Gender Discrimination towards Borrowers in Online P2PLending

Online peer-to-peer (P2P) lending has developed fast around the world in recent years; however, studies regarding gender discrimination and its rationality for developing countries are limited. Gender discrimination towards borrowers and its rationality in P2P lending in China are studied in this paper. Using data collected from PPdai.com, one of the largest P2P lending platforms in China, we found that, female borrowers are less likely to be funded than male borrowers, but their default rates are lower. Such results suggested that there is significant gender discrimination in P2P lending market in China, but such discrimination is out of prejudice rather than from rational reasoning. Eliminating such gender discrimination is not only beneficial to female borrowers, but also helpful for improving returns of lenders

Profit VS Non-Profit Business Based on P2P Lending: A Cross-Country Multiple Case Study

People-to-People (P2P) lending allows individuals to lend and borrow directly among each other online. Previous research mainly focuses on a single P2P lending marketplace – Prosper.com. This paper uses a multiple-case study approach to explore four P2P lending marketplaces. The study provides in-depth explanations on how different P2P lending marketplaces operate in different business models and in different countries. Our study thus extends current understanding about different operation models of P2P lending marketplaces, and suggests how future research should be done to cover more P2P lending platforms. We also contribute to the literature by indicating the interesting research questions originated from the cross-case analysis

The Roles of Social Capital in Online P2P Lending Markets Under Different Cultures: A Comparison of China and America

Online P2P (People-to-People or Peer-to-Peer) lending has very rapid development since it was appeared in 2005. In order to mitigate asymmetric information between borrowers and lenders, some online P2P market allows members building their social networks (such as Prosper, CommunityLend, PPDai etc). By empirical analyzing the transaction data of Prosper (largest P2P market in US) and PPDai (largest P2P market in China), the paper verifies that the social capital systems have a positive influence on borrower’s loan performance on the markets. However, on both markets, the loan interest rate mainly dependents on borrower’s hard information rather than their social capital. Furthermore, it concludes that borrower’ social network in PPDai is much more useful and effective than in Prosper by comparing the empirical results, which could be helpful for the credit system development of Chinese online P2P lending markets based on the conclusions

Research on Embedded Sensors for Concrete Health Monitoring Based on Ultrasonic Testing

In this article, embedded ultrasonic sensors were prepared using 1–3-type piezoelectric composite and piezoelectric ceramic as the piezoelectric elements, respectively. The frequency bandwidth of the novel embedded ultrasonic sensors was investigated. To obtain the relationship between the receiving ultrasonic velocity and compressive strength, as well as their response signals to crack damage, the sensors were fabricated and embedded into the cement mortar before testing. The results demonstrated that the piezoelectric composite sensor had wider frequency bandwidth than the piezoelectric ceramic sensor. The compressive strength and ultrasonic velocity had a positive linear relationship, with a correlation coefficient of 0.9216. The head wave amplitude of the receiving ultrasonic signal was sensitive to the changing crack damage and gradually decayed with the increasing degree of cement damage. Thus, the novel embedded ultrasonic sensors are suitable for concrete health monitoring via ultrasonic non-destructive testing

Design, Fabrication, and Properties of 2-2 Connectivity Cement/Polymer based Piezoelectric Composites with Varied Piezoelectric Phase Distribution

The laminated 2-2 connectivity cement/polymer based piezoelectric composites with variedpiezoelectric phase distribution were fabricated by employing Lead Zirconium Titanate ceramicas active phase, and mixture of cement powder, epoxy resin, and hardener as matrix phase with a mass proportion of 4:4:1. The dielectric, piezoelectric, and electromechanical coupling properties of the composites were studied. The composites with large total volume fraction ofpiezoelectric phase have large piezoelectric strain constant and relative permittivity, and thepiezoelectric and dielectric properties of the composites are independent of the dimensional variations of the piezoelectric ceramic layer. The composites with small total volume fraction of piezoelectric phase have large piezoelectric voltage constant, but also large dielectric loss. The composite with gradually increased dimension of piezoelectric ceramic layer has the smallest dielectric loss, and that with the gradually increased dimension of matrix layer has the largest piezoelectric voltage constant. The novel piezoelectric composites show potential applications in fabricating ultrasonic transducers with varied surface vibration amplitude of thetransducer

Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page

Entangled X-ray Photon Pair Generation by Free Electron Lasers

Einstein, Podolsky and Rosen's prediction on incompleteness of quantum mechanics was overturned by experimental tests on Bell's inequality that confirmed the existence of quantum entanglement. In X-ray optics, entangled photon pairs can be generated by X-ray parametric down conversion (XPDC), which is limited by relatively low efficiency. Meanwhile, free electron laser (FEL) has successfully lased at X-ray frequencies recently. However, FEL is usually seen as a classical light source, and its quantum effects are considered minor corrections to the classical theory. Here we investigate entangled X-ray photon pair emissions in FEL. We establish a theory for coherently amplified entangled photon pair emission from microbunched electron pulses in the undulator. We also propose an experimental scheme for the observation of the entangled photon pairs via energy and spatial correlation measurements. Such an entangled X-ray photon pair source is of great importance in quantum optics and other X-ray applications.Comment: 13 pages, 3 figure

Hierarchical layout-aware graph convolutional network for unified aesthetics assessment

Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. While previous state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs), our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more suited for modeling complex relations among image regions than vanilla convolutional layers. Specifically, we present a Hierarchical Layout-Aware Graph Convolutional Network (HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LA-GCN modules. The first LA-GCN module constructs an aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN module performs graph reasoning after aggregating significant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model outperforms the state-of-the-art on the AVA and AADB datasets across three different tasks. The code is available at http://github.com/days1011/HLAGCN

Nanocutting mechanism of 6H-SiC investigated by scanning electron microscope online observation and stress-assisted and ion implant-assisted approaches

Nanocutting mechanism of single crystal 6H-SiC is investigated through a novel scanning electron microscope setup in this paper. Various undeformed chip thicknesses on (0001) orientation are adopted in the nanocutting experiments. Phase transformation and dislocation activities involved in the 6H-SiC nanocutting process are also characterized and analyzed. Two methods of stress-assisted and ion implant-assisted nanocutting are studied to improve 6H-SiC ductile machining ability. Results show that stress-assisted method can effectively decrease the hydrostatic stress and help to activate dislocation motion and ductile machining; ion implant-induced damages are helpful to improve the ductile machining ability from MD simulation and continuous nanocutting experiments under the online observation platform.Peer reviewe