Circumferences and minimum degrees in 3-connected claw-free graphs

AbstractIn this paper, we prove that every 3-connected claw-free graph G on n vertices contains a cycle of length at least min{n,6δ−15}, thereby generalizing several known results

HierFedML: aggregator placement and UE assignment for hierarchical federated learning in mobile edge computing

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrent FL requests . Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications

Delineating the expanding phenotype associated with SCAPER gene mutation

This letter is freely available via Open Access. Click on the Publisher URL to access the full-text via the publisher's site

Mutations in REEP6 Cause Autosomal-Recessive Retinitis Pigmentosa

Retinitis pigmentosa (RP) is the most frequent form of inherited retinal dystrophy. RP is genetically heterogeneous and the genes identified to date encode proteins involved in a wide range of functional pathways, including photoreceptor development, phototransduction, the retinoid cycle, cilia, and outer segment development. Here we report the identification of biallelic mutations in Receptor Expression Enhancer Protein 6 (REEP6) in seven individuals with autosomal-recessive RP from five unrelated families. REEP6 is a member of the REEP/Yop1 family of proteins that influence the structure of the endoplasmic reticulum but is relatively unstudied. The six variants identified include three frameshift variants, two missense variants, and a genomic rearrangement that disrupts exon 1. Human 3D organoid optic cups were used to investigate REEP6 expression and confirmed the expression of a retina-specific isoform REEP6.1, which is specifically affected by one of the frameshift mutations. Expression of the two missense variants (c.383C>T [p.Pro128Leu] and c.404T>C [p.Leu135Pro]) and the REEP6.1 frameshift mutant in cultured cells suggest that these changes destabilize the protein. Furthermore, CRISPR-Cas9-mediated gene editing was used to produce Reep6 knock-in mice with the p.Leu135Pro RP-associated variant identified in one RP-affected individual. The homozygous knock-in mice mimic the clinical phenotypes of RP, including progressive photoreceptor degeneration and dysfunction of the rod photoreceptors. Therefore, our study implicates REEP6 in retinal homeostasis and highlights a pathway previously uncharacterized in retinal dystrophy

Node priority guided clustering algorithm

Density-based clustering algorithms have the advantages of clustering with arbitrary shapes and handling noise data, but cannot deal with unsymmetrical density distribution and high dimensionality dataset. Therefore, a node priority guided clustering algorithm (NPGC) is proposed. A direct K neighbor graph of dataset is set up based on KNN neighbor method. Then the local information of each node in graph is captured by using KNN kernel density estimate method, and the node priority is calculated by passing the local information through graph. Finally, a depth-first search on graph is applied to find out the clustering results based on the local kernel degree. Experiment results show that NPGC has the ability to deal with unsymmetrical density distribution and high dimensionality dataset

High dimensional clustering algorithm based on Local Significant Units

High dimensional clustering algorithm based on equal or random width density grid cannot guarantee high quality clustering results in complicated data sets. In this paper, a High dimensional Clustering algorithm based on Local Significant Unit (HC_LSU) is proposed to deal with this problem, based on the kernel estimation and spatial statistical theory. Firstly, a structure, namely Local Significant Unit (LSU) is introduced by local kernel density estimation and spatial statistical test; secondly, a greedy algorithm named Greedy Algorithm for LSU (GA_LSU) is proposed to quickly find out the local significant units in the data set; and eventually, the single-linkage algorithm is run on the local significant units with the same attribute subset to generate the clustering results. Experimental results on 4 synthetic and 6 real world data sets showed that the proposed high-dimensional clustering algorithm, HC_LSU, could effectively find out high quality clustering results from the highly complicated data sets. Dianzi Yu Xinxi Xueba

A Cooperation Scheme based on Reputation for Opportunistic Networks

International audienc

Bridge Displacement Monitoring Method Based on Laser Projection-Sensing Technology

Bridge displacement is the most basic evaluation index of the health status of a bridge structure. The existing measurement methods for bridge displacement basically fail to realize long-term and real-time dynamic monitoring of bridge structures, because of the low degree of automation and the insufficient precision, causing bottlenecks and restriction. To solve this problem, we proposed a bridge displacement monitoring system based on laser projection-sensing technology. First, the laser spot recognition method was studied. Second, the software for the displacement monitoring system was developed. Finally, a series of experiments using this system were conducted, and the results show that such a system has high measurement accuracy and speed. We aim to develop a low-cost, high-accuracy and long-term monitoring method for bridge displacement based on these preliminary efforts