Improve the effectiveness of the opinion retrieval and opinion polarity classification

Opinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval. It classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. This paper (1) proposes several new techniques that help improve the effectiveness of an existing opinion retrieval system; (2) presents a novel two-stage model to solve the opinion polarity classification problem. In this model, every query relevant opinionated sentence in a document retrieved by our opinion retrieval system is classified as positive or negative respectively by a SVM classifier. Then a second classifier determines the overall opinion polarity of the document. Experimental results show that both the opinion retrieval system with the proposed opinion retrieval techniques and the polarity classification model outperformed the best reported systems respectively. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Searc

Review of the Resources and Utilization of Bamboo in China

China has made a breakthrough in the development and scientific cultivation of bamboo. At present, China ranks first in bamboo research worldwide, because of numerous research units and strong technical force. This chapter focuses on the utilization of bamboo resources such as food, roofs and walls of houses, fences, and domestic and agricultural implements such as water containers, food and drink container hats, arrows, quiver, etc. A total of 861 species and infraspecific taxa belonging to 43 genera have been reported and include 707 species, 52 varieties, 98 forma, and 4 hybrids, which are naturally distributed in 21 provinces. The national bamboo forest covers 6.01 million ha, including 4.43 million ha of Moso bamboo and 1.58 million ha of other bamboo species. As the country develops and new economic activities emerge, bamboo production has shifted from harsh processing, such as bamboo basket, to finished machining, such as bamboo flooring. The bamboo industry has attracted new opportunities as a new energy source, particularly renewable energy, and may be considered a lignocellulose substrate for bioethanol production because of its environmental benefits and high annual biomass yield

Experience-driven Networking: A Deep Reinforcement Learning based Approach

Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based control framework, DRL-TE, for a fundamental networking problem: Traffic Engineering (TE). The proposed framework maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful Deep Neural Networks (DNNs). We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. To validate and evaluate the proposed framework, we implemented it in ns-3, and tested it comprehensively with both representative and randomly generated network topologies. Extensive packet-level simulation results show that 1) compared to several widely-used baseline methods, DRL-TE significantly reduces end-to-end delay and consistently improves the network utility, while offering better or comparable throughput; 2) DRL-TE is robust to network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL method (for continuous control), Deep Deterministic Policy Gradient (DDPG), which, however, does not offer satisfying performance.Comment: 9 pages, 12 figures, paper is accepted as a conference paper at IEEE Infocom 201

Neutron spin resonance as a probe of Fermi surface nesting and superconducting gap symmetry in Ba0.67_{0.67}K0.33_{0.33}(Fe1−x_{1-x}Cox_{x})2_{2}As2_{2}

We use inelastic neutron scattering to study energy and wave vector dependence of the superconductivity-induced resonance in hole-doped Ba$_{0.67}$K$_{0.33}$(Fe$_{1-x}$Co$_{x}$)$_{2}$As$_{2}$ ($x=0,0.08$ with $T_c\approx 37, 28$ K, respectively). In previous work on electron-doped Ba(Fe$_{0.963}$Ni$_{0.037}$)$_2$As$_2$ ($T_N=26$ K and $T_c=17$ K), the resonance is found to peak sharply at the antiferromagnetic (AF) ordering wave vector ${\bf Q}_{\rm AF}$ along the longitudinal direction, but disperses upwards away from ${\bf Q}_{\rm AF}$ along the transverse direction. For hole doped $x=0, 0.08$ without AF order, we find that the resonance displays ring-like upward dispersion away from ${\bf Q}_{\rm AF}$ along both the longitudinal and transverse directions. By comparing these results with calculations using the random phase approximation, we conclude that the dispersive resonance is a direct signature of isotropic superconducting gaps arising from nested hole-electron Fermi surfaces.Comment: 5 pages, 4 figure

Rule-based deduplication of article records from bibliographic databases

We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments