Access control for hybrid femtocell network based on AGV mechanism

As most of voice calls and data traffic originates indoors, femtocells have been one of the most promising trends in LTE, which are short-range, cost-beneficial and low-power cellular home base stations that can improve indoor coverage and voice/data quality of service (QoS). One of the major challenges for femtocell network is the access control. The hybrid access control mechanism, as a tradeoff between open and closed scenario, is the most promising access mechanism from which both users and operators benefit. Femtocell user equipments (FUEs) select femtocell access points (FAPs) according to their reported channel information which FAPs confidently own, and selfish FAPs have incentive to report larger information to win greater opportunity to be selected. Considering the aforementioned truth-telling in access control issue, this paper proposes access control scheme for hybrid femtocell network based on Arrow-d'Aspremont-Gerard-Varet (AGV) mechanism. Close form for the payment is given. Moreover, the access control scheme is nearly optimal performances with low computational complexity compared with the optimal access scheme. Furthermore, the simulation results demonstrate that the access control scheme can be apply to hybrid femtocell network. ? 2014 Global IT Research Institute (GIRI).EICPCI-S(ISTP)

The effect of proteoglycans inhibited by RNA interference on metastatic characters of human salivary adenoid cystic carcinoma

<p>Abstract</p> <p>Background</p> <p>Salivary adenoid cystic carcinoma (SACC) is one of the most common malignancies of salivary gland. Recurrence or/and early metastasis is its biological properties. In SACC, neoplastic myoepithelial cells secrete proteoglycans unconventionally full of the cribriform or tubular and glandular structures of SACC. Literatures have demonstrated that extracellular matrix provided an essential microenvironment for the biological behavior of SACC. However, there is rare study of the effect of proteoglycans on the potential metastasis of SACC.</p> <p>In this study, human xylosyltransferase-I (XTLY-I) gene, which catalyzes the rate-limited step of proteoglycans biosynthesis, was knocked down by RNA interference (RNAi) to inhibit the proteoglycans biosynthesis in SACC cell line with high tendency of lung metastasis (SACC-M). The impact of down-regulated proteoglycans on the metastasis characters of SACC-M cells was analyzed and discussed. This research could provide a new idea for the clinical treatment of SACC.</p> <p>Methods</p> <p>The eukaryotic expression vector of short hairpin RNA (shRNA) targeting XTLY-I gene was constructed and transfected into SACC-M cells. A stably transfectant cell line named SACC-M-WJ4 was isolated. The XTLY-I expression was measured by real-time PCR and Western blot; the reduction of proteoglycans was measured. The invasion and metastasis of SACC-M-WJ4 cells were detected; the effect of down-regulated proteoglycans on the potential lung metastasis of nude mice was observed, respectively.</p> <p>Results</p> <p>The shRNA plasmid targeting XTLY-I gene showed powerful efficiency of RNAi. The mRNA level of target gene decreased by 86.81%, the protein level was decreased by 80.10%, respectively. The silence of XTLY-I gene resulted in the reduction of proteoglycans significantly in SACC-M-WJ4 cells. The inhibitory rate of proteoglycans was 58.17% (24 h), 66.06% (48 h), 57.91% (72 h), 59.36% (96 h), and 55.65% (120 h), respectively. The reduction of proteoglycans suppressed the adhesion, invasion and metastasis properties of SACC-M cells, and decreased the lung metastasis of SACC-M cells markedly either.</p> <p>Conclusion</p> <p>The data suggested that the silence of XTLY-I gene in SACC-M cells could suppress proteoglycans biosynthesis and secretion significantly. The reduction of proteoglycans inhibited cell adhesion, invasion and metastasis of SACC-M cells. There is a close relationship between proteoglycans and the biological behavior of SACC.</p

Vortex solitons in quasi-phase-matched photonic crystals

We report solutions for stable compound solitons supported by a three-dimensional (3D) quasi-phase-matched (QPM) photonic crystal in a medium with the quadratic ($\chi ^{(2)}$) nonlinearity. The photonic crystals are introduced with a checkerboard structure, which can be realized by means of the available technology. The solitons are built as four-peak vortex modes of two types, rhombuses and squares. Their stability areas are identified in the system's parametric space, while all bright vortex solitons are subject to strong azimuthal instability in uniform $\chi ^{(2)}$ media. Possibilities for experimental realization of the solitons are outlined too.Comment: 6 pages, 6 figures, 39 reference

Dam failure environmental standards in China based on ecosystem service value

Dam failure risk standards are the foundation of risk decision-making for dam managers. However, as an important component of dam failure risk standards, there are currently no unified environmental risk standards. Drawing on research ideas of ecological economics on ecosystem service values and equivalent factor methods, this study quantified environmental values and effectively connected environmental standards with existing standards using the ALARP principle and the F-N curve. Considering the differences in environmental and economic conditions in different regions, a risk preference matrix was constructed to determine the risk preference of each region and formulate the dam failure environmental risk standards for China. This study presents a preliminary exploration of the formulation of dam failure environmental risk standards, providing new methods and ideas for subsequent research

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table