An Improved Way to Make Large-Scale SVR Learning Practical

<p/> <p>We first put forward a new algorithm of reduced support vector regression (RSVR) and adopt a new approach to make a similar mathematical form as that of support vector classification. Then we describe a fast training algorithm for simplified support vector regression, sequential minimal optimization (SMO) which was used to train SVM before. Experiments prove that this new method converges considerably faster than other methods that require the presence of a substantial amount of the data in memory.</p

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification

The Efficacy of Chinese Herbal Medicine as an Adjunctive Therapy for Advanced Non-small Cell Lung Cancer: A Systematic Review and Meta-analysis

Many published studies reflect the growing application of complementary and alternative medicine, particularly Chinese herbal medicine (CHM) use in combination with conventional cancer therapy for advanced non-small cell lung cancer (NSCLC), but its efficacy remains largely unexplored. The purpose of this study is to evaluate the efficacy of CHM combined with conventional chemotherapy (CT) in the treatment of advanced NSCLC. Publications in 11 electronic databases were extensively searched, and 24 trials were included for analysis. A sum of 2,109 patients was enrolled in these studies, at which 1,064 patients participated in CT combined CHM and 1,039 in CT (six patients dropped out and were not reported the group enrolled). Compared to using CT alone, CHM combined with CT significantly increase one-year survival rate (RR = 1.36, 95% CI = 1.15-1.60, p = 0.0003). Besides, the combined therapy significantly increased immediate tumor response (RR = 1.36, 95% CI = 1.19-1.56, p<1.0E-5) and improved Karnofsky performance score (KPS) (RR = 2.90, 95% CI = 1.62-5.18, p = 0.0003). Combined therapy remarkably reduced the nausea and vomiting at toxicity grade of III-IV (RR = 0.24, 95% CI = 0.12-0.50, p = 0.0001) and prevented the decline of hemoglobin and platelet in patients under CT at toxicity grade of I-IV (RR = 0.64, 95% CI = 0.51-0.80, p<0.0001). Moreover, the herbs that are frequently used in NSCLC patients were identified. This systematic review suggests that CHM as an adjuvant therapy can reduce CT toxicity, prolong survival rate, enhance immediate tumor response, and improve KPS in advanced NSCLC patients. However, due to the lack of large-scale randomized clinical trials in the included studies, further larger scale trials are needed. © 2013 Li et al.published_or_final_versio

Double Carbon Coated LiCoPO4 Nano Composite as High-Performance Cathode for Lithium Ion Batteries

Polyacene(PAS)/carbon and acetylene black(AB)/carbon coated lithium cobalt phosphate composites were synthesized via the solid state reaction method using co-precipitated Co3(PO4)2·8H2O and Li3PO4 mixture as its precursor. X-ray powder diffraction (XRD) was performed to investigate the structure and phase of all the samples. The transmission electron microscopy (TEM) shows that the double carbon layers coated on the surface of LiCoPO4 successfully. The LiCoPO4/C, LiCoPO4/PAS and LiCoPO4/AB delivered a capacity of T 120.92, 121.07 and 138.06 mAh×g-1 at 0.1C, respectively. The double carbon coated LiCoPO4 electrode delivered an initial discharge capacity of 147.12, 143.51 mAh×g-1 after AB/glucose, PAS/glucose coating, which maintained at 59.5% and 61.7% after 15 cycles at the 0.1C rate, respectively.Citiation: Yu, Y., Zhao, H., Chen, Y., Feng, Z.-k., Liu, X., and Yang, H. (2020). Double Carbon Coated LiCoPO4 Nano Composite as High-Performance Cathode for Lithium Ion Batteries. Trends in Renewable Energy, 6, 1-11. DOI: 10.17737/tre.2020.6.1.0010