Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.     

Selective sampling for approximate clustering of very large data sets

A key challenge in pattern recognition is how to scale the computational efficiency of clustering algorithms on large data sets. The extension of non‐Euclidean relational fuzzy c‐means (NERF) clustering to very large (VL = unloadable) relational data is called the extended NERF (eNERF) clustering algorithm, which comprises four phases: (i) finding distinguished features that monitor progressive sampling; (ii) progressively sampling from a N × N relational matrix R_N to obtain a n × n sample matrix R_n; (iii) clustering R_n with literal NERF; and (iv) extending the clusters in R_n to the remainder of the relational data. Previously published examples on several fairly small data sets suggest that eNERF is feasible for truly large data sets. However, it seems that phases (i) and (ii), i.e., finding R_n, are not very practical because the sample size n often turns out to be roughly 50% of n, and this over‐sampling defeats the whole purpose of eNERF. In this paper, we examine the performance of the sampling scheme of eNERF with respect to different parameters. We propose a modified sampling scheme for use with eNERF that combines simple random sampling with (parts of) the sampling procedures used by eNERF and a related algorithm sVAT (scalable visual assessment of clustering tendency). We demonstrate that our modified sampling scheme can eliminate over‐sampling of the original progressive sampling scheme, thus enabling the processing of truly VL data. Numerical experiments on a distance matrix of a set of 3,000,000 vectors drawn from a mixture of 5 bivariate normal distributions demonstrate the feasibility and effectiveness of the proposed sampling method. We also find that actually running eNERF on a data set of this size is very costly in terms of computation time. Thus, our results demonstrate that further modification of eNERF, especially the extension stage, will be needed before it is truly practical for VL data. © 2008 Wiley Periodicals, Inc.     

How optimal stimuli for sensory neurons are constrained by network architecture

Identifying the optimal stimuli for a sensory neuron is often a difficult process involving trial and error. By analyzing the relationship between stimuli and responses in feedforward and stable recurrent neural network models, we find that the stimulus yielding the maximum firing rate response always lies on the topological boundary of the collection of all allowable stimuli, provided that individual neurons have increasing input-output relations or gain functions and that the synaptic connections are convergent between layers with nondegenerate weight matrices. This result suggests that in neurophysiological experiments under these conditions, only stimuli on the boundary need to be tested in order to maximize the response, thereby potentially reducing the number of trials needed for finding the most effective stimuli. Even when the gain functions allow firing rate cutoff or saturation, a peak still cannot exist in the stimulus-response relation in the sense that moving away from the optimum stimulus always reduces the response. We further demonstrate that the condition for nondegenerate synaptic connections also implies that proper stimuli can independently perturb the activities of all neurons in the same layer. One example of this type of manipulation is changing the activity of a single neuron in a given processing layer while keeping that of all others constant. Such stimulus perturbations might help experimentally isolate the interactions of selected neurons within a network.     

RoadCompass: following rural roads with vision + ladar using vanishing point tracking

We present a vision- and ladar-based approach to autonomous driving on rural and desert roads that has been tested extensively in a closed-loop system. The vision component uses Gabor wavelet filters for texture analysis to find ruts and tracks from which the road vanishing point can be inferred via Hough-style voting, yielding a direction estimate for steering control. The ladar component projects detected obstacles along the road direction onto the plane of the front of the vehicle and tracks the 1-D obstacle “gap” presumed due to the road to yield a lateral offset estimate. Several image- and state-based tests to detect failure conditions such as off-road poses (i.e., there is no road to follow) and poor lighting due to sun glare or distracting shadows are also explained. The system’s efficacy is demonstrated with analysis of diverse logged data including from the 2005 DARPA Grand Challenge, as well as tests with full control of a vehicle over 15 km of difficult roads at up to 37 km/h with no waypoints.

