On Hearsay

I need to place the remarks that follow in context. And that means I need to acknowledge a number of heresies: I don’t like legal jargon; I don’t like the complexity of legal jargon; I don’t like the legal profession’s indifference to brevity; I don’t like the tendency of lawyers and judges always to be looking to the past for answers to novel questions; and I don’t consider law to be a science or remotely like a science. I want law to be simple and commonsensical and forward-looking. I take my judicial credo from a poem by the great Irish poet William Butler Yeats: “And I grew weary of the sun / Until my thoughts cleared up again, / Remembering that the best I have done / Was done to make it plain.” Or, in the words of another though lesser known poet, Ezra Pound, “MAKE IT NEW!

On Hearsay

I need to place the remarks that follow in context. And that means I need to acknowledge a number of heresies: I don’t like legal jargon; I don’t like the complexity of legal jargon; I don’t like the legal profession’s indifference to brevity; I don’t like the tendency of lawyers and judges always to be looking to the past for answers to novel questions; and I don’t consider law to be a science or remotely like a science. I want law to be simple and commonsensical and forward-looking. I take my judicial credo from a poem by the great Irish poet William Butler Yeats: “And I grew weary of the sun / Until my thoughts cleared up again, / Remembering that the best I have done / Was done to make it plain.” Or, in the words of another though lesser known poet, Ezra Pound, “MAKE IT NEW!

John R. Wooden, Stephen R. Covey and Servant Leadership: A Commentary

Ralph Waldo Emerson observed that consistency was the hobgoblin of little minds and that insight well underscores the challenges associated with accurately describing the leadership philosophy (and actions) of anyone - from coaches like John Wooden, leadership gurus like Steve Covey and Peter Drucker, philosophers like Aristotle and Socrates, or you and me. They are not always perfectly consistent, evolving, and even changing as situations and setting shift in both anticipated and unpredictable ways

Foreword

[Excerpt] I first met John Hutson in 2005 when I was the Executive Director of Human Rights First. Human Rights First was working to challenge the use of cruelty by U.S. officials in security detentions, practices that had been used at Abu Ghraib, Guantanamo, and elsewhere after the attacks on September 11, 2001. We knew that the human rights community acting alone could not influence this debate, so we went looking for national security experts to help us challenge these practices

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data -- as commonly encountered in robotics applications -- and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.Comment: Published in The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Video: https://youtu.be/cdeWCpfUGWc, Code: http://mrg.robots.ox.ac.uk/mrg_people/peter-ondruska

Social Costs of Monopoly and Regulation

When an industry is monopolized, price rises above and output falls below the competitive level. Those who continue to buy the product at the higher price suffer a loss, but this loss is exactly offset by the additional revenue that the monopolist obtains by charging the higher price. Other consumers, who are deflected by the higher price to substitute goods, suffer a loss, that is not offset by gains to the monopolist. This is the "deadweight loss" from monopoly, and in conventional analysis the only social cost of monopoly. The loss suffered by those who continue to buy the product at the higher cost is regarded merely as a transfer from consumers to owners of the monopoly seller and has not previously been factored into the social costs of monopoly. However, the existence of an opportunity to obtain monopoly profits will attract resources into efforts to obtain monopolies, and the opportunity costs of those resources are social costs of monopoly, too. Although the tendency of monopoly rents to be transformed into costs is no longer a novel insight, its implications both for the measurement of the aggregate social costs of monopoly and for a variety of other important issues relating to monopoly and public regulation (including tax policy) continue to be ignored. The present paper is an effort to rectify this neglect. Part I introduces the material. Part II presents a simple model of the social costs of monopoly, conceived as the sum of the deadweight loss and the additional loss resulting from the competition to become a monopolist. Part III uses the model to estimate the social costs of monopoly in the United States, and the social benefits of antitrust enforcement. Part IV explores the implications of the analysis for a variety of issues relating to monopoly and public regulation, such as public policy toward price discrimination and the choice between income and excise taxation.

A model to compare performance of space and ground network support of low-Earth orbiters

This article compares the downlink performance in a gross average sense between space and ground network support of low-Earth orbiters. The purpose is to assess what the demand for DSN support of future small, low-cost missions might be, if data storage for spacecraft becomes reliable enough and small enough to support the storage requirements needed to enable support only a fraction of the time. It is shown that the link advantage of the DSN over space reception in an average sense is enormous for low-Earth orbiters. The much shorter distances needed to communicate with the ground network more than make up for the speedup in data rate needed to compensate for the short contact times with the DSN that low-Earth orbiters have. The result is that more and more requests for DSN-only support of low-Earth orbiters can be expected