Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. The metadata provided with each image enables reasoning about location, time, sun angles, physical sizes, and other features when making predictions about objects in the image. Our dataset consists of over 1 million images from over 200 countries 1 . For each image, we provide at least one bounding box annotation containing one of 63 categories, including a "false detection" category. We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views. Our data, code, and pretrained models have been made publicly available.

This paper presents an approach to modeling the closure of the mitral valve using patient-specific anatomical information derived from 3D transesophageal echocardiography (3D TEE). Our approach uses physics-based modeling to solve for the stationary configuration of the closed valve structure from the patient-specific open valve structure, which is recovered using a user-in-the-loop, thin-tissue detector segmentation. The method utilizes a tensile shape finding approach based on energy minimization. This method is used to predict the aptitude of the mitral valve leaflets to coapt. We tested the method using ten intraoperative 3D TEE sequences by comparing (a) the closed valve configuration predicted from the segmented open valve, with (b) the segmented closed valve, taken as ground truth. Experiments show promising results, with prediction errors on par with 3D TEE resolution and with good potential for applications in pre-operative planning.

Randomized response techniques (RRT) are well-known as tools to procure trustworthy survey data on confidential issues. A review is attempted here of mostly published accounts on RRT covering qualitative and quantitative characters. Conflicting criteria of efficient estimation and protection of privacy are discussed. Infinite hypothetical and concrete finite population set-ups are treated separately.

In a federal form of government structure, the state-level governments generally receive supplementary budgetary resources from the central/federal government as support for the formers’ public expenditure activities. Such devolution of funds from the centre to the states takes the form of share of the revenue raised by central taxes and grants-in-aid. It is felt that such resource transfers should be made according to a policy based on the criteria of equity and efficiency. Formally, these criteria are defined with reference to individual state’s tax revenue collection relative to its taxable capacity. Formulation of a concrete transfer policy, however, crucially requires measures of individual state’s taxable capacity. Given an appropriate definition of a state’s taxable capacity, measurement of taxable capacity of states involves both conceptual and econometric issues. This paper proposes an econometric approach to the measurement of taxable capacity, which is similar to estimating a frontier production function using panel data. To illustrate the proposed method, it is applied to the panel data on annual state tax revenue and related variables of some selected Indian states for the period 1986-87 to 1996-97 and the relative taxable capacity and tax efforts of the states are compared.