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At least 73 records · Page 4

Log-normal model for microbial survival in heat sterilization

An analytical model is developed for the survival times of organisms in heat sterilization in which the probability of inactivation as a function of exposure time is log-normally distributed. Experimental data is examined relative to this model and it is concluded that the model is valid except during the initial period of heating when an additional interaction between the organisms and their surrounding medium appears to be present. At long heating times, the log-normal model appears more accurate for extrapolating to low survival probabilities than the usual logarithmic survivor curves and is generally more conservative.

SURVIVAL

Ultrastructure of Pseudomonas saccharophila at early and late log phase of growth.

Description of the fine structure of Pseudomonas saccarophila at the early log phase and the late log phase of growth, such as shown by electron microscopy with the aid of various techniques of preparation. The observations reported suggested that, under the experimental conditions applied, P. saccharophila multiplies by the method of constrictive division.

Young, H. L.

MAIL LOG, program summary and specifications

The summary and specifications to obtain the software package, MAIL LOG, developed for the Scout Project Automatic Data System, SPADS are provided. The MAIL LOG program has four modes of operation: (1) input - putting new records into the data base; (2) revise - changing or modifying existing records in the data base; (3) search - finding special records existing in the data base; and (4) archive - store or put away existing records in the data base. The output includes special printouts of records in the data base and results from the input and search modes.

Harris, D. K.

Log-Based Recovery in Asynchronous Distributed Systems

A log-based mechanism is described for restoring consistent states to replicated data objects after failures. Preserving a causal form of consistency based on the notion of virtual time is focused upon in this report. Causal consistency has been shown to apply to a variety of applications, including distributed simulation, task decomposition, and mail delivery systems. Several mechanisms have been proposed for implementing causally consistent recovery, most notably those of Strom and Yemini, and Johnson and Zwaenepoel. The mechanism proposed here differs from these in two major respects. First, a roll-forward style of recovery is implemented. A functioning process is never required to roll-back its state in order to achieve consistency with a recovering process. Second, the mechanism does not require any explicit information about the causal dependencies between updates. Instead, all necessary dependency information is inferred from the orders in which updates are logged by the object servers. This basic recovery technique appears to be applicable to forms of consistency other than causal consistency. In particular, it is shown how the recovery technique can be modified to support an atomic form of consistency (grouping consistency). By combining grouping consistency with casual consistency, it may even be possible to implement serializable consistency within this mechanism.

Kane, Kenneth Paul

Tracking algorithms using log-polar mapped image coordinates

The use of log-polar image sampling coordinates rather than conventional Cartesian coordinates offers a number of advantages for visual tracking and docking of space vehicles. Pixel count is reduced without decreasing the field of view, with commensurate reduction in peripheral resolution. Smaller memory requirements and reduced processing loads are the benefits in working environments where bulk and energy are at a premium. Rotational and zoom symmetries of log-polar coordinates accommodate range and orientation extremes without computational penalties. Separation of radial and rotational coordinates reduces the complexity of several target centering algorithms, described below.

Weiman, Carl F. R.

A modification of the fusion model for log polar coordinates

The fusion mechanism for application in stereo analysis of range restricted the depth of field and therefore required a shift variant mechanism in the peripheral area to find disparity. Misregistration was prevented by restricting the disparity detection range to a neighborhood spanned by the directional edge detection filters. This transformation was essentially accomplished by a nonuniform resampling of the original image in a horizontal direction. While this is easily implemented for digital processing, the approach does not (in the peripheral vision area) model the log-conformal mapping which is known to occur in the human mechanism. This paper therefore modifies the original fusion concept in the peripheral area to include the polar exponential grid-to-log conformal tesselation. Examples of the fusion process resulting in accurate disparity values are given.

Griswold, N. C.

