Book Reviews

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or $\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\mathcal{N}$-VAE, in low dimensions on other data types.Comment: GitHub repository: http://github.com/nicola-decao/s-vae-tf, Blogpost: https://nicola-decao.github.io/s-va

Community detection‐based deep neural network architectures: A fully automated framework based on Likert‐scale data.

Deep neural networks (DNNs) have emerged as a state‐of‐the‐art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio‐demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures

Removal of Hepatitis B virus surface HBsAg and core HBcAg antigens using microbial fuel cells producing electricity from human urine

© 2019, The Author(s). Microbial electrochemical technology is emerging as an alternative way of treating waste and converting this directly to electricity. Intensive research on these systems is ongoing but it currently lacks the evaluation of possible environmental transmission of enteric viruses originating from the waste stream. In this study, for the first time we investigated this aspect by assessing the removal efficiency of hepatitis B core and surface antigens in cascades of continuous flow microbial fuel cells. The log-reduction (LR) of surface antigen (HBsAg) reached a maximum value of 1.86 ± 0.20 (98.6% reduction), which was similar to the open circuit control and degraded regardless of the recorded current. Core antigen (HBcAg) was much more resistant to treatment and the maximal LR was equal to 0.229 ± 0.028 (41.0% reduction). The highest LR rate observed for HBsAg was 4.66 ± 0.19 h−1 and for HBcAg 0.10 ± 0.01 h−1. Regression analysis revealed correlation between hydraulic retention time, power and redox potential on inactivation efficiency, also indicating electroactive behaviour of biofilm in open circuit control through the snorkel-effect. The results indicate that microbial electrochemical technologies may be successfully applied to reduce the risk of environmental transmission of hepatitis B virus but also open up the possibility of testing other viruses for wider implementation

Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss

While global patterns of human genetic diversity are increasingly well characterized, the diversity of human languages remains less systematically described. Here we outline the Grambank database. With over 400,000 data points and 2,400 languages, Grambank is the largest comparative grammatical database available. The comprehensiveness of Grambank allows us to quantify the relative effects of genealogical inheritance and geographic proximity on the structural diversity of the world's languages, evaluate constraints on linguistic diversity, and identify the world's most unusual languages. An analysis of the consequences of language loss reveals that the reduction in diversity will be strikingly uneven across the major linguistic regions of the world. Without sustained efforts to document and revitalize endangered languages, our linguistic window into human history, cognition and culture will be seriously fragmented.Genealogy versus geography Constraints on grammar Unusual languages Language loss Conclusio

Hyperspherical Variational Auto-Encoders

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments, we show how such a hyperspherical VAE, or S-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, N-VAE, in low dimensions on other data types

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New YorkCity. The results demonstrate the superior performance of our model compared with the competing benchmarks.Peer reviewe