Essays on Selling Mechanisms with a Focus on Auctions

This thesis examines auctions as a selling mechanism in various market environments. There are in total three chapters. The rst two build theoretical frameworks to analyze equilibria and to investigate expected revenues in multiple period settings, while the last chapter is an empirical study on the pre-auction estimates in art auctions. The rst chapter studies sequential auctions with endogenous supply. In the setting of the model, whether more units of goods are to be auctioned is contingent on the performance of the current auction. More speci cally, two sellers, each of whom owns one unit of an identical item, but have di erent opportunity costs, face a xed number of bidders, each of whom has unit demand and private valuation. One unit is to be sold for certain by one of the sellers; the winner of the rst auction exits and the winning bid becomes common knowledge; the second seller then may o er her item depending on the price achieved in the rst auction. In other words, the sellers together endogenize the supply decision based on the information on the demand side revealed in the rst auction. We consider sequential auctions in both rst-price and second-price formats. We give conditions for a symmetric pure strategy equilibrium to exist for each auction format. Under the assumption of uniform distributions, we explicitly solve for the equilibrium. We show that the second-price auction format provides a higher expected payo to both sellers than the rst-price format, and that both sellers prefer that the low-cost seller conducts the rst auction while the high-cost seller conducts the contingent second auction. In addition, we conclude that the expected price declines if the second auction is held due to the uncertainty of the availability of the second unit of the item. This nding provides one possible explanation for the `declining price anomaly', a well-documented phenomenon that puzzles auction theorists. The second chapter investigates a market in which buyers with interdependent valuation arrive over time. A seller sells a single item with a second-price auction in such a market and wishes to achieve maximum ex ante pro t. Meanwhile, although the total number of buyers is xed, they arrive one by one in an exogenous sequence. We attempt to answer the following two questions in this setting: What is the optimal timing of auction for the seller? How well does the auction perform compared to another simple selling mechanism, posted price sale? We rst point out that with interdependent buyer valuation holding an auction that includes all buyers may not be pro t maximizing, even after we have assumed that everyone is su ciently patient and is not discounting the future transactions. We analyze both the e ciency loss and the improvement on the `winner's curse' concern that are associated with an early auction. Using uniform distribution examples and numerical solutions we show that under some conditions the optimal timing of auction can be earlier than the time when all buyers arrive. We conclude that the relative importance of the signals of other buyers plays a central role in characterizing the equilibrium. In the second part of the chapter, we compare auction and posted price sale. We argue that posted price sale is likely to be ex ante more pro table than auction when the total number of buyers is small and the signals of others are important to buyers. In the nal chapter, we empirically examine the pre-auction estimates in art auctions, which are given by art experts hired by auction houses. We rst address the question of whether these pre-auction estimates are unbiased indicators of the actual hammer prices, a question raised and studied by many economists in the literature. We have collected 3,923 auction records of paintings by a well-de ned group of American artists between 1987 and 2018. The sample size is large enough to allow us to be the rst to adopt a nonparametric approach to test the bias in the pre-auction estimates. Since the pre-auction estimates include a low estimate and a high estimate, we use the arithmetic mean of the low and high estimates in the regression model for the test. After correcting for the sample selection bias, we nd evidence of bias in the arithmetic mean as a predictor of the hammer price, although the size of the bias is small. Then we criticize the use of the arithmetic mean to test the bias of the pre-auction estimates, an approach adopted by all previous studies in the literature to our best knowledge. We build a simple model to illustrate that the distribution of the hammer price is left-skewed even if the distribution of buyer valuation is symmetric. If the pre-auction estimates are considered as a con dence interval for the hammer price, then we show that under some conditions art experts have incentives to place the low estimate closer to the mean of the hammer price. As a result, using the arithmetic mean of the low and high estimates is misleading, resulting in an upward bias. We also nd regression results that support our argument. We conclude that empirically the low estimate should be given more weight compared to the high estimate when one tries to interpret the pre-auction estimates and to predict the hammer price

Adaptive Graphical Model Network for 2D Handpose Estimation

In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image. The AGMN consists of two branches of deep convolutional neural networks for calculating unary and pairwise potential functions, followed by a graphical model inference module for integrating unary and pairwise potentials. Unlike existing architectures proposed to combine DCNNs with graphical models, our AGMN is novel in that the parameters of its graphical model are conditioned on and fully adaptive to individual input images. Experiments show that our approach outperforms the state-of-the-art method used in 2D hand keypoints estimation by a notable margin on two public datasets.Comment: 30th British Machine Vision Conference (BMVC

Association of opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with nicotine dependence

Background and Object Whether opioid-receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) is associated with nicotine dependence is controversial. We analyzed the combined results from published studies of this possibility. Methods: Literature reviews were performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Web of Science, Chinese National Science Infrastructure (CNKI), PubMed, Embase and Google Scholar database searches using MeSH terms were conducted to find all relevant researches up to October 2016. Odds ratios (ORs) and their 95% confidence intervals (95% CIs) were calculated in allele, homozygote, heterozygote, dominant and recessive models. Ethnicity-specific subgroup meta-analysis, heterogeneity, sensitivity analysis and publication bias were considered. Results: Seven eligible studies with 3313 patients were included. The ORs in the five genetic models mentioned above were 1.000 (95% CI: 0.906, 1.104; p = 0.999), 1.032 (95% CI: 0.771, 1.381; p = 0.834), 0.963 (95% CI: 0.799, 1.162; p = 0.696), 1.006 (95% CI: 0.916, 1.104; p = 0.907), 0.967 (95% CI: 0.715, 1.309; p = 0.830), respectively. Only in dominant model is the association significant. Upon ethnicity-specific subgroup analysis, there is no statistical significance. Conclusion: OPRM1-A118G polymorphism (A>G) is not associated with nicotine dependence

