Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9% macro F1 score on our task, and yields nearly 80% recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.

NASCAR will use the breadth and depth of AWS technologies to build cloud-based services and automate processes, including a new video series on NASCAR.com The video series will debut heading into the Monster Energy NASCAR Cup Series race at Michigan International Speedway, sharing the greatest historical moments in NASCAR racing with viewers. NASCAR is migrating its 18-petabyte video archive to AWS, and will leverage Amazon Rekognition--an AWS service that adds intelligent image and video analysis to applications--to automatically tag specific video frames with metadata, such as driver, car, race, lap, time, and sponsors so they can easily search those tags to surface the most iconic moments from past races. By using AWS's services, NASCAR expects to save thousands of hours of manual search time each year, and will be able to easily surface flashbacks like Dale Earnhardt Sr.'s 1987 "Pass in the Grass" or Denny Hamlin's 2016 Daytona 500 photo finish, and quickly deliver these to fans via video clips on NASCAR.com and social media channels. NASCAR will leverage AWS services to enhance its full range of media assets including websites, mobile applications, and social properties for its 80 million fans worldwide.

Agerris, an Australia-based robotics and AI platform for agriculture, announced over the weekend that it has raised $6.5 million (AUSD) in seed funding from Uniseed, Carthona Capital and BridgeLane Group. The startup was founded by Professor Salah Sukarrieh and began as research at the Australian Centre for Field Robotics at the University of Sydney (which is also a partner in Uniseed). From the looks of it, Agerris is building a modular robotics and AI platform that has broad applications for both plant and livestock farmers. According to a University of Sydney news post, Agerris has two main products. The "Swagbot" can autonomously monitor and identify weed issues, detect food and crops through computer vision, as well as herd livestock.

Microsoft Australia's national technology officer Lee Hickin has reaffirmed that artificial intelligence (AI) technologies are not replacing humans in the workforce, but are placing humans into positions where they can provide more value in their work. Speaking at ZDNet's Next Big Thing event in Sydney on Thursday, Hickin said the implementation of AI into work environments will augment the human experience, rather than replace it altogether. Using Microsoft's work at Northern Territory fisheries as an example, Hickin said the implementation of AI can "take away what we would call'grunt work' in jobs and functions". The AI fisheries project uses the company's Azure Cognitive Service to identify and count fish in waters without needing to sort through hours of under-water footage. The solution has already shown that the local golden snapper and black jewfish species are overfished.

Tinder is giving users more tools to express their sexuality. The dating app announced on Tuesday that users can now select up to three terms that they most identify with from a list of nine options. Tinder is giving users more tools to express their sexuality. Users can choose from nine orientations, including straight, gay, lesbian, bisexual, asexual, demisexual, pansexual, queer and questioning. From there, they can decide whether they want that information to show up on their public-facing profile.

The main theme of this work is a unifying algorithm, abbreviated as L2S, that can deal with (strongly) convex and nonconvex empirical risk minimization (ERM) problems. It broadens a recently developed variance reduction method known as SARAH. L2S enjoys a linear convergence rate for strongly convex problems, which also implies the last iteration of SARAH's inner loop converges linearly. For convex problems, different from SARAH, L2S can afford step and mini-batch sizes not dependent on the data size $n$, and the complexity needed to guarantee $\mathbb{E}[\|\nabla F(\mathbf{x}) \|^2] \leq \epsilon$ is ${\cal O}(n+ n/\epsilon)$. For nonconvex problems on the other hand, the complexity is ${\cal O}(n+ \sqrt{n}/\epsilon)$. Parallel to L2S there are a few side results. Leveraging an aggressive step size, D2S is proposed, which provides a more efficient alternative to L2S and SARAH-like algorithms. Specifically, D2S requires a reduced IFO complexity of ${\cal O}\big( (n+ \bar{\kappa}) \ln (1/\epsilon) \big)$ for strongly convex problems. Moreover, to avoid the tedious selection of the optimal step size, an automatic tuning scheme is developed, which obtains comparable empirical performance with SARAH using judiciously tuned step size.

Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable. To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a low-level agent that alters the sentence. We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents. Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.

Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorithm to generate class representatives for few-shot classification tasks. As a probabilistic model for learned features of inputs, we consider a mixture of von Mises-Fisher distributions which is known to be more expressive than Gaussian in a high dimensional space. Then, from a discriminative classifier perspective, we get a better class representative considering inter-class correlation which has not been addressed by conventional few-shot learning algorithms. We apply our method to \emph{mini}ImageNet and \emph{tiered}ImageNet datasets, and show that the proposed approach outperforms other comparable methods in few-shot classification tasks.

We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all recent algorithmic advances (e.g. neural nets, forests). We show that the estimated effect model is robust to estimation errors in the auxiliary models, by showing that the loss satisfies a Neyman orthogonality criterion. Our approach can be used to estimate projections of the true effect model on simpler hypothesis spaces. When these spaces are parametric, then the parameter estimates are asymptotically normal, which enables construction of confidence sets. We applied our method to estimate the effect of membership on downstream webpage engagement on TripAdvisor, using as an instrument an intent-to-treat A/B test among 4 million TripAdvisor users, where some users received an easier membership sign-up process. We also validate our method on synthetic data and on public datasets for the effects of schooling on income.