Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully observed variables and/or data from stationary time-series. We develop a causal discovery approach to handle a wide class of nonstationary time series that are _conditionally stationary_, where the nonstationary behaviour is modeled as stationarity conditioned on a set of latent state variables. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies, with provable identifiablity for the state-dependent causal structures. Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI's superior performance over baseline causal discovery methods. Improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method and motivate the use of causality-driven methods for forecasting.

The indexing services are an important element of researching process because they make publicly available research results and articles for the community. Furthermore, regional indices such Latindex have contributed to spreading scientific works and incentivizing research in the Latin-American context. However, the un-centralized publication approach that its member journals follow has made impossible to form a unified view of Latindex-indexed articles and its corresponding journals. This drawback has limited activities such biblio-metric studies and integration from the perspective of information systems. In this paper, a linking mechanism between journals and publications is outlined, which aims to identify explicitly whether or not an arbitrary article belongs to Latindex. The proposed approach leverages on the Linked Data principles and takes advantage of web search engines to validate its results. This proposal has successfully been evaluated on an Ecuadorian publications dataset obtaining a 0.91 f-score respect to a manually classified sample.

Data ambiguity from various sources remains as a complex problem that affects services provided by digital libraries. From the point of view of integration of information from different sources, the challenge of author ambiguity is one of the most important, and there are numerous methods proposed to deal with this issue using different approaches. They generally work for some scenarios but they have important limitations, specially when dealing with heterogeneous sources. In this work, we review a group of existing methods and then propose a technique that combines some of them, also incorporating a measure of distance using semantic technologies to solve the ambiguity of authors while integrating bibliographic data from various sources. This technique has been successfully tested in disambiguating Ecuadorian authors from both internal sources (institutional repositories) and external digital libraries.