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Yaveroglu, Omer Nebil. "Graphlet correlations for network comparison and modelling : World Trade Network example." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/25523.
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We propose methods on two fundamental graph theoretic problems: (1) network comparison, and (2) network modelling. Our methods are applied to five real-world network types, with an emphasis on world trade networks (WTNs), which we choose due to the world's current economic crisis. Finding topological similarities of complex networks is computationally intractable due to NP-Completeness of the subgraph isomorphism problem. Hence, simple heuristics have been used for this purpose. The most sophisticated heuristics are based on graph spectra and small subnetworks including graphlets. Among these, graphlets are preferred since spectra do not provide a direct real-world interpretation of network structure. However, current graphlet-based techniques can be improved. We improve graphlet-based heuristics by defining a new network topology descriptor, Graphlet Correlation Matrix (GCM), which eliminates all redundancies and quantifies the dependencies in graphlet properties. Then, we introduce a new network distance measure, Graphlet Correlation Distance (GCD), that compares GCMs of two networks. We show that GCD has the best network classification performance, is highly noise-tolerant, and is computationally efficient. Using this methodology, we highlight a three-layer organization in the WTNs: core, broker, and periphery. Furthermore, we uncover the link between the dynamic changes in oil price and trade network topology. Network models should shed light on the rules governing the formation of real networks. Using GCD, we identify models that fit five real-world network types. However, none of these standard network models fit WTNs. Hence, we introduce two new network models: one that mimics the Gravity Model of Trade, and the other that mimics brokerage / peripheral positioning of a country in WTN. Also, we show that economic wealth indicators of a country are predictive of its future brokerage position. Finally, we use exponential-family random graph modelling approach to build a generic framework that enables modelling based on any graphlet property.
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El, Boustani Sami. "Learning and coding correlations in stochastic network states." Paris 6, 2010. http://www.theses.fr/2010PA066279.
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L'activité des réseaux de neurones corticaux est caractérisée par des motifs de décharge stochastiques. Cependant, la nature exacte de ces états, et comment un apprentissage stable et un codage d'information peuvent se produire dans de tels états, restent mal compris. Nous avons d'abord étudié des modèles de réseaux de neurones capables de reproduire les régimes d'activité observés in vivo. Ces régimes asynchrones et irréguliers sont modélisés à l'aide d’une description Markovienne en utilisant une équation maîtresse phénoménologique. Afin de disséquer les corrélations présentes dans l'activité corticale, nous avons développé différents outils d'analyse. Nous trouvons que la réponse sous-liminaire de neurones dans V1 peut refléter les corrélations dans le stimulus visuel. Au niveau extracellulaire, nous avons développé un modèle d'Ising qui peut prédire précisément le taux d'occurrence des motifs spatio-temporels de décharge de plusieurs neurones enregistrés simultanément. Nous nous sommes ensuite intéressés à la question du codage de corrélations in vivo. Des enregistrements extracellulaires ont été effectués dans le cortex à tonneaux du rat anesthésié. Nous avons trouvé que le niveau de corrélation affecte les propriétés intégratives des neurones enregistrés. Un modèle fonctionnel suggère que ce résultat peut s'expliquer par des interactions entre cellules de sélectivité opposée. Finalement, nous avons proposé un modèle basé sur la règle de plasticité STDP qui peut stabiliser l'apprentissage dans l'activité spontanée. Ce modèle est équivalent à une règle BCM et peut reproduire des résultats dans l'hippocampe où la méta-plasticité a été observée pour la première fois.
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Jovanovic, Stojan. "Correlations of Higher Order in Networks of Spiking Neurons." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193316.
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The topic of this dissertation is the study of the emergence of higher-order correlations in recurrentlyconnected populations of brain cells.Neurons have been experimentally shown to form vast networks in the brain. In these networks, eachbrain cell communicates with tens of thousands of its neighbors by sending out and receiving electricalsignals, known as action potentials or spikes. The effect of a single action potential can propagate throughthe network and cause additional spikes to be generated. Thus, the connectivity of the neuronal networkgreatly influences the network's spiking dynamics. However, while the methods of action potentialgeneration are very well studied, many dynamical features of neuronal networks are still only vaguelyunderstood.The reasons for this mostly have to do with the difficulties of keeping track of the collective, non-linearbehavior of hundreds of millions of brain cells. Even when one focuses on small groups of neurons, all butthe most trivial questions about coordinated activity remain unanswered, due to the combinatorialexplosion that arises in all questions of this sort. In theoretical neuroscience one often needs to resort tomathematical models that try to explain the most important dynamical phenomena while abstractingaway many of the morphological features of real neurons.On the other hand, advances in experimental methods are making simultaneous recording of largeneuronal populations possible. Datasets consisting of collective spike trains of thousands of neurons arebecoming available. With these new developments comes the possibility of finally understanding the wayin which connectivity gives rise to the many interesting dynamical aspects of spiking networks.The main research question, addressed in this thesis, is how connectivity between neurons influences thedegree of synchrony between their respective spike trains. Using a linear model of spiking neurondynamics, we show that there is a mathematical relationship between the network's connectivity and theso-called higher-order cumulants, which quantify beyond-chance-level coordinated activity of groups ofneurons. Our equations describe the specific connectivity patterns that give rise to higher-ordercorrelations. In addition, we explore the special case of correlations of third-order and find that, in large,regular networks, it is the presence of a single subtree that is responsible for third-order synchrony.In summary, the results presented in this dissertation advance our understanding of how higher-ordercorrelations between spike trains of neurons are affected by certain patterns in synaptic connectivity.Our hope is that a better understanding of such complicated neuronal dynamics can lead to a consistenttheory of the network's functional properties.QC 20161003
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Charles, Adam Shabti. "Dynamics and correlations in sparse signal acquisition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.
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One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth.
One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited.
While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference.
Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].
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Calderini, Matias. "Linear Discriminant Analysis and Noise Correlations in Neuronal Activity." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39962.
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The effects of noise correlations on neuronal stimulus discrimination have been the subject of sustained debate. Both experimental and computational work suggest beneficial and detrimental contributions of noise correlations. The aim of this study is to develop an analytically tractable model of stimulus discrimination that reveals the conditions leading to improved or impaired performance from model parameters and levels of noise correlation.
We begin with a mean firing rate integrator model as an approximation of underlying spiking activity in neuronal circuits. We consider two independent units receiving constant input and time fluctuating noise whose correlation across units can be tuned independently of firing rate. We implement a perceptron-like readout with Fisher Linear Discriminant Analysis (LDA). We exploit its closed form solution to find explicit expressions for discrimination error as a function of network parameters (leak, shared inputs, and noise gain) as well as the strength of noise correlation.
