Military decorations and campaign service bars of the United States,

Mode of access: Internet.

Under the stars and bars; or, Memories of four years service with the Oglethorpes, of Augusta, Georgia.

Mode of access: Internet.

Behind the bars; 3148.

Mode of access: Internet.

Geomorphic Impacts of the 2013 Colorado Front Range Flood on Black Canyon Creek and North Fork Big Thompson River

In September 2013, the Colorado Front Range experienced a five-day storm that brought record-breaking precipitation to the region. As a consequence, many Front Range streams experienced flooding, leading to erosion, debris flows, bank failures and channel incision. I compare the effects that debris flows and flooding have on the channel bar frequency, frequency and location of wood accumulation, and on the shape and size of the channel along two flood impacted reaches located near Estes Park and Glen Haven, Colorado within Rocky Mountain National Park and Arapaho-Roosevelt National Forest: Black Canyon Creek (BCC) and North Fork Big Thompson River (NFBT). The primary difference between the two study areas is that BCC was inundated by multiple debris flows, whereas NFBT only experienced flooding. Fieldwork consisted of recording location and size of large wood and channel bars and surveying reaches to produce cross-sections. Additional observations were made on bank failures in NFBT and the presence of boulders in channel bars in BCC to determine sediment source. The debris flow acted to scour and incise BCC causing long-term alteration. The post-flood channel cross-sectional area is as much as 7 to 23 times larger than the pre-flood channel, caused by the erosion of the channel bed to bedrock and the elimination of riparian vegetation. Large wood was forced out of the stream channel and deposited outside of the bankfull channel. Flooding in NFBT caused bank erosion and widening that contributed sediment to channel bars, but accomplished little stream-bed scour. As a result, there was relatively little damage to mid-channel and riparian vegetation, and most large wood remained within the wetted channel.

Geology and landslide geomorphology of the Burpee Hills, Skagit County, Washington, USA

Landforms within the Skagit Valley record a complex history of land evolution from Late Pleistocene to the present. Late Pleistocene glacial deposits and subsequent incision by the Skagit River formed the Burpee Hills terrace. The Burpee Hills comprises an approximately 205-m-thick sequence of sediments, including glacio-lacustrine silts and clays, overlain by sandy advance outwash and capped by coarse till, creating a sediment-mantled landscape where mass wasting occurs in the form of debris flows and deep-seated landslides (Heller, 1980; Skagit County, 2014). Landslide probability and location are necessary metrics for informing citizens and policy makers of the frequency of natural hazards. Remote geomorphometric analysis of the site area using airborne LiDAR combined with field investigation provide the information to determine relative ages of landslide deposits, to classify geologic units involved, and to interpret the recent hillslope evolution. Thirty-two percent of the 28-km2 Burpee Hills landform has been mapped as landslide deposits. Eighty-five percent of the south-facing slope is mapped as landslide deposits. The mapped landslides occur predominantly within the advance outwash deposits (Qgav), this glacial unit has a slope angle ranging from 27 to 36 degrees. Quantifying surface roughness as a function of standard deviation of slope provides a relative age of landslide deposits, laying the groundwork for frequency analysis of landslides on the slopes of the Burpee Hills. The south-facing slopes are predominately affected by deep-seated landslides as a result of Skagit River erosion patterns within the floodplain. The slopes eroded at the toe by the Skagit River have the highest roughness coefficients, suggesting that areas with more frequent disturbance at the toe are more prone to sliding or remobilization. Future work including radiocarbon dating and hydrologic-cycle investigations will provide a more accurate timeline of the Burpee Hills hillslope evolution, and better information for emergency management and planners in the future.

Linking surficial geomorphology with vertical structure in high and low energy marine environments

Senior thesis written for Oceanography 445

Glacial geomorphology of the Altai and Western Sayan Mountains, Central Asia

In this article, we present a map of the glacial geomorphology of the Altai andWestern Sayan Mountains, covering an area of almost 600,000 km2. Although numerous studies provide evidence for restricted Pleistocene glaciations in this area, others have hypothesized the past existence of an extensive ice sheet. To provide a framework for accurate glacial reconstructions of the Altai and Western Sayan Mountains, we present a map at a scale of 1:1,000,000 based on a mapping from 30 m resolution ASTER DEM and 15 m/30 mresolution Landsat ETM+ satellite imagery. Four landform classes have been mapped: marginal moraines, glacial lineations, hummocky terrain, and glacial valleys. Our mapping reveals an abundance of glacial erosional and depositional landforms. The distribution of these glacial landforms indicates that the Altai and Western Sayan Mountains have experienced predominantly alpine-style glaciations, with some small ice caps centred on the higher mountain peaks. Large marginal moraine complexes mark glacial advances in intermontane basins. By tracing the outer limits of present-day glaciers, glacial valleys, and moraines, we estimate that the past glacier coverage have totalled to 65,000 km2 (10.9% of the mapped area), whereas present-day glacier coverage totals only 1300 km2 (0.2% of the mapped area). This demonstrates the usefulness of remote sensing techniques for mapping the glacial geomorphology in remote mountain areas and for quantifying the past glacier dimensions. The glacial geomorphological map presented here will be used for further detailed reconstructions of the paleoglaciology and paleoclimate of the region.

An upper bound on the Bayesian error bars for generalized linear regression

In the Bayesian framework, predictions for a regression problem are expressed in terms of a distribution of output values. The mode of this distribution corresponds to the most probable output, while the uncertainty associated with the predictions can conveniently be expressed in terms of error bars. In this paper we consider the evaluation of error bars in the context of the class of generalized linear regression models. We provide insights into the dependence of the error bars on the location of the data points and we derive an upper bound on the true error bars in terms of the contributions from individual data points which are themselves easily evaluated.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Edges and bars: where do people see features in 1-D images?

