MOTIVATION: With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process. METHODS: In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions. RESULTS: Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks. AVAILABILITY AND IMPLEMENTATION: A demo of our system is available at https://github.com/jinzanxia/biomedical-QA.

Known-item search is an everyday natural scenario that we search for a specific thing (maybe a song) while only remembering some details about it. Existing benchmarks generally focus on brief user requests which specify some metadata like the title, or the time. However, in most cases, the users can hardly recall such information accurately. In order to embrace the research of known-item search, we present a new publicly available known-item speech video search benchmark, namely TED-KISS, which takes TED talks as an example. The video collection is constructed with up-to-date nearly 80,000 TED and TEDx talks on Youtube. These talks cover various topics, and their titles, speakers, descriptions, full-text subtitles, as well as original links are extracted as metadata, which makes the researches on text-based retrieval and multimedia retrieval feasible. Unlike other benchmarks concerning visual contents in segments, the user requests in TED-KISS are generated through a more natural process, partly through original related topics posted on Reddit and Baidu Tieba, and partly through manual imitative requests annotated by volunteers in a scenario simulation. In addition, we analyze the characteristics of our benchmark through evaluations of several existing text-based IR and Neural-IR models, which also can be served as baselines for this task.

Recently many information retrieval (IR)-based approaches have been proposed to help locate software defects automatically by using information from bug report contents. However, some bug reports that do not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based system on these reports may produce false positives. In this paper, we propose a classification model for classifying a bug report as either helpful or unhelpful using a LSTM-network. By filtering our unhelpful reports before running an IR-based bug locating system, our approach helps reduce false positives and improve the ranking performance. We test our model over 9,000 bug reports from three software projects. The evaluation result shows that our model helps improve a state-of-the-art IR-based system's ranking performance under a trade-off between the precision and the recall. Our comparison experiments show that the LSTM-network achieves the best trade-off between precision and recall than other classification models including CNN, multilayer perceptron, and a simple baseline approach that classifies a bug report based its length. In the situation that precision is more important than recall, our classification model helps for bug locating.

The Quasi-Z-Source inverter (qZSI) applied to the photovoltaic (PV) system has nonlinear and unstable zero dynamic characteristics, which leads to the problem of narrow system bandwidth and slow dynamic response. A nonlinear control strategy is proposed based on the input-output feedback linearization method. The nonlinear affine model of qZSI is presented and transformed to a linearized and decoupled form with the help of the input-output feedback linearization technique. Based on the local linearized model, simple linear controllers are employed to regulate the input voltage, which significantly reduces the difficulty in controller design. The method of indirectly control of the converter input voltage with a inner inductor current feedback is used to solve the problem of unstable zero dynamics. The Quasi-Proportional-Resonant controller is used to eliminate the steady state error of grid-connected current. The proposed control method realizes decoupling control for DC side (boost control) and ac side (grid-connected current control of PV system). Simulation results of proposed method and conventional control method are compared. The results show that the proposed control strategy can track the maximum power point voltage reference quickly and accurately. The effect of unstable zero-dynamic on DC-link voltage is reduced, and the transferring of disturbance from DC side to the AC side of the qZSI is mitigated effectively. The system has higher stability and dynamic performance.