An Unsupervised Learning Model for Deformable Medical Image Registration

We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a convolutional neural network (CNN), and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201

An Experiential Comparative Tool for Board Games

In the field of game studies, contemporary board games have until now remained relatively unexplored. The recent years have allowed us to witness the emergence of the occasional academic texts focusing on board games – such as Eurogames (Woods, 2012), Characteristics of Games (Elias et al. 2013), and most recently Game Play: Paratextuality in Contemporary Board Games (Booth, 2015). The mentioned authors all explore board games from diverse viewpoints but none of these authors present a viable and practical analytical tool to allow us to examine and differentiate one board game from another. In this vein, this paper seeks to present an analytical comparative tool intended specifically for board games. The tool builds upon previous works (Aarseth et al. 2003; Elias et al. 2012; and Woods 2012) to show how four categories – rules, luck, interaction and theme – can interact on different levels to generate diverse gameplay experiences. Such a tool allows to score games objectively and separately in each of the categories to create a combined gameplay experience profile for each board game. Following this, the paper proceeds to present numerous practical examples of contemporary board games and how it can be used from a design perspective and an analytical perspective alike

Games for a new climate: experiencing the complexity of future risks

This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This report is a product of the Pardee Center Task Force on Games for a New Climate, which met at Pardee House at Boston University in March 2012. The 12-member Task Force was convened on behalf of the Pardee Center by Visiting Research Fellow Pablo Suarez in collaboration with the Red Cross/Red Crescent Climate Centre to “explore the potential of participatory, game-based processes for accelerating learning, fostering dialogue, and promoting action through real-world decisions affecting the longer-range future, with an emphasis on humanitarian and development work, particularly involving climate risk management.” Compiled and edited by Janot Mendler de Suarez, Pablo Suarez and Carina Bachofen, the report includes contributions from all of the Task Force members and provides a detailed exploration of the current and potential ways in which games can be used to help a variety of stakeholders – including subsistence farmers, humanitarian workers, scientists, policymakers, and donors – to both understand and experience the difficulty and risks involved related to decision-making in a complex and uncertain future. The dozen Task Force experts who contributed to the report represent academic institutions, humanitarian organization, other non-governmental organizations, and game design firms with backgrounds ranging from climate modeling and anthropology to community-level disaster management and national and global policymaking as well as game design.Red Cross/Red Crescent Climate Centr

Incentive Regulation for Electricity Networks

Elektrizitätswirtschaft, Anreizregulierung, Electric utility industry, Incentive regulation

Climate Engineering: Cost benefit and beyond

International efforts on abating climate change, focusing on reductions of greenhouse gas emissions, have thus far proved unsuccessful. This motivates exploration of other strategies such as climate engineering. We modify the Dynamic Integrated model of Climate and the Economy (DICE), and use it in a cost-benefit analysis of climate engineering specifically deposition of sulphur in the stratosphere. The model simulations show that climate engineering passes a cost-benefit test. The cost of postponing climate engineering by 20-30 years is relatively low. Going beyond these standard cost-benefit analyses, climate engineering may still fail. Voters may dislike the idea of climate engineering; they do not like the idea of tampering with nature, and their dislike stands independent of outcomes of cost-benefit analyses.Climate change; climate engineering; cost-benefit analyses; public choice.

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929

How Weyl stumbled across electricity while pursuing mathematical justice

It is argued that Weyl's theory of gravitation and electricity came out of `mathematical justice': out of the equal rights direction and length. Such mathematical justice was manifestly at work in the context of discovery, and is enough (together with a couple of simple and natural operations) to derive all of source-free electromagnetism. Weyl's repeated references to coordinates and gauge are taken to express equal treatment of direction and length