First, we derive equations for discrimination error as a function of noise correlation. We find that four qualitatively different sets of results exist, based on the ratios of the difference of means and variance of the distributions of neural activity. From network parameters, we find the conditions for which an increase in noise correlation can lead to monotonic decrease or monotonic increase of error, as well as conditions for which error evolves non-monotonically as a function of correlations. These results provide a potential explanation for previously reported contradictory effects of noise correlation.
Second, we expand on the dependency of the quantitative behaviour of the error curve on the tuning of specific subsets of network parameters. Particularly, when the noise gain of a pair of units is increased, the error rate as a function of noise correlation increases multiplicatively. However, when the noise gain of a single unit is increased, under certain conditions, the effect of noise can be beneficial to stimulus discrimination.
In sum, we present a framework of analysis that explains a series of non-trivial properties of neuronal discrimination via a simple linear classifier. We show explicitly how different configurations of parameters can lead to drastically different conclusions on the impact of noise correlations. These effects shed light on abundant experimental and computational results reporting conflicting effects of noise correlations. The derived analyses rely on few assumptions and may therefore be applicable to a broad class of neural models whose activity can be approximated by a multivariate distribution.
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Kawaguchi, Akio, Hiraku Okada, Takaya Yamazato, and Masaaki Katayama. "Correlations of noise wavements at different outlets in a power-line network." IEEE, 2006. http://hdl.handle.net/2237/7797.
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Thome, Alexander. "Experience-Dependent Network Modification in the Medial Temporal Lobe." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223358.
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Theoretical models of information storage in the brain have suggested that neurons may undergo an experience-dependent tuning or sharpening of their representations in order to maximize the amount of information that can be stored. Changes in the tuning profiles of neurons have been demonstrated to occur when animals must learn perceptual discriminations, however, whether similar changes occur in the absence of behavioral demands is unclear. To address these questions, the activity of simultaneously recorded medial temporal lobe (MTL) neurons was studied in relation to a passive visual recognition memory task. The structure of this task was such that it allowed for a comparison between novelty related responses as well as tuning properties of individual neurons. A total of 565 well isolated single neurons were recorded. The first contribution of this dissertation is the finding of a dissociation between different medial temporal lobe regions such that neurons in temporal area F (TF), but not perirhinal cortex (PRC) or the hippocampus, show an experience-dependent change in their stimulus selectivity. This finding indicates that tuning of stimulus representations may be an effective mechanism for maximizing information storage in some brain regions. The absence of stimulus tuning in higher level association regions (i.e. TF and PRC) suggests that tuning in these regions may be disadvantageous due to the need to construct unified representations across sensory modalities. A complimentary question to the question of network storage capacity is how networks avoid saturation in the connections between neurons. The second contribution of this dissertation is the finding that there exists a decrease in the magnitude of the short time scale correlations between pairs of neurons; suggesting that networks reduce the number of connections between neurons as a stimulus becomes familiar. Gamma oscillations have been proposed to be the mechanism by which groups of neurons coordinate their activity. However, network coordination has only been indirectly measured. The final contribution of this dissertation is the finding that the magnitude of gamma oscillations is strongly correlated with enhanced magnitude of correlations between neurons.
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Merve, Akis. "Temporal and Spatial Interference Correlations in Cognitive Radio Networks with Vertical Cooperation." Thesis, KTH, Kommunikationsnät, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91892.
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Cognitive radio technology provides a solution for the spectrum scarcity issue by allowing the unlicensed users which are the cognitive radio devices to share the licensed band with the licensed (primary) users. The abilities of cognitive radio device help the secondary (unlicensed) nodes to observe the licensed band and to adjust their transmission parameters for maintaining the primary communication since in cognitive radio networks, it is essential that the existence of the unlicensed users must not harm the licensed network. Under these circumstances, we consider vertical cooperative transmission where primary transmission is not severely damaged by the secondary interference since each primary pair (transmitter and receiver) selects a neighbor secondary user as a cooperative relay to assist their transmission. Cooperation provides an increase in the signal-tointerference-ratio (SIR) of the primary network which can be harnessed by the secondary network as an additional bandwidth for their communication. We propose three relay selection rules so the influence of the relay’s position over the temporal and spatial correlations can be evaluated for different network conditions. Additionally, we implement primary exclusive region (PER) for each primary pair in the network which covers primary nodes, and all secondary users locate inside the zone become inactive. According to the outage model proposed in paper [2], temporal and spatial correlations are assumed to be 1 and 0 respectively; however it is estimated that regarding the environmental factors and the relay’s location, these correlation values may vary. This thesis work is based on the validation of the assumptions provided in paper [2] and our results demonstrate that the temporal and spatial correlation values changes under different circumstances and with different relay selection rules. The simulation results also show that PER significantly stimulates the cooperation performance thereby increases the transmission quality of the primary network.
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Kraft, Tristan [Verfasser], and Otfried [Gutachter] Gühne. "Aspects of quantum resources: coherence, measurements, and network correlations / Tristan Kraft ; Gutachter: Otfried Gühne." Siegen : Universitätsbibliothek der Universität Siegen, 2020. http://d-nb.info/1225557860/34.
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Wu, Gang Mechanical & Manufacturing Engineering Faculty of Engineering UNSW. "The impact of inter-company network technology on correlations between supply chain drivers and performance measures." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2009. http://handle.unsw.edu.au/1959.4/43645.
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This research aims to examine how, and to what extent, the advanced network technology such as custom-built large-scale network, or internet-based technology contribute to the correlations between supply chain drivers and performance measures. The uniqueness of the research is to use network technology as a leverage factor, instead of merely one of the supply chain drivers, to analyse how it would impact on the correlations between supply chain drivers and performance measures. Through literature review, we identified the key drivers in supply chain and the key performance indicators as independent and dependent variables respectively for data analysis in the research. We consider the utilisation of network technology as a selection variable in the analysis. We also proposed a set of research questions and hypotheses resulting from the literature review. The subsequent data analyses attempted to find answers for these questions and test the validity of the hypotheses. This was achieved by a field survey for 1035 major Australian firms through a structured questionnaire. The response rate of the survey was 20.8%. All these data were analysed with statistical models such as reliability test, multi-collinearity test, MANOVA procedures, factor analysis, and multiple regression modelling to validate whether the survey was robust and how the leverage factor (network technology) would impact on the correlations between supply chain drivers and performance measures. Each research question and hypothesis was reviewed, validated, and concluded based on the results from data analysis. The key findings from the data analysis support the perception that the network technologies with their external customers and suppliers dramatically affect the correlations between supply chain drivers and performance measures. Statistically it actually determines whether the supply chain will success or fail when comparing firms using the technologies with firms not using them. In general, the impact on the correlations is directional and positive. A set of validated theoretical models was also proposed to depict the dynamics between supply chain variables under the influence of network technology. Implications of the findings are also provided in the thesis.
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Hwang, Tony. "A exploratory study into the correlations between the cultural composition of social support network and acculturative stress for international students." Thesis, California State University, Fullerton, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3581381.
