The Rescue of American International Group Module C: AIG Investment Program

In September 2008, the Federal Reserve Bank of New York (FRBNY) extended an $85 billion credit line to AIG to address its liquidity stresses, but AIG’s balance sheet remained under pressure. The insurance giant was projected to report large third-quarter losses and was at risk of being downgraded by major credit rating agencies. For these reasons, in early November 2008, the US Treasury invested$40 billion of Troubled Assets Relief Program (TARP) funds into AIG in exchange for 4 million shares of AIG Series D preferred stock and a warrant to purchase AIG common stock. The investment helped repay a portion of AIG’s debt to the FRBNY, restructured the terms of the credit line, and deleveraged AIG’s balance sheet. With similar concerns arising at the end of the first quarter of 2009, Treasury made a second TARP investment of $30 billion in exchange for 300,000 shares of Series F preferred stock and another common stock warrant. Treasury converted all the preferred stock from its TARP investments into AIG common stock in January 2011 and sold it over the following two years

The Rescue of American International Group Module A: The Revolving Credit Facility

On September 15, 2008, the big three rating agencies downgraded AIG’s credit ratings multiple levels, exacerbating liquidity strains that the company was experiencing due to increasing cash demands by securities borrowers and collateral calls by credit default swap (CDS) customers. To prevent AIG from filing for bankruptcy, the Federal Reserve (the Fed) announced on the following day that, pursuant to its emergency powers, it would provide the company with an $85 billion Revolving Credit Facility (RCF). The RCF was secured by AIG assets and interests in its subsidiaries and required AIG to grant the US Department of the Treasury a 79.9$6.4 billion in capitalized interest and fees from the program

The Rescue of American International Group Module F: The AIG Credit Facility Trust

In September 2008, American International Group, Inc. (AIG) experienced a liquidity crisis. To avoid the insurance giant’s bankruptcy, the Federal Reserve Bank of New York (FRBNY) extended an $85 billion emergency secured credit facility to AIG. In connection with the credit facility, AIG issued 100,000 shares of preferred stock, with voting rights equal to and convertible into 79.9% of the outstanding shares of AIG common stock, to an independent trust (the Trust) set up by the FRBNY. Three trustees held the stock for the sole benefit of the US Treasury, exercised the rights, powers, authorities, discretions, and duties of the preferred stock, and acted as the beneficial owner of AIG. On January 14, 2011, the Trust converted the preferred stock into AIG common stock, and, after transferring the common stock to the Treasury’s General Fund, the Trust effectively dissolved. Over the next two years, Treasury sold the common stock in a series of six public offerings returning a profit to the government. The government’s equity investment and the Trust were controversial, raising debate about nationalization, transparency, and independence of the Trustees

Bayesian Longitudinal Tensor Response Regression for Modeling Neuroplasticity

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power

Bayesian longitudinal tensor response regression for modeling neuroplasticity

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially-distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.Comment: 28 pages, 8 figures, 6 table

Mechanism of fluidization

The mechanism of fluidization has been investigated, in particular the method of initiation of movement. A detailed study of the discontinuities found in many liquid fluidized systems has been made. By means of an artificially created discontinuity or parvoid the mechanism of propagation of these parvoids has been studied and a correlation produced which agrees with the data obtained from the naturally occurring parvoids up to a superficial water velocity of 1.5 Umf. At higher superficial velocities the curve relating water velocity to parvoid velocity passed through a series of peaks or breaks. These can be explained on the basis of an inherent mechanism of speed control by means of reductions in the amount of dense phase existing between successive parvoids