In this document, a low-cost, portable, non-invasive method of collecting the 3D trajectories of flying bats is first presented. An array of commercially available camera and light components is used alongside a number of well-established calibration and triangulation techniques to resolve the motion of agents through a 3D volume. It is shown that this system is capable of accurately capturing the bats' flight paths in a field experiment. The use of non-visible illumination ensures that a natural cave environment is disturbed as little as possible for behavioral experiments. Following is a transfer entropy analysis approach applied to the 3D paths of bats flying in pairs. The 3D trajectories are one-dimensionally characterized as inverse curvature time series to allow for entropy calculations. In addition to a traditional formulation of information flow between pair members, a path coupling hypothesis is pursued with time-delay modifications implemented in such a way as to not change the Markovianity of the process. With this modification, trends are found that suggest a leader-follower interaction between the front bat and the rear bat, although statistical significance is not reached due to the small number of pairs considered. / Master of Science

Alzheimer’s disease (AD) is a neurodegenerative disorder resulting in the decline of cognitive function, memory formation and retrieval, and abrupt changes in personality. Damage to brain networks occur during prodromal stages of AD, prior to the development of clinical symptoms of dementia. Further characterising this state and identifying reliable biomarkers for early detection are priorities in AD research. I characterised neuronal changes within the dorsal CA1 and subiculum regions of the hippocampal formation (HF) in the well-characterised 3xTgAD mouse model of AD. These regions are well-established sites for early neurodegeneration in both AD patients and AD animal models. We inserted multi-electrode recording arrays into CA1 and subiculum of urethane anaesthetised 3xTgAD mice and recorded spontaneous local field potential activity. Using traditional and novel information theoretic approaches, I determined the information carrying capacity of the CA1- subiculum network during different network rhythms, and how this altered with age and AD-like pathology. A bipolar stimulating electrode was inserted into CA1, allowing the assessment of synaptic integrity between CA1 and subiculum. Results showed that synaptic and network changes occur in CA1 and subiculum during the early stages of AD-like pathology and correlates with the development of intracellular beta-amyloid. There is a progressive breakdown in synaptic facilitation as early as 3 months in the 3xTgAD mouse. These data support an advanced ageing-like phenotype in AD model mice, with an enhanced age/pathology-dependent breakdown in neuronal communication compared to age-matched controls. In agreement with other studies, 3xTgAD mice demonstrate evidence of pathology-related changes in the network rhythms of the HF. 3xTgAD mice show an increase in the power of alpha and beta rhythms, and a concurrent reduction in the power of delta oscillations. Application of novel information theoretic techniques results in a breakdown in the information carrying capacity of the hippocampal system. This deficit manifests as a reduction in information flow during delta-dominant periods of EEG rhythms, with a specific reduction during slow-wave ripple activity. This change in neuronal communication correlates with the onset of memory-retention/consolidation deficits. These network changes are complex, with alterations in the information carrying capacity of the system during theta rhythms at 6 months, and during slow-wave components by 9 months in the 3xTgAD mouse. This study provides the first evidence of an early and progressive decline in neuronal connectivity and communication that correlates with changes in cognition in the 3xTgAD mouse. Application of novel analytical techniques to multi-site EEG recording revealed early and measureable changes in information processing during the onset of AD-like pathology. These are important new biomarkers for early AD characterisation.

Recent popularization of technologies such as Smartphones, Wearables, Internet of Things, Social Networks and Video streaming increased data creation. Dealing with extensive data sets led the creation of term big data, often defined as when data volume, acquisition rate or representation demands nontraditional approaches to data analysis or requires horizontal scaling for data processing. Analysis is the most important Big Data phase, where it has the objective of extracting meaningful and often hidden information. One example of Big Data hidden information is causality, which can be inferred with Delayed Transfer Entropy (DTE). Despite DTE wide applicability, it has a high demanding processing power which is aggravated with large datasets as those found in big data. This research optimized DTE performance and modified existing code to enable DTE execution on a computer cluster. With big data trend in sight, this results may enable bigger datasets analysis or better statistical evidence. / A recente popularização de tecnologias como Smartphones, Wearables, Internet das Coisas, Redes Sociais e streaming de Video aumentou a criação de dados. A manipulação de grande quantidade de dados levou a criação do termo Big Data, muitas vezes definido como quando o volume, a taxa de aquisição ou a representação dos dados demanda abordagens não tradicionais para analisar ou requer uma escala horizontal para o processamento de dados. A análise é a etapa de Big Data mais importante, tendo como objetivo extrair informações relevantes e às vezes escondidas. Um exemplo de informação escondida é a causalidade, que pode ser inferida utilizando Delayed Transfer Entropy (DTE). Apesar do DTE ter uma grande aplicabilidade, ele possui uma grande demanda computacional, esta última, é agravada devido a grandes bases de dados como as encontradas em Big Data. Essa pesquisa otimizou e modificou o código existente para permitir a execução de DTE em um cluster de computadores. Com a tendência de Big Data em vista, esse resultado pode permitir bancos de dados maiores ou melhores evidências estatísticas.