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One of most significant challenges for international students studying in the U.S. is their ability to adjust to a new social setting. The maladjustment of international students in a host country has been associated with negative impacts to their psychosocial development, educational experience, and perception of the host culture. The increasing demand to recruit and enroll international students in colleges and universities across the U.S. prompts the need to further investigate the various factors that impact the cross-cultural and educational experiences of these sojourning scholars.
This correlational study was conducted using a 65-item online survey instrument. The population under investigation was international undergraduate students who have been studying in the U.S. for at least one academic year. The sample for this population was taken from three public higher education institutions in southern California. The total sample size of the study was 368 participants, One-way ANOVA and hierarchical multiple regressions were conducted to analyze the data and answer the research questions.
Overall, this study found that a relationship exists between the cultural composition of social support networks and the acculturative stress of international students. The results of the analysis indicate that international students who are more likely to seek support from members of their support network who are from a different culture experienced lower levels of acculturative stress. Furthermore, the findings suggest that the cultural composition of a social support network is positively correlated with feelings of homesickness.
The findings from this study can inform the practice of student affairs personnel responsible for working with international students. it can also inform institutional policies related to the strategic planning of increasing the enrollment of international students on a campus. This study contributes to the existing body of knowledge aimed at understanding the specific needs of international students by investigating the relationship between the cultural composition of social support networks and acculturative stress.
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Tonner, Anna. "Accessibility to green area qualities in the Stockholm region and their possible correlations to property values : A GIS-based network analysis." Thesis, KTH, Hållbar utveckling, miljövetenskap och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278156.
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The concept of accessibility has in recent years been more used in urban planning, where access to urban services and attractive places, such as green areas, is desirable. In a rapidly growing city region like Stockholm, accessibility is especially important in order to provide an attractive and sustainable region. Several studies have supported the positive correlation between green areas, human well- being and sustaining a good quality of life. However, it is rather the qualities possessed by a green area that is attractive and not necessarily the space itself. Moreover, access to green areas is considered to affect the property values and can further indicate whether a green area quality is demanded or not since it may differ depending on the green area type and its quality. Although studies concerning accessibility to green areas and the correlation to property values already have been conducted to some extent for Stockholm, the combination of qualitative green areas have not been extensively researched for the entire region with a network analysis approach. The purpose of this study is to measure the accessibility to green area qualities within the Stockholm region and further assess whether the measured distances correlate with the property values within the region. In this thesis, four green area qualities are selected, based on experience values, which are: Spacious, Quietness, Parks and Protected areas for biodiversity. These green area qualities are considered to contribute to individual’s quality of life, but for their usefulness from a regional planning perspective, a combination of different qualities could have been more attractive. The access is further measured from each dwelling in the Stockholm region to these green area qualities, via the pedestrian road network, for different geographical divisions within the region. These four divisions are: Archipelago areas, Urban countryside areas, Countryside areas and Urban areas. The accessibility analysis is conducted by using a GIS-based network analysis. The correlation between the measured distances and the property values per area is determined by using the Pearson correlation method. The results show that the access to spacious green areas generally is at least good in the entire region, while the access to quietness and protected areas for biodiversity generally is poor. The access to parks is only measured for Urban areas, due to limited mapping, and the results show that a majority have at least good access. Also, the results show that dwellings generally have best access to spacious green areas, while they have poorest access to quietness within the entire region. Dwellings located in the outer parts of the Urban areas had poorer access to Parks than in the more central areas. Moreover, the results indicate that there are differences in access for different geographical divisions within the region and it can be concluded that Urban areas have generally poorer access than the more rural areas. It is found that all different datasets have a positive correlation between the measured distance and the property values per areal, which means that dwellings located far away from these green area qualities have higher property values. The exceptions were for the green area quality: Parks, in Urban areas, and the green area quality: Protected areas for biodiversity, in Urban countryside areas, which instead had a negative correlation. Basically, this means that these dwellings located close to parks in Urban areas and close to protected areas for biodiversity in Urban countryside areas have higher property values. The study further shows that the correlation between all datasets is weak. These weak correlations are however assumed to be affected by other location factors that may have directly opposite correlations to these green area qualities, such as access to the city center, public transport and other urban services. However, all correlations in this study are found to be statistically significant, which mean it can be concluded that a true correlation exists, although it is weak. For the statistical results to be more useful from a regional planning perspective, other location factors could be analyzed as well, in order to get a better understanding of the statistical results. Finally, the results from both the accessibility analysis and the statistical analysis can be used as a basis for future planning and as a spatial decision support to sustain a good quality of life within the Stockholm region.Konceptet tillgänglighet har under de senaste åren använts mer frekvent inom stadsplanering, där tillgänglighet till urban service och attraktiva platser, så som grönområden, är önskvärt. I en snabbt växande stadsregion som Stockholm är tillgänglighet extra viktigt för att bidra till en attraktiv och hållbar region. Ett flertal studier stödjer att det finns en positiv korrelation mellan grönområden, mänskligt välmående och att upprätthålla en god livskvalitet. Det är dock snarare kvaliteterna ett grönområde besitter som är attraktivt och inte nödvändigtvis själva ytan. Följaktligen kan tillgänglighet till grönområden anses påverka fastighetsvärdet och kan vidare ge en indikation om vilka grönområdeskvaliteter som efterfrågas, då det kan variera för olika grönområdestyper med olika kvaliteter. Trots att studier angående tillgänglighet till grönområden och korrelationen till fastighetsvärden redan har utförts i viss utsträckning i Stockholm, saknas kombinationen med grönområdeskvaliteter på en regional nivå med ett nätverksanalytiskt angreppssätt. Syftet med denna studie är att mäta tillgängligheten till grönområdeskvaliteter i Stockholmsregionen och vidare att bedöma om det uppmätta avståndet mellan fastigheter och grönområden korrelerar med fastighetsvärden i regionen. I studien väljs fyra grönområdeskvaliteter baserat på upplevelsevärden, vilka är: