Supporting Document-Category Management: An Ontology-based Document Clustering Approach

Automated document-category management, particularly the document clustering, represents an appealing alternative of supporting a user\u27s search, access, and utilization of the ever-increasing corpora of textual. Traditional document clustering techniques generally emphasize on the analysis of document contents and measure document similarity on the basis of the overlap between or among the feature vectors representing individual document. However, it can be problematic and cannot address word mismatch or ambiguity effectively to cluster document at the lexical level. To address problems inherent to the traditional lexicon-based approach, we propose an Ontology-based Document Clustering (ODC) technique, which employs a domain-specific ontology to support the proceeding of document clustering at the conceptual level. We empirically evaluate the effectiveness of the proposed ODC technique, using the lexicon-based and LSI-based document clustering techniques (i.e., HAC and LSI-based HAC) for evaluation purpose. Our comparative analysis results show ODC to be partially effective than HAC and LSI-based HAC, showing higher cluster precision across all levels of cluster recall and statistically significant in F1 measure. In addition, our preliminary analysis on the effect of granularity of concept hierarchy suggests the usage of fine-grained concept hierarchy can make ODC reach to a better performance. Our findings have interesting implications to research and practice, which are discussed together with our future research directions

Observation of intervalley biexcitonic optical Stark effect in monolayer WS2

Coherent optical dressing of quantum materials offers technological advantages to control their electronic properties, such as the electronic valley degree of freedom in monolayer transition metal dichalcogenides (TMDs). Here, we observe a new type of optical Stark effect in monolayer WS2, one that is mediated by intervalley biexcitons under the blue-detuned driving with circularly polarized light. We found that such helical optical driving not only induces an exciton energy downshift at the excitation valley, but also causes an anomalous energy upshift at the opposite valley, which is normally forbidden by the exciton selection rules but now made accessible through the intervalley biexcitons. These findings reveal the critical, but hitherto neglected, role of biexcitons to couple the two seemingly independent valleys, and to enhance the optical control in valleytronics

Electronic transport and device prospects of monolayer molybdenum disulphide grown by chemical vapour deposition

Layered transition metal dichalcogenides display a wide range of attractive physical and chemical properties and are potentially important for various device applications. Here we report the electronic transport and device properties of monolayer molybdenum disulphide (MoS2) grown by chemical vapour deposition (CVD). We show that these devices have the potential to suppress short channel effects and have high critical breakdown electric field. However, our study reveals that the electronic properties of these devices are at present, severely limited by the presence of a significant amount of band tail trapping states. Through capacitance and ac conductance measurements, we systematically quantify the density-of-states and response time of these states. Due to the large amount of trapped charges, the measured effective mobility also leads to a large underestimation of the true band mobility and the potential of the material. Continual engineering efforts on improving the sample quality are needed for its potential applications.Comment: 23 pages, 5 figure

Valley-selective optical Stark effect in monolayer WS2

Breaking space-time symmetries in two-dimensional crystals (2D) can dramatically influence their macroscopic electronic properties. Monolayer transition-metal dichalcogenides (TMDs) are prime examples where the intrinsically broken crystal inversion symmetry permits the generation of valley-selective electron populations, even though the two valleys are energetically degenerate, locked by time-reversal symmetry. Lifting the valley degeneracy in these materials is of great interest because it would allow for valley-specific band engineering and offer additional control in valleytronic applications. While applying a magnetic field should in principle accomplish this task, experiments to date have observed no valley-selective energy level shifts in fields accessible in the laboratory. Here we show the first direct evidence of lifted valley degeneracy in the monolayer TMD WS2. By applying intense circularly polarized light, which breaks time-reversal symmetry, we demonstrate that the exciton level in each valley can be selectively tuned by as much as 18 meV via the optical Stark effect. These results offer a novel way to control valley degree of freedom, and may provide a means to realize new valley-selective Floquet topological phases in 2D TMDs

ProKware: integrated software for presenting protein structural properties in protein tertiary structures

Protein tertiary structure plays an essential role in deciphering protein functions, especially protein structural properties, including domains, active sites and post-translational modifications. These properties typically yield useful clues for understanding protein functions. This work presents an integrated software, named ProKware, that presents protein structural properties in protein tertiary structures, such as domains, functional sites, families, active sites, binding sites, post-translational modifications and domain–domain interaction. Using this web-based and Windows-based interface, users can manipulate and visualize three-dimensional protein structures, as well as the supported structural properties that are curated in the protein knowledge database. ProKware is an effective and convenient solution for investigating protein functions and structural relationships. This software can be accessed on the internet at

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers