Weakly supervised temporal action localization with proxy metric modeling

Temporal localization is crucial for action video recognition.Since the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but indispensable.In this paper,we propose a weakly-supervised temporal action localization approach in untrimmed videos.To settle this issue,we train the model based on the proxies of each action class.The proxies are used to measure the distances between action segments and different original action features.We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds.Compared with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.

Energy Procurement and Retail Pricing for Electricity Retailers via Deep Reinforcement Learning with Long Short-term Memory

The joint optimization problem of energy procurement and retail pricing for an electricity retailer is converted into separately determining the optimal procurement strategy and optimal pricing strategy,under the“price-taker”assumption.The aggregate energy consumption of end use customers(EUCs)is predicted to solve for the optimal procurement strategy vis a long short-term memory(LSTM)-based supervised learning method.The optimal retail pricing problem is formulated as a Markov decision process(MDP),which can be solved by using deep reinforcement learning(DRL)algorithms.However,the performance of existing DRL approaches may deteriorate due to their insufficient ability to extract discriminative features from the time-series vectors in the environmental states.We propose a novel deep deterministic policy gradient(DDPG)network structure with a shared LSTM-based representation network that fully exploits the Actor’s and Critic’s losses.The designed shared representation network and the joint loss function can enhance the environment perception capability of the proposed approach and further improve the optimization performance,resulting in a more profitable pricing strategy.Numerical simulations demonstrate the effectiveness of the proposed approach.

Asymmetric Synthesis of Dihydrospirotryprostatin B via a Silica Gel-Mediated Cyclization of Tryptamine-Ynamide

Comprehensive Summary An asymmetric synthesis of dihydrospirotryprostatin B was achieved in 15 steps(8 purifications)from L-tryptophan.The main feature of our synthetic strategy is the efficient construction of spirocyclic oxindole intermediate containing a chiral quaternary carbon center,involving the silica gel-mediated cyclization of tryptamine-ynamide and oxidation under neat conditions.