Tax Exceptionalism: Wanted Dead or Alive
Tax law has just not been the same since January 2011. Did Congress pass earthshaking legislation affecting the Internal Revenue Code? Did the IRS dramatically change regulations? If only it were that exciting. Instead, eight jurists sitting at One First Street in our nation’s capital transformed tax law in a less bloody, but no less profound, way. The thought must have gone through many a tax mind – is tax exceptionalism dead?
Tax Myopia, or Mamas Don't Let Your Babies Grow Up to Be Tax Lawyers
Two related myths have a remarkable currency among law students, lawyers and the general public. The first myth is that tax lawyers are somehow different from other lawyers. Part I of this Article documents how this misperception pervades law schools, the legal profession, and the popular culture. Although this Part chronicles the disparagement of tax lawyers in a light-hearted fashion (as suggested by the reference to the Willie Nelson song in the title), the Article focuses on the serious consequences caused by the related second myth that tax law is somehow different from other areas of the law. As suggested by the reference to tax myopia in the title, the Article contends that tax law too often is mistakenly viewed by lawyers, judges, and law professors as a self-contained body of law. Part II of the Article explains how this misperception has impaired the development of tax law by ignoring insights from other areas of law that should inform the tax debate. Similarly, other areas of law have been impoverished by the failure to consider how tax law can enrich their development. The Article advocates a synergistic relationship between tax and nontax law through which each benefits from the insights of the other. Part III of the Article addresses this theme through several fundamental issues concerning the allocation of legislative, executive, and judicial power in our constitutional system. The Article first injects various statutory construction and legislative process theories developed in the nontax area into the debate over whether the complexity of the Internal Revenue Code requires that tax legislative history be treated differently from its nontax counterparts. The Article then considers how recent Supreme Court administrative law decisions and the statutory reenactment doctrine affect the amount of deference that courts should give to the Treasury Department's interpretation of tax statutes. Finally, the Article examines how the empirical and nontax models of the tax litigation system should inform the litigant's choice of the appropriate tax forum, Congress's allocation of tax jurisdiction, and the federal courts' application of state law in tax controversies. Although I offer my views on these issues, the thrust of the Article is its advocacy of a cross-pollination approach, rather than a definitive resolution of these structural concerns that have bedeviled tax and nontax law for decades. By replacing the myopic vision of the tax law with an appreciation of the symbiotic relationship between tax and nontax law, the ultimate resolution of these tax issues will be facilitated by nontax learning, and tax, in turn, will provide a useful laboratory within which to test and refine these nontax principles.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.