Recent popularization of technologies such as Smartphones, Wearables, Internet of Things, Social Networks and Video streaming increased data creation. Dealing with extensive data sets led the creation of term big data, often defined as when data volume, acquisition rate or representation demands nontraditional approaches to data analysis or requires horizontal scaling for data processing. Analysis is the most important Big Data phase, where it has the objective of extracting meaningful and often hidden information. One example of Big Data hidden information is causality, which can be inferred with Delayed Transfer Entropy (DTE). Despite DTE wide applicability, it has a high demanding processing power which is aggravated with large datasets as those found in big data. This research optimized DTE performance and modified existing code to enable DTE execution on a computer cluster. With big data trend in sight, this results may enable bigger datasets analysis or better statistical evidence. / A recente popularização de tecnologias como Smartphones, Wearables, Internet das Coisas, Redes Sociais e streaming de Video aumentou a criação de dados. A manipulação de grande quantidade de dados levou a criação do termo Big Data, muitas vezes definido como quando o volume, a taxa de aquisição ou a representação dos dados demanda abordagens não tradicionais para analisar ou requer uma escala horizontal para o processamento de dados. A análise é a etapa de Big Data mais importante, tendo como objetivo extrair informações relevantes e às vezes escondidas. Um exemplo de informação escondida é a causalidade, que pode ser inferida utilizando Delayed Transfer Entropy (DTE). Apesar do DTE ter uma grande aplicabilidade, ele possui uma grande demanda computacional, esta última, é agravada devido a grandes bases de dados como as encontradas em Big Data. Essa pesquisa otimizou e modificou o código existente para permitir a execução de DTE em um cluster de computadores. Com a tendência de Big Data em vista, esse resultado pode permitir bancos de dados maiores ou melhores evidências estatísticas.

Made available in DSpace on 2017-07-21T19:26:01Z (GMT). No. of bitstreams: 1 Fabiano Alan Serafim Ferrari.pdf: 2088723 bytes, checksum: 870f9764d54466ce2b19e8b0e090108b (MD5) Previous issue date: 2012-02-27 / Chaotic systems are characterized by irregular behaviour, which some times are confused with random behaviour. The time evolution of these systems is described using an evolution rule. Even a chaotic system begins in two very close initial conditions, they will diverge exponentially to one another. Thus, this sensibility creates difficults to predict and get the chaotic orbit. Chaotic systems unpredictness make it more intersting studying its probability distribution than its time series directly. Through information theory is possible study dynamical systems by mean of its probability. Inside information theory an important quantity is the transfer entropy, that allows us to study statistic coherence in systemns which have their evolution through time. With this coherence it is possible transmit information using chaos in a safe way. Therefore, we present a model present in the literature and we suggest a new model of this safe transmission. / Sistemas caóticos se caracterizam por um comportamento irregular, aparentemente aleatório, mas cuja sua evolução temporal é descrita por uma regra de evolução. Mesmo quando um sistema caótico ´e iniciado de duas condições iniciais muito próximas elas tendem a divergir exponencialmente uma da outra com o tempo. Esta sensibilidade à condições iniciais torna a previsibilidade de uma órbita caótica difícil de se obter. A imprevisibilidade de sistemas caóticos faz com que muitas vezes seja mais interessante estudar sua distribuição de probabilidade ao invés de sua série temporal diretamente. Através da teoria da informação é possível estudar sistemas dinâmicos a partir de sua probalidade. Dentro da teoria da informação uma quantidade importante é a entropia de transferência que permite estudar coerências estatísticas em sistemas que evoluem no tempo. A partir desta coerência é possível transmitir informação de forma segura utilizando caos, apresentamos um modelo j´a existente na literatura e propomos um novo modelo para esta transmiss˜ao segura.

