Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.

Researchers in Norway are testing how salmon in a commercial fish farm might react to being regularly monitored by an underwater robots. While fish farms are typically uneventful environments, they still require oversight to ensure the captive fish are safe and healthy, a task most commercial fish farms assign to a human diver. Maarja Kruusmaa and a team of researchers at the Norwegian University of Science and Technology wanted to test how fish would respond to being watched over by robots instead of people. 'The happier the fish are, the healthier the fish are, the better they eat, the better they grow, the less parasites they have and the less they get sick,' Kruusmaa told New Scientist. The team used two different underwater robots to test whether the fish would react differently based on the size and propulsion method.

The company employs engineers in Seattle on machine–learning technology.

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.