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

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

Opinion Mining from Online Reviews: Consumer Satisfaction Analysis with B&B Hotels

Given the enormous growth and significant impact of user generated content in online hotel reviews, this study aims to mining the determinants of consumer satisfaction with B&Bs and build a hierarchical structure of these determinants. Content analysis was conducted based on the consumer review data from two well-known hotel booking websites. Ten determinants of customer satisfaction were identified. The interpretive structural modeling (ISM) technique was then used to develop a five-level hierarchical structural model based on these determinants to illustrate the influencing paths. Finally, the cross-impact matrix multiplication applied to classification (MICMAC) technique was used to analyze the driver and dependence power for each determinant. This study has the potential to make significant contributions from both the theoretical and practical perspectives in this research area

Effect of Zn doping on magnetic order and superconductivity in LaFeAsO

We report Zn-doping effect in the parent and F-doped LaFeAsO oxy-arsenides. Slight Zn doping in LaFe$_{1-x}$Zn$_{x}$AsO drastically suppresses the resistivity anomaly around 150 K associated with the antiferromagnetic (AFM) spin density wave (SDW) in the parent compound. The measurements of magnetic susceptibility and thermopower confirm further the effect of Zn doping on AFM order. Meanwhile Zn doping does not affect or even enhances the $T_c$ of LaFe$_{1-x}$Zn$_{x}$AsO$_{0.9}$F$_{0.1}$, in contrast to the effect of Zn doping in high-$T_c$ cuprates. We found that the solubility of Zn content ($x$) is limited to less than 0.1 in both systems and further Zn doping (i.e., $x$ $\geq$ 0.1) causes phase separation. Our study clearly indicates that the non-magnetic impurity of Zn$^{2+}$ ions doped in the Fe$_2$As$_2$ layers affects selectively the AFM order, and superconductivity remains robust against the Zn doping in the F-doped superconductors.Comment: 7 figures, 13 pages; revised version with more dat

Investigation of Cutting Rock by TBM Hob using a SPG Method

TBM (tunnel boring machine) hob is the core component of the TBM for rock cutting, whose cutting performance can directly determine the overall tunneling efficiency of the TBM. The understanding of cutting rock caused by TBM hobs is still not enough due to the complex contact features between the TBM hob and rock. To study the dynamic cutting process of the TBM hobs deeply, the rock cutting numerical model of the TBM hob is built based on the SPG (smooth particle Galerkin) method, the influence of hob penetration and hob spacing on rock breaking dynamic process, rock cutting forces and specific energy consumption are investigated. The results indicate that the dynamic process of sequential cutting of TBM hobs can be simulated well, and the rock breaking patterns caused by TBM hobs can be reflected with the SPG method. It also shows that the cutting forces of the hob are positively correlated with the hob penetration and hob spacing. For a given hob penetration, there exists an optimum hob spacing to acquire the highest rock cutting efficiency. The hob penetrations of 5, 7, 9, and 11 mm correspond to the optimum hob spacing of 60, 80, 90, and 100 mm respectively. Finally, the simulated results based on the SPG method are verified by comparing the experimental results and the CSM model. This study can provide a new method for simulating the rock cutting dynamic process of the TBM hobs