Inconsistent phenomenon of thermoelectric load resistance for photovoltaic–thermoelectric module

Combing PV with Thermoelectric (TE) would be dominant because it can employ the solar fully spectrum to produce electricity. But the TE efficiency is significantly lower than PV efficiency and the coupling effect between them will limit the performance of PV and TE. The analyze and comparison on the different characteristics among the hybrid module, the PV alone and TE alone is significant to obtain the highest the electrical efficiency. In this paper, the attention was paid to the inconsistent phenomenon of thermoelectric load resistance for photo-voltaic–thermoelectric modules. The model of PV-TE was built and verified based on two types of PV cells. The load resistance of TE for the maximum power output was also analyzed under different working conditions for the TE alone, TE in the PV-TE and PV-TE. The results showed that the load resistance of TE for the maximum power output of the TE alone, TE in the PV-TE and PV-TE are all different. For example, the PV-TE module based on the c-Si cell attains its peak value at the load electrical resistance of TE of 0.75 Ω, while the internal electrical resistance of the TE is 0.47 Ω. The PV-TE module based on the GaAs cell shows a maximum efficiency of PV-TE with a load resistance of approximately 1.6 Ω, while the internal electrical resistance of the TE is 2.0 Ω. Referring to the load resistance of TE alone is not suitable for PV-TE maximum power output. In addition, the TE maximum power output does not correspond to the PV-TE maximum power output since the TE load resistances in these two conditions are also different. The study will provide the reference for attaining the correct load resistance for the actual maximum power output of PV-TE module

Reinforcement Learning Experience Reuse with Policy Residual Representation

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may require experience across multiple granularities. In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience. PRR network is trained on a set of tasks with a multi-level architecture, where a module in each level corresponds to a subset of the tasks. Therefore, the PRR network represents the experience in a spectrum-like way. When training on a new task, PRR can provide different levels of experience for accelerating the learning. We experiment with the PRR network on a set of grid world navigation tasks, locomotion tasks, and fighting tasks in a video game. The results show that the PRR network leads to better reuse of experience and thus outperforms some state-of-the-art approaches.Comment: Conference version appears in IJCAI 201

Optimal input potential functions in the interacting particle system method

The assessment of the probability of a rare event with a naive Monte-Carlo method is computationally intensive, so faster estimation methods, such as variance reduction methods, are needed. We focus on one of these methods which is the interacting particle (IPS) system method. The method requires to specify a set of potential functions. The choice of these functions is crucial, because it determines the magnitude of the variance reduction. So far, little information was available on how to choose the potential functions. To remedy this, we provide the expression of the optimal potential functions minimizing the asymptotic variance of the estimator of the IPS method

Visualizing the dynamic behavior of poliovirus plus-strand RNA in living host cells

Dynamic analysis of viral nucleic acids in host cells is important for understanding virus–host interaction. By labeling endogenous RNA with molecular beacon, we have realized the direct visualization of viral nucleic acids in living host cells and have studied the dynamic behavior of poliovirus plus-strand RNA. Poliovirus plus-strand RNA was observed to display different distribution patterns in living Vero cells at different post-infection time points. Real-time imaging suggested that the translocation of poliovirus plus-strand RNA is a characteristic rearrangement process requiring intact microtubule network of host cells. Confocal-FRAP measurements showed that 49.4 ± 3.2% of the poliovirus plus-strand RNA molecules diffused freely (with a D-value of 9.6 ± 1.6 × 10(−10) cm(2)/s) within their distribution region, while the remaining (50.5 ± 2.9%) were almost immobile and moved very slowly only with change of the RNA distribution region. Under the electron microscope, it was found that virus-induced membrane rearrangement is microtubule-associated in poliovirus-infected Vero cells. These results reveal an entrapment and diffusion mechanism for the movement of poliovirus plus-strand RNA in living mammalian cells, and demonstrate that the mechanism is mainly associated with microtubules and virus-induced membrane structures

Cross-Language Question Re-Ranking

We study how to find relevant questions in community forums when the language of the new questions is different from that of the existing questions in the forum. In particular, we explore the Arabic-English language pair. We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings. We observe that both approaches degrade the performance of the system when working on the translated text, especially the kernel-based system, which depends heavily on a syntactic kernel. We address this issue using a cross-language tree kernel, which compares the original Arabic tree to the English trees of the related questions. We show that this kernel almost closes the performance gap with respect to the monolingual system. On the neural network side, we use the parallel corpus to train cross-language embeddings, which we then use to represent the Arabic input and the English related questions in the same space. The results also improve to close to those of the monolingual neural network. Overall, the kernel system shows a better performance compared to the neural network in all cases.Comment: SIGIR-2017; Community Question Answering; Cross-language Approaches; Question Retrieval; Kernel-based Methods; Neural Networks; Distributed Representation