Submitted by Mario BC (mario@bc.ufrpe.br) on 2018-05-08T13:01:59Z No. of bitstreams: 1 Leila Milfont Rameh.pdf: 1237678 bytes, checksum: 124c9f70bd10e5a122c60275796bdc90 (MD5) / Made available in DSpace on 2018-05-08T13:01:59Z (GMT). No. of bitstreams: 1 Leila Milfont Rameh.pdf: 1237678 bytes, checksum: 124c9f70bd10e5a122c60275796bdc90 (MD5) Previous issue date: 2018-02-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The Brazilian energy infrastructure in the transportation sector has unique characteristics, with a large participation of bioenergy. The Brazilian government obliges the addition of ethanol in gasoline and this percentage varies as a political mechanism of price stabilization, increasing or decreasing depending on the industrial capacity to produce ethanol, the price of oil. The price of sugar cane also has a role in this decision-making. The domestic market switches the destination of sugarcane to ethanol or sugar production depending on market prices. Much of the ethanol production is destined for the domestic market. While sugar, besides being a product of the Brazilian basic food basket, is a strong product of exports. Thus, sugar, ethanol and petroleum commodities have a very large price correlation in the Brazilian financial market. The correlation between the prices of ethanol, sugar and oil leads to the need to study this exchange of information and to evaluate the directionality of this flow of information. In the analysis of multivariate time series, a common subject of interest is the coupling between the variables. In order to study the transfer of information in the series of prices of these commodities we use the Transfer Entropy that allows, in addition to measuring the coupling force between the series, to identify the directionality of the coupling. As these commodities are highly influenced by the international market, we analyze the behavior of their time series of prices in the face of the global financial crisis that led to the collapse of Lehman Brothers Bank. In this way, we study these commodities for the periods before, during and after the Subprime Crisis. Our main objective in this study was to investigate time series of ethanol, oil and sugar prices using the Transfer Entropy method. The results show that the financial crisis affected the relationship between sugar, ethanol and oil commodity prices. Transfer Entropy was efficient in quantifying the flow of information between the time series of prices of these commodities and elucidating the directionality of the transfer. We highlight some inversions observed in the intensity of the flow of information, for example between sugar and oil, during the periods during and after the crisis, the behavior of Transfer Entropy reversed and the direction of oil sugar became bigger than the other direction. Between sugar and ethanol and between oil and ethanol there was also an inversion in the behavior of information transfer during the period during the crisis. Overall, the method detected the change in price behavior caused by the financial crisis. The advantage of the technique is, besides measuring the intensity between these couplings, to indicate the directionality of the interactions. Among the results obtained with the Multiscale Transfer Entropy method we highlight that between oil and ethanol, when analyzing the different time scales, we could not conclude which direction transfers more information. This feature may be related to policy interventions that lower the price of gasoline artificially, change the percentage of ethanol in gasoline, causing the relationship between ethanol and oil prices not to occur naturally. / A infraestrutura energética brasileira no setor de transportes tem características únicas, com uma grande participação de bioenergia. O governo brasileiro obriga a adição de etanol na gasolina e esse percentual varia como um mecanismo político de estabilização dos preços, aumentando ou diminuindo a depender da capacidade industrial de produzir etanol e do preço do petróleo. O preço da cana-de-açúcar também tem um papel fundamental nesta tomada de decisão. O mercado interno alterna o destino da cana-de-açúcar para produção de etanol ou de açúcar dependendo dos preços do mercado. Grande parte da produção de etanol é destinada ao mercado interno. Enquanto o açúcar, além de ser um produto da cesta básica brasileira, é um forte produto de exportações. Dessa forma, as commodities açúcar, etanol e petróleo possuem uma correlação muito grande com relação aos preços no mercado financeiro brasileiro. A correlação existente entre os preços do etanol, açúcar e petróleo leva a necessidade de estudar esta troca de informações e avaliar a direcionalidade desse fluxo de informação. Para estudar a transferência de informações nas séries de preço destas commodities utilizamos a Transfer Entropy que permite, além de medir a intensidade de acoplamento entre as séries, identificar a direcionalidade do acoplamento. Como essas commodities são altamente influenciadas pelo mercado internacional, analisamos o comportamento de suas séries temporais de preços diante da crise financeira mundial que acarretou a quebra do Banco Lehman Brothers. Dessa forma, estudamos essas commodities para os períodos antes, durante e após a Crise Subprime. Nosso principal objetivo neste estudo foi investigar séries temporais de preço de etanol, petróleo e açúcar utilizando o método Transfer Entropy. Os resultados obtidos mostram que a crise financeira afetou a relação entre os preços das commodities açúcar, etanol e petróleo. ATransfer Entropy se mostrou eficiente para quantificar o fluxo de informações entre as séries temporais de preços dessas commodities e elucidar a direcionalidade da transferência. Destacamos algumas inversões observadas na intensidade do fluxo de informações, por exemplo, entre açúcar e petróleo, nos períodos durante e após a crise o comportamento da Transfer Entropy inverteu e o sentido petróleo - açúcar se tornou maior que o outro sentido. Entre açúcar e etanol e entre petróleo e etanol também houve inversão no comportamento da transferência de informações no período durante a crise. No geral, o método detectou a alteração no comportamento dos preços ocasionada pela crise financeira. Avantagem da técnica é, além de medir a intensidade entre esses acoplamentos, indicar a direcionalidade das interações. Entre os resultados obtidos com o método Multiscale Transfer Entropy destacamos que entre o petróleo e o etanol, ao analisar as diferentes escalas de tempo, não pudemos concluir qual sentido transfere mais informação. Essa característica pode ser relacionada com as intervenções políticas que baixam o preço da gasolina artificialmente, alteram o percentual de etanol na gasolina, fazendo com que a relação entre os preços do etanol e petróleo não ocorram naturalmente.

