In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: The utility of Twitter data as a medium to support populationlevel mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.

Output: Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health

Input: Consider Input: In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entitiestasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.

Output: Towards Combining HTN Planning and Geometric Task Planning

Input: Consider Input: The computation and storage requirements for Deep Neural Networks (DNNs) are usually high. This issue limit their deployability on ubiquitous computing devices such as smart phones or wearables. In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient. We train these TNNs using a teacher-student approach. Using only ternary weights and ternary neurons, with a step activation function of two-thresholds, the student ternary network learns to mimic the behaviour of its teacher network. We propose a novel, layer-wise greedy methodology for training TNNs. During training, a ternary neural network inherently prunes the smaller weights by setting them to zero. This makes them even more compact thus more resource-friendly. We devise a purpose-built hardware design for TNNs and implement it on FPGA. The benchmark results with our purpose-built hardware running TNNs reveal that, with only 1.24J per image, we can achieve 97.76% accuracy with 5.37s latency and with a rate of 255K images per second on MNIST.
Output: Ternary Neural Networks for Resource-Efficient AI Applications