UNDERSTANDING INVESTMENT INTENTION TOWARDS P2P LENDING: AN EMPIRICAL STUDY

P2P lending is an innovation of micro-financial operation pattern, which is mainly used to meet the petty loan and investment demands of small and micro businesses and individuals. Given the rapid development of P2P market, there is a pressing need to understand lenders’ initial investment intentions in P2P platform. Although there are some studies exploring the factors explaining P2P lenders’ investment intentions, none of research has been reported from the perspective of the platform. This study extended technology acceptance model with perceived risk and initial trust as a theoretical framework to examine the roles of individual factors and platform factors in determining P2P lenders’ initial investment intentions. This study suggests that risk appetite, trust propensity, perceived ease of use, perceived security assurance, perceived privacy protection, perceived reputation, third-party certification, perceived risk and initial trust together provide a strong explanation for initial investment intention in P2P lending. The finding of this research provided a theoretical foundation for future academic studies as well as practical guidance for rapid development of P2P platform

Fast generation of mock galaxy catalogues with COLA

We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance matching (SHAM) procedure with COLA simulations, utilizing only three free parameters: the scatter magnitude ($\sigma_{\rm scat}$) in SHAM, the initial redshift ($z_{\rm init}$) of the COLA simulation, and the time stride ($da$) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range $z\in [0.45, 0.55]$. We perform $\mathtt{GADGET}$ simulation and low-resolution COLA simulations with various combinations of $(z_{\rm init}, da)$, each using $1024^{3}$ particles in an $800~h^{-1}{\rm Mpc}$ box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal $\sigma_{\rm scat}$. We have found that by setting $z_{\rm init}=29$ and $da=1/30$, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4 to $20~h^{-1}{\rm Mpc}$, with a computational cost two orders of magnitude lower than that of the N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows similar performance between GADGET mock and COLA mock catalogues with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogues across different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogues for emulators and machine learning analyses in exploring the large-scale structure of the Universe.Comment: 24 pages, 14 figures, 4 table

M-OFDFT: Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. In this work, we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep-learning functional model. We build the essential nonlocality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those in training, which unleashes the appealing scaling for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry

Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

Multistage Effort and the Equity Structure of Venture Investment Based on Reciprocity Motivation

For venture capitals, it is a long process from an entry to its exit. In this paper, the activity of venture investment will be divided into multistages. And, according to the effort level entrepreneurs will choose, the venture capitalists will provide an equity structure at the very beginning. As a benchmark for comparison, we will establish two game models on multistage investment under perfect rationality: a cooperative game model and a noncooperative one. Further, as a cause of pervasive psychological preference behavior, reciprocity motivation will influence the behavior of the decision-makers. Given this situation, Rabin’s reciprocity motivation theory will be applied to the multistage game model of the venture investment, and multistage behavior game model will be established as well, based on the reciprocity motivation. By looking into the theoretical derivations and simulation studies, we find that if venture capitalists and entrepreneurs both have reciprocity preferences, their utility would have been Pareto improvement compared with those under perfect rationality

Preliminary research on the operation mode of virtual-real integration in fully-mechanized mining face based on industrial metaverse

The key to promoting intelligent construction is to integrate the digital twin technology form the operation mode of virtual and real integration. And the industrial metaverse based on digital twin is the future development direction of intelligent mining face. The concept of virtual and real integration operation mode of fully-mechanized mining face based on virtual reality-digital twin-cyber physical system-industrial metaverse is proposed. It has six connotation characteristics, such as display and off-line simulation, monitoring and auxiliary operation, online simulation and preview. It is an evolution process from low-level display simulation to high-level deep integration function. Finally, it have four abilities : the ability of reproduction mapping from real to virtual precision, the ability of reasoning and forecasting decision-making in virtual iteration, the ability of reproduction control from virtual to real, the ability of seamless cooperation between virtual and real human-computer, and the ability of lean management. The four capabilities of industrial metaverse and the key technologies to realize industrial metaverse are analyzed.Based on the existing monitoring, decision-making and control capabilities, AR remote assistance technology that can strengthen the cooperation ability between field operators and remote operators, robot cooperation technology that can strengthen the safety of operators, and virtual human technology that can use AI-driven operation in virtual space are integrated to build a hydraulic support adjusting experimental system based on industrial metaveise,and preliminary understanding of the application of industrial metaverse in coal mining