Survey of modular ontology techniques and their applications in the biomedical domain

In the past several years, various ontologies and terminologies such as the Gene Ontology have been developed to enable interoperability across multiple diverse medical information systems. They provide a standard way of representing terms and concepts thereby supporting easy transmission and interpretation of data for various applications. However, with their growing utilization, not only has the number of available ontologies increased considerably, but they are also becoming larger and more complex to manage. Toward this end, a growing body of work is emerging in the area of modular ontologies where the emphasis is on either extracting and managing "modules" of an ontology relevant to a particular application scenario (ontology decomposition) or developing them independently and integrating into a larger ontology (ontology composition). In this paper, we investigate state-of-the-art approaches in modular ontologies focusing on techniques that are based on rigorous logical formalisms as well as well-studied graph theories. We analyze and compare how such approaches can be leveraged in developing tools and applications in the biomedical domain. We conclude by highlighting some of the limitations of the modular ontology formalisms and put forward additional requirements to steer their future development.     

A Managed IPv6 Transitioning Architecture for Large Network Deployments

With IPv6 transitioning still in a relative state of flux, the need has emerged for methods to control this crucial aspect of IPv6 deployments. This article makes the case for transitioning architectures as a realistic solution to this issue, specifically within large-scale networks. The Site Transitioning Architecture (STA) supports the deployment, operation, and management of IPv6 transitioning mechanisms. The authors provide an overview of the STA's design and operation, noting that its flexible, simple, and secure architecture is suitable for the duration of the IPv6 deployment process, regardless of the underlying network environment.     

Subjects presented at ASHP Midyear Clinical Meetings, 1967-90

The subject matter and trends of presentations made at ASHP Midyear Clinical Meetings (MCMs) were studied. A computerized database of information from MCM and Annual Meeting (AM) program and abstract books was created. The data were analyzed to determine the distribution of MCM presentations by subject and by author for the period 1967 to 1990, determine if there were differences in subjects covered between MCMs and AMs, explore the proposition that there has been duplication of material in MCM presentations, and evaluate the frequency with which MCM presentations have been published in ASHP journals. The total number of presentations made at MCMs from 1967 through 1990 was 8180, while the total for the AMs was 1547 for the two periods (1962-71 and 1985-90) studied. The most common keywords in titles were "pharmacy," "drug," "patient," "hospital," and "service." All International Pharmaceutical Abstracts subject categories and two other categories were represented; presentations in the institutional pharmacy practice category were the most frequent, while pharmacognosy-related presentations were least frequent. The overwhelming number of authors made only one presentation and were listed as the first author. The subjects of presentations were similar between AMs and MCMs. A tendency toward duplication of material was found. Of the 8180 MCM presentations, at least 1005 were published in an ASHP journal. Between 1967 and 1990, presentations at MCMs covered a wide range of subjects but were sometimes duplicative or not on the cutting edge.     

Severity of delayed (“secondary”) cerebral energy failure after acute hypoxia-ischemia is related to the time integral of acute ATP depletion

The aim of this study was to reproduce the delayed (secondary) cerebral energy failure previously described in birth-asphyxiated newborn infants and to investigate relationships between primary insult severity and the extent of the delayed energy failure. Phosphorus (³¹P) magnetic resonance spectroscopy (MRS) at 7 T was used to study the brains of 12 newborn piglets during an acute, reversible, cerebral hypoxic-ischemic episode which continued until nucleotide triphosphates (NTP) were depleted. After reperfusion and reoxygenation, spectroscopy was continued for 48 h. High-energy metabolite concentrations returned to near normal levels after the insult, but later they fell as delayed energy failure developed. The time integral of NTP depletion in the primary insult correlated strongly with the minimum [phosphocreatine (PCr)]/[inorganic orthophosphate (P_i)] observed 24–48 h after the insult. (Linear regression analysis gave slope –8.04 h^–1; ordinate intercept=1.23;r=0.92;P<0.0001.) This model is currently being used to investigate the therapeutic potential of various cerebroprotective strategies including hypothermia.