Rymd, Rofylldhet, Park och Skyddade områden för biodiversitet. Dessa kvaliteter antas bidra till ökad livskvalitet, men dess användbarhet inom regional planering skulle kunna vara mer attraktiv om de kombineras med andra kvaliteter i analysen. Tillgängligheten mäts från varje bostad i Stockholmsregionen till dessa grönområdeskvaliteter, via gångvägsnätet, för olika geografiska områden inom regionen, vilka är: Skärgård, Tätortsnära landsbygd, Landsbygd och Tätort. Detta utförs genom att använda en GIS-baserad nätverksanalys. Korrelationen mellan det uppmätta avståndet och fastighetsvärden per areal bestäms genom att använda Pearsons korrelationsmetod. Resultatet från tillgänglighetanalysen visar på att tillgängligheten till rymliga grönområden generellt sett är bra eller mycket bra i hela regionen, medan tillgängligheten till rofylldhet och skyddade områden för biodiversitet generellt sett är dålig. Tillgängligheten till parker är endast uppmätt för urbana områden på grund av begränsad kartering och resultatet visar på att majoriteten har bra eller mycket bra tillgänglighet. Följaktligen indikerar resultaten att tillgängligheten skiljer sig för de olika geografiska områdena inom regionen och det kan konstateras att urbana områden generellt sett har sämre tillgänglighet än landsbygdsområden. Dessutom indikerar resultaten att tillgängligheten varierar mellan de olika grönområdeskvaliteterna. Användbarheten av tillgänglighetsresultaten antas vara relevant för framtida regionala planerare som ämnar fokusera på dessa specifika kvaliteter. För att få ett mer heltäckande perspektiv angående befolkningens livskvalitet i regionen behövs dock även andra grundläggande kvaliteter inkluderas i tillgänglighetsanalysen. Kvaliteterna i denna studie kan dock bidra som en del i bedömningen av regional tillgänglighet med fokus på att upprätthålla en god livskvalitet. Den statistiska analysen visar att alla olika dataset har en positiv korrelation mellan det uppmätta avståndet och fastighetsvärdet per areal, vilket innebär att fastighetsvärdet ökar för bostäder som ligger långt ifrån dessa grönområdeskvaliteter. Undantagen för detta är: Park, i urbana områden, och Skyddade områden för biodiversitet, i tätortsnära landsbygd, som istället har en negativ korrelation. Detta innebär att fastighetsvärdet ökar för bostäder som ligger nära parker i urbana områden och skyddade områden för biodiversitet i tätortsnära landsbygd. Studien visar även på att korrelationerna mellan samtliga nämnda variabler är svaga. Dessa svaga korrelationer antas bero på inverkan av andra samverkande lägesfaktorer, såsom tillgänglighet till stadskärnan, kollektivtrafik och annan urban service, som möjligtvis har direkt motsatt korrelation till dessa grönområdeskvaliteter. Samtliga korrelationer är dock statistiskt signifikanta, vilket innebär att slutsatsen kan dras att det finns en sann korrelation, även om den är svag. För att få det statistiska resultatet mer användbart för regional planering kan även andra lägesfaktorer analyseras för att få en bättre förståelse för det statistiska resultatet. Slutligen kan resultaten från både tillgänglighetsanalysen och den statistiska analysen användas som ett underlag för framtida planering och som en del av ett rumsligt beslutsstöd för att upprätthålla en god livskvalitet inom Stockholmsregionen.
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Dash, Shreeram J. "Aging of Selenium glass probed by MDSC and Raman Scattering Experiments: Growth of inter-chain structural correlations leading to network compaction." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1490354472387536.
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Leo, Yannick. "Deep dive into social network and economic data : a data driven approach for uncovering temporal ties, human mobility, and socioeconomic correlations." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEN066/document.
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Dans cette thèse, j'étudie des jeux de données concernant des liens sociaux entre personnes (appels et SMS), leur mobilité ainsi que des informations économiques sur ces personnes, comme leur revenu et leurs dépenses. Les sept travaux couvrent un spectre assez large et apportent des contributions en informatique des réseaux mais aussi en sociologie, économie et géographie. Les questions posées sont très diverses. Comment quantifier la perte d'information causée par une agrégation de flot de liens en série de graphe ? Comment inférer les mouvements des utilisateurs quand on ne connaît que les localisations des utilisateurs aux moments des appels, et que l'on ne détecte donc que les mouvements qui ont eu lieu entre deux appels consécutifs, sans connaître leur nombre ni les instants auxquels ils ont lieu ? Est-il possible de transmettre des SMS dans une région dense en utilisant la densité des téléphones, la mobilité des utilisateurs ainsi que la localité des messages échangés ? Est-il possible de comprendre les inégalités sociales avec une approche Big Data ? Cette dernière question fait l'objet d'une première étude socio-économique approfondie au prisme du Big Data. Il a été possible d'étudier à grande échelle la stratification de la société, l'existence de clubs de riches, la ségrégation spatiale et la structure des dépenses par classe sociale.Au delà de la variété de ces études et de ces nombreuses applications, cette thèse montre que l'analyse de données individuelles riches à l'échelle d'une population permettent de répondre à de nouvelles questions et à d'anciennes hypothèses avec une approche Big Data. Cette thèse tient à mettre l'accent sur la potentialité d'une approche Big Data mais aussi de sa complémentarité avec les approches classiques (modélisation, sociologie avec enquêtes, …). Un effort particulier a été mis dans l'explication des étapes qui amènent aux résultats et dans la prise en compte des biais ce qui est trop souvent négligéIn this thesis, I have carried out data-driven studies based on rich, large-scale combined data sets including social links between users (calls and SMS), their demographic parameters (age and gender), their mobility and their economic information such as income and spendings. These seven studies bring insights in network science but also in sociology, economy and geography. The questions asked are very diversified. How can one quantify the loss of temporal information caused by the aggregation of link streams into series of graphs? How can one infer mobility of a user from his or her localisations of calls? Is it possible to transmit SMS in a dense region by using the density of phones, the mobility of users and the locality of the messages? How can one quantify and prove empirically the social stratification of the society at a large population scale? I present, for this last question, a first socio-economic study with a data-driven approach. It has been possible to study, at a very large scale, the stratification of the society, the existence of "rich-clubs", the spatial segregation and purchase patterns for each social class. Beyond the variety of studies and their numerous applications, this thesis shows that the analysis of individual rich combined datasets at a large population scale gives the opportunity to answer long-standing hypotheses and to address novel questions. This work not only points out the potentiality of Big Data approach but also its complementarity to classical approaches (modelization, surveys, …). Particular attention was given in order to explain each steps that lead to results and to take into account biases which is too often neglected
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Czischek, Stefanie [Verfasser], and Thomas [Akademischer Betreuer] Gasenzer. "Simulating Strongly Interacting Quantum Spin Systems–From Critical Dynamics Towards Entanglement Correlations in a Classical Artificial Neural Network / Stefanie Czischek ; Betreuer: Thomas Gasenzer." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/119790431X/34.
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Murgham, Haithem Abualasaad. "Enhancing and Expanding Conventional Simulation Models of Refrigeration Systems for Improved Correlations." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544524232130074.
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Tupikina, Liubov. "Temporal and spatial aspects of correlation networks and dynamical network models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17746.
