Fabrication of a microresonator-fiber assembly maintaining a high-quality factor by CO2 laser welding

We demonstrate fabrication of a microtoroid resonator of a high-quality (high-Q) factor using femtosecond laser three-dimensional (3D) micromachining. A fiber taper is reliably assembled to the microtoroid using CO2 laser welding. Specifically, we achieve a high Q-factor of 2.12*10^6 in the microresonator-fiber assembly by optimizing the contact position between the fiber taper and the microtoroid.Comment: 7 pages, 5 figure

Design of amine-functionalized metal-organic frameworks for CO2 separation: the more amine, the better?

A total of 41,825 metal-organic frameworks (MOFs) were computationally screened toward the design of amine-functionalized MOFs for CO2 separation. Both the optimal species and number of amine functional groups were examined for eight MOFs with good performance in terms of CO2 uptake and selectivity. It was revealed that more amine functional groups grafted on the MOFs do not lead to a better CO2 separation capability, and the concept of saturation degree of functional groups was proposed. The ethylene-diamine-functionalized MOF-74 membrane was predicted to possess high CO2 permeation separation capability, which was confirmed by the parallel experimental test of gas permeation.National Key Basic Research Program of China/2013CB73350National Natural Science Foundation of China/21376089National Natural Science Foundation of China/91334202China Postdoctoral Science Foundation/2014M560663Guangdong Science Foundation/2014A03031200Guangdong Science Foundation/2014A03031026Fundamental Research Funds for the Central Universities/SCUT-2014ZB0012Fundamental Research Funds for the Central Universities/2015ZP03State Key Lab of Pulp and Paper Engineering Program/201444National University of Singapore for CENGa

On-chip electro-optic tuning of a lithium niobate microresonator with integrated in-plane microelectrodes

We demonstrate electro-optic tuning of an on-chip lithium niobate microresonator with integrated in-plane microelectrodes. First two metallic microelectrodes on the substrate were formed via femtosecond laser process. Then a high-Q lithium niobate microresonator located between the microelectrodes was fabricated by femtosecond laser direct writing accompanied by focused ion beam milling. Due to the efficient structure designing, high electro-optical tuning coefficient of 3.41 pm/V was observed.Comment: 6 pages, 3 figure

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication burden on resource-constrained workers and protect model privacy, split federated learning (SFL) has been released by integrating both data and model parallelism. Despite resource limitations, SFL still faces two other critical challenges in EC, i.e., statistical heterogeneity and system heterogeneity. To address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. Concretely, feature merging aims to merge the features from workers into a mixed feature sequence, which is approximately equivalent to the features derived from IID data and is employed to promote model accuracy. While batch size regulation aims to assign diverse and suitable batch sizes for heterogeneous workers to improve training efficiency. Moreover, MergeSFL explores to jointly optimize these two strategies upon their coupled relationship to better enhance the performance of SFL. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson edge devices, and the experimental results show that MergeSFL can improve the final model accuracy by 5.82% to 26.22%, with a speedup by about 1.74x to 4.14x, compared to the baselines

Abnormal Event Detection Based on Deep Autoencoder Fusing Optical Flow

International audienceAs an important research topic in computer vision, abnormal detection has gained more and more attention. In order to detect abnormal events effectively, we propose a novel method using optical flow and deep autoencoder. In our model, optical flow of the original video sequence is calculated and visualized as optical flow image, which is then fed into a deep autoencoder. Then the deep autoencoder extract features from the training samples which are compressed to low dimension vectors. Finally, the normal and abnormal samples gather separately in the coordinate axis. In the evaluation, we show that our approach outperforms the existing methods in different scenes, in terms of accuracy

Generative Neural Networks for Anomaly Detection in Crowded Scenes

International audienc