Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. To minimize the effects of buoy disruption on a buoy network, we propose a more robust buoy placement using dropout k-means and dropout k-median. We apply dropout k-means and dropout k-median to determine locations for deploying marine buoys in the Gabonese waters near West Africa. We simulated the passage of ships using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median, taking into account that the current sensor detection radius is 10km. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%.

We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations. We propose a method for learning these relational features for a Gaifman model by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over classical rule-learning.

Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification. Specifically, we propose a kernelized support tensor train machine that accepts tensorial input and preserves the intrinsic kernel property. The main contributions are threefold. First, we propose a TT-based feature mapping procedure that maintains the TT structure in the feature space. Second, we demonstrate two ways to construct the TT-based kernel function while considering consistency with the TT inner product and preservation of information. Third, we show that it is possible to apply different kernel functions on different data modes. In principle, our method tensorizes the standard SVM on its input structure and kernel mapping scheme. Extensive experiments are performed on real-world tensor data, which demonstrates the superiority of the proposed scheme under few-sample high-dimensional inputs.

Machine learning (ML) is quickly emerging as a powerful tool with diverse applications across an extremely broad spectrum of disciplines and commercial endeavors. Typically, ML is used as a black box that provides little illuminating rationalization of its output. In the current work, we aim to better understand the generic intuition underlying unsupervised ML with a focus on physical systems. The systems that are studied here as test cases comprise of six different 2-dimensional (2-D) particulate systems of different complexities. It is noted that the findings of this study are generic to any unsupervised ML problem and are not restricted to materials systems alone. Three rudimentary unsupervised ML techniques are employed on the adjacency (connectivity) matrix of the six studied systems: (i) using principal eigenvalue and eigenvectors of the adjacency matrix, (ii) spectral decomposition, and (iii) a Potts model based community detection technique in which a modularity function is maximized. We demonstrate that, while solving a completely classical problem, ML technique produces features that are distinctly connected to quantum mechanical solutions. Dissecting these features help us to understand the deep connection between the classical non-linear world and the quantum mechanical linear world through the kaleidoscope of ML technique, which might have far reaching consequences both in the arena of physical sciences and ML.

The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models for multiple grassland types based on an unmanned aerial vehicle (UAV)-borne imaging spectroscopy and (ii) to generate forage quality distribution maps using the best models obtained. The study examined data from eight grasslands in northern Hesse, Germany, which largely differed in terms of vegetation type and cutting regime. The UAV with a hyperspectral camera on board was utilised to acquire spectral images from the grasslands, and crude protein (CP) and acid detergent fibre (ADF) concentration of the forage was assessed at each cut.

Let's get the basic definition out of the way: Markov Chain Monte Carlo (MCMC) methods let us compute samples from a distribution even though we can't compute it. Let's back up and talk about Monte Carlo Sampling. What are Monte Carlo methods? "Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results." And you are tasked with determining the area enclosed by this shape.

Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the wherewithal, in terms of money, manpower, and might, to chart the future. However, concentration of power can lead to marginalization, causing severe inequalities. Regulatory agencies and governments across the globe are creating national policies, and laws around these technologies to protect the rights of the digital citizens, as well as to empower them. Even private, not-for-profit organizations are also contributing to democratizing the technologies by making them \emph{accessible} and \emph{affordable}. However, accessibility and affordability are all but a few of the facets of democratizing the field. Others include, but not limited to, \emph{portability}, \emph{explainability}, \emph{credibility}, \emph{fairness}, among others. As one can imagine, democratizing AI is a multi-faceted problem, and it requires advancements in science, technology and policy. At \texttt{mlsquare}, we are developing scientific tools in this space. Specifically, we introduce an opinionated, extensible, \texttt{Python} framework that provides a single point of interface to a variety of solutions in each of the categories mentioned above. We present the design details, APIs of the framework, reference implementations, road map for development, and guidelines for contributions.

January 3, 2020 Abstract Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter. Keywords: Deep Learning, Graph Representation, Network Embedding 1 Introduction Many real-world systems, such as Facebook/Twitter social systems, DBLP author-citation systems and roadmap transportation systems etc., can be formulated in the form of graphs or networks, making analyzing these systems equivalent to mining their corresponding graphs or networks. Literature on mining graphs or networks has two names: ...

Motivated by the concept of network motifs we construct certain clustering methods (functors) which are parametrized by a given collection of motifs (or representers).

Kernel methods form a theoretically-grounded, powerful and versatile framework to solve nonlinear problems in signal processing and machine learning. The standard approach relies on the \emph{kernel trick} to perform pairwise evaluations of a kernel function, leading to scalability issues for large datasets due to its linear and superlinear growth with respect to the training data. Recently, we proposed \emph{no-trick} (NT) kernel adaptive filtering (KAF) that leverages explicit feature space mappings using data-independent basis with constant complexity. The inner product defined by the feature mapping corresponds to a positive-definite finite-rank kernel that induces a finite-dimensional reproducing kernel Hilbert space (RKHS). Information theoretic learning (ITL) is a framework where information theory descriptors based on non-parametric estimator of Renyi entropy replace conventional second-order statistics for the design of adaptive systems. An RKHS for ITL defined on a space of probability density functions simplifies statistical inference for supervised or unsupervised learning. ITL criteria take into account the higher-order statistical behavior of the systems and signals as desired. However, this comes at a cost of increased computational complexity. In this paper, we extend the NT kernel concept to ITL for improved information extraction from the signal without compromising scalability. Specifically, we focus on a family of fast, scalable, and accurate estimators for ITL using explicit inner product space (EIPS) kernels. We demonstrate the superior performance of EIPS-ITL estimators and combined NT-KAF using EIPS-ITL cost functions through experiments.