Prematurity is a significant cause of mortality in neonates. Knowledge of an infant's gestational age is critical in post-delivery treatment plans to reduce neonatal deaths. In high-income countries, prenatal ultrasound scans – the ground truth measure – are the gold-standard method to track gestational aging, but in lower-income countries, access to ultrasound technology and medical experts is limited. "If we could accurately estimate gestational age for newborns using simple, portable technology, we would be able to administer a proper post-treatment plan to reduce the risk of mortality in many under-serviced regions," says Arjun Desai from Duke University, first-author on a new study into an automatic system for gestational aging. The cross-disciplinary team, led by Sina Farsiu, has developed a system based on the previously reported inverse correlation between blood vessel density in the anterior lens capsule region, and gestational age.

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.

Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we construct NCP with sparse regularization (sparse NCP) by l1-norm. Several popular optimization methods in block coordinate descent framework are employed to solve the sparse NCP, all of which are deeply analyzed with mathematical solutions. We compare these methods by experiments on synthetic and real tensor data, both of which contain third-order and fourth-order cases. After comparison, the methods that have fast computation and high effectiveness to impose sparsity will be concluded. In addition, we proposed an accelerated method to compute the objective function and relative error of sparse NCP, which has significantly improved the computation of tensor decomposition especially for higher-order tensor.

This paper studies whether rationality can be computed. Rationality is defined as the use of complete information, which is processed with a perfect biological or physical brain, in an optimized fashion. To compute rationality one needs to quantify how complete is the information, how perfect is the physical or biological brain and how optimized is the entire decision making system. The rationality of a model (i.e. physical or biological brain) is measured by the expected accuracy of the model. The rationality of the optimization procedure is measured as the ratio of the achieved objective (i.e. utility) to the global objective. The overall rationality of a decision is measured as the product of the rationality of the model and the rationality of the optimization procedure. The conclusion reached is that rationality can be computed for convex optimization problems.

Using machine learning and the IoT, tractor fleet owners will be able to view and manage fleet utilization and predictive maintenance as well as forecast future tractor utilizations based on history, real-time weather and remote sensing satellite data. In sub-Saharan Africa, more than 60 percent of crops are managed manually, with less than 20 percent managed by tractors and other machinery, an unsustainable model as food demand increases due to population growth averaging 11 million per year. In addition, up to 50 percent of farmers suffer post-harvest losses annually due to poor planting practices. This was the motivation behind startup Hello Tractor, a mobile platform that enables farmers to access tractor services on demand. Using a mobile app, the service aggregates tractor service requests and pairs them with recommended tractors and operators, while simultaneously tracking how many hours each piece of equipment is in the field and area serviced.

Although science and the public celebrated the amazing predictive power of the new machine learningmethods, many researchers are left with some unease, simply because prediction does not imply causation. The ability to uncover causal relations is, however, at the core of most questions concerning the effects of particular policies, medical treatments, marketing campaigns, businessdecisions, etc. (see Athey, 2017, for a recent discussion). The recently rapidly expanding causal machine learning literature holds great promise for the improved estimation of causal effects by merging the statistics and econometrics literature oncausality with the supervised statistical and machine learning (ML) literature focussing on prediction. The classical causality literature clarifies the conditions needed for being able to estimate causal effects. It also shows how to transform a counterfactual causal problem into specific prediction problems (e.g., Imbens and Wooldridge, 2009). The latter literature on ML provides tools that can be highly effective in solving prediction problems (e.g.

Earlier this month, an intergovernmental conference in Marrakesh, Morocco brought together leaders from around the world to address global migration. After two days of deliberations, some 150 nations signed the Global Compact for Migration (GCM) agreement, which called for the implementation of more humane policies to ensure "safe, orderly and regular migration". But the conference and the agreement were very much a platform for Western doublespeak and hypocrisy. European countries and the United States are by far not concerned with the "safety" of migrants and refugees heading for their borders. In fact, the border security industry in Europe and the US is thriving and in both places, Israel with its infamous militarised security policies is serving as a role model and a major technology supplier.

Climbing teams spent over two weeks laser scanning Mount Rushmore in May 2010. High atop the Thomas Jefferson Memorial in Washington, D.C., is a layer of biofilm covering the dome, darkening and discoloring it. Biofilm is "a colony of microscopic organisms that adheres to stone surfaces," according to the U.S. National Park Service, which needed to get a handle on its magnitude to get an accurate cost estimate for the work to remove it. Enter CyArk, a non-profit organization that uses three-dimensional (3D) laser scanning and photogrammetry to digitally record and archive some of the world's most significant cultural artifacts and structures. CyArk spent a week covering "every inch" of the dome, processed the data, and returned a set of engineering drawings to the Park Service "to quantify down to the square inch how much biofilm is on the monument," says CEO John Ristevski.

Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.

Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work we perform an analytic study of the performances of one of them, the Langevin algorithm, in the context of noisy high-dimensional inference. We employ the Langevin algorithm to sample the posterior probability measure for the spiked matrix-tensor model. The typical behaviour of this algorithm is described by a system of integro-differential equations that we call the Langevin state evolution, whose solution is compared with the one of the state evolution of approximate message passing (AMP). Our results show that, remarkably, the algorithmic threshold of the Langevin algorithm is sub-optimal with respect to the one given by AMP. We conjecture this phenomenon to be due to the residual glassiness present in that region of parameters. Finally we show how a landscape-annealing protocol, that uses the Langevin algorithm but violate the Bayes-optimality condition, can approach the performance of AMP.