Innocence in the Eyes of the Internal Revenue Service

The filing of a joint return by a married couple usually results in overall tax savings. The downside of filing jointly, however, is that the parties to the joint return are together, and individually, responsible for the accuracy of the return, for the full tax liability, and for any interest or penalty relating to the return. This so-called joint and several liability extends not only to the tax shown on the return, but also to any tax that should have been but was not reported on the return. Consequently, each spouse becomes responsible for the tax infractions of the other. Because the joint and several liability rule was found to be overly harsh, since 1971, the tax law has contained a provision under which a spouse could seek relief from liability if he or she met the definition of an innocent spouse. As it turned out, however, the requirements that had to be met in order to obtain innocent spouse relief were very stringent and failed to protect spouses, in most cases the wife or ex-wife, where fairness dictated that relief was warranted. Accordingly, in 1998, Congress significantly liberalized the rules for obtaining innocent spouse relief by enacting a new section of the Internal Revenue Code and expunging the prior provisions. This paper analyzes the new Code section and also analyzes a recent Revenue Procedure, issued in 2000, that details the requirements for obtaining innocent spouse relief under one of the provisions of the new law, namely, equitable relief

Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment

This paper introduces the unsupervised assignment flow that couples the assignment flow for supervised image labeling with Riemannian gradient flows for label evolution on feature manifolds. The latter component of the approach encompasses extensions of state-of-the-art clustering approaches to manifold-valued data. Coupling label evolution with the spatially regularized assignment flow induces a sparsifying effect that enables to learn compact label dictionaries in an unsupervised manner. Our approach alleviates the requirement for supervised labeling to have proper labels at hand, because an initial set of labels can evolve and adapt to better values while being assigned to given data. The separation between feature and assignment manifolds enables the flexible application which is demonstrated for three scenarios with manifold-valued features. Experiments demonstrate a beneficial effect in both directions: adaptivity of labels improves image labeling, and steering label evolution by spatially regularized assignments leads to proper labels, because the assignment flow for supervised labeling is exactly used without any approximation for label learning.Comment: 34 pages, 13 figures, published in Journal of Mathematical Imaging and Vision (JMIV

Tax Court Reverses Course on Deductions for MBA Costs

Obtaining an MBA degree is an expensive and time-consuming process. The cost is ameliorated, however, if a tax deduction can be taken. Although many in the business community believe they are entitled to deduct the costs of obtaining an MBA, the fact of the matter is that a deduction is not a certainty. There have been numerous litigated cases concerning whether a deduction for the cost of an MBA should be allowed. The cases reveal, however, that it all depends on the particular facts and circumstances. This paper reviews the case law concerning the deductibility of the costs of obtaining an MBA: first some older cases, then some more recent cases, and finally the most recent case decided in 2005. Based upon two cases decided by the Tax Court in 2002 and 2004, in which the taxpayers lost, tax practitioners were concerned that a tax deduction for the cost of an MBA was virtually foreclosed for everyone. But in the case decided in 2005, it appears that the Tax Court softened its position. Accordingly, it seems that there is still the possibility of deducting the cost of an MBA. But again, it’s not automatic and will depend on the facts

Estate Tax Aggregation Theory: IRS Continues Its Losing Ways

Sophisticated estate planning encompasses the concept of valuation discounting. Discounting can be accomplished by transferring fractional parts of a property interest, or arranging things so that only a fractional part is owned at death. A minority and/or market discount is claimed for the fractional interest. The IRS has unsuccessfully challenged fractionalization using an aggregation theory under which interests of family members in the property are combined into a majority interest, precluding a minority discount. In two 1999 Tax Court cases, the IRS also lost when it attempted to expand its aggregation theory to include stock held in a so-called QTIP trust. This paper examines these cases and also explores other estate tax issues

Silence is Golden: Publicly-Traded Corporations Should Not Speak Out on Social or Political Issues

In a social and political landscape that is becoming quite divided, the role of publicly traded corporations in this landscape is up for much debate. On one end, speaking out can improve a corporation’s overall brand image, help relate to socially conscious customers, and help maintain customer loyalty. On the other end, however, it creates significant risks. Speaking out can alienate stakeholders, create damaging backlash, and generate detrimental financial repercussions. This significant paradox asks an important question that must be answered by each company at some point: Should corporations remain neutral on contentious issues to perhaps protect their business interests, or is it tactfully advantageous for them to publicly voice their opinion? While there are some that argue corporate silence is preferred, others insist there is an expectation for companies to use their platform to respond according to the expectations of their stakeholders. Ultimately, it can be argued that publicly traded corporations should not speak out on social or political issues as it will negatively affect their business and consumer relations

