The N2-P3 complex of the evoked potential and human performance

The N2-P3 complex and other endogenous components of human evoked potential provide a set of tools for the investigation of human perceptual and cognitive processes. These multidimensional measures of central nervous system bioelectrical activity respond to a variety of environmental and internal factors which have been experimentally characterized. Their application to the analysis of human performance in naturalistic task environments is just beginning. Converging evidence suggests that the N2-P3 complex reflects processes of stimulus evaluation, perceptual resource allocation, and decision making that proceed in parallel, rather than in series, with response generation. Utilization of these EP components may provide insights into the central nervous system mechanisms modulating task performance unavailable from behavioral measures alone. The sensitivity of the N2-P3 complex to neuropathology, psychopathology, and pharmacological manipulation suggests that these components might provide sensitive markers for the effects of environmental stressors on the human central nervous system

Attention, effort, and fatigue: Neuropsychological perspectives

Models of attention, effort, and fatigue are reviewed. Methods are discussed for measuring these phenomena from a neuropsychological and psychophysiological perspective. The following methodologies are included: (1) the autonomic measurement of cognitive effort and quality of encoding; (2) serial assessment approaches to neurophysiological assessment; and (3) the assessment of subjective reports of fatigue using multidimensional ratings and their relationship to neurobehavioral measures

Sherlock: Scalable Fact Learning in Images

We study scalable and uniform understanding of facts in images. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, and interactions. We propose a setting where all these facts can be modeled simultaneously with a capacity to understand unbounded number of facts in a structured way. The training data comes as structured facts in images, including (1) objects (e.g., $$), (2) attributes (e.g.,$$), (3) actions (e.g., $$), and (4) interactions (e.g.,$$). Each fact has a semantic language view (e.g., $$) and a visual view (an image with this fact). We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding. We propose and investigate recent and strong approaches from the multiview learning literature and also introduce two learning representation models as potential baselines. We applied the investigated methods on several datasets that we augmented with structured facts and a large scale dataset of more than 202,000 facts and 814,000 images. Our experiments show the advantage of relating facts by the structure by the proposed models compared to the designed baselines on bidirectional fact retrieval.Comment: Jan 7 Updat

Deep GrabCut for Object Selection

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.Comment: BMVC 201

Estimating the equity risk premium for economies in the Asian region

The Equity Risk Premium (ERP) is widely used in economic and financial analysis, yet it is difficult to find empirical estimates of the ERP that are generally accepted. The paucity of data in Asian economies exacerbates the problems of estimation. This study estimates the ERP for the larger market-orientated Asian economies and compares the estimates with those of the United States. Surprisingly, of the seven economies examined, the ERP of four cannot be statistically differentiated from that of the United States