Fabrication and deterministic transfer of high quality quantum emitter in hexagonal boron nitride

Color centers in solid state crystals have become a frequently used system for single photon generation, advancing the development of integrated photonic devices for quantum optics and quantum communication applications. In particular, defects hosted by two-dimensional (2D) hexagonal boron nitride (hBN) are a promising candidate for next-generation single photon sources, due to its chemical and thermal robustness and high brightness at room temperature. The 2D crystal lattice of hBN allows for a high extraction efficiency and easy integration into photonic circuits. Here we develop plasma etching techniques with subsequent high temperature annealing to reliably create defects. We show how different fabrication parameters influence the defect formation probability and the emitter brightness. A full optical characterization reveals the higher quality of the created quantum emitters, represented by a narrow spectrum, short excited state lifetime and high single photon purity. We also investigated the photostability on short and very long timescales. We utilize a wet chemically-assisted transfer process to reliably transfer the single photon sources onto arbitrary substrates, demonstrating the feasibility for the integration into scalable photonic quantum information processing networks.Comment: revised versio

In-band label extractor based on Cascaded Si ring resonators enabling 160 Gb/s optical packet switching modules

Photonic integration of optical packet switching modules is crucial to compete with existing electronic switching fabrics in large data center networks. The approach of coding the forwarding packet information in an in-band label enables a spectral-efficient and scalable way of building low-latency large port count modular optical packet switching architecture. We demonstrate the error-free operation of the four in-band label extraction from 160 Gb/s optical data packets based on photonic integrated silicon-on- insulator ring resonators. Four low-loss cascaded ring resonators using the quasi-TM mode are used as narrowband filters to ensure the detection of four optical labels as well as the error-free forwarding of the payload at limited power penalty. Due to the low-loss and less-confined optical quasi-TM mode the resonators can be very narrowband and have low insertion loss. The effect of the bandwidth of the four ring resonators on the quality of the payload is investigated. We show that using four rings with 3dB bandwidth of 21 pm and only an insertion loss of 3 dB, the distortion on the payload is limited (< 1.5 dB power penalty), even when the resonances are placed very close to the packet's central wavelength. We also investigate the optical power requirements for error-free detection of the label as function of their spectral position relative to the center of the payload. The successful in-band positioning of the labels makes this component very scalable in amount of labels

Building scalable digital library ingestion pipelines using microservices

CORE, a harvesting service offering access to millions of open access research papers from around the world, has shifted its harvesting process from following a monolithic approach to the adoption of a microservices infrastructure. In this paper, we explain how we rearranged and re-scheduled our old ingestion pipeline, present CORE's move to managing microservices and outline the tools we use in a new and optimised ingestion system. In addition, we discuss the ineffciencies of our old harvesting process, the advantages, and challenges of our new ingestion system and our future plans. We conclude that via the adoption of microservices architecture we managed to achieve a scalable and distributed system that would assist with CORE's future performance and evolution

The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

Scalable ASL sign recognition using model-based machine learning and linguistically annotated corpora

We report on the high success rates of our new, scalable, computational approach for sign recognition from monocular video, exploiting linguistically annotated ASL datasets with multiple signers. We recognize signs using a hybrid framework combining state-of-the-art learning methods with features based on what is known about the linguistic composition of lexical signs. We model and recognize the sub-components of sign production, with attention to hand shape, orientation, location, motion trajectories, plus non-manual features, and we combine these within a CRF framework. The effect is to make the sign recognition problem robust, scalable, and feasible with relatively smaller datasets than are required for purely data-driven methods. From a 350-sign vocabulary of isolated, citation-form lexical signs from the American Sign Language Lexicon Video Dataset (ASLLVD), including both 1- and 2-handed signs, we achieve a top-1 accuracy of 93.3% and a top-5 accuracy of 97.9%. The high probability with which we can produce 5 sign candidates that contain the correct result opens the door to potential applications, as it is reasonable to provide a sign lookup functionality that offers the user 5 possible signs, in decreasing order of likelihood, with the user then asked to select the desired sign

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues