Deep Semantic Classification for 3D LiDAR Data

Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.Comment: 8 pages to be published in IROS 201

The number of privately treated tuberculosis cases in India: an estimation from drug sales data

Background Understanding the amount of tuberculosis managed by the private sector in India is crucial to understanding the true burden of the disease in the country, and thus globally. In the absence of quality surveillance data on privately treated patients, commercial drug sales data offer an empirical foundation for disease burden estimation. Methods We used a large, nationally representative commercial dataset on sales of 189 anti-tuberculosis products available in India to calculate the amount of anti-tuberculosis treatment in the private sector in 2013–14. We corrected estimates using validation studies that audited prescriptions against tuberculosis diagnosis, and estimated uncertainty using Monte Carlo simulation. To address implications for numbers of patients with tuberculosis, we explored varying assumptions for average duration of tuberculosis treatment and accuracy of private diagnosis. Findings There were 17·793 million patient-months (95% credible interval 16·709 million to 19·841 million) of anti-tuberculosis treatment in the private sector in 2014, twice as many as the public sector. If 40–60% of private-sector tuberculosis diagnoses are correct, and if private-sector tuberculosis treatment lasts on average 2–6 months, this implies that 1·19–5·34 million tuberculosis cases were treated in the private sector in 2014 alone. The midpoint of these ranges yields an estimate of 2·2 million cases, two to three times higher than currently assumed. Interpretation India's private sector is treating an enormous number of patients for tuberculosis, appreciably higher than has been previously recognised. Accordingly, there is a re-doubled need to address this burden and to strengthen surveillance. Tuberculosis burden estimates in India and worldwide require revision

Across the Digital Divide: A Cross-Country Multi-Technology Analysis of the Determinants of IT Penetration

This paper studies the country-level digital divide across successive generations of IT, providing detailed insights into the magnitude and changing nature of the divide. We examine a panel of 40 countries from 1985-2001, based on data from three distinct generations of IT: mainframes, personal computers, and the Internet. Using two measures of IT penetration, we conduct an empirical investigation of socio-economic factors driving the digital divide. We find that IT penetration is positively associated with national income for all three technology generations, and the association between penetration and income is stronger for countries with higher levels of IT penetration. We also examine other demographic and economic factors, going beyond income, and find significant differences in the nature of their effects across countries at different stages of IT adoption. Importantly, factors that previously may have been expanding the divide with earlier technologies are narrowing the gap as the Internet becomes the defining technology of the Information Age

Investigating RFC1 expansions in sporadic amyotrophic lateral sclerosis

A homozygous AAGGG repeat expansion within the RFC1 gene was recently described as a common cause of CANVAS syndrome. We examined 1069 sporadic ALS patients for the presence of this repeat expansion. We did not discover any carriers of the homozygous AAGGG expansion in our ALS cohort, indicating that this form of RFC1 repeat expansions is not a common cause of sporadic ALS. However, our study did identify a novel repeat conformation and further expanded on the highly polymorphic nature of the RFC1 locus