There have been two main approaches to feature detection in human and computer vision - based either on the luminance distribution and its spatial derivatives, or on the spatial distribution of local contrast energy. Thus, bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of features in images? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square-wave and all Fourier components have a common phase. Observers used a cursor to mark where bars and edges were seen for different test phases (Experiment 1) or judged the spatial alignment of contours that had different phases (e.g. 0 degrees and 45 degrees ; Experiment 2). The feature positions defined by both tasks shifted systematically to the left or right according to the sign of the phase offset, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks (bars) and gradient peaks (edges), but not by energy peaks which (by design) predicted no shift at all. These results encourage models based on a Gaussian-derivative framework, but do not support the idea that human vision uses points of phase alignment to find local, first-order features. Nevertheless, we argue that both approaches are presently incomplete and a better understanding of early vision may combine insights from both. (C)2004 Elsevier Ltd. All rights reserved.

Bars and edges: what makes a feature for human vision?

There have been two main approaches to feature detection in human and computer vision - luminance-based and energy-based. Bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of elements in a 3-element contour-alignment task? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square wave and Fourier components in a given image have a common phase. Observers judged whether the centre element (eg ±458 phase) was to the left or right of the flanking pair (eg 0º phase). Lateral offset of the centre element was varied to find the point of subjective alignment from the fitted psychometric function. This point shifted systematically to the left or right according to the sign of the centre phase, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks and other derivative-based features, but not by energy peaks which (by design) predicted no shift at all. These results on contour alignment agree well with earlier ones from a more explicit feature-marking task, and strongly suggest that human vision does not use local energy peaks to locate basic first-order features. [Supported by the Wellcome Trust (ref: 056093)]

Mach Bands: multi-scale spatial filtering and co-operative coding of edges and bars

Perception of Mach bands may be explained by spatial filtering ('lateral inhibition') that can be approximated by 2nd derivative computation, and several alternative models have been proposed. To distinguish between them, we used a novel set of ‘generalised Gaussian’ images, in which the sharp ramp-plateau junction of the Mach ramp was replaced by smoother transitions. The images ranged from a slightly blurred Mach ramp to a Gaussian edge and beyond, and also included a sine-wave edge. The probability of seeing Mach Bands increased with the (relative) sharpness of the junction, but was largely independent of absolute spatial scale. These data did not fit the predictions of MIRAGE, nor 2nd derivative computation at a single fine scale. In experiment 2, observers used a cursor to mark features on the same set of images. Data on perceived position of Mach bands did not support the local energy model. Perceived width of Mach bands was poorly explained by a single-scale edge detection model, despite its previous success with Mach edges (Wallis & Georgeson, 2009, Vision Research, 49, 1886-1893). A more successful model used separate (odd and even) scale-space filtering for edges and bars, local peak detection to find candidate features, and the MAX operator to compare odd- and even-filter response maps (Georgeson, VSS 2006, Journal of Vision 6(6), 191a). Mach bands are seen when there is a local peak in the even-filter (bar) response map, AND that peak value exceeds corresponding responses in the odd-filter (edge) maps.

Bayesian error bars for regression

Regression problems are concerned with predicting the values of one or more continuous quantities, given the values of a number of input variables. For virtually every application of regression, however, it is also important to have an indication of the uncertainty in the predictions. Such uncertainties are expressed in terms of the error bars, which specify the standard deviation of the distribution of predictions about the mean. Accurate estimate of error bars is of practical importance especially when safety and reliability is an issue. The Bayesian view of regression leads naturally to two contributions to the error bars. The first arises from the intrinsic noise on the target data, while the second comes from the uncertainty in the values of the model parameters which manifests itself in the finite width of the posterior distribution over the space of these parameters. The Hessian matrix which involves the second derivatives of the error function with respect to the weights is needed for implementing the Bayesian formalism in general and estimating the error bars in particular. A study of different methods for evaluating this matrix is given with special emphasis on the outer product approximation method. The contribution of the uncertainty in model parameters to the error bars is a finite data size effect, which becomes negligible as the number of data points in the training set increases. A study of this contribution is given in relation to the distribution of data in input space. It is shown that the addition of data points to the training set can only reduce the local magnitude of the error bars or leave it unchanged. Using the asymptotic limit of an infinite data set, it is shown that the error bars have an approximate relation to the density of data in input space.

Mach bands and multiscale models of spatial vision:the role of first, second, and third derivative operators in encoding bars and edges

Ernst Mach observed that light or dark bands could be seen at abrupt changes of luminance gradient in the absence of peaks or troughs in luminance. Many models of feature detection share the idea that bars, lines, and Mach bands are found at peaks and troughs in the output of even-symmetric spatial filters. Our experiments assessed the appearance of Mach bands (position and width) and the probability of seeing them on a novel set of generalized Gaussian edges. Mach band probability was mainly determined by the shape of the luminance profile and increased with the sharpness of its corners, controlled by a single parameter (n). Doubling or halving the size of the images had no significant effect. Variations in contrast (20%-80%) and duration (50-300 ms) had relatively minor effects. These results rule out the idea that Mach bands depend simply on the amplitude of the second derivative, but a multiscale model, based on Gaussian-smoothed first- and second-derivative filtering, can account accurately for the probability and perceived spatial layout of the bands. A key idea is that Mach band visibility depends on the ratio of second- to first-derivative responses at peaks in the second-derivative scale-space map. This ratio is approximately scale-invariant and increases with the sharpness of the corners of the luminance ramp, as observed. The edges of Mach bands pose a surprisingly difficult challenge for models of edge detection, but a nonlinear third-derivative operation is shown to predict the locations of Mach band edges strikingly well. Mach bands thus shed new light on the role of multiscale filtering systems in feature coding. © 2012 ARVO.