A SINDA '85 nodal heat transfer rate calculation user subroutine

This paper describes a subroutine, GETQ, which was developed to compute the heat transfer rates through all conductors attached to a node within a SINDA '85 thermal submodel. The subroutine was written for version 2.3 of SINDA '85. Upon calling GETQ, the user supplies the submodel name and node number which the heat transfer rate computation is desired. The returned heat transfer rate values are broken down into linear, nonlinear, source and combined heat loads

Faces as a "Model Category" for Visual Object Recognition

Visual recognition is an important ability that is central to many everyday tasks such as reading, navigation and social interaction, and is therefore actively studied in neuroscience, cognitive psychology and artificial intelligence. There exist thousands of object categories, all of which pose similar challenges to biological and artificial visual systems: accurate recognition under varying location, scale, view angle, illumination and clutter. In many areas of science, important discoveries have been made using "model organisms" such as fruit flies, mice and macaques. For the thousands of object categories, the important and well-studied category of faces could potentially serve as a "model category" upon which efforts are focused, and from which fundamental insights are drawn. However, it has been hotly debated whether faces are processed by the brain in a manner fundamentally different from other categories. Here we show that "neural tuning size" -- a single parameter in a computational model of object processing -- is able to account for important face-specific phenomena. Thus, surprisingly, "face-like" processing is explainable by physiological mechanisms that differ only quantitatively from "object-like" processing. Our computational proof-of-principle provides specific neural tuning properties that correspond to the so-far qualitative and controversial notion of "holistic" face processing. Overall, faces may be a viable model category. Since faces are highly amenable to complementary experimental techniques like functional MRI, electrophysiology, electroencephalography and transcranial magnetic stimulation, this further raises the odds that the algorithms and neural circuits underlying visual recognition may first be solved for faces. With faces serving as a model category, the great scientific challenge of understanding and reverse-engineering general visual recognition can be greatly accelerated

Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing

Faces are an important and unique class of visual stimuli, and have been of interest to neuroscientists for many years. Faces are known to elicit certain characteristic behavioral markers, collectively labeled “holistic processing”, while non-face objects are not processed holistically. However, little is known about the underlying neural mechanisms. The main aim of this computational simulation work is to investigate the neural mechanisms that make face processing holistic. Using a model of primate visual processing, we show that a single key factor, “neural tuning size”, is able to account for three important markers of holistic face processing: the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole-Part Effect (WPE). Our proof-of-principle specifies the precise neurophysiological property that corresponds to the poorly-understood notion of holism, and shows that this one neural property controls three classic behavioral markers of holism. Our work is consistent with neurophysiological evidence, and makes further testable predictions. Overall, we provide a parsimonious account of holistic face processing, connecting computation, behavior and neurophysiology.National Science Foundation (U.S.) (STC award CCF-1231216

Throwing Down the Visual Intelligence Gauntlet

In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes---the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence.This research was sponsored by grants from DARPA (IPTO and DSO), National Science Foundation (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05-C-7262). Additional support was provided by: Adobe, Honda Research Institute USA, King Abdullah University Science and Technology grant to B. DeVore, NEC, Sony and especially by the Eugene McDermott Foundation

An integrated model of visual attention using shape-based features

Apart from helping shed some light on human perceptual mechanisms, modeling visual attention has important applications in computer vision. It has been shown to be useful in priming object detection, pruning interest points, quantifying visual clutter as well as predicting human eye movements. Prior work has either relied on purely bottom-up approaches or top-down schemes using simple low-level features. In this paper, we outline a top-down visual attention model based on shape-based features. The same shape-based representation is used to represent both the objects and the scenes that contain them. The spatial priors imposed by the scene and the feature priors imposed by the target object are combined in a Bayesian framework to generate a task-dependent saliency map. We show that our approach can predict the location of objects as well as match eye movements (92% overlap with human observers). We also show that the proposed approach performs better than existing bottom-up and top-down computational models

Neural representation of action sequences: how far can a simple snippet-matching model take us?

The macaque Superior Temporal Sulcus (STS) is a brain area that receives and integrates inputs from both the ventral and dorsal visual processing streams (thought to specialize in form and motion processing respectively). For the processing of articulated actions, prior work has shown that even a small population of STS neurons contains sufficient information for the decoding of actor invariant to action, action invariant to actor, as well as the specific conjunction of actor and action. This paper addresses two questions. First, what are the invariance properties of individual neural representations (rather than the population representation) in STS? Second, what are the neural encoding mechanisms that can produce such individual neural representations from streams of pixel images? We find that a baseline model, one that simply computes a linear weighted sum of ventral and dorsal responses to short action “snippets”, produces surprisingly good fits to the neural data. Interestingly, even using inputs from a single stream, both actor-invariance and action-invariance can be produced simply by having different linear weights

A study of the potential impacts of space utilization

Because the demand for comprehensive impact analysis of space technologies will increase with the use of space shuttles, the academic social sciences/humanities community was surveyed in order to determine their interests in space utilization, to develop a list of current and planned courses, and to generate a preliminary matrix of relevant social sciences. The academic scope/focus of a proposed social science space-related journal was identified including the disciplines which should be represented in the editorial board/reviewer system. The time and funding necessary to develop a self-sustaining journal were assessed. Cost income, general organizational structure, marking/distribution and funding sources were analyzed. Recommendations based on the survey are included

Deep Convolutional Networks are Hierarchical Kernel Machines

We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, respectively. Under the assumption of normalized inputs, we show that appropriate linear combinations of rectifying nonlinearities are equivalent to radial kernels. If pooling is present an equivalent kernel also exist. Thus present-day DCNs (Deep Convolutional Networks) can be exactly equivalent to a hierarchy of kernel machines with pooling and non-pooling layers. Finally, we describe a conjecture for theoretically understanding hierarchies of such modules. A main consequence of the conjecture is that hierarchies of eHW modules minimize memory requirements while computing a selective and invariant representation.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216