This paper presents a new approach for dimension reduction of data observed in a sphere. Several dimension reduction techniques have recently developed for the analysis of non-Euclidean data. As a pioneer work, Hauberg (2016) attempted to implement principal curves on Riemannian manifolds. However, this approach uses approximations to deal with data on Riemannian manifolds, which causes distorted results. In this study, we propose a new approach to construct principal curves on a sphere by a projection of the data onto a continuous curve. Our approach lies in the same line of Hastie and Stuetzle (1989) that proposed principal curves for Euclidean space data. We further investigate the stationarity of the proposed principal curves that satisfy the self-consistency on a sphere. Results from real data analysis with earthquake data and simulation examples demonstrate the promising empirical properties of the proposed approach.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

They are a fundamental datatype in all the modern programming languages, and operations on strings frequently occur in disparate fields such as software analysis, model checking, database applications, web security, bioinformatics and so on[3, 11, 19, 21, 27, 28, 49, 60, 67]. Reasoning over strings requires solving arbitrarily complex string constraints, i.e., relations defined on a number of string variables. Typical examples of string constraints are string length, (dis-)equality, concatenation, substring, regular expression matching. With the term "string constraint solving" (in short, string solving or SCS) we refer to the process of modelling, processing, and solving combinatorial problems involving string constraints. We may see SCS as a declarative paradigm which falls at the intersection between constraint solving and combinatorics on words: the user states a problem with string variables and constraints, and a suitable string solver seeks a solution for that problem. Although works on the combinatorics of words were already published in the 1940s [110], the dawn of SCS dates back to the late 1980s in correspondence with the rise of Constraint Programming (CP) [114] and Constraint Logic Programming(CLP)[73] paradigms. Pioneers in this field were for example Trilogy[142], a language providing strings, integer and real constraints, and CLP(Σ) [144], an instance of the CLP scheme representing strings as regular sets. The latter in particular was the first known attempt to use string constraints like regular membership to denote regular sets.

Artificial Intelligence these days has become a new key driver of economic growth. It is a significant field in technology right now. While several countries are racing towards AI supremacy, Canada is attracting the world's tech giants that are pouring mammoth amounts in the region. The country is currently in the midst of the AI boom as companies like Microsoft, Facebook, Google, Huawei, among others are spending huge capital on research hubs in Quebec, Ontario and Alberta. Canada is a world research leader and home to extraordinary AI-driven businesses, and has played a vital role in the advancement of AI.

Considered by some to be the fourth industrial revolution, the capabilities of AI are ever-growing with new personnel, data and financial power pushing it to greater feats each day, week, month and year. With the wealth of data available to us today, the potential of AI is undeniable. With current debates roaring on which sectors will reap the benefits most, including healthcare, finance, education and more, the only certainty is change. In our top cities in AI blog from 2019, we forecasted (with some help from our industry friends), which cities would emerge as tech hubs, so we thought we'd have another go in 2020! Our list, in no particular order, details some of the cities we think will see some great advancements over the next 11 months.

Microsoft announced the investment plans for Mexico in an official press release. The announcement comes a month after Microsoft CEO Satya Nadela expressed his vision to "power broad economic growth through tech intensity" at Davos WEF 2020. He had said that Microsoft will ensure that this economic growth is inclusive. Mexico is now part of this inclusive global digital revolution. Mexico's digital revolution roadmap includes Microsoft's Cloud Services allocated from the local datacenters.

The main pillar of the plan is focused on accelerating Mexico's digital transformation through democratizing the access to technology. The company announced plans to establish a new cloud datacenter region in Mexico to deliver its intelligent and trusted cloud services to serve Mexico's public entities, organizations and Mexican society, including Microsoft Azure, Office 365, Dynamics 365 and the Power Platform. This datacenter region is an important part of Microsoft's $1.1 billion investment plan in Mexico over the next five years. The plan also includes a robust education and skilling program with different initiatives the first one being the creation of three laboratories and a virtual classroom, in collaboration with public universities to create an education platform for digital skills, to expand employability in future generations. The first initiative of the commitment to apply artificial intelligence to create societal impact is an investment in the project "Artificial Intelligence to Monitor Pelagic Sharks in the Mexican Pacific Ocean" (Shark ID), focused on the conservation of Mako shark species, driven by Mexico Azul, as part of the initiative AI for Earth, creating societal impact.

National Oceanic and Atmospheric Administration scientists are preparing to use machine learning (ML) to more easily monitor threatened ice seal populations in Alaska between April and May. Ice flows are critical to seal life cycles but are melting due to climate change -- which has hit the Arctic and sub-Arctic regions hardest. So scientists are trying to track species' population distributions. But surveying millions of aerial photographs of sea ice a year for ice seals takes months. And the data is outdated by the time statisticians analyze it and share it with the NOAA assistant regional administrator for protected resources in Juneau, according to a Microsoft blog post.

Moreland's project combines AI technology with improved cameras on a NOAA turboprop airplane that will fly over the Beaufort Sea north of Alaska this April and May, scanning and classifying the imagery to produce a population count of ice seals and polar bears that will be ready in hours instead of months. Her colleague Manuel Castellote, a NOAA affiliate scientist, will apply a similar algorithm to the recordings he'll pick up from equipment scattered across the bottom of Alaska's Cook Inlet, helping him quickly decipher how the shrinking population of endangered belugas spent its winter.

On Saturday morning, I used an app on my phone to unlock a vehicle from Gig, a car sharing startup, and set off for a Valentine's Day weekend trip to northern California with my partner. By late Sunday afternoon, we were sitting on the side of a remote highway, a software issue on our smart car rendering it unusable. It was getting dark, we had no way of getting home, and I was contemplating the limits of the sharing economy and the ultimate costs of convenience. Gig is a company that rents a fleet of hybrid Toyota Priuses and electric Chevrolet Bolts in the Bay Area and Sacramento to 65,000 users, according to a spokesman for the company. It is part of a growing field of car-sharing services – including Zipcar, the now-defunct Share Now, and recently Uber and Lyft – that allow users to rent standardized vehicles on the go.