Academia Sinica

About: Academia Sinica is a facility organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Gene. The organization has 52086 authors who have published 65998 publications receiving 1728114 citations. The organization is also known as: Central Research Academy.

Topological chiral crystals with helicoid-arc quantum states

Abstract: The quantum behaviour of electrons in materials is the foundation of modern electronics and information technology1-11, and quantum materials with topological electronic and optical properties are essential for realizing quantized electronic responses that can be used for next generation technology. Here we report the first observation of topological quantum properties of chiral crystals6,7 in the RhSi family. We find that this material class hosts a quantum phase of matter that exhibits nearly ideal topological surface properties originating from the crystals' structural chirality. Electrons on the surface of these crystals show a highly unusual helicoid fermionic structure that spirals around two high-symmetry momenta, indicating electronic topological chirality. The existence of bulk multiply degenerate band fermions is guaranteed by the crystal symmetries; however, to determine the topological invariant or charge in these chiral crystals, it is essential to identify and study the helicoid topology of the arc states. The helicoid arcs that we observe on the surface characterize the topological charges of ±2, which arise from bulk higher-spin chiral fermions. These topological conductors exhibit giant Fermi arcs of maximum length (π), which are orders of magnitude larger than those found in known chiral Weyl fermion semimetals5,8-11. Our results demonstrate an electronic topological state of matter on structurally chiral crystals featuring helicoid-arc quantum states. Such exotic multifold chiral fermion semimetal states could be used to detect a quantized photogalvanic optical response, the chiral magnetic effect and other optoelectronic phenomena predicted for this class of materials6.

Reduced Support Vector Machines: A Statistical Theory

Abstract: In dealing with large data sets, the reduced support vector machine (RSVM) was proposed for the practical objective to overcome some computational difficulties as well as to reduce the model complexity. In this paper, we study the RSVM from the viewpoint of sampling design, its robustness, and the spectral analysis of the reduced kernel. We consider the nonlinear separating surface as a mixture of kernels. Instead of a full model, the RSVM uses a reduced mixture with kernels sampled from certain candidate set. Our main results center on two major themes. One is the robustness of the random subset mixture model. The other is the spectral analysis of the reduced kernel. The robustness is judged by a few criteria as follows: 1) model variation measure; 2) model bias (deviation) between the reduced model and the full model; and 3) test power in distinguishing the reduced model from the full one. For the spectral analysis, we compare the eigenstructures of the full kernel matrix and the approximation kernel matrix. The approximation kernels are generated by uniform random subsets. The small discrepancies between them indicate that the approximation kernels can retain most of the relevant information for learning tasks in the full kernel. We focus on some statistical theory of the reduced set method mainly in the context of the RSVM. The use of a uniform random subset is not limited to the RSVM. This approach can act as a supplemental algorithm on top of a basic optimization algorithm, wherein the actual optimization takes place on the subset-approximated data. The statistical properties discussed in this paper are still valid

Roles of plant hormones in the regulation of host-virus interactions.

Abstract: Hormones are tuners of plant responses to biotic and abiotic stresses. They are involved in various complicated networks, through which they modulate responses to different stimuli. Four hormones primarily regulate plant defence to pathogens: salicylic acid (SA), jasmonic acid (JA), ethylene (Et) and abscisic acid (ABA). In susceptible plants, viral infections result in hormonal disruption, which manifests as the simultaneous induction of several antagonistic hormones. However, these antagonistic hormones may exhibit some sequential accumulation in resistant lines. Virus propagation is usually restricted by the activation of the small interfering RNA (siRNA) antiviral machinery and/or SA signalling pathway. Several studies have investigated these two systems, using different model viruses. However, the roles of hormones other than SA, especially those with antagonistic properties, such as ABA, have been neglected. Increasing evidence indicates that hormones control components of the small RNA system, which regulates many processes (including the siRNA antiviral machinery and the microRNA system) at the transcriptional or post-transcriptional level. Consequently, cross-talk between the antagonistic SA and ABA pathways modulates plant responses at multiple levels. In this review, we summarize recent findings on the different roles of hormones in the regulation of plant-virus interactions, which are helping us to elucidate the fine tuning of viral and plant systems by hormones.

PAT-tree-based keyword extraction for Chinese information retrieval

Abstract: urgent need to promote Chinese in this paper we will raise the significance of keyword extraction using a new PAT-treebased approach, which is efficient in automatic keyword extraction from a set of relevant Chinese documents. This approach has been successfully applied in several IR researches, such as document classification, book indexing and relevance feedback. Many Chinese language processing applications therefore step ahead from character level to word/phrase level,

Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia.

Abstract: To identify common genetic variants that contribute to lung cancer susceptibility, we conducted a multistage genome-wide association study of lung cancer in Asian women who never smoked. We scanned 5,510 never-smoking female lung cancer cases and 4,544 controls drawn from 14 studies from mainland China, South Korea, Japan, Singapore, Taiwan and Hong Kong. We genotyped the most promising variants (associated at P < 5 × 10(-6)) in an additional 1,099 cases and 2,913 controls. We identified three new susceptibility loci at 10q25.2 (rs7086803, P = 3.54 × 10(-18)), 6q22.2 (rs9387478, P = 4.14 × 10(-10)) and 6p21.32 (rs2395185, P = 9.51 × 10(-9)). We also confirmed associations reported for loci at 5p15.33 and 3q28 and a recently reported finding at 17q24.3. We observed no evidence of association for lung cancer at 15q25 in never-smoking women in Asia, providing strong evidence that this locus is not associated with lung cancer independent of smoking.