Networked Federated Learning

We develop the theory and algorithmic toolbox for networked federated learning in decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of similarity between local datasets. Different notions of similarity are induced by spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate networked federated learning using a generalized total variation minimization. This formulation unifies and considerably extends existing federated multi-task learning methods. It is highly flexible and can be combined with a broad range of parametric models including Lasso or deep neural networks. Our main algorithmic contribution is a novel networked federated learning algorithm which is well suited for distributed computing environments such as edge computing over wireless networks. This algorithm is robust against inexact computations arising from limited computational resources including processing time or bandwidth. For local models resulting in convex problems, we derive precise conditions on the local models and their network structure such that our algorithm learns nearly optimal local models. Our analysis reveals an interesting interplay between the convex geometry of local models and the (cluster-) geometry of their network structure

MicroRNA-101 is a potential prognostic indicator of laryngeal squamous cell carcinoma and modulates CDK8

BACKGROUND: Various microRNAs (miRNAs) negatively modulate genes that are involved in cellular proliferation, differentiation, invasion, and apoptosis. In many types of cancer, the expression profiles of these miRNAs are altered. Recently, miR-101 was identified as a tumour suppressor and was found to be expressed at low levels in various types of tumours, including prostate, breast, endometrium, and bladder cancers. However, the function(s) of miR-101 in laryngeal carcinoma remain unknown. METHODS: The expression levels of miR-101 in laryngeal squamous cell carcinoma (LSCC) tissues and cells were detected by qPCR. Cell proliferation, migration, cell cycle, and apoptosis assay were applied to assess the function(s) of miR-101 in vitro. Nude mice subcutaneous tumour model was used to perform in vivo study. Moreover, we identified Cyclin-dependent kinase 8 (CDK8) as the target of miR-101 by a luciferase assay. The possible downstream effectors of CDK8 were investigated in Wnt/β-catenin signaling pathway. Changes of CDK8, β-catenin, and cyclin D1 protein levels were analyzed by western blotting and immunohistochemical staining. The prognostic effect of miR-101 was evaluated using the Kaplan–Meier method. RESULTS: Expression of miR-101 was down-regulated in the LSCC tissues compared with the adjacent normal tissues. Furthermore, downregulation of miR-101 correlated with T3–4 tumour grade, lymph node metastasis, and an advanced clinical stage in the LSCC patients examined (P < 0.05). The low level of miR-101 expression was associated with poor prognosis (P < 0.05). CDK8 was identified as the target gene of miR-101 by luciferase reporter assay. Moreover, we showed that up-regulation of miR-101 expression suppressed humen LSCC Hep-2 cells proliferation and migration, and induced cell-cycle arrest. Increased expression of miR-101 induced cells apoptosis both in vitro and in vivo. Correspondingly, exogenous expression of miR-101 significantly reduced the growth of tumour in a LSCC xenograft model. Furthermore, the miR-101 level was inversely correlated with levels of CDK8, β-catenin, and cyclin D1 in western blotting assay and immunohistochemical staining assay. CONCLUSIONS: These results indicate that miR-101 is a potent tumour repressor that directly represses CDK8 expression. Thus, detection and targeting of miR-101 may represent a novel diagnostic and therapeutic strategy for LSCC patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-015-0626-6) contains supplementary material, which is available to authorized users

Long noncoding RNA NEAT1 promotes laryngeal squamous cell cancer through regulating miR-107/CDK6 pathway

BACKGROUND: Long noncoding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) plays key role in the progression of some human cancers. However, the role of NEAT1 in human laryngeal squamous cell cancer (LSCC) is still unknown. We therefore investigated the expression and function of NEAT1 in LSCC. METHODS: NEAT1 level in LSCC and adjacent non-neoplastic tissues were detected by qRT-PCR. NEAT1 was knockdown in LSCC cells and cell proliferation, apoptosis and cell cycle were examined. The growth of xenografts with NEAT1 knockdown LSCC cells was analyzed. RESULTS: NEAT1 level was significantly higher in LSCC than in corresponding adjacent non-neoplastic tissues, and patients with neck nodal metastasis or advanced clinical stage had higher NEAT1 expression. Moreover, siRNA mediated NEAT1 knockdown significantly inhibited the proliferation and induced apoptosis and cell cycle arrest at G1 phase in LSCC cells. The growth of LSCC xenografts was significantly suppressed by the injection of NEAT1 siRNA lentivirus. Furthermore, NEAT1 regulated CDK6 expression in LSCC cells which was mediated by miR-107. CONCLUSION: NEAT1 plays an oncogenic role in the tumorigenesis of LSCC and may serve as a potential target for therapeutic intervention

Emerging Computation and Information teChnologies for Education : Proceeding of 2012 International Conference on Emerging Computation and Information teChnologies for Education

The 2012 International Conference on Emerging Computation and Information teChnologies for Education (ECICE 2012) was  held on Jan. 15-16, 2012, Hangzhou, China.  The main results of the conference are presented in this proceedings book of carefully reviewed and accepted paper addressing the hottest issues in emerging computation and information technologies used for education. The volume covers a wide series of topics in the area, including Computer-Assisted Education, Educational Information Systems, Web-based Learning, etc

Detection method for surface scratches of composite automotive components with high reflection and complicated background color

Abstract Current testing status have the problems that the surface background color pattern of high reflective carbon fiber auto parts is complex, the shape of scratch defects is irregular, and the shallow and micro scratches are not easy to be detected. Aiming at the problems, a scratch detection method combining innovative morphological processing and optimized Canny edge detection algorithm is proposed. The image acquired by an innovative image acquisition platform. After gray processing and open operation denoising, the self-defined oval kernel secondary expansion is introduced in the morphological processing part. When threshold segmentation and minimum connected domain screening are done, the optimized Canny edge detection algorithm is used. Through the non-maximum suppression of gradient amplitude, a wide range of dual-threshold parameters are selected to detect the edge of the target image. The results show that the detection rate of the proposed method is 18.57% higher than that of the traditional way, and the detection rate is up to 95.71%. At the same time, the proposed method can reflect the scratch morphology more intuitively.</jats:p