University of Colorado Colorado Springs

About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.

A randomized controlled trial comparing acetaminophen, acetaminophen and ibuprofen, and acetaminophen and codeine for postoperative pain relief after Mohs surgery and cutaneous reconstruction.

Abstract: BACKGROUNDThere are no population-based data comparing analgesics after Mohs micrographic surgery (MMS) and reconstruction.OBJECTIVETo compare the efficacy in pain management of three analgesic combinations.METHODSIn a randomized, double-blind, controlled study, patients undergoing MMS and reconstru

Improving Performance of Heterogeneous MapReduce Clusters with Adaptive Task Tuning

Abstract: Datacenter-scale clusters are evolving toward heterogeneous hardware architectures due to continuous server replacement. Meanwhile, datacenters are commonly shared by many users for quite different uses. It often exhibits significant performance heterogeneity due to multi-tenant interferences. The deployment of MapReduce on such heterogeneous clusters presents significant challenges in achieving good application performance compared to in-house dedicated clusters. As most MapReduce implementations are originally designed for homogeneous environments, heterogeneity can cause significant performance deterioration in job execution despite existing optimizations on task scheduling and load balancing. In this paper, we observe that the homogeneous configuration of tasks on heterogeneous nodes can be an important source of load imbalance and thus cause poor performance. Tasks should be customized with different configurations to match the capabilities of heterogeneous nodes. To this end, we propose a self-adaptive task tuning approach, Ant , that automatically searches the optimal configurations for individual tasks running on different nodes. In a heterogeneous cluster, Ant first divides nodes into a number of homogeneous subclusters based on their hardware configurations. It then treats each subcluster as a homogeneous cluster and independently applies the self-tuning algorithm to them. Ant finally configures tasks with randomly selected configurations and gradually improves tasks configurations by reproducing the configurations from best performing tasks and discarding poor performing configurations. To accelerate task tuning and avoid trapping in local optimum, Ant uses genetic algorithm during adaptive task configuration. Experimental results on a heterogeneous physical cluster with varying hardware capabilities show that Ant improves the average job completion time by 31, 20, and 14 percent compared to stock Hadoop (Stock), customized Hadoop with industry recommendations (Heuristic), and a profiling-based configuration approach (Starfish), respectively. Furthermore, we extend Ant to virtual MapReduce clusters in a multi-tenant private cloud. Specifically, Ant characterizes a virtual node based on two measured performance statistics: I/O rate and CPU steal time. It uses k-means clustering algorithm to classify virtual nodes into configuration groups based on the measured dynamic interference. Experimental results on virtual clusters with varying interferences show that Ant improves the average job completion time by 20, 15, and 11 percent compared to Stock, Heuristic and Starfish, respectively.

Regional decline of an iconic amphibian associated with elevation, land-use change, and invasive species.

Abstract: Ecological theory predicts that species with restricted geographic ranges will have the highest probability of extinction, but species with extensive distributions and high population densities can also exhibit widespread population losses. In the western United States populations of northern leopard frogs (Lithobates pipiens)-historically one of the most widespread frogs in North America-have declined dramatically in abundance and geographic distribution. To assess the status of leopard frogs in Colorado and evaluate causes of decline, we coupled statewide surveys of 196 historically occupied sites with intensive sampling of 274 wetlands stratified by land use. We used an information-theoretic approach to evaluate the contributions of factors at multiple spatial extents in explaining the contemporary distribution of leopard frogs. Our results indicate leopard frogs have declined in Colorado, but this decline was regionally variable. The lowest proportion of occupied wetlands occurred in eastern Colorado (2-28%), coincident with urban development and colonization by non-native bullfrogs (Lithobates catesbeianus). Variables at several spatial extents explained observed leopard frog distributional patterns. In low-elevation wetlands introduced fishes, bullfrogs, and urbanization or suburbanization associated negatively with leopard frog occurrence, whereas wetland area was positively associated with occurrence. Leopard frogs were more abundant and widespread west of the Continental Divide, where urban development and bullfrog abundance were low. Although the pathogenic chytrid Batrachochytrium dendrobatidis (Bd) was not selected in our best-supported models, the nearly complete extirpation of leopard frogs from montane wetlands could reflect the individual or interactive effects of Bd and climate patterns. Our results highlight the importance of considering multiple, competing hypotheses to explain species declines, particularly when implicated factors operate at different spatial extents.

Fine-grain software distributed shared memory on SMP clusters

Abstract: Commercial SMP nodes are an attractive building block for software distributed shared memory systems. The advantages of using SMP nodes include fast communication among processors within the same node and potential gains from clustering where remote data fetched by one processor is used by other processors on the same node. This paper describes a major extension to the Shasta distributed shared memory system to run efficiently on a cluster of SMP nodes. The Shasta system keeps shared data coherent across nodes at a fine granularity by inserting inline code that checks the cache state of shared data before each load or store in an application. However allowing processors to share memory within the same SMP is complicated by race conditions that arise because the inline state check is non-atomic with respect to the actual load or store of shared data. We present a novel protocol that avoids such race conditions without the use of costly synchronization in the inline checking code. To characterize the benefits of using SMP nodes in the context of Shasta, we present detailed performance results for nine SPLASH-2 applications running on a cluster of Alpha multiprocessors.

Layer Number Dependence of MoS2 Photoconductivity Using Photocurrent Spectral Atomic Force Microscopic Imaging

Abstract: Atomically thin MoS2 is of great interest for electronic and optoelectronic applications because of its unique two-dimensional (2D) quantum confinement; however, the scaling of optoelectronic properties of MoS2 and its junctions with metals as a function of layer number as well the spatial variation of these properties remain unaddressed. In this work, we use photocurrent spectral atomic force microscopy (PCS-AFM) to image the current (in the dark) and photocurrent (under illumination) generated between a biased PtIr tip and MoS2 nanosheets with thickness ranging between n = 1 to 20 layers. Dark current measurements in both forward and reverse bias reveal characteristic diode behavior well-described by Fowler–Nordheim tunneling with a monolayer barrier energy of 0.61 eV and an effective barrier scaling linearly with layer number. Under illumination at 600 nm, the photocurrent response shows a marked decrease for layers up to n = 4 but increasing thereafter, which we describe using a model that accounts fo...