Stop and Sbottom LSP with R-parity Violation

Considering a third-generation squark as the lightest supersymmetric particle (LSP), we investigate R-parity violating collider signatures with bilinear LH or trilinear LQD operators that may contribute to observed neutrino masses and mixings. Reinterpreting the LHC 7+8 TeV results of SUSY and leptoquark searches, we find that third-generation squark LSPs decaying to first- or second-generation leptons are generally excluded up to at least about 660 GeV at 95%C.L.. One notable feature of many models is that sbottoms can decay to top quarks and charged leptons that lead to a broader invariant mass spectrum and weaker collider constraints. More dedicated searches with $b$-taggings or top reconstructions are thus encouraged. Finally, we discuss that the recently observed excesses in the CMS leptoquark search can be accommodated by the decay of sbottom LSPs in the LQD$_{113+131}$ model.Comment: 17 pages, v2: figure 5 is corrected and more references are cite

Learning Cities: a need for learning to develop mutually beneficial tourist-resident relations

Tourism is among the fastest-growing industries in all corners of the world. It brings substantial economic benefit but at the same time could become an issue of political and civic concern to many world-famous cities, heritage sites and even to whole nations. In the recent PASCAL seminar “Making Learning Happen [2],” held at UCL, London on the 4th of May 2018, I raised a discussion topic pertaining to the scope of mutual learning among tourists and residents of cities, which connected for some in the room. I thought I should write a bit more to get the discussion going beyond this PASCAL event

A variational Bayesian method for inverse problems with impulsive noise

We propose a novel numerical method for solving inverse problems subject to impulsive noises which possibly contain a large number of outliers. The approach is of Bayesian type, and it exploits a heavy-tailed t distribution for data noise to achieve robustness with respect to outliers. A hierarchical model with all hyper-parameters automatically determined from the given data is described. An algorithm of variational type by minimizing the Kullback-Leibler divergence between the true posteriori distribution and a separable approximation is developed. The numerical method is illustrated on several one- and two-dimensional linear and nonlinear inverse problems arising from heat conduction, including estimating boundary temperature, heat flux and heat transfer coefficient. The results show its robustness to outliers and the fast and steady convergence of the algorithm.Comment: 20 pages, to appear in J. Comput. Phy

A deformable elastic matching model for handwritten Chinesecharacter recognition

Conference Theme: Intelligent Systems for the 21st CenturyThis paper describes a deformable elastic matching approach to handwritten Chinese character recognition (HCCR). Handwritten character is regarded as a kind of deformable object, with elastic property. For the same category of character, we assume that different handwriting variations share the same topological structure, but may differ in shape details. The variations between different handwriting characters are modelled by a set of stroke displacement vectors (SDV). According to the SDV derived, a model character is deformed gradually, in an effort to transform itself much closer to an input character. Experiments show that the proposed elastic matching model can efficiently deal with local shape changes and variations between characters.published_or_final_versio

FMCW multiplexing of fiber Bragg grating sensors

2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Off-line Chinese handwriting recognition using multi-stage neural network architecture

In this paper, we propose a Multi-stage Neural Network Architecture (MNNA) which integrates several neural networks and various feature extraction approaches into a unique pattern recognition system. General mechanism for designing the MNNA is presented. A three-stage fully connected feedforward neural networks system is designed for Handwritten Chinese Character Recognition (HCCR). Different feature extraction methods are employed at each stage. Experiments show that the three-stage neural network HCCR system has achieved impressive performance and the preliminary results are very encouraging.published_or_final_versio

Severe acute respiratory syndrome coronavirus nucleocapsid protein does not modulate transcription of the human FGL2 gene

Among the structural and nonstructural proteins of severe acute respiratory syndrome coronavirus (SARS-CoV), the nucleocapsid (N) protein plays pivotal roles in the biology and pathogenesis of viral infection. N protein is thought to dysregulate cell signalling and the transcription of cellular genes, including FGL2, which encodes a prothrombinase implicated in vascular thrombosis, fibrin deposition and pneumocyte necrosis. Here, we showed that N protein expressed in cultured human cells was predominantly found in the cytoplasm and was competent in repressing the transcriptional activity driven by interferon-stimulated response elements. However, the expression of N protein did not influence the transcription from the FGL2 promoter. More importantly, N protein did not modulate the expression of FGL2 mRNA or protein in transfected or SARS-CoV-infected cells. Taken together, our findings did not support the model in which SARS-CoV N protein specifically modulates transcription of the FGL2 gene to cause fibrosis and vascular thrombosis. © 2009 SGM.published_or_final_versio

A centrosomal target of human t-cell leukemia virus oncoprotein tax

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Consistent relaxation matching for handwritten Chinese character recognition

Due to the complexity in structure and the various distortions (translation, rotation, shifting, and deformation) in different writing styles of Handwritten Chinese Characters(HCCs), it is more suitable to use a structural matching algorithm for computer recognition of HCC. Relaxation matching is a powerful technique which can tolerate considerable distortion. However, most relaxation techniques so far developed for Handwritten Chinese Character Recognition (HCCR) are based on a probabilistic relaxation scheme. In this paper, based on local constraint of relaxation labelling and optimization theory, we apply a new relaxation matching technique to handwritten character recognition. From the properties of the compatibility constraints, several rules are devised to guide the design of the compatibility function, which plays an important role in the relaxation process. By parallel use of local contextual information of geometric relaxationship among strokes of two characters, the ambiguity between them can be relaxed iteratively to achieve optimal consistent matching.published_or_final_versio