Abstract-These days, many traditional end-user applications are said to "run fast enough" on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging.This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six worldclass machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in "clean C" so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.

Color blindness is a highly prevalent vision impairment that inhibits people's ability to understand colors. Although classified as a mild disability, color blindness has important effects on the daily activity of people, preventing them from performing their tasks in the most natural and effective ways. In order to address this issue we developed Chroma, a wearable augmented-reality system based on Google Glass that allows users to see a filtered image of the current scene in real-time. Chroma automatically adapts the scene-view based on the type of color blindness, and features dedicated algorithms for color saliency. Based on interviews with 23 people with color blindness we implemented four modes to help colorblind individuals distinguish colors they usually can't see. Although Glass still has important limitations, initial tests of Chroma in the lab show that colorblind individuals using Chroma can improve their color recognition in a variety of real-world activities. The deployment of Chroma on a wearable augmented-reality device makes it an effective digital aid with the potential to augment everyday activities, effectively providing access to different color dimensions for colorblind people.

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This paper proposes a use of multi-factor seemingly unrelated regression (SUR) in event study analysis to study mergers and acquisitions in Singapore's financial industry. We also study the cross-sector (banking and insurance) domestic acquisitions in Singapore's financial industry. In contrast to the use of ordinary least squares (OLS) method, it is found that OLS method seems to underestimate the value of the sample cumulative abnormal returns as compared to SUR. The study also found that firms post mergers and takeovers in the banking and insurance industries tend to have high a possibility of negative returns.