Reading Scene Text in Deep Convolutional Sequences

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence (AAAI-16), 201

Less-than-truckload Dynamic Pricing Model in Physical Internet

International audienceThis paper investigates a decision-making problem consisting of less-than-truckload dynamic pricing (LTLDP) under Physical Internet (PI). PI can be seen as the interconnection of logistics networks via open PI-hubs, which can be considered as spot freight markets where LTL requests of different volume/destination continuously arrive over time for a short-stay. Carriers can bid for the requests by using short-term contract. This paper proposes a dynamic pricing model to optimise carrier’s bid price to maximise his expected profits. Three influencing factors are investigated: requests quantity, carrier’s capacity and cost. The results provide useful guidelines to carriers on pricing decisions in PI-hub

Formation of in-volume nanogratings with sub-100 nm periods in glass by femtosecond laser irradiation

We present direct experimental observation of the morphological evolution during the formation of nanogratings with sub-100-nm periods with the increasing number of pulses. Theoretical simulation shows that the constructive interference of the scattering light from original nanoplanes will create an intensity maximum located between the two adjacent nanoplanes, resulting in shortening of the nanograting period by half. The proposed mechanism enables explaining the formation of nanogratings with periods beyond that predicted by the nanoplasmonic model.Comment: 4 pages, 3 figure