering uncertainty in both price and power production.The paper Supporting Regulatory Measures in the Context of Big Data Applications for Smart Grids by Mihai A. Mladin discusses the policy and regulatory aspects. This paper focuses in particular on big data applications to the ongoing "energy transition" process built on higher renewable energy integration and digitalization, and discusses how this can help regulatory measures through societal acceptance and involvement.The paper Data Consistency for Data-Driven Smart Energy Assessment by Gianfranco Chicco addresses the issue of data consistency and discusses data-versus model-based approaches.

Data science is a popular and lucrative profession, and despite pandemic-era slowdowns, it's still one of the sexiest jobs around. As businesses seek to employ the power of data to increasingly digital commerce, companies across industries are on the lookout for data scientists and vice versa. These data-powered professionals have a lot to offer. From manufacturing to hospitality, data scientists can bring invaluable insights that transform the ways we conduct business, leading to greater solutions and cost-reduction opportunities. While career growth may shift by industry and economic activity, the rise of data science is on an overall upward trend.

Half the battle in a successful data science project can be expressing the problem in a way that ensures a optimal data-driven solution, with a clear set of realistic, achievable objectives. What exactly will be the commercial benefit of solving this problem? If you have properly addressed the first 3 points, this should be a yes, but it always worth this final check. It is at points 3 and 4 that seemingly well-structured data projects often become unstuck. A granular analysis at this stage can save much subsequent hair-tearing and disappointment.

Managing stakeholders in the world of data science projects is a tricky prospect. I have seen a lot of executives and professionals get swept up in the hype around data science without properly understanding what a full-blown project entails. And I don't say this lightly – my career has been at the very cusp of machine learning and delivery. I hold a Ph.D. in Data Science and Machine Learning from one of the best institutions in the world and have several years of experience working with some of the top industry research labs. I moved to Yodlee, a FinTech organization, in 2016 to run the data sciences product delivery division.