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

Research on a New Signature Scheme on Blockchain

With the rise of Bitcoin, blockchain which is the core technology of Bitcoin has received increasing attention. Privacy preserving and performance on blockchain are two research points in academia and business, but there are still some unresolved issues in both respects. An aggregate signature scheme is a digital signature that supports making signatures on many different messages generated by many different users. Using aggregate signature, the size of the signature could be shortened by compressing multiple signatures into a single signature. In this paper, a new signature scheme for transactions on blockchain based on the aggregate signature was proposed. It was worth noting that elliptic curve discrete logarithm problem and bilinear maps played major roles in our signature scheme. And the security properties of our signature scheme were proved. In our signature scheme, the amount will be hidden especially in the transactions which contain multiple inputs and outputs. Additionally, the size of the signature on transaction is constant regardless of the number of inputs and outputs that the transaction contains, which can improve the performance of signature. Finally, we gave an application scenario for our signature scheme which aims to achieve the transactions of big data on blockchain

C1q/TNF-related protein 3 (CTRP3) and 9 (CTRP9) concentrations are decreased in patients with heart failure and are associated with increased morbidity and mortality.

BACKGROUND: Biochemical marker has revolutionized the approach to the diagnosis of heart failure. However, it remains difficult to assess stability of the patient. As such, novel means of stratifying disease severity are needed. C1q/TNF-Related Protein 3 (CTRP3) and C1q/TNF-Related Protein 9 (CTRP9) are novel adipokines that contribute to energy homeostasis with additional anti-inflammatory and anti-ischemic properties. The aim of our study is to evaluate concentrations of CTRP3 and CTRP9 in patients with HFrEF (heart failure with reduced ejection fraction) and whether associated with mortality. METHODS: Clinical data and plasma were obtained from 176 healthy controls and 168 patients with HFrEF. CTRP3 and CTRP9 levels were evaluated by enzyme-linked immunosorbent assay. RESULTS: Both CTRP3 and CTRP9 concentrations were significantly decreased in the HFrEF group compared to the control group (p \u3c 0.001). Moreover, patients with higher New York Heart Association class had significantly lower CTRP3 or CTRP9 concentrations. Correlation analysis revealed that CTRP3 and CTRP9 levels were positively related with LVEF% (CTRP3, r = 0.556, p \u3c 0.001; CTRP9, r = 0.526, p \u3c 0.001) and negatively related with NT-proBNP levels (CTRP3, r = - 0.454, p \u3c 0.001; CTRP9, r = - 0.483, p \u3c 0.001). After a follow up for 36 months, after adjusted for age, LVEF and NT-proBNP, we observed that CTRP3 or CTRP9 levels below the 25th percentile was a predictor of total mortality (CTRP3,HR:1.93,95%CI1.03~3.62,P = 0.042;CTRP9,HR:1.98,95%CI:1.02~3.85,P = 0.044) and hospitalizations (CTRP3,HR:2.34,95% CI:1.43~3.82,P = 0.001;CTRP9,HR:2.67,95%CI:1.58~4.50,P \u3c 0.001). CONCLUSIONS: CTRP3 and CTRP9 are decreased in patients with HFrEF, proportionate to disease severity, and each is associated with increased morbidity and mortality. TRIAL REGISTRATION: NCT01372800 . Registered May 2011

Nitrogen Photofixation over IIIâ Nitride Nanowires Assisted by Ruthenium Clusters of Low Atomicity

In many heterogeneous catalysts, the interaction of supported metal species with a matrix can alter the electronic and morphological properties of the metal and manipulate its catalytic properties. IIIâ nitride semiconductors have a unique ability to stabilize ultraâ small ruthenium (Ru) clusters (ca. 0.8â nm) at a high loading density up to 5â wtâ %. nâ Type IIIâ nitride nanowires decorated with Ru subâ nanoclusters offer controlled surface charge properties and exhibit superior UVâ and visibleâ light photocatalytic activity for ammonia synthesis at ambient temperature. A metal/semiconductor interfacial Schottky junction with a 0.94â eV barrier height can greatly facilitate photogenerated electron transfer from IIIâ nitrides to Ru, rendering Ru an electron sink that promotes Nâ ¡N bond cleavage, and thereby achieving lowâ temperature ammonia synthesis.IIIâ Nitridâ Halbleiter stabilisieren Rutheniumcluster mit Beladungsdichten bis 5â Gew.â %. Der Schottkyâ à bergang an der Grenzfläche zwischen Metall und Halbleiter begünstigt den Transfer von Photoelektronen aus den IIIâ Nitriden auf das Ruthenium, das dadurch die Spaltung der Nâ ¡Nâ Bindung in einer Niedertemperatursynthese von Ammoniak bewirken kann.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137688/1/ange201703301_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137688/2/ange201703301-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137688/3/ange201703301.pd

Study on Target Detection & Recognition Using Laser 3D Vision Systems for Automatic Ship Loader

This paper purposes a solution of the target detection and identification for automatic ship loader. For automatic ship loaders, the operation target should be detected and identified continuously and real-timely. By using the laser measurement systems (LMS), the ship cargo holds and the bulk cargo can be rebuilt as a group of 3D points. Then the image processing algorithm can identify the positions, sizes and shapes of the cargo holds and the bulk cargo from the 3D points. Based on the target information identified by the image processing algorithm, the ship loader can finish the loading operation automatically. At last, this paper describes and analyzes the experiment of the cargo height detection using LMS in Coal Terminal of Tianjin Port

Combinatorial hydrogels with biochemical gradients for screening 3D cellular microenvironments

3D microenvironmental parameters control cell behavior, but can be challenging to investigate over a wide range of conditions. Here, a combinatorial hydrogel platform is developed that uses light-mediated thiol-norbornene chemistry to encapsulate cells within hydrogels with biochemical gradients made by spatially varied light exposure. Specifically, mesenchymal stem cells are photoencapsulated in norbornene-modified hyaluronic acid hydrogels functionalized with gradients (0–5 mM) of peptides that mimic cell-cell or cell-matrix interactions, either as single or orthogonal gradients. Chondrogenesis varied spatially in these hydrogels based on the local biochemical formulation, as indicated by Sox9 and aggrecan expression levels. From 100 combinations investigated, discrete hydrogels are formulated and early gene expression and long-term cartilage-specific matrix production are assayed and found to be consistent with screening predictions. This platform is a scalable, highthroughput technique that enables the screening of the effects of multiple biochemical signals on 3D cell behavior

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