Calcified amorphous tumor: A rare cause of central retinal artery occlusion.

PurposeWe report the case of a central retinal artery occlusion secondary to presumed embolus from a calcified amorphous tumor of the heart, a very rare non-neoplastic cardiac mass.ObservationsA 60-year-old female presented with acute unilateral vision loss of the left eye. Examination revealed hand motion visual acuity of the left eye and a left relative afferent pupillary defect. Fundoscopy showed whitening of the macula with a cherry red spot, consistent with a central retinal artery occlusion. Initial workup was unremarkable, including hypercoagulability labs, magnetic resonance imaging of the brain, and magnetic resonance angiography of the head and neck. Transthoracic echocardiogram (TTE) showed calcification of the mitral valve but no masses. Subsequently, transesophageal echocardiogram (TEE) was performed, which revealed a mobile calcified amorphous tumor of the heart.ConclusionsCalcified amorphous tumor of the heart is a very rare cardiac mass that may cause retinal artery occlusion. TEE is a more sensitive imaging modality to assess for potential cardio-embolic sources if TTE is unrevealing

Performance analysis of low-flux least-squares single-pixel imaging

A single-pixel camera is able to computationally form spatially resolved images using one photodetector and a spatial light modulator. The images it produces in low-light-level operation are imperfect, even when the number of measurements exceeds the number of pixels, because its photodetection measurements are corrupted by Poisson noise. Conventional performance analysis for single-pixel imaging generates estimates of mean-square error (MSE) from Monte Carlo simulations, which require long computational times. In this letter, we use random matrix theory to develop a closed-form approximation to the MSE of the widely used least-squares inversion method for Poisson noise-limited single-pixel imaging. We present numerical experiments that validate our approximation and a motivating example showing how our framework can be used to answer practical optical design questions for a single-pixel camera.This work was supported in part by the Samsung Scholarship and in part by the US National Science Foundation under Grant 1422034. (Samsung Scholarship; 1422034 - US National Science Foundation)Accepted manuscrip

How Much Do Taxes Discourage Incorporation.

One of the most basic distortions created by the double taxation of corporate income is the disincentive to incorporate. In this paper, we investigate the extent to which the aggregate allocation of assets and taxable income in the U.S. between corporate vs. noncorporate forms of organization during the period 1959-86 has responded to the size of the tax distortion discouraging firms from incorporating. In theory, profitable firms should shift out of the corporate sector when the tax distortion to incorporating is larger, and conversely for firms with tax losses. Our empirical results provide strong support for these theoretical forecasts, and hold consistently across a wide variety of specifications and measures of the tax variables. Measured effects are small, however, throwing doubt on the economic importance of tax-induced changes in organizational form.

Why Is There Corporate Taxation In a Small Open Economy? The Role of Transfer Pricing and Income Shifting

Several recent papers argue that corporate income taxes should not be used by small, open economies. With capital mobility, the burden of the tax falls on fixed factors (e.g., labor), and the tax system is more efficient if labor is taxed directly. However, corporate taxes not only exist but rates are roughly comparable with the top personal tax rates. Past models also forecast that multinationals should not invest in countries with low corporate tax rates, since the surtax they owe when profits are repatriated puts them at a competitive disadvantage. Yet such foreign direct investment is substantial. We suggest that the resolution of these puzzles may be found in the role of income shifting, both domestic (between the personal and corporate tax bases) and cross-border (through transfer pricing). Countries need cash-flow corporate taxes as a backstop to labor taxes to discourage individuals from converting their labor income into otherwise untaxed corporate income. We explore how these taxes can best be modified to deal as well with cross-border shifting.

Effects of the Tax Reform Act of 1986 on Corporate Financial Policy and Organizational Form

We examine the effects of the Tax Reform Act of 1986 on the financial decisions made by firms. We review the theory and empirical predictions of prior literature for corporate debt policy, for dividend and equity repurchase payouts to shareholders, and for the choice of organizational form. We then compare the predictions to post-1986 experience. The change in debt/value ratios has been substantially smaller than expected. Dividend payouts increased as predicted, but stock repurchases increased even more rapidly which was unexpected and is difficult to understand. Based on very scant data, it appears that some activities have shuffled among organizational forms; in particular, loss activities may have been moved into corporate form where they are deducted at a higher tax rate, while gain activities may have shifted towards noncorporate form, to be taxed at the lower personal rates. In addition, several interesting new issues are raised. One concerns previously neglected implications for the effective tax on retained earnings that follow from optimal trading strategies when long- and short-term capital gains are taxed at different rates. Also, new interest allocation rules for multinational corporations provide a substantial incentive for many firms to shift their borrowing abroad.

Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors

Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum-likelihood based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.Comment: 11 pages, 8 figure

Improving Learning Performance by Applying Economic Knowledge

Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their position as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.

Computational multi-depth single-photon imaging

We present an imaging framework that is able to accurately reconstruct multiple depths at individual pixels from single-photon observations. Our active imaging method models the single-photon detection statistics from multiple reflectors within a pixel, and it also exploits the fact that a multi-depth profile at each pixel can be expressed as a sparse signal. We interpret the multi-depth reconstruction problem as a sparse deconvolution problem using single-photon observations, create a convex problem through discretization and relaxation, and use a modified iterative shrinkage-thresholding algorithm to efficiently solve for the optimal multi-depth solution. We experimentally demonstrate that the proposed framework is able to accurately reconstruct the depth features of an object that is behind a partially-reflecting scatterer and 4 m away from the imager with root mean-square error of 11 cm, using only 19 signal photon detections per pixel in the presence of moderate background light. In terms of root mean-square error, this is a factor of 4.2 improvement over the conventional method of Gaussian-mixture fitting for multi-depth recovery.This material is based upon work supported in part by a Samsung Scholarship, the US National Science Foundation under Grant No. 1422034, and the MIT Lincoln Laboratory Advanced Concepts Committee. We thank Dheera Venkatraman for his assistance with the experiments. (Samsung Scholarship; 1422034 - US National Science Foundation; MIT Lincoln Laboratory Advanced Concepts Committee)Accepted manuscrip

Thermodynamics predicts how confinement modifies hard-sphere dynamics

We study how confining the equilibrium hard-sphere fluid to restrictive one- and two-dimensional channels with smooth interacting walls modifies its structure, dynamics, and entropy using molecular dynamics and transition-matrix Monte Carlo simulations. Although confinement strongly affects local structuring, the relationships between self-diffusivity, excess entropy, and average fluid density are, to an excellent approximation, independent of channel width or particle-wall interactions. Thus, thermodynamics can be used to predict how confinement impacts dynamics.Comment: 4 pages, 4 figure