The Three Giants of Bankruptcy Law in St. Louis

This Article reviews the significant roles three Washing University School of Law alumni had in shaping bankruptcy law—Jay L. Torrey, Walter D. Coles, and Barry Schermer. The Author notes Torrey’s contributions to the very first national permanent bankruptcy statute, Coles’s almost four decades of influence as a referee under the Bankruptcy Act of 1898, and Schermer’s contemporary contributions to bankruptcy law. The Article notes how these three individuals and their work span the almost 125 years of bankruptcy law across the country

When Doctors Kill Patients: Vital Organ Transplants

This paper attempts to discern exactly what is happening in some medical situations involving patients who are, in different ways, near death. In order to arrive at a correct moral evaluation of these practices, it is necessary to begin with a careful analysis of exactly what is happening, and then proceed to moral evaluation. This paper argues that declarations of death in many vital organ transplants are unjustified. Thus, probably there are killings occurring in these cases. However, there is no reason to think that these killings are morally unacceptable

Political leadership and conflict resolution: An African example

Challenges to post-conflict leadership in African states highlight the need for democratic capacity building, with clear participatory processes involving communities and the leadership as a necessary condition to mitigate new or resurrected conflicts. This article explores transformational leadership and how it relates to democratic capacity building in Rwanda. We argue that community capacity building through grassroots leadership is a necessary and sufficient ingredient for the development and sustenance of democracy in postconflict societies. Reconciliation through justice, political reforms including decentralisation, and women’s empowerment as critical variables in this process characterise a transformational agenda to gradually achieve stability at the grassroots. Despite dilemmas of justice and democracy, transformative leadership in Rwanda continues to evolve at both state and grassroots levels through processes based on indigenous knowledge and practices like gacaca and ingando to achieve the greater good of reconciliation

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201

Linking phytoplankton community metabolism to the individual size distribution

This is the final version of the article. Available from the publisher via the DOI in this recordQuantifying variation in ecosystem metabolism is critical to predicting the impacts of environmental change on the carbon cycle. We used a metabolic scaling framework to investigate how body size and temperature influence phytoplankton community metabolism. We tested this framework using phytoplankton sampled from an outdoor mesocosm experiment, where communities had been either experimentally warmed (+ 4 °C) for 10 years or left at ambient temperature. Warmed and ambient phytoplankton communities differed substantially in their taxonomic composition and size structure. Despite this, the response of primary production and community respiration to long- and short-term warming could be estimated using a model that accounted for the size- and temperature dependence of individual metabolism, and the community abundance-body size distribution. This work demonstrates that the key metabolic fluxes that determine the carbon balance of planktonic ecosystems can be approximated using metabolic scaling theory, with knowledge of the individual size distribution and environmental temperature.NERC. Grant Number: PASW06

Approximating Hit Rate Curves using Streaming Algorithms

A hit rate curve is a function that maps cache size to the proportion of requests that can be served from the cache. (The caching policy and sequence of requests are assumed to be fixed.) Hit rate curves have been studied for decades in the operating system, database and computer architecture communities. They are useful tools for designing appropriate cache sizes, dynamically allocating memory between competing caches, and for summarizing locality properties of the request sequence. In this paper we focus on the widely-used LRU caching policy. Computing hit rate curves is very efficient from a runtime standpoint, but existing algorithms are not efficient in their space usage. For a stream of m requests for n cacheable objects, all existing algorithms that provably compute the hit rate curve use space linear in n. In the context of modern storage systems, n can easily be in the billions or trillions, so the space usage of these algorithms makes them impractical. We present the first algorithm for provably approximating hit rate curves for the LRU policy with sublinear space. Our algorithm uses O( p^2 * log(n) * log^2(m) / epsilon^2 ) bits of space and approximates the hit rate curve at p uniformly-spaced points to within additive error epsilon. This is not far from optimal. Any single-pass algorithm with the same guarantees must use Omega(p^2 + epsilon^{-2} + log(n)) bits of space. Furthermore, our use of additive error is necessary. Any single-pass algorithm achieving multiplicative error requires Omega(n) bits of space