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In der vorliegenden Arbeit untersuchte ich die komplexen Strukturen von Netzwerken, deren zeitliche Entwicklung, die Interpretationen von verschieden Netzwerk-Massen und die Klassen der Prozesse darauf. Als Erstes leitete ich Masse für die Charakterisierung der zeitlichen Entwicklung der Netzwerke her, um räumlich Veränderungsmuster zu erkennen. Als Nächstes führe ich eine neue Methode zur Konstruktion komplexer Netzwerke von Flussfeldern ein, bei welcher man das Set-up auch rein unter Berufung Berufung auf das Geschwindigkeitsfeld ändern kann. Diese Verfahren wurden für die Korrelationen skalarer Grössen, z. B. Temperatur, entwickelt, welche eine Advektions-Diffusions-Dynamik in der Gegenwart von Zwingen und Dissipation. Die Flussnetzwerk-Methode zur Zeitreihenanalyse konstruiert die Korrelationsmatrizen und komplexen Netzwerke. Dies ermöglicht die Charakterisierung von Transport in Flüssigkeiten, die Identifikation verschiedene Misch-Regimes in dem Fluss und die Anwendung auf die Advektions-DiffusionsDynamik, Klimadaten und anderen Systemen, in denen Teilchentransport eine entscheidende Rolle spielen. Als Letztes, entwickelte ich ein neuartiges Heterogener Opinion Status Modell (HOpS) und Analysetechnik basiert auf Random Walks und Netzwerktopologie Theorien, um dynamischen Prozesse in Netzwerken zu studieren, wie die Verbreitung von Meinungen in sozialen Netzwerken oder Krankheiten in der Gesellschaft. Ein neues Modell heterogener Verbreitung auf einem Netzwerk wird als Beispielssystem für HOpS verwendent, um die vergleichsweise Einfachheit zu nutzen. Die Analyse eines diskreten Phasenraums des HOPS-Modells hat überraschende Eigenschaften, welches sensibel auf die Netzwerktopologie reagieren. Sie können verallgemeinert werden, um verschiedene Klassen von komplexen Netzwerken zu quantifizieren, Transportphänomene zu charakterisieren und verschiedene Zeitreihen zu analysieren.In the thesis I studied the complex architectures of networks, the network evolution in time, the interpretation of the networks measures and a particular class of processes taking place on complex networks. Firstly, I derived the measures to characterize temporal networks evolution in order to detect spatial variability patterns in evolving systems. Secondly, I introduced a novel flow-network method to construct networks from flows, that also allows to modify the set-up from purely relying on the velocity field. The flow-network method is developed for correlations of a scalar quantity (temperature, for example), which satisfies advection-diffusion dynamics in the presence of forcing and dissipation. This allows to characterize transport in the fluids, to identify various mixing regimes in the flow and to apply this method to advection-diffusion dynamics, data from climate and other systems, where particles transport plays a crucial role. Thirdly, I developed a novel Heterogeneous Opinion-Status model (HOpS) and analytical technique to study dynamical processes on networks. All in all, methods, derived in the thesis, allow to quantify evolution of various classes of complex systems, to get insight into physical meaning of correlation networks and analytically to analyze processes, taking place on networks.
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Sowgath, Md Tanvir. "Neural network based hybrid modelling and MINLP based optimisation of MSF desalination process within gPROMS : development of neural network based correlations for estimating temperature elevation due to salinity, hybrid modelling and MINLP based optimisation of design and operation parameters of MSF desalination process within gPROMS." Thesis, University of Bradford, 2007. http://hdl.handle.net/10454/10998.
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Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.
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Nkhumeleni, Thizwilondi Moses. "Correlation and comparative analysis of traffic across five network telescopes." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1011668.
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Monitoring unused IP address space by using network telescopes provides a favourable environment for researchers to study and detect malware, worms, denial of service and scanning activities. Research in the field of network telescopes has progressed over the past decade resulting in the development of an increased number of overlapping datasets. Rhodes University's network of telescope sensors has continued to grow with additional network telescopes being brought online. At the time of writing, Rhodes University has a distributed network of five relatively small /24 network telescopes. With five network telescope sensors, this research focuses on comparative and correlation analysis of traffic activity across the network of telescope sensors. To aid summarisation and visualisation techniques, time series' representing time-based traffic activity, are constructed. By employing an iterative experimental process of captured traffic, two natural categories of the five network telescopes are presented. Using the cross- and auto-correlation methods of time series analysis, moderate correlation of traffic activity was achieved between telescope sensors in each category. Weak to moderate correlation was calculated when comparing category A and category B network telescopes' datasets. Results were significantly improved by studying TCP traffic separately. Moderate to strong correlation coefficients in each category were calculated when using TCP traffic only. UDP traffic analysis showed weaker correlation between sensors, however the uniformity of ICMP traffic showed correlation of traffic activity across all sensors. The results confirmed the visual observation of traffic relativity in telescope sensors within the same category and quantitatively analysed the correlation of network telescopes' traffic activity.
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Gu, Guofei. "Correlation-based Botnet Detection in Enterprise Networks." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24634.
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Most of the attacks and fraudulent activities on the Internet are carried out by malware. In particular, botnets, as state-of-the-art malware, are now considered as the largest threat to Internet security.
In this thesis, we focus on addressing the botnet detection problem in an enterprise-like network environment. We present a comprehensive correlation-based framework for multi-perspective botnet detection consisting of detection technologies demonstrated in four complementary systems: BotHunter, BotSniffer, BotMiner, and BotProbe. The common thread of these systems is correlation analysis, i.e., vertical correlation (dialog correlation), horizontal correlation, and cause-effect correlation. All these Bot* systems have been evaluated in live networks and/or real-world network traces. The evaluation results show that they can accurately detect real-world botnets for their desired detection purposes with a very low false positive rate.
We find that correlation analysis techniques are of particular value for detecting advanced malware such as botnets. Dialog correlation can be effective as long as malware infections need multiple stages. Horizontal correlation can be effective as long as malware tends to be distributed and coordinated. In addition, active techniques can greatly complement passive approaches, if carefully used. We believe our experience and lessons are of great benefit to future malware detection.
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Póvoa, Luís. "Correlation of network information." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/7145.
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Mestrado em Engenharia de Computadores e TelemáticaNos dias de hoje, e imposs vel para empresas ou para um utilizador comum de computador, trabalhar sem internet, pesquisar, comunicar, partilhar informa c~ao . . . Todos os dias, novas amea cas s~ao criadas e podem afectar milh~oes de computadores e centenas de empresas numa quest~ao de minutos ou horas. As solu c~oes existentes ainda dependem da interven c~ao Humana e an alise de tr afego, o que signi ca que existe sempre mais margem para erro e falhas. Tudo muda rapidamente e a informa c~ao tende a aumentar mais e mais, tanto em redes privadas como na Internet, seja essa informa c~ao sens vel ou n~ao. Ent~ao, toda esta informa c~ao deve ser automaticamente reunida e processada, deixando o m nimo poss vel de responsabilidade para o Administrador de rede. Esta disserta c~ao pretende lembrar que a seguran ca n~ao deve nunca ser vista como uma tarefa secund aria. E com este princ pio em mente, o principal objectivo e criar um sistema, f acil de usar, instalar e de administrar, para obter a informa c~ao dispon vel de servidores e equipamentos de toda a rede. A informa c~ao recolhida pode ser depois utilizada para detectar anomalias de rede e deve tamb em ter a capacidade de implementar as contra-medidas mais apropriadas .
