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.
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.