Multiterminal Nonreciprocal Routing in an Optomechanical Plaquette via Synthetic Magnetism

Optomechanical systems with parametric coupling between optical (photon) and mechanical (phonon) modes provide a useful platform to realize various magnetic-free nonreciprocal devices, such as isolators, circulators, and directional amplifiers. However, nonreciprocal router with multiaccess channels has not been extensively studied yet. Here, we propose a nonreciprocal router with one transmitter, one receiver, and two output terminals, based on an optomechanical plaquette composing of two optical modes and two mechanical modes. The time-reversal symmetry of the system is broken via synthetic magnetism induced by driving the two optical modes with phase-correlated laser fields. The prerequisites for nonreciprocal routing are obtained both analytically and numerically, and the robustness of the nonreciprocity is demonstrated numerically. Multi-terminal nonreciprocal router in optomechanical plaquette provides a useful quantum node for development of quantum network information security and realization of quantum secure communication.Comment: 12 pages, 5 figure

An Application Case Study on Multi-sensor Data fusion System for Intelligent Process Monitoring

AbstractMulti-sensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Focusing on the indirect method, an attempt was made to build up a multi-sensor data fusion system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent monitoring system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The application case indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole

Broadband optical nonreciprocity via nonreciprocal band structure

As a promising approach for optical nonreciprocity without magnetic materials, optomechanically induced nonreciprocity has great potential for all-optical controllable isolators and circulators on chips. However, as a very important issue in practical applications, the bandwidth for nonreciprocal transmission with high isolation has not been fully investigated yet. In this study we review the nonreciprocity in a Brillouin optomechanical system with single cavity and point out the challenge in achieving broad bandwidth with high isolation. To overcome this challenge, we propose a one dimensional optomechanical array to realize broadband optical nonreciprocity via nonreciprocal band structure. We exploit nonreciprocal band structure by the stimulated Brillouin scattering induced transparency with directional optical pumping, and show that it is possible to demonstrate optical nonreciprocity with both broad bandwidth and high isolation. Such Brillouin optomechanical lattices with nonreciprocal band structure, offer an avenue to explore nonreciprocal collective effects in different electromagnetic and mechanical frequency regimes, such as nonreciprocal topological photonic and phononic phases.Comment: 10 pages, 6 figure

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret $\mathcal{O}(\sqrt{T})$ over $T$ time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods

Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network. RESULTS: For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. DISCUSSION/CONCLUSION: In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted