Remote information concentration and multipartite entanglement in multilevel systems

Remote information concentration (RIC) in $d$-level systems (qudits) is studied. It is shown that the quantum information initially distributed in three spatially separated qudits can be remotely and deterministically concentrated to a single qudit via an entangled channel without performing any global operations. The entangled channel can be different types of genuine multipartite pure entangled states which are inequivalent under local operations and classical communication. The entangled channel can also be a mixed entangled state, even a bound entangled state which has a similar form to the Smolin state, but has different features from the Smolin state. A common feature of all these pure and mixed entangled states is found, i.e., they have $d^2$ common commuting stabilizers. The differences of qudit-RIC and qubit-RIC ($d=2$) are also analyzed.Comment: 10 pages, 3 figure

Mott Physics and Topological Phase Transition in Correlated Dirac Fermions

We investigate the interplay between the strong correlation and the spin-orbital coupling in the Kane-Mele-Hubbard model and obtain the qualitative phase diagram via the variational cluster approach. We identify, through an increase of the Hubbard $U$, the transition from the topological band insulator to either the spin liquid phase or the easy-plane antiferromagnetic insulating phase, depending on the strength of the spin-orbit coupling. A nontrivial evolution of the bulk bands in the topological quantum phase transition is also demonstrated.Comment: 5 pages, 4 figures. Due to the limit of file size, a clear Fig.4 is available on reques

miR-638 is a new biomarker for outcome prediction of non-small cell lung cancer patients receiving chemotherapy.

MicroRNAs (miRNAs), a class of small non-coding RNAs, mediate gene expression by either cleaving target mRNAs or inhibiting their translation. They have key roles in the tumorigenesis of several cancers, including non-small cell lung cancer (NSCLC). The aim of this study was to investigate the clinical significance of miR-638 in the evaluation of NSCLC patient prognosis in response to chemotherapy. First, we detected miR-638 expression levels in vitro in the culture supernatants of the NSCLC cell line SPC-A1 treated with cisplatin, as well as the apoptosis rates of SPC-A1. Second, serum miR-638 expression levels were detected in vivo by using nude mice xenograft models bearing SPC-A1 with and without cisplatin treatment. In the clinic, the serum miR-638 levels of 200 cases of NSCLC patients before and after chemotherapy were determined by quantitative real-time PCR, and the associations of clinicopathological features with miR-638 expression patterns after chemotherapy were analyzed. Our data helped in demonstrating that cisplatin induced apoptosis of the SPC-A1 cells in a dose- and time-dependent manner accompanied by increased miR-638 expression levels in the culture supernatants. In vivo data further revealed that cisplatin induced miR-638 upregulation in the serum derived from mice xenograft models, and in NSCLC patient sera, miR-638 expression patterns after chemotherapy significantly correlated with lymph node metastasis. Moreover, survival analyses revealed that patients who had increased miR-638 levels after chemotherapy showed significantly longer survival time than those who had decreased miR-638 levels. Our findings suggest that serum miR-638 levels are associated with the survival of NSCLC patients and may be considered a potential independent predictor for NSCLC prognosis

Multiple Model Rao-Blackwellized Particle Filter for Manoeuvring Target Tracking

Particle filters can become quite inefficient when applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, a novel multiple model Rao-Blackwellized particle filter (MMRBPF)-based algorithm has been proposed for manoeuvring target tracking in a cluttered environment. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the probabilistic data association filter, and the model selection by sequential importance sampling. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Moreover, to reduce the particle-degeneracy problem, the resampling approach is selectively carried out. Finally, experiment results, show that the proposed algorithm, has advantages over the conventional IMM-PDAF algorithm in terms of robust and  efficiency.Defence Science Journal, 2009, 59(3), pp.197-204, DOI:http://dx.doi.org/10.14429/dsj.59.151

Classification of Diabetic Foot Ulcers Using Class Knowledge Banks

Diabetic foot ulcers (DFUs) are one of the most common complications of diabetes. Identifying the presence of infection and ischemia in DFU is important for ulcer examination and treatment planning. Recently, the computerized classification of infection and ischaemia of DFU based on deep learning methods has shown promising performance. Most state-of-the-art DFU image classification methods employ deep neural networks, especially convolutional neural networks, to extract discriminative features, and predict class probabilities from the extracted features by fully connected neural networks. In the testing, the prediction depends on an individual input image and trained parameters, where knowledge in the training data is not explicitly utilized. To better utilize the knowledge in the training data, we propose class knowledge banks (CKBs) consisting of trainable units that can effectively extract and represent class knowledge. Each unit in a CKB is used to compute similarity with a representation extracted from an input image. The averaged similarity between units in the CKB and the representation can be regarded as the logit of the considered input. In this way, the prediction depends not only on input images and trained parameters in networks but the class knowledge extracted from the training data and stored in the CKBs. Experimental results show that the proposed method can effectively improve the performance of DFU infection and ischaemia classifications