Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Bank competition, information specialization and innovation

Complementary to rich existing evidence on bank competition and corporate innovation, this paper aims to investigate the impacts of bank competition on innovation efficiencies, in terms of both R&D input and output at firm level. By acknowledging the role played by information asymmetries in financing innovation, we also examine the moderating effects of information specialization at both industry and firm level on corporate innovation. Analyzing innovation and bank structure data from U.S. between 1992 and 2010, we show novel evidence that increased bank competition improves innovation efficiencies in terms of both R&D input (investment) and output (patents and profits generated by R&D). In addition, we find bank competition has a greater favorable effect on innovation for those firms with more specialized information, such as those operating in an industry with more dispersed productivity growth and those with more concentrated patent types. Overall, our findings support market power hypothesis and banking strategic theory where bank competition improves credit supply to corporate innovation

ELSI: A Unified Software Interface for Kohn-Sham Electronic Structure Solvers

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aims to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.Comment: 55 pages, 14 figures, 2 table

Screening of patients with tuberculosis for diabetes mellitus in China.

Objective  There is a high burden of both diabetes (DM) and tuberculosis (TB) in China, and this study aimed to assess feasibility and results of screening patients with TB for DM within the routine healthcare setting of six health facilities. Method  Agreement on how to screen, monitor and record was reached in May 2011 at a stakeholders' meeting, and training was carried out for staff in the six facilities in July 2011. Implementation started in September 2011, and we report on 7 months of activities up to 31 March 2012. Results  There were 8886 registered patients with TB. They were first asked whether they had DM. If the answer was no, they were screened with a random blood glucose (RBG) followed by fasting blood glucose (FBG) in those with RBG ≥ 6.1 mm (one facility) or with an initial FBG (five facilities). Those with FBG ≥ 7.0 mm were referred to DM clinics for diagnostic confirmation with a second FBG. Altogether, 1090 (12.4%) patients with DM were identified, of whom 863 (9.7%) had a known diagnosis of DM. Of 8023 patients who needed screening for DM, 7947 (99%) were screened. This resulted in a new diagnosis of DM in 227 patients (2.9% of screened patients), and of these, 226 were enrolled to DM care. In addition, 575 (7.8%) persons had impaired fasting glucose (FBG 6.1 to <7.0 mm). Prevalence of DM was significantly higher in patients in health facilities serving urban populations (14.0%) than rural populations (10.6%) and higher in hospital patients (13.5%) than those attending TB clinics (8.5%). Conclusion  This pilot project shows that it is feasible to screen patients with TB for DM in the routine setting, resulting in a high yield of patients with known and newly diagnosed disease. Free blood tests for glucose measurement and integration of TB and DM services may improve the diagnosis and management of dually affected patients