First large transport drone takes off in China

USATODAY - Tech Top Stories

The world's first unmanned large transport drone made its maiden flight on Thursday, according the China's state broadcaster CCTV. It claimed the drone has a maximum carrying capacity of 1.5 metric tons and can fly at an altitude of 20,000 feet.

The intraflagellar transport train


Assembly of the cilium requires bidirectional intraflagellar transport (IFT) of building blocks along microtubules to and from the site of assembly at its tip. Dynein-1b motors are required to power retrograde transport and are believed to reach the ciliary tip by kinesin-2–driven anterograde IFT. It is unclear which mechanism prevents a tug-of-war between these oppositely directed microtubule motors. Jordan et al. used cryo–electron tomography to examine the architecture of IFT trains in Chlamydomonas cilia in situ. Their findings revealed the relative positions of IFT motors on anterograde versus retrograde trains.

Seeking to revive an ancient transport network in the UK

Al Jazeera

The UK has thousands of kilometres of inland waterways - navigable rivers and canals - built to move goods in the 19th century. For many years they have been neglected. But as roads get busier, businesses and environmental groups are looking for new ways to revive a forgotten transport network.

Private and public-sector big data transport policies explor


Researchers have published a comprehensive report on private- and public-sector big data policies affecting transport in EU countries and abroad. The travel behaviours and transport preferences of city dwellers are changing. Transport researchers and policymakers are therefore faced with numerous challenges as they strive to create efficient, safe and sustainable transportation systems, notes CORDIS, the EU Research and innovation news service. In order to address these issues, the EU-funded project LeMO is exploring the opportunities provided by big data in the field of transport research. LeMO will disseminate its findings to stakeholders, including transport authorities and industry, leading to better understanding of travellers' and consumers' behaviour, targeted information and identify policy interventions.

Mapping Estimation for Discrete Optimal Transport

Neural Information Processing Systems

We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ linked to the original Monge problem. Consequently, it lowers the potential usage of such methods in contexts where out-of-samples computations are mandatory. In this paper we propose a new way to jointly learn the coupling and an approximation of the transport map. We use a jointly convex formulation which can be efficiently optimized.