Parallel O(log n) algorithms for open- and closed-chain rigid multibody systems based on a new mass matrix factorization technique

In this paper, parallel O(log n) algorithms for computation of rigid multibody dynamics are developed. These parallel algorithms are derived by parallelization of new O(n) algorithms for the problem. The underlying feature of these O(n) algorithms is a drastically different strategy for decomposition of interbody force which leads to a new factorization of the mass matrix (M). Specifically, it is shown that a factorization of the inverse of the mass matrix in the form of the Schur Complement is derived as M(exp -1) = C - B(exp *)A(exp -1)B, wherein matrices C, A, and B are block tridiagonal matrices. The new O(n) algorithm is then derived as a recursive implementation of this factorization of M(exp -1). For the closed-chain systems, similar factorizations and O(n) algorithms for computation of Operational Space Mass Matrix lambda and its inverse lambda(exp -1) are also derived. It is shown that these O(n) algorithms are strictly parallel, that is, they are less efficient than other algorithms for serial computation of the problem. But, to our knowledge, they are the only known algorithms that can be parallelized and that lead to both time- and processor-optimal parallel algorithms for the problem, i.e., parallel O(log n) algorithms with O(n) processors. The developed parallel algorithms, in addition to their theoretical significance, are also practical from an implementation point of view due to their simple architectural requirements.

Fijany, Amir

Ulysses log 1992

The Ulysses Log tells the story of some intriguing problems that we (=The Spacecraft Team) have encountered. Ulysses was launched on 6 Oct. 1990, and it made the fastest trip to Jupiter (8 Feb. 1992). It is presently going out of the ecliptic. This paper presents log entries from the following areas: (1) ingenious maneuvers; (2) telecommunication problems; and (3) surprises.

Perez, Raul Garcia

Integrating PCLIPS into ULowell's Lincoln Logs: Factory of the future

We are attempting to show how independent but cooperating expert systems, executing within a parallel production system (PCLIPS), can operate and control a completely automated, fault tolerant prototype of a factory of the future (The Lincoln Logs Factory of the Future). The factory consists of a CAD system for designing the Lincoln Log Houses, two workcells, and a materials handling system. A workcell consists of two robots, part feeders, and a frame mounted vision system.

Mcgee, Brenda J.

The Flux of Carbon from Selective Logging, Fire, and Regrowth in Amazonia

The major goal of this work was to develop a spatial, process-based model (CARLUC) that would calculate sources and sinks of carbon from changes in land use, including logging and fire. The work also included Landsat data, together with fieldwork, to investigate fire and logging in three different forest types within Brazilian Amazonia. Results from these three activities (modeling, fieldwork, and remote sensing) are described, individually, below. The work and some of the personnel overlapped with research carried out by Dr. Daniel Nepstad's LBA team, and thus some of the findings are also reported in his summaries.

Houghton, R. A.

Close-Call Action Log Form

"Close Call Action Log Form" ("CCALF") is the name of both a computer program and a Web-based service provided by the program for creating an enhanced database of close calls (in the colloquial sense of mishaps that were avoided by small margins) assigned to the Center Operations Directorate (COD) at Johnson Space Center. CCALF provides a single facility for on-line collaborative review of close calls. Through CCALF, managers can delegate responses to employees. CCALF utilizes a pre-existing e-mail system to notify managers that there are close calls to review, but eliminates the need for the prior practices of passing multiple e-mail messages around the COD, then collecting and consolidating them into final responses: CCALF now collects comments from all responders for incorporation into reports that it generates. Also, whereas it was previously necessary to manually calculate metrics (e.g., numbers of maintenance-work orders necessitated by close calls) for inclusion in the reports, CCALF now computes the metrics, summarizes them, and displays them in graphical form. The reports and all pertinent information used to generate the reports are logged, tracked, and retained by CCALF for historical purposes.

Spuler, Linda M.

Position Paper - pFLogger: The Parallel Fortran Logging framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or logger) similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Fortran

POSITION PAPER - pFLogger: The Parallel Fortran Logging Framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Clune, Thomas L.

pFlogger: The Parallel Fortran Logging Utility

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger)' similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Clune, Tom

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]