SPS Feed

Channel estimation is of paramount importance in most communication systems in order to optimize the data rate/energy consumption tradeoff. In modern systems, the possibly large number of transmit/receive antennas and subcarriers makes this task difficult. Designing pilot sequences of reasonable size yielding good performance is thus critical. 

Control over noisy communication-channels” invented by Sahai-Mitter-and-Tatikonda is a prominent topic. In this context, the latency-and-reliability trade-off is considered by responding to the following: How much fast? How much secure? For a stochastic-mean-field-game (S-MFG), we assign the source-codes as the agents. Additionally, the total-Reward is the Volume of the maximum secure lossy source-coding-rate achievable between a set of Sensors, and the Fusion-Centre (FC) set – including intercepting-Byzantines.

Video summarization is an important technique to browse, manage and retrieve a large amount of videos efficiently. The main objective of video summarization is to minimize the information loss when selecting a subset of video frames from the original video, hence the summary video can faithfully represent the overall story of the original video. Recently developed unsupervised video summarization approaches are free of requiring tedious annotation on important frames to train a video summarization model and thus are practically attractive.

Scene text plays a significant role in image and video understanding, which has made great progress in recent years. Most existing models on text detection in the wild have the assumption that all the texts are surrounded by a rotated rectangle or quadrangle. While there also exist lots of curved texts in the wild, which would not be bounded by a regular bounding box. 

This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples.