EQG-RACE: Examination-Type Question Generation

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e.g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.Comment: Accepted by AAAI-202

Spontaneous edge-defect formation and defect-induced conductance suppression in graphene nanoribbons

We present a first-principles study of the migration and recombination of edge defects (carbon adatom and/or vacancy) and their influence on electrical conductance in zigzag graphene nanoribbons (ZGNRs). It is found that at room temperature, the adatom is quite mobile while the vacancy is almost immobile along the edge of ZGNRs. The recombination of an adatom-vacancy pair leads to a pentagon-heptagon ring defect structure having a lower energy than the perfect edge, implying that such an edge-defect can be formed spontaneously. This edge defect can suppresses the conductance of ZGNRs drastically, which provides some useful hints for understanding the observed semiconducting behavior of the fabricated narrow GNRs.Comment: 6 pages, 4 figures, to appear in PR

Amygdalin isolated from Amygdalus mongolica protects against hepatic fibrosis in rats

The aim of this research was to investigate the effect of amygdalin on hepatic fibrosis in rats. Amygdalin was purified and identified from the seeds of Amygdalus mongolica. Sprague Dawley rats in the control and model groups were administered water. Sprague Dawley rats were divided into the low-, middle-, and high-dose amygdalin groups that received 20, 40, and 80 mg kg–1 amygdalin, respectively. whereas the silymarin group was treated with 50 mg kg–1 silymarin. The control and model groups were administered water. Liver tissue analysis revealed significantly lower activities of ALT, AST, ALP, SOD, and MDA in the drug-treated groups compared to the model group. Serum analysis revealed significantly lower HYC and C-IV in the middle-dose amygdalin-treated group compared to the model group. The histopathological changes were less severe in the drug-treated groups as observed by the formation of pseudolobuli and decreased collagen fiber deposition. Hepatic fibrosis-related genes were expressed at significantly lower levels in the amygdalin-treated groups than in the model group. Amygdalin from A. mongolica represents a therapeutic candidate for hepatic fibrosis prevention and treatment

Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications

Processing, mining, and learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning models from data with a complex structure (e.g., multirelational data, XML data, text data, image data, time series, sequences, graphs, streaming data, and trees) [1–5]. These kinds of data are commonly encountered in many social, economic, scientific, and engineering applications. Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for processing, mining, and learning them. Traditional data analysis methods often require the data to be represented as vectors [6]. However, many data objects in real-world applications, such as chemical compounds in biopharmacy, brain regions in brain health data, users in business networks, and time-series information in medical data, contain rich structure information (e.g., relationships between data and temporal structures). Such a simple feature-vector representation inherently loses the structure information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized. Meanwhile, the data may come from heterogeneous domains [7], such as traditional tabular-based data, sequential patterns, graphs, time-series information, and semistructured data. Novel data analytics methods are desired to discover meaningful knowledge in advanced applications from data objects with complex characteristics. This special issue contributes to the fundamental research in processing, mining, and learning complex data, focusing on the analysis of complex data sources

Dirac Fermion in Strongly-Bound Graphene Systems

It is highly desirable to integrate graphene into existing semiconductor technology, where the combined system is thermodynamically stable yet maintain a Dirac cone at the Fermi level. Firstprinciples calculations reveal that a certain transition metal (TM) intercalated graphene/SiC(0001), such as the strongly-bound graphene/intercalated-Mn/SiC, could be such a system. Different from free-standing graphene, the hybridization between graphene and Mn/SiC leads to the formation of a dispersive Dirac cone of primarily TM d characters. The corresponding Dirac spectrum is still isotropic, and the transport behavior is nearly identical to that of free-standing graphene for a bias as large as 0.6 V, except that the Fermi velocity is half that of graphene. A simple model Hamiltonian is developed to qualitatively account for the physics of the transfer of the Dirac cone from a dispersive system (e.g., graphene) to an originally non-dispersive system (e.g., TM).Comment: Apr 25th, 2012 submitte