Nowadays, it is impossible for companies or a common computer user, to work without Internet, searching, communicating, sharing information. . . Every day new threats are created and can a ect millions of computers and hundreds of companies in a matter of minutes or hours. Existing solutions still depend on Human intervention and network tra c analysis, which means they are open to breaches. Everything changes faster, runs faster, and information tends to increase more and more, both in private networks and on the Internet, whether it is sensitive or not. So, all this information should be automatically gathered and processed, letting as less as possible behind the responsibility of the Network Administrator. This dissertation intends to remember that security should never be seen as a secondary task. Having this principle in mind, the main purpose was to create a system, easy to use, install and administrate in order to gather information from the servers and equipments of the entire Network. Collected information can then be used to detect network anomalies and the system should also have the ability to deploy them most appropriate countermeasures.
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Li, Xiaobo. "Neural network based exchange-correlation functional." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B3955899X.
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Li, Xiaobo, and 李曉博. "Neural network based exchange-correlation functional." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B3955899X.
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Würflinger, Lars Erik. "Nonlocality in multipartite correlation networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/129918.
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Despite the success of quantum mechanics in predicting the outcomes of experiments in many branches of physics, the foundations of the theory have remained subject of research and dispute. At the basis of this struggle with the theory lie the phenomena of nonlocality and entanglement. Since it was first predicted by Bell in 1964, nonlocality was not only verified in numerous experiments, but also identified as a useful resource for quantum information processing. Thus, the study of nonlocality is important both from a fundamental point of view and with respect to new applications in quantum information
theory, such as secure cryptography and randomness generation. The identification of entanglement as a resource for quantum information led to a strong theoretical effort devoted to its characterisation and detection. Many of the resulting mathematical tools find application in several domains of physics. Although the only known way to create nonlocal correlations is to measure entangled quantum systems, it has been shown that entanglement and nonlocality constitute two inequivalent properties. Therefore, in the light of the success of entanglement theory, it is of interest to also devise a resource theory of nonlocality.
The task of this thesis is to develop such a theoretical framework for the characterisation of nonlocality as a resource. To gain a better understanding of nonlocal correlations it will be necessary to investigate correlation scenarios that go beyond the situation originally considered by Bell. In doing so, this thesis provides new description of nonlocality that also have implications for the characterisation of quantum correlations and the detection of new forms of nonlocality. The first question we address is how nonlocality can consistently be defined in a scenario of arbitrarily many parties that may collaborate among each other. To this end we recognise which are the allowed physical operations in this situation and then define nonlocality as the resource that cannot be created by these operations. Our approach shows that the conventional definition of multipartite nonlocality, adopted by the community so far, is inconsistent with this operational definition; we further propose and analyse new models that do not suffer from these inconsistencies.
Furthermore, we show that our findings have implications for the characterisation of quantum correlations. A recent approach to describe the set of quantum correlation consists in using principles inspired from information theory. By using a special instance of the models we defined earlier we show a fundamental limitation of this approach: no bipartite information principle is sufficient to single out the set of quantum correlations from the set of nonsignalling correlations. We then developed a description of nonlocality in an even more generalised scenario of several parties. Motivated by a result of
Popescu we study scenarios where the parties are allowed to perform not only a single but sequences of measurements on their systems. Characterising nonlocality also in this scenario in operational terms and defining local models compatible with this definition, we show that a new form of nonlocality can be detected Lastly, we examine the problem of detecting the presence of nonlocality in a multipartite scenario when one is given only partial access to the global system. We find that one can verify that the total system must display nonlocality, even though the accessible subsystems only exhibit local correlations.
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Teng, Chunhui. "Alarm correlation in ATM networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0005/MQ43348.pdf.
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Teng, Chunhui Carleton University Dissertation Engineering Systems and Computer. "Alarm correlation in ATM networks." Ottawa, 1999.
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Kang, Wei. "Correlation and cooperation in network information theory." College Park, Md. : University of Maryland, 2008. http://hdl.handle.net/1903/8355.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Shawash, J. "Generalised correlation higher order neural networks, neural network operation and Levenberg-Marquardt training on field programmable gate arrays." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1344090/.
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Higher Order Neural Networks (HONNs) were introduced in the late 80's as a solution to the increasing complexity within Neural Networks (NNs). Similar to NNs HONNs excel at performing pattern recognition, classification, optimisation particularly for non-linear systems in varied applications such as communication channel equalisation, real time intelligent control, and intrusion detection. This research introduced new HONNs called the Generalised Correlation Higher Order Neural Networks which as an extension to the ordinary first order NNs and HONNs, based on interlinked arrays of correlators with known relationships, they provide the NN with a more extensive view by introducing interactions between the data as an input to the NN model. All studies included two data sets to generalise the applicability of the findings. The research investigated the performance of HONNs in the estimation of short term returns of two financial data sets, the FTSE 100 and NASDAQ. The new models were compared against several financial models and ordinary NNs. Two new HONNs, the Correlation HONN (C-HONN) and the Horizontal HONN (Horiz-HONN) outperformed all other models tested in terms of the Akaike Information Criterion (AIC). The new work also investigated HONNs for camera calibration and image mapping. HONNs were compared against NNs and standard analytical methods in terms of mapping performance for three cases; 3D-to-2D mapping, a hybrid model combining HONNs with an analytical model, and 2D-to-3D inverse mapping. This study considered 2 types of data, planar data and co-planar (cube) data. To our knowledge this is the first study comparing HONNs against NNs and analytical models for camera calibration. HONNs were able to transform the reference grid onto the correct camera coordinate and vice versa, an aspect that the standard analytical model fails to perform with the type of data used. HONN 3D-to-2D mapping had calibration error lower than the parametric model by up to 24% for plane data and 43% for cube data. The hybrid model also had lower calibration error than the parametric model by 12% for plane data and 34% for cube data. However, the hybrid model did not outperform the fully non-parametric models. Using HONNs for inverse mapping from 2D-to-3D outperformed NNs by up to 47% in the case of cube data mapping. This thesis is also concerned with the operation and training of NNs in limited precision specifically on Field Programmable Gate Arrays (FPGAs). Our findings demonstrate the feasibility of on-line, real-time, low-latency training on limited precision electronic hardware such as Digital Signal Processors (DSPs) and FPGAs. This thesis also investigated the effects of limited precision on the Back Propagation (BP) and Levenberg-Marquardt (LM) optimisation algorithms. Two new HONNs are compared against NNs for estimating the discrete XOR function and an optical waveguide sidewall roughness dataset in order to find the Minimum Precision for Lowest Error (MPLE) at which the training and operation are still possible. The new findings show that compared to NNs, HONNs require more precision to reach a similar performance level, and that the 2nd order LM algorithm requires at least 24 bits of precision. The final investigation implemented and demonstrated the LM algorithm on Field Programmable Gate Arrays (FPGAs) for the first time in our knowledge. It was used to train a Neural Network, and the estimation of camera calibration parameters. The LM algorithm approximated NN to model the XOR function in only 13 iterations from zero initial conditions with a speed-up in excess of 3 x 10^6 compared to an implementation in software. Camera calibration was also demonstrated on FPGAs; compared to the software implementation, the FPGA implementation led to an increase in the mean squared error and standard deviation of only 17.94% and 8.04% respectively, but the FPGA increased the calibration speed by a factor of 1:41 x 106.
