Showing papers by "Guo-Jun Qi published in 2014"
TL;DR: In this paper, the authors identified the major unresolved issues and the potential improvement approaches of realizing sizable improvements in the solar cells' efficiency, thus providing a guide as to where research efforts should be focused.
Abstract: Cu2ZnSnS4 (CZTS) and its related materials such as Cu2ZnSnSe4 (CZTSe) and Cu2ZnSn(S,Se)4 (CZTSSe) have attracted considerable attention as an absorber material for thin film solar cells due to the non-toxicity, elemental abundance, and large production capacity of their constituents. Despite the similarities between CZTS-based materials and Cu(In,Ga)Se2(CIGS), the record efficiency of CZTS-based solar cells remains significantly lower than that of CIGS solar cells. Considering that the difference between the two lies in the choice of the absorber material, the cause of the lower efficiency of CZTS-based solar cells can be isolated to the issues associated with CZTS-based materials and their related interfaces. Herein, these issues and the work done to understand and resolve them is reviewed. Unlike existing review papers, every unique region of CZTS-based solar cells that contributes to its lower efficiency, namely: (1) the bulk of the absorber, (2) the grain boundaries of the absorber, (3) the absorber/buffer layer interface, and (4) the absorber/back contact interface are surveyed. This review also intends to identify the major unresolved issues and the potential improvement approaches of realizing sizable improvements in the solar cells' efficiency, thus providing a guide as to where research efforts should be focused. (© 2014 WILEY-VCH Verlag GmbH &Co. KGaA, Weinheim)
128 citations
TL;DR: The underlying philosophy is that as long as the multilabel dependency cannot be well explained, the principle of parsimony should be applied to the modeling process of the label correlations, which makes the learned model more generalizable to future samples.
Abstract: The goal of multilabel classification is to reveal the underlying label correlations to boost the accuracy of classification tasks. Most of the existing multilabel classifiers attempt to exhaustively explore dependency between correlated labels. It increases the risk of involving unnecessary label dependencies, which are detrimental to classification performance. Actually, not all the label correlations are indispensable to multilabel model. Negligible or fragile label correlations cannot be generalized well to the testing data, especially if there exists label correlation discrepancy between training and testing sets. To minimize such negative effect in the multilabel model, we propose to learn a sparse structure of label dependency. The underlying philosophy is that as long as the multilabel dependency cannot be well explained, the principle of parsimony should be applied to the modeling process of the label correlations. The obtained sparse label dependency structure discards the outlying correlations between labels, which makes the learned model more generalizable to future samples. Experiments on real world data sets show the competitive results compared with existing algorithms.
78 citations
14 Dec 2014
TL;DR: A Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework and is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices.
Abstract: The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as Sim Rank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge-loss alternatively. To facilitate efficient computation on large scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA datasets. The results show that FSL is robust, efficient, and outperforms the state-of-the-art.
60 citations
TL;DR: This special issue includes five papers focusing on different aspects of social media mining and knowledge discovery, including a sparse semantic metric learning method that exploits heterogeneous information from the visual features and the tagging information of images, and formulates the learning problem as a sparse constrained one.
Abstract: applications, it is of high interest to discover potentially important knowledge by social media mining in this nascent field. Recently, more and more research efforts have been dedicated to the aforementioned challenges and opportunities. This special issue includes five papers focusing on different aspects of social media mining and knowledge discovery. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval performance. In “Sparse Semantic Metric Learning for Image Retrieval”, Liu et al. propose a sparse semantic metric learning method by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from the visual features and the tagging information of images, and formulates the learning problem as a sparse constrained one. Extensive experiments were conducted on a real-world dataset to validate the effectiveness of the proposed approach. In most cases, visual information can be regarded as an enhanced content of the textual document. In “Relative Image Similarity Learning with Contextual Information for Internet Crossmedia Retrieval”, to make image-to-image similarity being more consistent with document-to-document similarity, Jiang et al. propose a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted, and the ranking structure is established by comparing the relative relations of textual information. The proposed method With the rapid advances of Internet and Web 2.0, social networking and social media become more and more popular in humans’ daily lives. The ubiquitous nature of webenabled devices, including desktops, laptops, tablets, and mobile phones, enables users to participate and interact with each other in various web communities, including photo and video sharing platforms, forums, newsgroups, blogs, micro-blogs, bookmarking services, and locationbased services. The rapidly evolving social networks provide a platform for communication, information sharing, and collaboration among friends, colleagues, alumnus, business partners, and many other social relations. To be accompanied by, increasingly rich and massive heterogeneous media data have been generated by the users, such as images, videos, audios, tweets, tags, categories, titles, geo-locations, comments, and viewer ratings, which offer an unprecedented opportunity for studying novel theories and technologies for social media analysis and mining. While researchers from multidisciplinary areas have proposed intelligent methods for processing social media data and employing such rich multi-modality data for various
7 citations