Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Temporal structure is skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefronatal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables such as time-to-contact. At a finer scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over- shoot the amounts needed for precise acts. Each context of action may require a different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive patterns of analog signals. From some parts of the cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine design to serve the lowest and highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between leveels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.National Institute of Mental Health (R01 DC02582

Subset Warping: Rubber Sheeting with Cuts

Image warping, often referred to as "rubber sheeting" represents the deformation of a domain image space into a range image space. In this paper, a technique is described which extends the definition of a rubber-sheet transformation to allow a polygonal region to be warped into one or more subsets of itself, where the subsets may be multiply connected. To do this, it constructs a set of "slits" in the domain image, which correspond to discontinuities in the range image, using a technique based on generalized Voronoi diagrams. The concept of medial axis is extended to describe inner and outer medial contours of a polygon. Polygonal regions are decomposed into annular subregions, and path homotopies are introduced to describe the annular subregions. These constructions motivate the definition of a ladder, which guides the construction of grid point pairs necessary to effect the warp itself

Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement

The goal of many early visual filtering processes is to remove noise while at the same time sharpening contrast. An historical succession of approaches to this problem, starting with the use of simple derivative and smoothing operators, and the subsequent realization of the relationship between scale-space and the isotropic dfffusion equation, has recently resulted in the development of "geometry-driven" dfffusion. Nonlinear and anisotropic diffusion methods, as well as image-driven nonlinear filtering, have provided improved performance relative to the older isotropic and linear diffusion techniques. These techniques, which either explicitly or implicitly make use of kernels whose shape and center are functions of local image structure are too computationally expensive for use in real-time vision applications. In this paper, we show that results which are largely equivalent to those obtained from geometry-driven diffusion can be achieved by a process which is conceptually separated info two very different functions. The first involves the construction of a vector~field of "offsets", defined on a subset of the original image, at which to apply a filter. The offsets are used to displace filters away from boundaries to prevent edge blurring and destruction. The second is the (straightforward) application of the filter itself. The former function is a kind generalized image skeletonization; the latter is conventional image filtering. This formulation leads to results which are qualitatively similar to contemporary nonlinear diffusion methods, but at computation times that are roughly two orders of magnitude faster; allowing applications of this technique to real-time imaging. An additional advantage of this formulation is that it allows existing filter hardware and software implementations to be applied with no modification, since the offset step reduces to an image pixel permutation, or look-up table operation, after application of the filter

Improved Cross-correlation for Template Matching on the Laplacian Pyramid

Template matching via cross-correlation on Laplacian pyramid image architectures has been traditionally performed in a "coarse" to "fine" fashion. In the present paper, we show that by computing cross-correlation within each level of the pyramid independently, and considering the su, across (expanded) levels, a significant improvement in Peak to Correlation Energy (PCE) [9] is obtained. This result is illustrated with a number of numerical examples

Real-Time Restoration of Images Degraded by Uniform Motion Blur in Foveal Active Vision Systems

Foveated, log-polar, or space-variant image architectures provide a high resolution and wide field workspace, while providing a small pixel computation load. These characteristics are ideal for mobile robotic and active vision applications. Recently we have described a generalization of the Fourier Transform (the fast exponential chirp transform) which allows frame-rate computation of full-field 2D frequency transforms on a log-polar image format. In the present work, we use Wiener filtering, performed using the Exponential Chirp Transform, on log-polar (fovcated) image formats to de-blur images which have been degraded by uniform camera motion.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-96-C-0178); Office of Naval Research Multidisciplinary University Research Initiative (N00014-95-1-0409

Predicting unobserved exposures from seasonal epidemic data

We consider a stochastic Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model with a contact rate that fluctuates seasonally. Through the use of a nonlinear, stochastic projection, we are able to analytically determine the lower dimensional manifold on which the deterministic and stochastic dynamics correctly interact. Our method produces a low dimensional stochastic model that captures the same timing of disease outbreak and the same amplitude and phase of recurrent behavior seen in the high dimensional model. Given seasonal epidemic data consisting of the number of infectious individuals, our method enables a data-based model prediction of the number of unobserved exposed individuals over very long times.Comment: 24 pages, 6 figures; Final version in Bulletin of Mathematical Biolog

