Oligopsony Distortions and Welfare Implications of Trade

While imperfect competition in the output market has garnered extensive focus in the new trade theory literature, input market imperfection has received considerably less attention. Since market power in input purchase has been growing in recent years, it is worth examining the welfare implications of trade arising from oligopsony power. We develop a model consisting of two final goods, one intermediate good, and two primary factors (capital and labor). One final good and the intermediate good employ primary factors, whereas the other final good uses labor and the intermediate input. All markets operate under perfect competition except for the intermediate input, which is oligopsonistic. Using this model, we show that oligopsony can lead to some anomalies such as an increase in the oligopsony output, reward to the intensive-factor in the oligopsony sector, national welfare, and deterioration of terms of trade, but it always decreases the reward to the intermediate input.Marketing,

Employee Needs and Job-Related Opportunities: From The Person-Environment Fit Framework

The work environment presents employees with many opportunities for meaningful experiences associated with personal and professional growth. When these opportunities match what employees need, they have favorable attitudes toward the job and the organization. My dissertation addresses questions related to work design, employees’ experiences of leadership and leaders’ attitudes towards their own leadership behaviors through the lens of Person-Environment (P-E) Fit theory. In the first part of my dissertation, I revisited the Job Characteristics Model (JCM) which predicted positive attitudes and behavior when jobs were designed to increase five key job characteristics (variety; autonomy; feedback, identity, and significance). I re-conceptualized GNS as variation in employees’ needs for the five job characteristics by applying the person-environment fit (P-E) framework to the JCM The second part of my dissertation suggested that visionary leadership might also engender negative effects because it required employees’ exceptional and relentless persistence and effort. I examined the joint effect of the visionary leadership employees’ receive and the amount of visionary leadership employees’ need on their work attitudes. Core self-evaluation (CSE) was predicted to moderate the relationship between visionary leadership needed and received on work attitudes. The final part of my dissertation examined the effects of leadership on the leaders themselves. I proposed that leadership roles might also be harmful for leaders because the increased responsibility for subordinates and their performance requires them to enact leadership behaviors that deviate from what is comfortable, increasing their work overload and strain. Results showed that as supplies deviated from needs for both deficiency and excess, employees’ outcomes (attitudes, well-being) decreased; when the needed amounts of job-related opportunities s were matched with the supplied amounts, outcomes were most positive. Moreover when needs and supplies were both high vs. when both were low, outcomes were more positive. My dissertation demonstrated that desirable behaviors and experience can have negative effects on both employees and leaders when individual variations in employees’ and leaders’ needs are not considered. My findings suggest ways in which common advice to leaders is associated with unfavorable outcomes for employees, leaders, and their organizations

Deep Network Flow for Multi-Object Tracking

Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features. In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, which outperform hand-crafted costs in all settings. The integration and combination of various sources of inputs becomes easy and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.Comment: Accepted to CVPR 201

Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses

We present an approach to multi-target tracking that has expressive potential beyond the capabilities of chain-shaped hidden Markov models, yet has significantly reduced complexity. Our framework, which we call tracking-by-selection, is similar to tracking-by-detection in that it sepa-rates the tasks of detection and tracking, but it shifts tempo-ral reasoning from the tracking stage to the detection stage. The core feature of tracking-by-selection is that it reasons about path hypotheses that traverse the entire video instead of a chain of single-frame object hypotheses. A traditional chain-shaped tracking-by-detection model is only able to promote consistency between one frame and the next. In tracking-by-selection, path hypotheses exist across time, and encouraging long-term temporal consistency is as sim-ple as rewarding path hypotheses with consistent image fea-tures. One additional advantage of tracking-by-selection is that it results in a dramatically simplified model that can be solved exactly. We adapt an existing tracking-by-detection model to the tracking-by-selection framework, and show im-proved performance on a challenging dataset (introduced in [18]). 1

Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .Comment: Accepted by CVPR 202

Understanding Complex Human Behaviour in Images and Videos.

Understanding human motions and activities in images and videos is an important problem in many application domains, including surveillance, robotics, video indexing, and sports analysis. Although much progress has been made in classifying single person's activities in simple videos, little efforts have been made toward the interpretation of behaviors of multiple people in natural videos. In this thesis, I will present my research endeavor toward the understanding of behaviors of multiple people in natural images and videos. I identify four major challenges in this problem: i) identifying individual properties of people in videos, ii) modeling and recognizing the behavior of multiple people, iii) understanding human activities in multiple levels of resolutions and iv) learning characteristic patterns of interactions between people or people and surrounding environment. I discuss how we solve these challenging problems using various computer vision and machine learning technologies. I conclude with final remarks, observations, and possible future research directions.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99956/1/wgchoi_1.pd