NBC News Scripts
WBAP-TV (Television station : Fort Worth, Tex.)
1954-12-31
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70 records were found.
Obra perteneciente al Fondo Antiguo de la Biblioteca de la USAL
Taking into account the environmental impacts of biofuel production is essential to develop new and innovative low-emission processes. The assessment of life cycle GreenHouse Gas (GHG) emissions of biofuel is mandatory for the countries of the European Union. New biomass resources that hardly compete with food crops are been developed increasingly. Microalgae are an interesting alternative to terrestrial biomass thanks to their high photosynthetic efficiency and their ability to accumulate lipids. This article provides an analysis of potential environmental impacts of the production of algal biofuel for aviation using the Life Cycle Assessment (LCA). Evaluated impacts are GHG emissions and the primary energy consumption, from extraction of raw materials to final waste treatment. This study compared two management choices for oilcakes g...
Many kinds of Evolutionary Algorithms (EAs) have been described in the literature since the last 30 years. However, though most of them share a common structure, no existing software package allows the user to actually shift from one model to another by simply changing a few parameters, e.g. in a single window of a Graphical User Interface. This paper presents GUIDE, a Graphical User Interface for DREAM Experiments that, among other user-friendly features, unifies all kinds of EAs into a single panel, as far as evolution parameters are concerned. Such a window can be used either to ask for one of the well known ready-to-use algorithms, or to very easily explore new combinations that have not yet been studied. Another advantage of grouping all necessary elements to describe virtually all kinds of EAs is that it creates a fantastic pedag...
We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.
Markov chain Stochastic comparison Quasistationary measure
In this paper, we propose a new approach to the performance supervision of complex and heterogeneous infrastructures found in hybrid cloud networks, which typically consist of hundreds or thousands of interconnected servers and networking devices. This hardware and the quality of the interconnections is monitored by sampling specific metrics (such as bandwidth usage, CPU time, packet loss...) using probes, and raising alarms in case of an anomaly. We study an Artificial Immune Ecosystem model derived from the Artificial Immune Systems (AIS) algorithms to perform distributed analysis of the data collected throughout the network by these probes. In particular, we use the low variability of the measured data to derive statistical approaches to outlier detection, instead of the traditional stochastic antibody generation and selection metho...
Adaptive dynamics so far has been put on a rigorous footing only for clonal inheritance. We extend this to sexually reproducing diploids, although admittedly still under the restriction of an unstructured population with Lotka-Volterra-like dynamics and single locus genetics (as in Kimura's 1965 infinite allele model). We prove under the usual smoothness assumptions, starting from a stochastic birth and death process model, that, when advantageous mutations are rare and mutational steps are not too large, the population behaves on the mutational time scale (the 'long' time scale of the literature on the genetical foundations of ESS theory) as a jump process moving between homozygous states (the trait substitution sequence of the adaptive dynamics literature). Essential technical ingredients are a rigorous estimate for the probability o...
This paper presents an algorithm which has been used by PPSN VI organisers to allocate papers to reviewers, which is typically a Constraint Satisfaction Problem. Its aim is not to present a new revolutionary competitive method to solve CSPs, but rather to show how such a problem can be simply implemented using EASEA, a language designed specifically to write evolutionary algorithms.
The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.