Computing with the Integrate and Fire Neuron: Weber's Law, Multiplication and Phase Detection

The integrate and fire model (Stein, 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold and refractory period. Integrate and fire (IAF) neurons have mainly been used to model physiologically realistic spike trains but little application of the IAF model appears to have been made in an explicitly computational context. In this paper we show that the transfer function of an IAF neuron provides, over a wide parameter range, a compressive nonlinearity sufficiently close to that of the logarithm so that IAF neurons can be used to multiply neural signals by mere addition of their outputs. Thus, although the IAF transfer function is not explicitly logarithmic, its compressive parameter regime supports a simple, single neuron model for multiplication. A simulation of the IAF multiplier shows that under a wide choice of parameters, the IAF neuron can multiply its inputs to within a 5% relative error. We also show that an IAF neuron under a different, yet biologically reasonable, parameter regime can have a quasi-linear transfer function, acting as an adder or a gain node. We then show an application in which the compressive transfer function of the IAF model provides a simple mechanism for phase-detection: multiplication of 40Hz phasic inputs followed by low-pass filtering yields an output that is a quasi-linear function of the relative phase of the inputs. This is a neural version of the heterodyne phase detection principle. Finally, we briefly discuss the precision and dynamic range of an IAF multiplier that is restricted to reasonable firing rates (in the range of 10-300 Hz) and reasonable computation time (in the range of 25-200 milliseconds).National Institute of Mental Health (5R01MH45969-04); Office of Naval Research (N00014-95-1-0409

Estimating Sensor Motion from Wide-Field Optical Flow on a Log-Dipolar Sensor

Log-polar image architectures, motivated by the structure of the human visual field, have long been investigated in computer vision for use in estimating motion parameters from an optical flow vector field. Practical problems with this approach have been: (i) dependence on assumed alignment of the visual and motion axes; (ii) sensitivity to occlusion form moving and stationary objects in the central visual field, where much of the numerical sensitivity is concentrated; and (iii) inaccuracy of the log-polar architecture (which is an approximation to the central 20°) for wide-field biological vision. In the present paper, we show that an algorithm based on generalization of the log-polar architecture; termed the log-dipolar sensor, provides a large improvement in performance relative to the usual log-polar sampling. Specifically, our algorithm: (i) is tolerant of large misalignmnet of the optical and motion axes; (ii) is insensitive to significant occlusion by objects of unknown motion; and (iii) represents a more correct analogy to the wide-field structure of human vision. Using the Helmholtz-Hodge decomposition to estimate the optical flow vector field on a log-dipolar sensor, we demonstrate these advantages, using synthetic optical flow maps as well as natural image sequences

How Bioethics Principles Can Aid Design of Electronic Health Records to Accommodate Patient Granular Control

Ethics should guide the design of electronic health records (EHR), and recognized principles of bioethics can play an important role. This approach was adopted recently by a team of informaticists designing and testing a system where patients exert granular control over who views their personal health information. While this method of building ethics in from the start of the design process has significant benefits, questions remain about how useful the application of bioethics principles can be in this process, especially when principles conflict. For instance, while the ethical principle of respect for autonomy supports a robust system of granular control, the principles of beneficence and non-maleficence counsel restraint due to the danger of patients being harmed by restrictions on provider access to data. Conflict between principles has long been recognized by ethicists and has even motivated attacks on approaches that state and apply principles. In this paper we show how using ethical principles can help in the design of EHRs by first, explaining how ethical principles can and should be used generally, and then by, discuss how attention to details in specific cases can show that the tension between principles is not as bad as it initially appeared. We conclude by suggesting further ways in which the application of these (and other) principles can add value to the ongoing discussion of patient involvement in their health care. This is a new approach to linking principles to informatics design that we expect will stimulate further interest

To Be or Not to Be – A Research Subject

Most people do not know there are different kinds of medical studies; some are conducted on people who already have a disease or medical condition, and others are performed on healthy volunteers who want to help science find answers. No matter what sort of research you are invited to participate in, or whether you are a patient when you are asked, it’s entirely up to you whether or not to do it. This decision is important and may have many implications for your health and well-being, as well as those of other patients now and in the future. Making a good decision – the right one for you – requires you to become educated about topics you may not have thought about before, some of which may be quite complicated. This chapter explains the key issues to help you make a good decision