Investing with Fast Thinking

Using data from a major online peer-to-peer lending market, we document that investors follow a simple rule of thumb under time pressure: they rush to invest in loans with high interest rates without sufficiently examining credit ratings, which are freely available on the trading interface. Our experiments show that making credit rating information more salient “nudges” investors into better decisions. Firsthand experience matters for learning for non-informational reasons: An investor responds differently when observing a default of her own loan, relative to observing a default of another investor’s loan

Research on the evolution of innovation behavior of new generation entrepreneurs in different scenarios

Innovation of new generation entrepreneurs is crucial to the development of a country. Empirical research method can analyze the history and current situation, but it is difficult to reflect the dynamic process and evolution trend under different scenarios. In this paper, we adopt computational experiment method to model the decision-making process of new generation entrepreneurs. Multi-agent evolution model is constructed to simulate individual behavior of different types of new generation entrepreneurs under different scenarios. By the comparison of different results, it analyses the evolutionary rules of innovation behaviors and explores guidance policies to promote entrepreneurs’ innovation behavior and achieve better innovation performance. The experimental results show that although internal elements such as individual’s innovative spirit, innovative ability and cognition of social capital determine the innovation intention, the capital, technology and talent conditions are also very important for innovation implementation. New generation entrepreneurs with different risk preferences should objectively evaluate and treat innovation risks according to their own characteristics. This helps to reduce the negative impact of innovation risk on continuous innovation. Meanwhile, government should pay attention to establishing risk guarantee mechanism such as innovation insurance fund to promote the innovation of new generation entrepreneurs. First published online 17 April 202

A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences

Longitudinal atlas construction plays an important role in medical image analysis. Given a set of longitudinal images from different subjects, the task of longitudinal atlas construction is to build an atlas sequence which can represent the trend of anatomical changes of the population. The major challenge for longitudinal atlas construction is how to effectively incorporate both the subject-specific information and population information to build the unbiased atlases. In this paper, a novel groupwise longitudinal atlas construction framework is proposed to address this challenge, and the main contributions of the proposed framework lie in the following aspects: (1) The subject-specific longitudinal information is captured by building the growth model for each subject. (2) The longitudinal atlas sequence is constructed by performing groupwise registration among all the subject image sequences, and only one transformation is needed to transform each subject’s image sequence to the atlas space. The constructed longitudinal atlases are unbiased and no explicit template is assumed. (3) The proposed method is general, where the number of longitudinal images of each subject and the time points at which they are taken can be different. The proposed method is extensively evaluated on two longitudinal databases, namely the BLSA and ADNI databases, to construct the longitudinal atlas sequence. It is also compared with a state-of-the-art longitudinal atlas construction algorithm based on kernel regression on the temporal domain. Experimental results demonstrate that the proposed method consistently achieves higher registration accuracies and more consistent spatial-temporal correspondences than the compared method on both databases

ABSORB: Atlas building by self-organized registration and bundling

To achieve more accurate and consistent registration in an image population, a novel hierarchical groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this new framework, the global structure, i.e., the relative distribution of subject images is always preserved during the registration process by constraining each subject image to deform only locally with respect to its neighbors within the learned image manifold. To achieve this goal, two novel strategies, i.e., the self-organized registration by warping one image towards a set of its eligible neighbors and image bundling to cluster similar images, are specially proposed. By using these two strategies, this new framework can perform groupwise registration in a hierarchical way. Specifically, in the high level, it will perform on a much smaller dataset formed by the representative subject images of all subgroups that are generated in the previous levels of registration. Compared to the other groupwise registration methods, our proposed framework has several advantages: 1) It explores the local data distribution and uses the obtained distribution information to guide the registration; 2) The possible registration error can be greatly reduced by requiring each individual subject to move only towards its nearby subjects with similar structures; 3) It can produce a smoother registration path, in general, from each subject image to the final built atlas than other groupwise registration methods. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise registration methods, in terms of both registration accuracy and robustness

Intermediate templates guided groupwise registration of diffusion tensor images

Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image

Observation of intervalley quantum interference in epitaxial monolayer WSe2

Monolayer (ML) transition metal dichalcogenides (TMDs) have been attracting great research attentions lately for their extraordinary properties, in particular the exotic spin-valley coupled electronic structures that promise future spintronic and valleytronic applications1-3. The energy bands of ML TMDs have well separated valleys that constitute effectively an extra internal degree of freedom for low energy carriers3-12. The large spin-orbit coupling in the TMDs makes the spin index locked to the valley index, which has some interesting consequences such as the magnetoelectric effects in 2H bilayers13. A direct experimental characterization of the spin-valley coupled electronic structure can be of great interests for both fundamental physics and device applications. In this work, we report the first experimental observation of the quasi-particle interference (QPI) patterns in ML WSe2 using low-temperature (LT) scanning tunneling microscopy/spectroscopy (STM/S). We observe intervalley quantum interference involving the Q-valleys in the conduction band due to spin-conserved scattering processes, while spin-flip intervalley scattering is absent. This experiment establishes unequivocally the presence of spin-valley coupling and affirms the large spin-splitting at the Q valleys. Importantly, the inefficient spin-flip intervalley scattering implies long valley and spin lifetime in ML WSe2, which represents a key figure of merit for valley-spintronic applications.Comment: 15 pages, 4 figure

Dense network of one-dimensional mid-gap metallic modes in monolayer MoSe2 and their spatial undulations

We report the observation of a dense triangular network of one-dimensional (1D) metallic modes in a continuous and uniform monolayer of MoSe2 grown by molecular-beam epitaxy. High-resolution transmission electron microscopy and scanning tunneling microscopy and spectroscopy (STM/STS) studies show these 1D modes are mid-gap states at inversion domain boundaries. STM/STS measurements further reveal intensity undulations of the metallic modes, presumably arising from the superlattice potentials due to moire pattern and the quantum confinement effect. A dense network of the metallic modes with high density of states is of great potential for heterocatalysis applications. The interconnection of such mid-gap 1D conducting channels may also imply new transport behaviors distinct from the 2D bulk