**Maximum Distance Sub-Lattice Problem (MDSP)**

In this paper, we define a problem on lattices called the Maximum Distance Sub-lattice Problem (MDSP). The decision version of this problem is shown to be in NP. We prove that MDSP is isomorphic to a well-known problem called closest vector problem (CVP). We give an exact and a heuristic algorithm for MDSP. Using experimental results we show that the LLL algorithm can be accelerated when it is combined with the heuristic algorithm for MDSP. … **Multiscale Network (MS)**

This paper proposes a dimension reduction process for computing the Dijkstra’s shortest path algorithm in a complex network. This is done through a novel multiscale (MS) network decomposition into base-elements: links and landmark-nodes. All of them result to be essential for keeping all the network connectivity information and for speeding up the exact computation of the Dijkstra’s shortest path. The multiscale shortest path (MS-SP) algorithm shows to be advantageous when dealing with big-size utility networks in comparison with other shortest-path algorithms: unfeasible for the curse of the dimensionality for traditional approaches or providing approximate solution in other cases. The novel methodology is of high interest when it is computed on urban utility networks as it explodes several of their inherent properties. However, the proposal extends straightforwardly to another kind of networks. MS-SP has been successfully applied for 2 water utility networks (medium and big size). In both cases, MS-SP provides the exact solution that the obtained by applying the Dijkstra’s shortest path while showing its efficiency in terms of computational time. … **Explainable Time Series Tweaking**

Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking. The classifier under investigation is the random shapelet forest classifier. Moreover, we propose two algorithmic solutions for the two problems along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions. … **IRGAN**

Generative adversarial nets (GANs) have been widely studied during the recent development of deep learning and unsupervised learning. With an adversarial training mechanism, GAN manages to train a generative model to fit the underlying unknown real data distribution under the guidance of the discriminative model estimating whether a data instance is real or generated. Such a framework is originally proposed for fitting continuous data distribution such as images, thus it is not straightforward to be directly applied to information retrieval scenarios where the data is mostly discrete, such as IDs, text and graphs. In this tutorial, we focus on discussing the GAN techniques and the variants on discrete data fitting in various information retrieval scenarios. (i) We introduce the fundamentals of GAN framework and its theoretic properties; (ii) we carefully study the promising solutions to extend GAN onto discrete data generation; (iii) we introduce IRGAN, the fundamental GAN framework of fitting single ID data distribution and the direct application on information retrieval; (iv) we further discuss the task of sequential discrete data generation tasks, e.g., text generation, and the corresponding GAN solutions; (v) we present the most recent work on graph/network data fitting with node embedding techniques by GANs. Meanwhile, we also introduce the relevant open-source platforms such as IRGAN and Texygen to help audience conduct research experiments on GANs in information retrieval. Finally, we conclude this tutorial with a comprehensive summarization and a prospect of further research directions for GANs in information retrieval. …

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