This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.

Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications. In this paper, we propose a novel type of model where interpretable models compete and collaborate with black-box models. We present the Model-Agnostic Linear Competitors (MALC) for partially interpretable classification. MALC is a hybrid model that uses linear models to locally substitute any black-box model, capturing subspaces that are most likely to be in a class while leaving the rest of the data to the black-box. MALC brings together the interpretable power of linear models and good predictive performance of a black-box model. We formulate the training of a MALC model as a convex optimization. The predictive accuracy and transparency (defined as the percentage of data captured by the linear models) balance through a carefully designed objective function and the optimization problem is solved with the accelerated proximal gradient method. Experiments show that MALC can effectively trade prediction accuracy for transparency and provide an efficient frontier that spans the entire spectrum of transparency.

This paper proposes a formulation of the Active Debris Removal (ADR) Mission Design problem as a modified Time-Dependent Traveling Salesman Problem (TDTSP). The TDTSP is a well-known combinatorial optimization problem, whose solution is the cheapest mono-cyclic tour connecting a number of non-stationary cities in a map. The problem is tackled with an optimization procedure based on Simulated Annealing, that efficiently exploits a natural encoding and a careful choice of mutation operators. The developed algorithm is used to simultaneously optimize the targets sequence and the rendezvous epochs of an impulsive ADR mission. Numerical results are presented for sets comprising up to 20 targets. INTRODUCTION The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, whose solution is the cheapest tour which allows a salesman to visit, only once, a number of cities in a map; the cost of each city-to-city transfer is, typically, the traveled distance or the fuel consumption. Active Debris Removal (ADR) missions can be seen as peculiar instances of the TDTSP, where an active (chaser) spacecraft is asked to visit, that is, to perform a rendezvous, with a certain number of targets (space debris), making the best use of the on-board propellant. Such kind of missions are increasing in popularity among space agencies all over the world, as the sustainability of the extra-atmospheric environment is becoming compromised by the huge amount of "space garbage" now orbiting Earth. A cost-competitive space program would involve the removal of several dozens of small debris with each single mission; such a complex scenario could became feasible only with the best possible use of the propellant on-board of the chaser spacecraft. As a consequence, a well-designed ADR mission would require the optimization of a multi-target rendezvous trajectory. A number of authors dealt with long term or time-free ADR missions aimed at removing a small number of debris from Sun synchronous orbits (at a rate of three to ten per year). These missions heavily rely on J 2 orbital perturbation for the alignment of the orbital planes of consecutive targets before starting the rendezvous maneuver, in order to reduce the mission cost.

The Clarke subdifferential is not suited to tackle nonsmooth deep learning issues: backpropagation, mini-batches and steady states are not properly modelled. As a remedy, we introduce set valued conservative fields as surrogates to standard subdifferential mappings. We study their properties and provide elements of a calculus. Functions having a conservative field are called path differentiable. Convex/concave, semi-algebraic, or Clarke regular Lipschitz continuous functions are path differentiable as their corresponding subdifferentials are conservative. Another concrete and considerable class of examples of conservative fields, which are not subdifferential mappings, is given by the automatic differentiation oracle, as for instance the "subgradients" provided by the backpropagation algorithm in deep learning. Our differential model is eventually used to ensure subsequential convergence for nonsmooth stochastic gradient methods in the tame Lipschitz continuous setting offering the possibility of using mini-batches, the actual backpropagation oracle and $o(1/\log k)$ stepsizes.

In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.

A term "Deep learning" refers to training neural networks and sometimes very large neural networks. They are massively used for problems of classification and regression. The main goal is to optimize parameters \( w \) and \( b \) ( both in logistic regression and in neural networks). Gradient Descent achieved amazing results in solving these optimization tasks. Not so new, developed in the 1940s, 1950s and 1960s.

Do athletes specialize towards sports disciplines that are well aligned with their anthropometry? Novel machine-learning algorithms now enable scientists to cluster athletes based on their individual anthropometry while integrating multiple anthropometric dimensions, which may provide new perspectives on anthropometry-dependent sports specialization. We aimed to identify clusters of competitive cyclists based on their individual anthropometry using multiple anthropometric measures, and to evaluate whether athletes with a similar anthropometry also competed in the same cycling discipline. Additionally, we assessed differences in sprint and endurance performance between the anthropometric clusters. Twenty-four nationally and internationally competitive male cyclists were included from sprint, pursuit and road disciplines.

Data visualization: In this section, you will learn how to create simple plots like scatter plot histogram bar, etc. Data manipulation: You will learn in detail about data manipulation. GUI Programming: This section is a combination of life instructor-led training and self-paced learning. Developing web Maps and representing information using plots: In this section, you will understand how to design Python applications. Computer vision using open CV and visualization using bokeh: You will also learn designing Python application in the section.

-- In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy. CCORDING to the National Institute of Mental Health, anxiety disorders affect more than 18% of the U.S. adult population every year [1]. Additionally, the National Survey of Drug Use and Health reports that 6.7% of the U.S. adult population experienced at least one major depressive disorder episode in the past year [2]. This work was supported by NSF-IIP 1631871 from the National Science Foundation (NSF), Division of Industrial Innovation and Partnerships (IIP). Rashidi are with the University of Florida, Gainesville, FL 32611 USA (email: shickelb@ufl.edu; S. Benton is with T AO Connect, Inc., St. Petersburg, FL 33701 USA (email: sherry .benton@taoconnect.org).

Excessive computational cost for learning large data and streaming data can be alleviated by using stochastic algorithms, such as stochastic gradient descent and its variants. Recent advances improve stochastic algorithms on convergence speed, adaptivity and structural awareness. However, distributional aspects of these new algorithms are poorly understood, especially for structured parameters. To develop statistical inference in this case, we propose a class of generalized regularized dual averaging (gRDA) algorithms with constant step size, which improves RDA (Xiao, 2010; Flammarion and Bach, 2017). Weak convergence of gRDA trajectories are studied, and as a consequence, for the first time in the literature, the asymptotic distributions for online l1 penalized problems become available. These general results apply to both convex and non-convex differentiable loss functions, and in particular, recover the existing regret bound for convex losses (Nemirovski et al., 2009). As important applications, statistical inferential theory on online sparse linear regression and online sparse principal component analysis are developed, and are supported by extensive numerical analysis. Interestingly, when gRDA is properly tuned, support recovery and central limiting distribution (with mean zero) hold simultaneously in the online setting, which is in contrast with the biased central limiting distribution of batch Lasso (Knight and Fu, 2000). Technical devices, including weak convergence of stochastic mirror descent, are developed as by-products with independent interest. Preliminary empirical analysis of modern image data shows that learning very sparse deep neural networks by gRDA does not necessarily sacrifice testing accuracy.