The Theme Park Throwdown: The Successes and Failures of IP-Related Attractions and Expansions in Disney Theme Parks and Strategies for the Future

The Disney brand invokes strong feelings of loyalty from supporters and dissatisfaction from its opponents, and it many facets play an important part in society (Havard et al., 2021). It is certainly true that “the Magical World of Disney remains unsurpassed in terms of brand name recognition, planning complexity, and detailed execution. It is the paradigmatic ideal of how architecture can establish a universe around its landscape” (Dalov, 2019, p.10). For nearly 70 years, the Imagineers cultivated an idea from the mind of one man into a global phenomenon of twelve theme parks and counting (Tang & Iwerks, 2019). The Disney park is a grandiose land made accessible to mere humans. One forgets about aching feet as he races from one adventure to the next. The clean, safe environment is welcoming to guests of all ages. Complete strangers are impeccably kind to one another as they wait in line making pleasant conversation. Regardless of where the park is in the world, it is meant to be an escape: “Disney is not just offering entertainment: The Company is also selling… an antidote to everyday life. What we buy [at Disney] is not just fun and souvenirs but is also a welcome civility on a human scale” (Fjellman, 2019, p. 31)

Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling

In this thesis we focus on the image labeling problem, which is used as a subroutine in many image processing applications. Our work is based on the assignment flow which was recently introduced as a novel geometric approach to the image labeling problem. This flow evolves over time on the manifold of row-stochastic matrices, whose elements represent label assignments as assignment probabilities. The strict separation of assignment manifold and feature space enables the data to lie in any metric space, while a smoothing operation on the assignment manifold results in an unbiased and spatially regularized labeling. The first part of this work focuses on theoretical statements about the asymptotic behavior of the assignment flow. We show under weak assumptions on the parameters that the assignment flow for data in general position converges towards integral probabilities and thus ensures unique assignment decisions. Furthermore, we investigate the stability of possible limit points depending on the input data and parameters. For stable limits, we derive conditions that allow early evidence of convergence towards these limits and thus provide convergence guarantees. In the second part, we extend the assignment flow approach in order to impose global convex constraints on the labeling results based on linear filter statistics of the assignments. The corresponding filters are learned from examples using an eigendecomposition. The effectiveness of the approach is numerically demonstrated in several academic labeling scenarios. In the last part of this thesis we consider the situation in which no labels are given and therefore these prototypical elements have to be determined from the data as well. To this end we introduce an additional flow on the feature manifold, which is coupled to the assignment flow. The resulting flow adapts the prototypes in time to the assignment probabilities. The simultaneous adaptation and assignment of prototypes not only provides suitable prototypes, but also improves the resulting image segmentation, which is demonstrated by experiments. For this approach it is assumed that the data lie on a Riemannian manifold. We elaborate the approach for a range of manifolds that occur in applications and evaluate the resulting approaches in numerical experiments

Determining Whether an Activity is a Trade or Business

With respect to business expenses, it was not practicable to enact individual Code sections for the innumerable types of expenditures that businesses might incur. Consequently, the Code contains a generic provision, Section 162, which allows as a deduction “all the ordinary and necessary expenses paid or incurred in carrying on any trade or business.” The section contains a reasonableness standard for compensation and disallows travel expenses that are “lavish or extravagant.” There are also public policy considerations. No deduction is permitted for fines and penalties. Nor is a deduction allowed for illegal bribes, kickbacks and certain other payments. A frequently recurring adversarial issue is whether an activity in which a taxpayer is engaged constitutes a “trade or business,” which impliedly requires a profit motive. If the activity is not engaged in for profit, no deductions attributable to the activity are allowed, except to the extent gross income derived from the activity exceeds deductions that are allowable otherwise. Moreover, deductions to the extent of gross income are allowed only if the taxpayer itemizes deductions. Even then, the amount deductible would be reduced by 2% of adjusted gross income. Where non-business expenditures exceed gross income, or where there is none, such expenditures are considered of a personal nature and are not deductible unless there is a provision expressly providing otherwise. Although business expenditures of a capital nature must be capitalized, the expenditure may be recoverable through depreciation or amortization deductions