Conciousness Expanded

Many kinds of human states of consciousness have been distinguished, including colourful or anomalous experiences that are felt to have spiritual significance by most people who have them. The neurosciences have isolated brain-state correlates for some of these colourful states of consciousness, thereby strengthening the hypothesis that these experiences are mediated by the brain. This result both challenges metaphysically dualist accounts of human nature and suggests that any adequate causal explanation of colourful experiences would have to make detailed reference to the evolutionary and genetic conditions that give rise to brains capable of such conscious phenomena. This paper quickly surveys types of conscious states and neurological interpretations of them. In order to deal with the question of the significance of such experiences, the paper then attempts to identify evolutionary and genetic constraints on proposals for causal explanations of such experiences. The conclusion is that a properly sensitive evolutionary account of human consciousness supports a rebuttal of the argument that the cognitive content of colourful experiences is pure delusion, but that this evolutionary account also heavily constrains what might be inferred theologically from such experiences. They are not necessarily delusory, therefore, but they are often highly misleading. Their significance must be construed consistently with this conclusion.

Diagnosing Network-Wide Traffic Anomalies

Anomalies are unusual and significant changes in a network's traffic levels, which can often involve multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data. In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively using Principal Component Analysis. Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow. We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.

Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.