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Brunet, Richard. "Temporal alarm correlation in communication networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0004/MQ32426.pdf.
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Brunet, Richard Carleton University Dissertation Engineering Systems and Computer. "Temporal alarm correlation in communication networks." Ottawa, 1998.
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Paisley, Jonathan. "Application and network traffic correlation of grid applications." Thesis, Connect to e-thesis, 2006. http://theses.gla.ac.uk/535/.
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Thesis (Ph.D.) - University of Glasgow, 2006.Ph.D. thesis submitted to the Department of Computing Science, University of Glasgow, 2006. Includes bibliographical references. Print version also available.
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Guo, Lihao. "Correlation Transfer in Striatal Network via Morphology Changes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292949.
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The activity of striatum is significantly modulated by dopamine, and long- term effects if an altered dopamine signalling can also lead to degeneration and regeneration of synapses. This thesis report investigates the signal transfer, especially correlation transfer, in the striatum. Simulations using a biophysically detailed MSN model and a simplified striatal network model are conducted to generate data to form hypotheses. At the single neuron level, synaptic pruning reduces the effects of the input. Compensation through regeneration of new synapses is much more effective than scaling up the existing synaptic strengths. At the network level, mutual inhibition between different neuron populations enhances the difference of their output firing rates when inputs are weakly correlated, and reduces the difference of output correlations when inputs are strongly correlated. In addition, the purely inhibitory striatal network can potentially either increase or decrease output correlations when changing the connection probability. The recurrent inhibition within a certain population plays a more important role in modulating the correlation transfer compared to mutual inhibition between populations. Finally, limitations of the models are discussed, and this motivates the introduction of connectivity preferences and neuron type diversity in future work.Striatums aktivitet moduleras signifikant av dopamin, och långtidseffekter av en förändrad dopaminsignalering kan även leda till degeneration och regeneration av synapser. Denna masteruppsats undersöker signalöverföringen, och specifikt överföringen när korrelerade signaler används för att aktivera neuron i striatum. Simuleringar av dels en biofysiskt detaljerad MSN modell samt av ett förenklat striatalt nätverk görs för att generera data så att nya hypoteser kan formuleras. Om man tittar på en enstaka cell, så leder synaptisk pruning till en minskad effekt av det input som används för att aktivera neuronet. Kompensation genom regeneration av nya synapser är här mycket effektivare än att skala upp de kvarvarande synapsernas styrka. På nätverksnivån kan ömsesidig inhibition mellan olika neuronpopulationer öka skillnaderna i deras aktivitetsnivåer om man använder svagt synkroniserat input för att aktivera nätverkets celler, och när inputs istället är starkt korrelerade minskar skillnaderna beträffande hur korrelerat neuronen spikar. Dessutom, i inhibitoriskt kopplade nätverk kan man då man ändrar kopplingssannolikheten potentiellt se antingen en ökning eller en minskning i hur korrelerad aktiviteten i neuronen är. Rekurrent inhibition inom en viss population har en större betydelse för överföringen av korrelerade signaler jämfört med ömsesidig inhibition mellan neuronpopulationer. Slutligen diskuteras begränsningar av modellerna, vilket motiverar att man i framtida studier kan introducera mer specifika kopplingssannolikheter mellan celltyperna samt införa en variabilitet mellan enskilda neuron.
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Neise, Patrick. "Graph-based Event Correlation for Network Security Defense." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10785425.
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Organizations of all types and their computer networks are constantly under threat of attack. While the overall detection time of these attacks is getting shorter, the average detection time of weeks to months allows the attacker ample time to potentially cause damage to the organization. Current detection methods are primarily signature based and typically rely on analyzing the available data sources in isolation. Any analysis of how the individual data sources relate to each other is usually a manual process, and will most likely occur as a forensic endeavor after the attack identification occurs via other means. The use of graph theory and the graph databases built to support its application can provide a repeatable and automated analysis of the data sources and their relationships. By aggregating the individual data sources into a graph database based on a model that supports the data types and relationships, database queries can extract information relevant to the detection of attack behavior within the network. The work in this Praxis shows how the graph model and database queries will reduce the overall time to detection of a successful attack by enabling defenders to understand better how the data elements and what they represent are related.
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Lindlöf, Angelica. "Deriving Genetic Networks from Gene Expression Data and Prior Knowledge." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-589.
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In this work three different approaches for deriving genetic association networks were tested. The three approaches were Pearson correlation, an algorithm based on the Boolean network approach and prior knowledge. Pearson correlation and the algorithm based on the Boolean network approach derived associations from gene expression data. In the third approach, prior knowledge from a known genetic network of a related organism was used to derive associations for the target organism, by using homolog matching and mapping the known genetic network to the related organism. The results indicate that the Pearson correlation approach gave the best results, but the prior knowledge approach seems to be the one most worth pursuing
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Ziebell, Sara E. "A Powerful Correlation Method for Microbial Co-Occurrence Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595812.
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Motivation: Network interpretation using correlations has several known difficulties. Firstly, the data structure has discrete counts with an excess of zeros creating non-normal non-continuous data. Secondly, correlations, often used as similarity measures in network inference, are not causal. Thirdly, there is a masking effect of mutualism on commensalism and competition on amensalism in ecological networks that interfere with interpretation (Faust and Raes, 2012). More explicitly, the symmetric nature of correlations (cor(X,Y)=cor(Y,X)) can mask the affect of the asymmetric ecology relationship (commensalism and amensalism). We aim to solve the third issue which may speed up targeted drug therapies or disease diagnosis based on specific relationships in gut microbiomes. Methods: We apply a non-symmetric correlation method, Gini Correlations which should serve as a better classifier of ecological relationships revealing a fuller picture of microbiomes. First, create simulated correlated and independent Zero-Inflated Negative Binomial data. Second, validate Gini correlations by comparing Gini with Pearson Spearman and Kendall correlations; calculate false positive rate, true positive rate, accuracy, ROC, AUC after applying Benjamini-Hochberg (1995) multiple testing correction. Simulation Result: Gini is consistent and out performs other methods for small sample sizes of 10 and 25 producing consistently low false positive rates across 64+ simulation settings as well as consistently high accuracy rates. When sample size is increased to 50 Gini performs as well as other methods. Real Data Result: For well-defined microbial communities Gini correlations found novel biologically and medically relevant relationships. However, Gini's ability to unmask non-symmetric ecological relationships is yet to be determined.
