Evaluating search and matching models using experimental data

This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context

Attributes and action recognition based on convolutional neural networks and spatial pyramid VLAD encoding

© Springer International Publishing AG 2017.Determination of human attributes and recognition of actions in still images are two related and challenging tasks in computer vision, which often appear in fine-grained domains where the distinctions between the different categories are very small. Deep Convolutional Neural Network (CNN) models have demonstrated their remarkable representational learning capability through various examples. However, the successes are very limited for attributes and action recognition as the potential of CNNs to acquire both of the global and local information of an image remains largely unexplored. This paper proposes to tackle the problem with an encoding of a spatial pyramid Vector of Locally Aggregated Descriptors (VLAD) on top of CNN features. With region proposals generated by Edgeboxes, a compact and efficient representation of an image is thus produced for subsequent prediction of attributes and classification of actions. The proposed scheme is validated with competitive results on two benchmark datasets: 90.4% mean Average Precision (mAP) on the Berkeley Attributes of People dataset and 88.5% mAP on the Stanford 40 action dataset

Gain curves in depletable food patches: A test of five models with European starlings

A forager's gain curve, the cumulative number of prey harvested from a patch as a function of time spent in the patch, influences optimal patch departure rules and interpretations of patch use data. We describe models of five different search strategies that yield different gain curves. Hence they would influence a forager's decision for patch departure differently and, consequently, how researchers should interpret patch residence times and giving-up densities. However, the models are virtually impossible to separate based on data of the gain curves per se. Therefore, we develop a series of diagnostic tests that can be used to discriminate among models. These tests consider how the instantaneous harvest rate within patches depends on initial (IPD) and current prey density (CPD) and search time. We applied these tests to data collected from European starlings (Sturnus vulgaris) foraging in experimental food patches of known initial prey density. The starlings' harvest rate increased with CPD, an indication of diminishing returns. However, a given CPD yielded a lower instantaneous intake rate the higher the IPD. Thus, the two models most commonly assumed in foraging studies, systematic and random search, can be unequivocally rejected. Instead, we found support for a new model, negative stirring, in which the starlings spoil their own future foraging returns by aggregating the remaining prey items as they search

Rapid growth of an intact human liver transplanted into a recipient larger than the donor

Two individuals undergoing orthotopic hepatic transplantation received livers from donors who were on average 10 kg smaller than themselves based on recipient ideal body weight. As a result, the donor livers in these 2 cases were 29%-59% smaller than would be expected had the donor liver and recipient been matched ideally. The liver grafts in the recipients steadily increased in size, as determined by serial computed tomography scanning, to achieve new volumes consistent with those that would have been expected in a normal individual of the recipient's size, sex, and age. Fasting plasma levels of amino acids, glucagon, insulin, and standard liver injury tests were monitored to determine which measure best reflected the changes observed in the size of the grafts over time. No relationship between the changes observed in any of these parameters and hepatic growth was apparent. In both cases, the liver increased in volume at a rate of ~70 ml/day. These data demonstrate that a small-for-size liver transplanted into a larger recipient increases in size at a rate of ~70 ml/day until it achieves a liver volume consistent with that expected given the recipient's size, age, and sex. © 1987

Gaussian tree constraints applied to acoustic linguistic functional data

Evolutionary models of languages are usually considered to take the form of trees. With the development of so-called tree constraints the plausibility of the tree model assumptions can be addressed by checking whether the moments of observed variables lie within regions consistent with trees. In our linguistic application, the data set comprises acoustic samples (audio recordings) from speakers of five Romance languages or dialects. We wish to assess these functional data for compatibility with a hereditary tree model at the language level. A novel combination of canonical function analysis (CFA) with a separable covariance structure provides a method for generating a representative basis for the data. This resulting basis is formed of components which emphasize language differences whilst maintaining the integrity of the observational language-groupings. A previously unexploited Gaussian tree constraint is then applied to component-by-component projections of the data to investigate adherence to an evolutionary tree. The results indicate that while a tree model is unlikely to be suitable for modeling all aspects of the acoustic linguistic data, certain features of the spoken Romance languages highlighted by the separable-CFA basis may indeed be suitably modeled as a tree.NS acknowledges the support of Economics and Social Science Research Council grant ES/I90427/1. JADA acknowledges the support of UK Engineering and Physical Sciences Research Council grant EP/K021672/2. JSC acknowledges the support of UK Arts and Humanities Research Council grant AH/M002993/1.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jmva.2016.09.01

A proposed testbed for detector tomography

Measurement is the only part of a general quantum system that has yet to be characterized experimentally in a complete manner. Detector tomography provides a procedure for doing just this; an arbitrary measurement device can be fully characterized, and thus calibrated, in a systematic way without access to its components or its design. The result is a reconstructed POVM containing the measurement operators associated with each measurement outcome. We consider two detectors, a single-photon detector and a photon-number counter, and propose an easily realized experimental apparatus to perform detector tomography on them. We also present a method of visualizing the resulting measurement operators.Comment: 9 pages, 4 figure

Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking

Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID