On March 8, International Women's Day (IWD) we celebrate the political, socioeconomic and cultural achievements of women and the women right's movement towards gender equality. The aim of this article is to show what is being done and is available, especially in the European Union, for us to contribute as individuals and as a community towards gender equality in the field of robotics.

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (e.g., planning, business process management, run-time monitoring, reactive synthesis). Several studies approached the respective satisfiability problem. In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications. We provide four algorithms for extracting an unsatisfiable core leveraging the adaptation of state-of-the-art approaches to LTLf satisfiability checking. We implement the different approaches within the respective tools and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable ones. The results show the feasibility, effectiveness, and complementarities of the different algorithms and tools.

BAN MAI, Thailand – As a child, Manit Boonkhiew watched his grandparents plow their rice farm near Bangkok with water buffaloes, and harvest by hand. His parents switched to tractors and threshers, while he now uses a zippy drone to spray pesticide on his field. Manit, who grows rice, orchids and fruit trees on about 40 acres (16 hectares) of land in Ban Mai, is part of a community enterprise that recently acquired a drone under a Thai government program to digitize agriculture. Drones to plant seeds, and spray pesticide and fertilizers are growing in popularity in the Southeast Asian country as it grapples with a labor shortage that worsened during the coronavirus pandemic, with restrictions on movement of workers. "Labor is the biggest challenge for us -- it's hard to get, and it's expensive," said Manit, 56, a leader of the Ban Mai Community Rice Center farm that comprises 57 members with nearly 400 acres of land.

AI has been involved in medicine since as early as the 1950s when physicians made the first attempts to improve their diagnoses using computer-aided programs. In 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. The USA tops the list of countries with the maximum number of articles (215), followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. One of the notable aspects of AI techniques is potential support for comprehensive health services management.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. Artificial Intelligence: The future is data capture, not machine learning.

Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences.

This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies. To achieve our goal, we use a heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. We identify that the Covid-19 dataset may project onto two low-dimensional manifolds without significant information loss. The low dimensionality suggests strong dependency among the standardised growth rates of cases and deaths per capita and the OxCGRT Covid-19 Stringency Index for a country over 2020-2021. Given the low dimensional structure, it may be feasible to model observable Covid-19 dynamics with few parameters. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. Moreover, we highlight that high-income countries are more likely to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from Covid-19. Finally, we temporally stratify the dataset to examine the intrinsic dimension at a more granular level throughout the Covid-19 pandemic.

A global audience of a half-billion gamers have gotten to know the axolotl, which largely cluster in the canals around Mexico City and look like little dragons with a goofy smile. The videogame "Fortnite" trotted out axolotl characters in 2020, and "Minecraft" followed suit last summer. Roblox, a platform with millions of user-made games, has dozens of axolotl-centric ones, including "Axolotl Tycoon" and "Axolotl Paradise." Axolotls appear in "Adopt Me!," one of the most-played games on Roblox. All of the exposure has spawned axolotl memes, YouTube videos, coloring books and nonfungible tokens.

The world of pi is an all-out pi extravaganza with labs and demos, streaming icons, and free swag. Check out this video to see for yourself. Choose from tech demos and hands-on labs to talks about Linux, Graal, MySQL, and Oracle Cloud Infrastructure (OCI) including deep dives into analytics and AI/machine learning. All attendees receive a Pi Day 2022 swag kit. And the first 500 to sign up for a free-tier trial account for OCI get a bonus gift.

We present a planning framework for minimising the deterministic worst-case error in sparse Gaussian process (GP) regression. We first derive a universal worst-case error bound for sparse GP regression with bounded noise using interpolation theory on reproducing kernel Hilbert spaces (RKHSs). By exploiting the conditional independence (CI) assumption central to sparse GP regression, we show that the worst-case error minimisation can be achieved by solving a posterior entropy minimisation problem. In turn, the posterior entropy minimisation problem is solved using a Gaussian belief space planning algorithm. We corroborate the proposed worst-case error bound in a simple 1D example, and test the planning framework in simulation for a 2D vehicle in a complex flow field. Our results demonstrate that the proposed posterior entropy minimisation approach is effective in minimising deterministic error, and outperforms the conventional measurement entropy maximisation formulation when the inducing points are fixed.