Simple and Bias-Corrected Matching Estimators for Average Treatment Effects

Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. In this article, we develop a new framework to analyze the properties of matching estimators and establish a number of new results. First, we show that matching estimators include a conditional bias term which may not vanish at a rate faster than root-N when more than one continuous variable is used for matching. As a result, matching estimators may not be root-N-consistent. Second, we show that even after removing the conditional bias, matching estimators with a fixed number of matches do not reach the semiparametric efficiency bound for average treatment effects, although the efficiency loss may be small. Third, we propose a bias-correction that removes the conditional bias asymptotically, making matching estimators root-N-consistent. Fourth, we provide a new estimator for the conditional variance that does not require consistent nonparametric estimation of unknown functions. We apply the bias-corrected matching estimators to the study of the effects of a labor market program previously analyzed by Lalonde (1986). We also carry out a small simulation study based on Lalonde's example where a simple implementation of the biascorrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias and root-mean-squared-error. Software for implementing the proposed estimators in STATA and Matlab is available from the authors on the web.

Student Attendance System Based on Fingerprint Recognition and One to Many Matching

Our project aims at designing an student attendance system which could effectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identification. For student identification, a fingerprint recognition based identification system is used. Fingerprints are considered to be the best and fastest method for biometric identification. They are secure to use, unique for every person and does not change in one's lifetime. Fingerprint recognition is a mature field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. It was our responsibility to improve the fingerprint identification system for implementation on large databases e.g. of an institute or a country etc. In this project, many new algorithms have been used e.g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identification system which is faster in implementation than any other available today in the market. Although we are using this fingerprint identification system for student identification purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identification was done with existing identification technique i.e. one to one identification on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn^2). The fingerprint identification system was tested on FVC2004 and Verifinger databases

Attendance Management System for Industrial Worker using Finger Print Scanner

Attendance management is the act of managing attendance or presence in a work setting, which maximizes and motivates employee attendance thereby minimizing loss. Not only does it affect productivity, it can cost the company profits or even additional contracts. For the industrial sector attendance management system can develop alacrity among the workers to work regularly and also help them to motivate their co- worker to attend work regularly. Fingerprints are considered to be the best and fastest method for biometric identification. They are secure to use, unique for every person and do not change in one's lifetime. Fingerprint recognition is a mature field to-day, but still identifying individual from a set of enrolled fingerprints is a time taking process. This paper illustrates improvement of attendance management system based on fingerprint identification for implementation on large databases e.g. of an industry or a garments factory etc. In this project, many new algorithms have been used e.g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms a new attendance management system has been developed which is faster and cheaper in implementation than any other available today in the market

Radial-Distance Based Shape Descriptor for Image Retrieval

Shape analysis is used in many application fields including emerging virtual environments or 3D model market, security applications, medical imaging and many more. A Shape descriptor (or Signature) is the simplified representation of images. These shape descriptors carry important image information to store and makes easy the comparing of different shapes. The proposed shape descriptor is based on radial-distances. The type of shape descriptor used here is contour-based shape descriptor. Distance from center of bounding box encompassing the edge image to farthest point on the edge is calculated. A circle is drawn using the distance mentioned above as radius. The ratio of Euclidean distances of an edge pixel and the radius is considered as a feature. A set of such ratios for all the edge pixels forms a shape descriptor. The descriptor is divided into segments so as to avoid global distribution. A rotational matching scheme ensures invariance to rotation. As the computation of feature set is compact, implementation of this method results in quick retrieval of images invariant to scaling, translation and rotation

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi

Distributed Channel Assignment in Cognitive Radio Networks: Stable Matching and Walrasian Equilibrium

We consider a set of secondary transmitter-receiver pairs in a cognitive radio setting. Based on channel sensing and access performances, we consider the problem of assigning channels orthogonally to secondary users through distributed coordination and cooperation algorithms. Two economic models are applied for this purpose: matching markets and competitive markets. In the matching market model, secondary users and channels build two agent sets. We implement a stable matching algorithm in which each secondary user, based on his achievable rate, proposes to the coordinator to be matched with desirable channels. The coordinator accepts or rejects the proposals based on the channel preferences which depend on interference from the secondary user. The coordination algorithm is of low complexity and can adapt to network dynamics. In the competitive market model, channels are associated with prices and secondary users are endowed with monetary budget. Each secondary user, based on his utility function and current channel prices, demands a set of channels. A Walrasian equilibrium maximizes the sum utility and equates the channel demand to their supply. We prove the existence of Walrasian equilibrium and propose a cooperative mechanism to reach it. The performance and complexity of the proposed solutions are illustrated by numerical simulations.Comment: submitted to IEEE Transactions on Wireless Communicaitons, 13 pages, 10 figures, 4 table

Crafting Next Generation Eco-Label Policy

Eco-labels present a promising policy tool in the effort to achieve sustainable consumption. Many questions remain, however, about the extent to which eco-labels can contribute to sustainability efforts and how to maximize their effectiveness. This Article deploys research from evolutionary psychology, behavioral law and economics, and norm theory to offer specific insights for the design and implementation of eco-labels to enhance their influence on sustainable consumer choice. Notably, this research suggests possibilities for eco-labels to shape or expand consumer preferences for green goods, and thereby enhance eco-label influence on consumer behavior by extending it beyond eco-minded consumers. We suggest that public exposure of the label (so that people see it) and the exposure of the purchasing behavior (so that other people can see that you have bought the product) are key elements to the success of eco-labels--the social context around product purchasing may be as important as the eco-label itself. We recommend that behavioral insights be used to improve eco-labeling as traditionally understood by incorporating knowledge about behavioral tendencies into label design so as to allow for more accurate matching of consumers\u27 preexisting environmental preferences to eco-labeled goods, and develop next-generation eco-labeling policy with the potential to significantly expand the market for eco-labeled goods. Specifically, 1) Eco-labels could be purposefully designed and implemented to attract consumers motivated by social norms; 2) Eco-labels could appeal to a wider range of abstract norm alternate more broadly or locally accepted and strong abstract that are stronger and/or more broadly accepted or locally-salient; and 3) Eco-labels could highlight private, near and near-term benefits