The squeaky wheel gets the grease: Violent civil unrest and global social assistance provision.

What are the contemporary determinates of social assistance provision? What is the role of contentious politics? Social assistance literature is dominated by economic and demographic accounts, which under-examine the possibility that governments extend social assistance to contain social unrest. We test factors associated with these "structuralist" and "political" theories on a new panel dataset which includes 54 OECD and emerging market countries between 2002 and 2015. The results indicate social assistance coverage has a significant positive relationship with riots. We explain this outcome as policymakers expanding social assistance as a means of containing violent civil unrest. This effect is more significant in emerging markets, suggesting that the domination of structural explanations is a result of sample bias toward the OECD. Finally, we find that governments consider World Bank social policy recommendations only insofar as there is violent unrest.

Computer-Aided Medical Image Annotation: Preliminary Results With Liver Lesions in CT.

The increasing volume of medical image data, as well as the need for multicenter data consolidation for big data analytics, require computer-aided medical image annotation (CMIA). Majority of the methods proposed so far do not exploit interdependencies between annotations explicitly. They further limit their annotations at a higher level than diagnostics and/or do not consider a standardized lexicon. A radiologist-in-the-loop semi-automatic CMIA system is proposed. It is based on a Bayesian tree structured model, linked to RadLex, and present preliminary results with liver lesions in computed tomography images. The proposed system guides the radiologist to input the most critical information in each iteration and uses a network model to update the full annotation online. The effectiveness of the system using this model-based interactive annotation scheme is shown by contrasting the domain-blind and domain-aware models. Preliminary results show that on average 7.50 (out of 29) manual annotations are sufficient for ${\text{95}}\%$ accuracy, which is ${\text{32.8}}\%$ less than the required manual effort when there is no guidance. The results also suggest that the domain-aware models perform better than the domain-blind models learned from data. Further analysis with larger datasets and in domains other than the liver lesions is needed.

A comprehensive statistical model for cell signaling.

Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses microarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data.

Shape-based hand recognition.

The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a pre-processing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.

A physical model for DT-MRI based connectivity map computation.

In this study we address the problem of extracting a robust connectivity metric for brain white matter. We defined the connectivity problem as an energy minimization task, by associating the DT-field to a physical system composed of nodes and springs, with their constants defined as a function of local structure. Using a variational approach we formulated a fast and stable map evolution, which utilizes an anisotropic kernel smoothing scheme equivalent to a diffusion PDE. The proposed method provides connectivity maps that correlate with normal anatomy on real patient data.