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Vuran, Mehmet Can. "Correlation-based Cross-layer Communication in Wireless Sensor Networks." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16135.
Full textAbstract:
Wireless sensor networks (WSN) are event based systems that rely on the collective effort of densely deployed sensor nodes continuously observing a physical phenomenon. The spatio-temporal correlation between the sensor observations and the cross-layer design advantages are significant and unique to the design of WSN. Due to the high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the energy-radiating physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. This unique characteristic of WSN can be exploited through a cross-layer design of communication functionalities to improve energy efficiency of the network.
In this thesis, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to capture the spatial and temporal correlations in WSN and to enable the development of efficient communication protocols. Based on this framework, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is described, which exploits the spatial correlation in the WSN in order to achieve efficient medium access. Furthermore, the cross-layer module (XLM), which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes, is developed. The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN. Finally, the cross-layer packet size optimization framework is described.
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Gou, Zhenkun. "Canonical correlation analysis and artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269409.
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Ginn, Patrick W. "Correlation analysis of fleet information warfare center network incidents." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA396275.
Full textAbstract:
Thesis (M.S. in Information Technology Management) Naval Postgraduate School, June 2001.Thesis advisors, Raymond Buettner, Dan C. Boger. Includes bibliographical references (p. 51). Also Available online.
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Mandere, Edward Ondieki. "Financial Networks and Their Applications to the Stock Market." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1234473233.
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Lotun, Reza M. E. "wypy : an extensible, online interference detection tool for wireless networks." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/515.
Full textAbstract:
WiFi networks have become ubiquitous. However, due to the nature of the
radio-wave medium, the performance of 802.11 is unpredictable and highly
dependent on the environment. This problem is fundamental to 802.11's
decentralized, signal-based airspace arbitration mechanism. When devices have
incomplete and inconsistent channel conditions for an overlapping
interference domain, their signals alone cannot ensure a fair competition for
airspace. As a result, competing flows may suffer from unfair bandwidth
distribution if the shared airspace is congested.
A useful tool to visualize and diagnose problematic wireless networks is the
set of devices interfering with each other at a given time. We say two
devices a and b interfere when one of two possible situations occur. First,
a is able to sense b's radio signals, though not necessarily decode them,
resulting in a unable to send data. Second, a and b aren't in radio range,
but their destination devices are, resulting in packet collisions. We call
such a set of mutually interfering devices the interference neighbourhood.
We present wypy, an online system which merges trace-files and produces a map
of interfering devices contained within the trace. wypy is able to identify
pairs of devices exhibiting either hidden or exposed terminal interference
using a pipeline that consists of trace merging and reconstruction, filtering
of simultaneously sending devices, throughput and delay signal calculations,
and a test for interference correlation. We evaluate wypy using an in-lab
testbed set up in known interference scenarios.
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Ganapathy, Vinayak. "An Epistemic Event-based Correlation Scheme for Pervasive Network Management." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/190.
Full textAbstract:
Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.
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Shangguan, Yihong 1965. "Event correlations in active networks by using active filters." Thesis, McGill University, 1999. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=30744.
Full textAbstract:
With the development of modern computer and network technologies, computer systems and applications become more and more complex and dynamic. One of the problems of complex and dynamic systems is the difficulty of management of changes. Network management comprises of network monitoring and control, its aims include the detection and handling of faults, performance inefficiencies and security compromises. Centralized network management seriously limits the scalability of network management. The shortcomings of the centralized approach show up during the periods of heavy congestion when management intervention is particularly important. Therefore, in order to cope with problems (such as passive, static, rigid, etc.) arising from centralization, the network management functionality must be decentralized and should become more active and flexible. In this thesis, we use mobile agent and active network technologies to implement a network management tool, active filter, which could be very useful in various network management activities, such as, real-time resource management; load-sensitive flow rerouting; application-specific filtering in wireline/wireless network; and real-time multicast.
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Shangguan, Yihong. "Event correlations in active networks by using active filters." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq64449.pdf.
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Lai, Pei Ling. "Neural implementations of canonical correlation analysis." Thesis, University of the West of Scotland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311771.
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Gardner, Robert David. "Event handling techniques in high speed networks." Thesis, University of Strathclyde, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248548.
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Schatzmann, Dominik [Verfasser]. "Correlating Flow-based Network Measurements for Service Monitoring and Network Troubleshooting / Dominik Schatzmann." Aachen : Shaker, 2013. http://d-nb.info/1051573653/34.
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Zhang, Yinghua. "Stock Market Network Topology Analysis Based on a Minimum Spanning Tree Approach." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1245347181.
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Whitacre, Timothy P. "A Neural Network Receiver for EM-MWD Communication." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/534.
Full textAbstract:
Baseband digital communication in electro-magnetic measurement while drilling (EM-MWD) systems is often corrupted by non-white surface noise. The inability to reliably decode the transmitted signals in a noisy environment limits the depth at which EM-MWD systems can operate. Correlation receivers, which are optimal in the presence of additive white Gaussian noise, can be sub-optimal in the presence of various types of field noise at different drilling sites.
This thesis investigates the application of artificial neural networks (ANN) as communication receivers in EM-MWD baseband digital communication systems. The performances of various ANN architectures and training algorithms are studied and compared with conventional correlation receivers via computer simulations. Standard symbol error rate (SER) test results show that the NN receiver is able to adapt to site-specific noise and thus outperforms the traditional correlation receiver.
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Dai, Rui. "Correlation-based communication in wireless multimedia sensor networks." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42736.
Full textAbstract:
Wireless multimedia sensor networks (WMSNs) are networks of interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the observations of camera sensors with overlapped coverage areas, which introduces substantial data redundancy in the network. In this dissertation, efficient communication schemes are designed for WMSNs by leveraging the correlation of visual information observed by camera sensors. First, a spatial correlation model is developed to estimate the correlation of visual information and the joint entropy of multiple correlated camera sensors. The compression performance of correlated visual information is then studied. An entropy-based divergence measure is proposed to predict the compression efficiency of performing joint coding on the images from correlated cameras. Based on the predicted compression efficiency, a clustered coding technique is proposed that maximizes the overall compression gain of the visual information gathered in WMSNs. The correlation of visual information is then utilized to design a network scheduling scheme to maximize the lifetime of WMSNs. Furthermore, as many WMSN applications require QoS support, a correlation-aware QoS routing algorithm is introduced that can efficiently deliver visual information under QoS constraints. Evaluation results show that, by utilizing the correlation of visual information in the communication process, the energy efficiency and networking performance of WMSNs could be improved significantly.
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Frey, Nathan J. "Hierarchical Latent Networks for Image and Language Correlation." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1320847134.
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