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Search Results: 1 - 10 of 15251 matches for " Jiawei Ren "
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Linear-Combined-Code-Based Unambiguous Code Discriminator Design for Multipath Mitigation in GNSS Receivers
Jiawei Ren,Huihua Chen,Weimin Jia,Minli Yao
Radioengineering , 2012,
Abstract: Unambiguous tracking and multipath mitigation for Binary Offset Carrier (BOC) signals are two important requirements of modern Global Navigation Satellite Systems (GNSS) receivers. A GNSS discriminator design method based on optimization technique is proposed in this paper to meet these requirements. Firstly, the discriminator structure based on a linear-combined code is given. Then the requirements of ideal discriminator function are converted into the mathematical constraints and the objective function to form a non-linear optimization problem. Finally, the problem is solved and the local code is generated according to the results. The theoretical analysis and simulation results indicate that the proposed method can completely remove the false lock points for BOC signals and provide superior multipath mitigation performance compared with traditional discriminator and high revolution correlator (HRC) technique. Moreover, the proposed discriminator is easy to implement for not increasing the number of correlators.
Exploring The Contribution of Unlabeled Data in Financial Sentiment Analysis
Jimmy SJ. Ren,Wei Wang,Jiawei Wang,Stephen Shaoyi Liao
Computer Science , 2013,
Abstract: With the proliferation of its applications in various industries, sentiment analysis by using publicly available web data has become an active research area in text classification during these years. It is argued by researchers that semi-supervised learning is an effective approach to this problem since it is capable to mitigate the manual labeling effort which is usually expensive and time-consuming. However, there was a long-term debate on the effectiveness of unlabeled data in text classification. This was partially caused by the fact that many assumptions in theoretic analysis often do not hold in practice. We argue that this problem may be further understood by adding an additional dimension in the experiment. This allows us to address this problem in the perspective of bias and variance in a broader view. We show that the well-known performance degradation issue caused by unlabeled data can be reproduced as a subset of the whole scenario. We argue that if the bias-variance trade-off is to be better balanced by a more effective feature selection method unlabeled data is very likely to boost the classification performance. We then propose a feature selection framework in which labeled and unlabeled training samples are both considered. We discuss its potential in achieving such a balance. Besides, the application in financial sentiment analysis is chosen because it not only exemplifies an important application, the data possesses better illustrative power as well. The implications of this study in text classification and financial sentiment analysis are both discussed.
Comparative Document Analysis for Large Text Corpora
Xiang Ren,Yuanhua Lv,Kuansan Wang,Jiawei Han
Computer Science , 2015,
Abstract: This paper presents a novel research problem on joint discovery of commonalities and differences between two individual documents (or document sets), called Comparative Document Analysis (CDA). Given any pair of documents from a document collection, CDA aims to automatically identify sets of quality phrases to summarize the commonalities of both documents and highlight the distinctions of each with respect to the other informatively and concisely. Our solution uses a general graph-based framework to derive novel measures on phrase semantic commonality and pairwise distinction}, and guides the selection of sets of phrases by solving two joint optimization problems. We develop an iterative algorithm to integrate the maximization of phrase commonality or distinction measure with the learning of phrase-document semantic relevance in a mutually enhancing way. Experiments on text corpora from two different domains---scientific publications and news---demonstrate the effectiveness and robustness of the proposed method on comparing individual documents. Our case study on comparing news articles published at different dates shows the power of the proposed method on comparing document sets.