Survivin overexpression is potentially associated with pituitary adenoma invasiveness

Background and objective Survivin is an inhibitor of apoptosis. Its role in guiding the treatment of neoplasms, making diagnosis and predicting prognosis has been reported. However, there is little information on the implications and uses of survivin in predicting pituitary adenoma (PA) invasiveness. Existing information is unclear and controversial. We thus conducted this meta-analysis to explore whether the surviving expression levels in invasive PAs (IPA) and regular PAs are different or not. We considered both non-secreting and secreting tumors together. Methods: A global search strategy was systematically applied among five databases including Cochrane Library, Embase, PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) up to June 18th, 2017. With a specially designed form including PAs’ invasive features, etc., data was collected. The included studies should present the data representing the surviving levels in IPA groups and regular PA groups, respectively. Differences were expressed as standard mean differences (SMDs) or odds ratios (ORs) with 95% confidence interval (CI). To estimate the heterogeneities, I2 test, Cochran's Q-test and Galbr figure were all conducted. A sensitivity-analysis and potential-publication bias were also performed. Results: In the present meta-analysis, 9 studies containing 489 patients were included. Seven studies with dichotomous-data showed that survivin over-expression in PA tissue was closely associated with a high invasive tendency (OR 6.226, 95% CI 3.970, 9.765; P<0.001), but 2 continuous-data studies revealed that there was no significant association (SMD −5.043, 95% CI-10.965, 0.878; p=0.095). A sensitivity-analysis suggested a statistically stable result. We did not find publication bias. Conclusion: We suggest that survivin overexpression is potentially associated with PA invasiveness. More research based on medical big data is needed to confirm this finding

PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation

Recently, the vision transformer and its variants have played an increasingly important role in both monocular and multi-view human pose estimation. Considering image patches as tokens, transformers can model the global dependencies within the entire image or across images from other views. However, global attention is computationally expensive. As a consequence, it is difficult to scale up these transformer-based methods to high-resolution features and many views. In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens. Furthermore, we extend our PPT to multi-view human pose estimation. Built upon PPT, we propose a new cross-view fusion strategy, called human area fusion, which considers all human foreground pixels as corresponding candidates. Experimental results on COCO and MPII demonstrate that our PPT can match the accuracy of previous pose transformer methods while reducing the computation. Moreover, experiments on Human 3.6M and Ski-Pose demonstrate that our Multi-view PPT can efficiently fuse cues from multiple views and achieve new state-of-the-art results.Comment: ECCV 2022. Code is available at https://github.com/HowieMa/PP

Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

Reconstructing 3D hand meshes from monocular RGB images has attracted increasing amount of attention due to its enormous potential applications in the field of AR/VR. Most state-of-the-art methods attempt to tackle this task in an anonymous manner. Specifically, the identity of the subject is ignored even though it is practically available in real applications where the user is unchanged in a continuous recording session. In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject. We demonstrate the importance of the identity information by comparing the proposed identity-aware model to a baseline which treats subject anonymously. Furthermore, to handle the use case where the test subject is unseen, we propose a novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject. Experiments on two large scale public datasets validate the state-of-the-art performance of our proposed method.Comment: ECCV 2022. Github https://github.com/deyingk/PersonalizedHandMeshEstimatio

Diffeomorphic Image Registration with Neural Velocity Field

Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to learn the transformation directly, achieve fast inference, but face challenges in accuracy due to the difficulties in capturing the small local deformations and generalization ability. Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations. A multilayer perceptron (MLP) with sinusoidal activation function is used to represent the continuous velocity field and assigns a velocity vector to every point in space, providing the flexibility of modeling complex deformations as well as the convenience of optimization. Moreover, we propose a cascaded image registration framework (Cas-DNVF) by combining the benefits of both optimization and learning based methods, where a fully convolutional neural network (FCN) is trained to predict the initial deformation, followed by DNVF for further refinement. Experiments on two large-scale 3D MR brain scan datasets demonstrate that our proposed methods significantly outperform the state-of-the-art registration methods.Comment: WACV 202

Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR begins with explicit deformations of template meshes to obtain coarsely reconstructed cortical surfaces, based on which the oriented point clouds are estimated for the subsequent differentiable poisson surface reconstruction. By doing so, our method unifies explicit (oriented point clouds) and implicit (indicator function) cortical surface reconstruction. Compared to explicit representation-based methods, our hybrid approach is more friendly to capture detailed structures, and when compared with implicit representation-based methods, our method can be topology aware because of end-to-end training with a mesh-based deformation module. In order to address topology defects, we propose a new topology correction pipeline that relies on optimization-based diffeomorphic surface registration. Experimental results on three brain datasets show that our approach surpasses existing implicit and explicit cortical surface reconstruction methods in numeric metrics in terms of accuracy, regularity, and consistency