POMDP Model Learning for Human Robot Collaboration

Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion planning or HRC interface design, we are particularly interested in a formal design of HRC with respect to high-level complex missions, where it is of critical importance to obtain an accurate and meanwhile tractable human model. Instead of assuming the human model is given, we ask whether it is reasonable to learn human models from observed perception data, such as the gesture, eye movements, head motions of the human in concern. As our initial step, we adopt a partially observable Markov decision process (POMDP) model in this work as mounting evidences have suggested Markovian properties of human behaviors from psychology studies. In addition, POMDP provides a general modeling framework for sequential decision making where states are hidden and actions have stochastic outcomes. Distinct from the majority of POMDP model learning literature, we do not assume that the state, the transition structure or the bound of the number of states in POMDP model is given. Instead, we use a Bayesian non-parametric learning approach to decide the potential human states from data. Then we adopt an approach inspired by probably approximately correct (PAC) learning to obtain not only an estimation of the transition probability but also a confidence interval associated to the estimation. Then, the performance of applying the control policy derived from the estimated model is guaranteed to be sufficiently close to the true model. Finally, data collected from a driver-assistance test-bed are used to train the model, which illustrates the effectiveness of the proposed learning method

Effects of <i>ELOVL4</i> gene overexpress on the synthesis efficiency of n3 and n6 very long chain polyunsaturated fatty acids

AIM:To compare the synthesis efficiency of n3 and n6 very long chain polyunsaturated fatty acid(VLC-PUFA)by overexpressing ELOVL4 protein, providing guidance for treating Stargardt-like macular dystrophy(STGD3).<p>METHODS:To establish recombinant adenovirus with the ELOVL4 protein and green fluorescent protein, transferred into cultured PC12 cells. The cells were divided into 3 groups: PC12, PC12+Ad-<i>GFP</i> and PC12+Ad- <i>ELOVL4</i>, former two groups serve as controls. <i>ELOVL4</i> gene expression was quantified by qRT-PCRs. ELOVL4 protein was analyzed by Western-Blot(WB). The transduced cells were treated with both EPA and AA(1:1). After 48h of incubation, cells were collected, total lipids extracted and fatty acid methyl esters prepared and analyzed by gas chromatography-mass spectrometry(GC-MS). <p>RESULTS: When supplemented together, 20:5n3(EPA)and 20:4n6(AA)were efficiently taken up at almost the same amounts in the PC12 cells regardless of ELOVL4 expression. The ELOVL4-expressing cells elongated both EPA and AA to a series of n3 and n6 VLC-PUFAs. From 20:5n3/EPA, 34:5n3 and 36:5n3 account for 0.71% and 1.6%, respectively. From 20:4n6/DHA, 34:4n6 and 36:4n6 were only 0.46% and 0.61%, respectively. The total relative mol% of n3 VLC-PUFAs synthesized from EPA was almost two times that of n6 VLC-PUFAs synthesized from AA.<p>CONCLUSION: <i>ELOVL4</i> protein preferentially elongates n3 PUFA to VLC-PUFAs over n6 PUFA. Dietary supplementation of appropriate n3/n6 PUFAs may provide STGD3 patients with some therapeutic benefits

Scalable fault-tolerant quantum computation in DFS blocks

We investigate how to concatenate different decoherence-free subspaces (DFSs) to realize scalable universal fault-tolerant quantum computation. Based on tunable $XXZ$ interactions, we present an architecture for scalable quantum computers which can fault-tolerantly perform universal quantum computation by manipulating only single type of parameter. By using the concept of interaction-free subspaces we eliminate the need to tune the couplings between logical qubits, which further reduces the technical difficulties for implementing quantum computation.Comment: 4 papges, 2 figure

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance