Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Flow updating: fault-tolerant aggregation for dynamic networks

Documento submetido para revisão pelos pares. A publicar em Journal of Parallel and Distributed Computing. ISSN 0743-7315

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Primary pineal tumors: outcome and prognostic factors-a study from the Rare Cancer Network (RCN).

PURPOSE: To better define outcome and prognostic factors in primary pineal tumors. MATERIALS AND METHODS: Thirty-five consecutive patients from seven academic centers of the Rare Cancer Network diagnosed between 1988 and 2006 were included. Median age was 36 years. Surgical resection consisted of biopsy in 12 cases and resection in 21 (2 cases with unknown resection). All patients underwent radiotherapy and 12 patients received also chemotherapy. RESULTS: Histological subtypes were pineoblastoma (PNB) in 21 patients, pineocytoma (PC) in 8 patients and pineocytoma with intermediate differentiation in 6 patients. Six patients with PNB had evidence of spinal seeding. Fifteen patients relapsed (14 PNB and 1 PC) with PNB cases at higher risk (p = 0.031). Median survival time was not reached. Median disease-free survival was 82 months (CI 50 % 28-275). In univariate analysis, age younger than 36 years was an unfavorable prognostic factor (p = 0.003). Patients with metastases at diagnosis had poorer survival (p = 0.048). Late side effects related to radiotherapy were dementia, leukoencephalopathy or memory loss in seven cases, occipital ischemia in one, and grade 3 seizures in two cases. Side effects related to chemotherapy were grade 3-4 leucopenia in five cases, grade 4 thrombocytopenia in three cases, grade 2 anemia in two cases, grade 4 pancytopenia in one case, grade 4 vomiting in one case and renal failure in one case. CONCLUSIONS: Age and dissemination at diagnosis influenced survival in our series. The prevalence of chronic toxicity suggests that new adjuvant strategies are advisable.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Information Mobility: Information Dissemination in Mobile Networks

En el nostre projecte, considerem un escenari urbà o interurbà on persones amb dispositius mòbils (smartphones) o vehicles equipats amb interfícies de comunicació, estan interessats en compartir fitxers entre ells o descarregar-los al creuar Punts d’Accés (APs) propers a la carretera. Estudiem la possibilitat d’utilizar la cooperació en les trobades casuals entre nodes per augmentar la velocitat de descàrrega global. Amb aquest objectiu, plantejem algoritmes per a la selecció de quins paquets, per a quins destins i quins transportistes s’escullen en cada moment. Mitjançant extenses simulacions, mostrem com les cooperacions carry&forward dels nodes augmenten significativament la velocitat de descàrrega dels usuaris, i com aquest resultat es manté per a diversos patrons de mobilitat, col•locacions d'AP i càrregues de la xarxa. Per altra banda, aparells com els smartphones, on la targeta de WiFi està encesa contínuament, consumeixen l'energia de la bateria en poques hores. En molts escenaris, una targeta WiFi sempre activa és poc útil, perque sovint no hi ha necessitat de transmissió o recepció. Aquest fet es veu agreujat en les Delay Tolerant Networks (DTN), on els nodes intercanvien dades quan es creuen i en tenen l’oportunitat. Les tècniques de gestió de l’estalvi d’energia permeten extendre la duració de les bateries. El nostre projecte analitza els avantatges i inconvenients que apareixen quan els nodes apaguen períodicament la seva targeta wireless per a estalviar energia en escenaris DTN. Els nostres resultats mostren les condicions en que un node pot desconnectar la bateria sense afectar la probabilitat de contacte amb altres nodes, i les condicions en que aquesta disminueix. Per exemple, es demostra que la vida del node pot ser duplicada mantenint la probabilitat de contacte a 1. I que aquesta disminueix ràpidament en intentar augmentar més la vida útil.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption

An ATM distributed simulator for network management research

Due to the high cost of a large ATM network working up to full strength to apply our ideas about network management, i.e., dynamic virtual path (VP) management and fault restoration, we developed a distributed simulation platform for performing our experiments. This platform also had to be capable of other sorts of tests, such as connection admission control (CAC) algorithms, routing algorithms, and accounting and charging methods. The platform was posed as a very simple, event-oriented and scalable simulation. The main goal was the simulation of a working ATM backbone network with a potentially large number of nodes (hundreds). As research into control algorithms and low-level, or rather cell-level methods, was beyond the scope of this study, the simulation took place at a connection level, i.e., there was no real traffic of cells. The simulated network behaved like a real network accepting and rejecting SNMP ones, or experimental tools using the API node

Availability analysis of GMPLS connections based on physical network topology

This paper presents a study of connection availability in GMPLS over optical transport networks (OTN) taking into account different network topologies. Two basic path protection schemes are considered and compared with the no protection case. The selected topologies are heterogeneous in geographic coverage, network diameter, link lengths, and average node degree. Connection availability is also computed considering the reliability data of physical components and a well-known network availability model. Results show several correspondences between suitable path protection algorithms and several network topology characteristics

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Final Report for the Iowa Livestock Industry Waste Characterization and Methane Recovery Information Dissemination Project, 2003

The Iowa livestock industry generates large quantities of manure and other organic residues; composed of feces, urine, bedding material, waste feed, dilution water, and mortalities. Often viewed as a waste material, little has been done to characterize and determine the usefulness of this resource. The Iowa Department of Natural Resources initiated the process to assess in detail the manure resource and the potential utilization of this resource through anaerobic digestion coupled with energy recovery. Many of the pieces required to assess the manure resource already exist, albeit in disparate forms and locations. This study began by interpreting and integrating existing Federal, State, ISU studies, and other sources of livestock numbers, housing, and management information. With these data, models were analyzed to determine energy production and economic feasibility of energy recovery using anaerobic digestion facilities on livestock faxms. Having these data individual facilities and clusters that appear economically feasible can be identified specifically through the use of a GIs system for further investigation. Also livestock facilities and clusters of facilities with high methane recovery potential can be the focus of targeted educational programs through Cooperative Extension network and other outreach networks, providing a more intensive counterpoint to broadly based educational efforts.