<abstract> <p>The fairness preference in the principal-agent relationship is a vital factor that can even determine the success or failure of one program. Under normal circumstances, the capital invested by VC is often several times that of EN, which is one of the reasons for the profit gap between EN and VC. Therefore, when establishing a principal-agent model with fairness preferences, it is necessary to project the utility of VC to the level of EN and compare it with the utility of venture entrepreneurs, which will better reflect the profit gap between the two. On the basis of previous studies, this paper considers the amount of contribution of the participants, builds four principal-agent models to find the optimal distribution of income between the Venture Entrepreneur (EN) and the Venture Capital (VC) in a venture capital investment program, two without fairness preference and others with fairness preference. After the simulation we confirm that the fairness preference coefficient exerts a great impact on the distribution of income in both situations where information is symmetric and asymmetric, and a strong fairness preference will lead to a greater net profit gap between the EN and the VC. Thus, the EN should carefully choose the level of his efforts to realize the maximum return for him. In the case of information asymmetry, EN's optimal effort level decreases as the fairness preference coefficient increases.This will affect project revenue. And then affect the VC income.</p> </abstract>

An efficient algorithm is proposed for detecting vertical corner lines on buildings in quasi-Manhattan world scene. The vertical corner lines are useful geometric information of buildings in urban scene, whose importance is similar to that of corners on planar curves for various applications in computer vision. The algorithm employs a bottom-up and step-by-step pipeline processing procedure. First, straightline segments are extracted as low-level features from the input building image by using a fast and accurate line segment detector. Secondly, the extracted straight-line segments are clustered to groups and associated to different vanishing points, which are computed by a J-Linkage estimator and act as the mid-level features. Finally, high-level features, vertical corner lines, are detected with several geometric constraints in quasi-Manhattan world. Experimental results demonstrate the efficiency of the new algorithm.

Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion detection, and rare disease diagnosis. Rare category analysis (RCA) refers to the task of detecting, characterizing, and comprehending the behaviors of minority classes in a highly-imbalanced data distribution. While the vast majority of existing work on RCA has focused on improving the prediction performance, a few fundamental research questions heretofore have received little attention and are less explored: How confident or uncertain is a prediction model in rare category analysis? How can we quantify the uncertainty in the learning process and enable reliable rare category analysis?To answer these questions, we start by investigating miscalibration in existing RCA methods. Empirical results reveal that stateof-the-art RCA methods are mainly over-confident in predicting minority classes and under-confident in predicting majority classes. Motivated by the observation, we propose a novel individual calibration framework, named CaliRare, for alleviating the unique challenges of RCA, thus enabling reliable rare category analysis. In particular, to quantify the uncertainties in RCA, we develop a node-level uncertainty quantification algorithm to model the overlapping support regions with high uncertainty; to handle the rarity of minority classes in miscalibration calculation, we generalize the distribution-based calibration metric to the instance level and propose the first individual calibration measurement on graphs named Expected Individual Calibration Error (EICE). We perform extensive experimental evaluations on real-world datasets, including rare category characterization and model calibration tasks, which demonstrate the significance of our proposed framework.

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This paper studies the consumer strategy behavior impact on earnings of enterprises perishable products. It mainly considers the impact on the income from the income and cost analysis, and other factors how to influence the two factors, thus indirectly affect the retailer's cost. The conclusion of the study laid the theoretical foundation for the dynamic pricing for perishable products.