Coherence assisted resonance with sub-lifetime-limited linewidth

We demonstrate a novel approach to obtain resonance linewidth below that limited by coherence lifetime. Cross correlation between induced intensity modulation of two lasers coupling the target resonance exhibits a narrow spectrum. 1/30 of the lifetime-limited width was achieved in a proof-of-principle experiment where two ground states are the target resonance levels. Attainable linewidth is only limited by laser shot noise in principle. Experimental results agree with an intuitive analytical model and numerical calculations qualitatively. This technique can be easily implemented and should be applicable to many atomic, molecular and solid state spin systems for spectroscopy, metrology and resonance based sensing and imaging.Comment: 5 pages 5 figure

The college students' response to customized information services based on Library2.0 technologies at universities in Nanjing

Through a questionnaire survey of students' response from 6 universities in Nanjing, this paper aims to determine their varying degrees of satisfaction about the customized information service based on Library2.0 technologies. In so doing, the authors carefully examined the data collected from the returned questionnaires about such key issues as the students' perceptions about the customized information service via a Library 2.0 platform, self-initiated use experience of such a mechanism, their achieved information searching results&nbsp; vis-&agrave;-vis their expectations, etc. In addition, the authors also made a comparative study between information providers (i.e. librarians) and information consumers (i.e. students) at Chinese and American academic libraries.</p

Role of the porous structure of the bioceramic scaffolds in bone tissue engineering

The porous structure of biomaterials plays a critical role in improving the efficiency of biomaterials in tissue engineering. Here we fabricate successfully porous bioceramics with accurately controlled pore parameters, and investigate the effect of pore parameters on the mechanical property, the cell seeding proliferation and the vascularization of the scaffolds. This study shows that the porosity play an important role on the mechanical property of the scaffolds, which is affected not only by the macropores size, but also by the interconnections of the scaffolds. Larger pores are beneficial for cell growth in scaffolds. In contrast, the interconnections do not affect cell growth much. The interconnections appear to limit the number of blood vessels penatrating through adjacent pores, and both the pores size and interconnections can determine the size of blood vessels. The results may be referenced on the selective design of porous structure of biomaterials to meet the specificity of biological application

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to rely on and upload their private data to big data companies they may not trust. Due to the compliance, legal, or privacy constraints, most users are willing to contribute only their encrypted data, and lack interests or resources to join the training of DNNs in cloud. To train a DNN on encrypted data in a completely non-interactive way, a recent work proposes a fully homomorphic encryption (FHE)-based technique implementing all activations in the neural network by \textit{Brakerski-Gentry-Vaikuntanathan (BGV)}-based lookup tables. However, such inefficient lookup-table-based activations significantly prolong the training latency of privacy-preserving DNNs. In this paper, we propose, Glyph, a FHE-based scheme to fast and accurately train DNNs on encrypted data by switching between TFHE (Fast Fully Homomorphic Encryption over the Torus) and BGV cryptosystems. Glyph uses logic-operation-friendly TFHE to implement nonlinear activations, while adopts vectorial-arithmetic-friendly BGV to perform multiply-accumulation (MAC) operations. Glyph further applies transfer learning on the training of DNNs to improve the test accuracy and reduce the number of MAC operations between ciphertext and ciphertext in convolutional layers. Our experimental results show Glyph obtains the state-of-the-art test accuracy, but reduces the training latency by $99\%$ over the prior FHE-based technique on various encrypted datasets.Comment: 10 pages, 8 figure