New Trends in Photonic Switching and Optical Network Architecture for Data Centre and Computing Systems

AI/ML for data centres and data centres for AI/ML are defining new trends in cloud computing. Disaggregated heterogeneous reconfigurable computing systems realized by photonic interconnects and photonic switching expect greatly enhanced throughput and energy-efficiency for AI/ML workloads, especially when aided by an AI/ML control plane

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014-June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baseline interferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurement on a point source and a variable width slit; analyzed and studied the testbed results. (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterize the optical throughput and heater response for Si3N4 PIC for low and high resolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin)

Low-Mass Planar Photonic Imaging Sensor

Continuing on the successful progress of NIAC (NASA Innovative Advanced Concepts) Phase I, this report summarizes the technical progress achieved under NIAC Phase II during the performance period September 19, 2014 to June 18, 2017. During this period, the research team has made the following accomplishments: designed and layout a silica photonic integrated circuit (PIC) as a two baselineinterferometric imager; constructed an experiment to utilize the two baselines for complex visibility measurementon a point source and a variable width slit; analyzed and studied the testbed results (in collaboration with Lockheed Martin); designed and layout Si3N4 PICs for the low-resolution and high-resolution SPIDER (Segmented Planar Imaging Detector for Electro-Optical Reconnaissance) telescope; fabricated the multi-layer Si3N4 PIC for low and high resolution SPIDER telescope; characterized the optical throughput and heater response for Si3N4 PIC for low and highresolution SPIDER telescopes; carried out imaging experiments using the Si3N4 PIC low-resolution version (in collaboration with Lockheed Martin); investigated signal-to-noise (SNR) ratio of SPIDER imager compared to the conventional panchromatic imager (in collaboration with Lockheed Martin); fulfilled the SNR simulation upon SPIDER imager (in collaboration with Lockheed Martin)

Distributed tuning of boundary resources: the case of Apple's iOS service system

The digital age has seen the rise of service systems involving highly distributed, heterogeneous, and resource-integrating actors whose relationships are governed by shared institutional logics, standards, and digital technology. The cocreation of service within these service systems takes place in the context of a paradoxical tension between the logic of generative and democratic innovations and the logic of infrastructural control. Boundary resources play a critical role in managing the tension as a firm that owns the infrastructure can secure its control over the service system while independent firms can participate in the service system. In this study, we explore the evolution of boundary resources. Drawing on Pickering’s (1993) and Barrett et al.’s (2012) conceptualizations of tuning, the paper seeks to forward our understanding of how heterogeneous actors engage in the tuning of boundary resources within Apple’s iOS service system. We conduct an embedded case study of Apple’s iOS service system with an in-depth analysis of 4,664 blog articles concerned with 30 boundary resources covering 6 distinct themes. Our analysis reveals that boundary resources of service systems enabled by digital technology are shaped and reshaped through distributed tuning, which involves cascading actions of accommodations and rejections of a network of heterogeneous actors and artifacts. Our study also shows the dualistic role of power in the distributed tuning process

Which can Accelerate Distributed Machine Learning Faster: Hybrid Optical/Electrical or Optical Reconfigurable DCN?

We run various distributed machine learning (DML) architectures in a hybrid optical/electrical DCN and an optical DCN based on Hyper-FleX-LION. Experimental results show that Hyper-FleX-LION gains faster DML acceleration and improves acceleration ratio by up to 22.3%

Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation

Aflatoxin is a carcinogenic toxin to humans and animals produced by mold fungi in staple crops. Surveys of Aflatoxin are expensive, and the results are usually not available for implementing within season mitigation strategies. Identification of high and low risk areas and years is essential to reduce the number of samples analyzed for Aflatoxin concentration. Previously a risk factors approach was developed to determine county level Aflatoxin contamination risk in southern Georgia, but Aflatoxin concentrations and risk factor data were not analyzed simultaneously and all risk factors had equal weight which is unrealistic. In the current paper we propose a regression approach to overcome these problems. Spatial Poisson profile regression identified clusters of counties which have similar Aflatoxin risk and risk factor profiles, whilst explicitly taking into account multicollinearity in the risk factor data and spatial autocorrelation in the Aflatoxin data. This approach allows examination of the utility of different highly correlated variables including remotely sensed data that could give information at the sub-county level. The results identify plausible clusters compared to previous work but also give the relative importance of the risk factors associated with those clusters. The approach also helps show that some factors like well-drained soil behave differently from expectations and irrigation data is not useful

Collaborative learning in multi-domain optical networks

This paper presents a collaborative learning framework for multi-domain optical networks to enable cognitive end-to-end networking while guaranteeing the autonomy of each administrative domain

Machine-Learning-Aided Bandwidth and Topology Reconfiguration for Optical Data Center Networks

We present an overview of the application of machine learning for traffic engineering and network optimization in optical data center networks. In particular, we discuss the application of supervised and unsupervised learning for bandwidth and topology reconfiguration

First Demonstration of Monolithic Silicon Photonic Integrated Circuit 32×32 Thin-CLOS AWGR for All-to-All Interconnections

We designed, fabricated, and demonstrated the first monolithic silicon photonic Thin-CLOS AWGR. The fabricated Thin-CLOS has 32 ports and four 16-port silicon nitride AWGRs integrated by compact multilayer waveguide routing. Experimental results show 4 dB insertion loss and-20 dB crosstalk