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

A Practice in Enrollment Prediction with Markov Chain Models

Enrollment projection is a critical aspect of university management, guiding decisions related to resource allocation and revenue forecasting. However, despite its importance, there remains a lack of transparency regarding the methodologies utilized by many institutions. This paper presents an innovative approach to enrollment projection using Markov Chain modeling, drawing upon a case study conducted at Eastern Michigan University (EMU). Markov Chain modeling emerges as a promising approach for enrollment projection, offering precise predictions based on historical trends. This paper outlines the implementation of Enhanced Markov Chain modeling at EMU, detailing the methodology used to compute transition probabilities and evaluate model performance. Despite challenges posed by external uncertainties such as the COVID-19 pandemic, Markov Chain modeling has demonstrated impressive accuracy, with an average difference of less than 1 percent between predicted and actual enrollments. The paper concludes with a discussion of future directions and opportunities for collaboration among institutions

Zero-Sum Stochastic Stackelberg Games

Zero-sum stochastic games have found important applications in a variety of fields, from machine learning to economics. Work on this model has primarily focused on the computation of Nash equilibrium due to its effectiveness in solving adversarial board and video games. Unfortunately, a Nash equilibrium is not guaranteed to exist in zero-sum stochastic games when the payoffs at each state are not convex-concave in the players' actions. A Stackelberg equilibrium, however, is guaranteed to exist. Consequently, in this paper, we study zero-sum stochastic Stackelberg games. Going beyond known existence results for (non-stationary) Stackelberg equilibria, we prove the existence of recursive (i.e., Markov perfect) Stackelberg equilibria (recSE) in these games, provide necessary and sufficient conditions for a policy profile to be a recSE, and show that recSE can be computed in (weakly) polynomial time via value iteration. Finally, we show that zero-sum stochastic Stackelberg games can model the problem of pricing and allocating goods across agents and time. More specifically, we propose a zero-sum stochastic Stackelberg game whose recSE correspond to the recursive competitive equilibria of a large class of stochastic Fisher markets. We close with a series of experiments that showcase how our methodology can be used to solve the consumption-savings problem in stochastic Fisher markets.Comment: 29 pages 2 figures, Appeared in NeurIPS'2

Fisher Markets with Social Influence

A Fisher market is an economic model of buyer and seller interactions in which each buyer's utility depends only on the bundle of goods she obtains. Many people's interests, however, are affected by their social interactions with others. In this paper, we introduce a generalization of Fisher markets, namely influence Fisher markets, which captures the impact of social influence on buyers' utilities. We show that competitive equilibria in influence Fisher markets correspond to generalized Nash equilibria in an associated pseudo-game, which implies the existence of competitive equilibria in all influence Fisher markets with continuous and concave utility functions. We then construct a monotone pseudo-game, whose variational equilibria and their duals together characterize competitive equilibria in influence Fisher markets with continuous, jointly concave, and homogeneous utility functions. This observation implies that competitive equilibria in these markets can be computed in polynomial time under standard smoothness assumptions on the utility functions. The dual of this second pseudo-game enables us to interpret the competitive equilibria of influence CCH Fisher markets as the solutions to a system of simultaneous Stackelberg games. Finally, we derive a novel first-order method that solves this Stackelberg system in polynomial time, prove that it is equivalent to computing competitive equilibrium prices via t\^{a}tonnement, and run experiments that confirm our theoretical results

Region-specific and activity-dependent regulation of SVZ neurogenesis and recovery after stroke.

Stroke is the leading cause of adult disability. Neurogenesis after stroke is associated with repair; however, the mechanisms regulating poststroke neurogenesis and its functional effect remain unclear. Here, we investigate multiple mechanistic routes of induced neurogenesis in the poststroke brain, using both a forelimb overuse manipulation that models a clinical neurorehabilitation paradigm, as well as local manipulation of cellular activity in the peri-infarct cortex. Increased activity in the forelimb peri-infarct cortex via either modulation drives increased subventricular zone (SVZ) progenitor proliferation, migration, and neuronal maturation in peri-infarct cortex. This effect is sensitive to competition from neighboring brain regions. By using orthogonal tract tracing and rabies virus approaches in transgenic SVZ-lineage-tracing mice, SVZ-derived neurons synaptically integrate into the peri-infarct cortex; these effects are enhanced with forelimb overuse. Synaptic transmission from these newborn SVZ-derived neurons is critical for spontaneous recovery after stroke, as tetanus neurotoxin silencing specifically of the SVZ-derived neurons disrupts the formation of these synaptic connections and hinders functional recovery after stroke. SVZ-derived neurogenesis after stroke is activity-dependent, region-specific, and sensitive to modulation, and the synaptic connections formed by these newborn cells are functionally critical for poststroke recovery

Meridional heat transport variability induced by mesoscale processes in the subpolar North Atlantic

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 9 (2018): 1124, doi:10.1038/s41467-018-03134-x.The ocean’s role in global climate change largely depends on its heat transport. Therefore, understanding the oceanic meridional heat transport (MHT) variability is a fundamental issue. Prevailing observational and modeling evidence suggests that MHT variability is primarily determined by the large-scale ocean circulation. Here, using new in situ observations in the eastern subpolar North Atlantic Ocean and an eddy-resolving numerical model, we show that energetic mesoscale eddies with horizontal scales of about 10–100 km profoundly modulate MHT variability on time scales from intra-seasonal to interannual. Our results reveal that the velocity changes due to mesoscale processes produce substantial variability for the MHT regionally (within sub-basins) and the subpolar North Atlantic as a whole. The findings have important implications for understanding the mechanisms for poleward heat transport variability in the subpolar North Atlantic Ocean, a key region for heat and carbon sequestration, ice–ocean interaction, and biological productivity.J.Z. was financially supported by the Postdoctoral Scholar Program at WHOI, with funding provided by the Ocean and Climate Change Institute. This work was also supported by the US National Science Foundation (OCE-1258823 and OCE-1634886), as well as by China’s national key research and development projects (2016YFA0601803), the National Natural Science Foundation of China (41521091 and U1606402), the Qingdao National Laboratory for Marine Science and Technology (2015ASKJ01), and the Fundamental Research Funds for the Central Universities (201424001 and 201362048)