An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection
Jimmy SJ. Ren,Wei Wang,Jiawei Wang,Stephen Liao
Computer Science , 2013,
Abstract: Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.
p38α MAP kinase phosphorylates RCAN1 and regulates its interaction with calcineurin
Lei Ma,HaiPing Tang,Yan Ren,HaiTeng Deng,JiaWei Wu,ZhiXin Wang
Science China Life Sciences , 2012, DOI: 10.1007/s11427-012-4340-9
Abstract: RCAN1, also known as DSCR1, is an endogenous regulator of calcineurin, a serine/threonine protein phosphatase that plays a critical role in many physiological processes. In this report, we demonstrate that p38α MAP kinase can phosphorylate RCAN1 at multiple sites in vitro and show that phospho-RCAN1 is a good protein substrate for calcineurin. In addition, we found that unphosphorylated RCAN1 noncompetitively inhibits calcineurin protein phosphatase activity and that the phosphorylation of RCAN1 by p38α MAP kinase decreases the binding affinity of RCAN1 for calcineurin. These findings reveal the molecular mechanism by which p38α MAP kinase regulates the function of RCAN1/calcineurin through phosphorylation.
Recent progress in neurodegenerative disorder research in China
JiaWei Zhou
Science China Life Sciences , 2010, DOI: 10.1007/s11427-010-0061-0
Abstract: Neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), are common disorders of the central nervous system among aging populations. In the last 10 years insights concerning the etiology, diagnosis and pathogenesis of these diseases have come from research carried out by Chinese neuroscientists. Their findings include the description of Chinese patients with autosomal recessive early-onset PD, the function of the tau protein, molecular mechanisms underlying protein aggregation, and the identification of biomarkers for AD diagnosis and molecules/compounds with potential neuroprotective activities.
Suppression of neuroinflammation by the dopamine receptor D2 via alphaB-crystallin
Zhou Jiawei
Molecular Neurodegeneration , 2012, DOI: 10.1186/1750-1326-7-s1-l15
QED Test at LEP200 Energies in the Reaction $\rm e^+ e^-\to γγ (γ)$
Jiawei Zhao
Physics , 2000,
Abstract: The measurements of the QED reaction $ \EEGG $ performed with the L3 detector are used to search for new physics phenomena beyond the Standard Model. No evidence for these phenomena is found and new limits on their parameters are set. First the reaction is used to constrain a model of an excited electron and second to study contact interactions. The total and differential cross sections for the process $ \EEGG $, are measured at energies from 91 GeV to 202 GeV using the data collected with the L3 detector from 1991 to 1999. The L3 data set lower limits on the mass of an excited electron $ \MESTAR > 402 $ GeV, on the QED cutoff parameters $ \LAMP > 415 $ GeV, $ \LAMM > 258 $ GeV and on the contact interaction energy scale $ \Lambda > 1687 $ GeV. The last parameter limits the size of the interaction area to $ R < 1.17\times 10^{-17} $ cm. Some limits on the string and quantum gravity scales are also discussed.
An Improved Procedure for Selecting the Profiles of Perfectly Matched Layers
Jiawei Zhang
Mathematics , 2007,
Abstract: The perfectly matched layers (PMLs), as a boundary termination over an unbounded spatial domain, are widely used in numerical simulations of wave propagation problems. Given a set of discretization parameters, a procedure to select the PML profiles based on minimizing the discrete reflectivity is established for frequency domain simulations. We, by extending the function class and adopting a direct search method, improve the former procedure for traveling waves.
On Equilibria of N-seller and N-buyer Bargaining Games
Jiawei Li
Computer Science , 2015,
Abstract: A group of players that contains n sellers and n buyers bargain over the partitions of n pies. A seller(/buyer) has to reach an agreement with a buyer (/seller) on the division of a pie. The players bargain in a system like the stock market: each seller(buyer) can either offer a selling(buying) price to all buyers(sellers) or accept a price offered by another buyer(seller). The offered prices are known to all. Once a player accepts a price offered by another one, the division of a pie between them is determined. Each player has a constant discounting factor and the discounting factors of all players are common knowledge. In this article, we prove that the equilibrium of this bargaining problem is a unanimous division rate that is exactly equivalent to Nash bargaining equilibrium of a two-player bargaining game in which the discounting factors of two players are the average of n buyers and the average of n sellers respectively. This result is nontrivial for studying general equilibrium of markets.
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