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Most bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed various strategies to prevent interference which may lead to different behavior when flying with conspecifics than when flying alone. This thesis seeks to explore the role of this sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. Results show that there is evidence of information transfer in both the speed of the bats and their turning behavior, and that such evidence is absent when we consider their heading directions. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader-follower interaction. / Master of Science / Manyanimalspeciesexhibitcollectivebehaviorwheregroupsofanimalscoordinatetheir motion, as in flocking or schooling. Many species of bats also demonstrate this behavior. Bats are unique among these animals in that they use echolocation as their primary means of navigation. Bats produce ultrasonic pulses or calls and listen to the returning echo to "visualize" their environment. Bats using echolocation in large groups run the risk of other bat calls interfering with their ability to hear their own calls. They have developed various waystopreventinterferencewhichmayleadtodifferentbehaviorwhenflyingwithotherbats thanwhenflyingalone. Fielddatafromamaternitycolonyofgraybatswerecollectedusing a system of cameras and microphones. These data were analyzed to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. Results show that there is evidence of information transfer about both the speed of the bats and their turning behavior. There was also evidence of a possible leader-follower interaction in some subsets of the data.

Information diffusion is significant in fields such as propagation prediction and influence maximization, with applications in viral marketing and rumor control. Despite conceptual differences, existing diffusion models may not represent identical underlying generative structures. A classification of diffusion of information models is developed based on infection requirements and stochasticity. The study involves analyzing seven existing DOI models on directed scale-free networks. The distinctive properties of each model are identified through simulations and analysis of experimental results. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, the final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application. We further investigate how the network structure and clustering affect the diffusion of information. Our findings reveal that clustering does not consistently accelerate the spread of information. Instead, the extent to which clustering facilitates the dissemination of information is influenced by the interplay between the specific network structure types and the information diffusion model employed. Another significant aspect of information diffusion is the effect of influential nodes. Identifying highly influential nodes is of great interest for strategic targeting in various applications such as viral marketing and information campaigns. Our follow-up study aims to identify influential nodes using a transfer entropy-based method. In this work, we use our method to identify influential users in Twitter data and compare the results against other existing methods. Finally, we developed a methodology based on Transfer Entropy to evaluate influence in the context of information diffusion. This methodology demonstrated its superiority in predicting user adoption against retweet-based metrics, marking it as a direct and reliable metric for understanding influential users and information diffusion trends.

This thesis describes how transmission of the 2009 A/H1N1 influenza pandemic in the United States varied geographically, with emphasis on population distribution and age structure. This is made possible by the availability of medical claims records maintained in the private sector that capture the weekly incidence of influenza-like illness in 834 US cities. First, a probabilistic method is developed to infer each city's outbreak onset time. This reveals a clear wave-like pattern of transmission originating in the south-eastern US. Then, a mechanistic mathematical model is constructed to describe the between-city transmission of the epidemic. A model selection procedure reveals that transmission to a city is modulated by its population size, surrounding population density, and possibly by students mixing in schools. Geographic variation in transmissibility is explored further by nesting a latent Gaussian process within the mechanistic transmission model, revealing a possible region of elevated transmissibility in the south-eastern US. Then, using the mechanistic model and a probabilistic back-tracing procedure, the geographic introduction sites (the `transmission hubs') of the outbreak are identified. The transmission hubs of the 2009 pandemic were generally mid-sized cities, contrasting with the conventional perspective that major outbreaks should start in large population centres with high international connectivity. Transmission is traced forward from these hubs to identify `basins of infection', or regions where outbreaks can be attributed with high probability to a particular hub. The city-level influenza data is also separated into 12 age categories. Techniques adapted from signal processing reveal that school-aged children may have been key drivers of the epidemic. Finally, to provide a point of comparison, the procedures described above are applied to the 2003-04 and 2007-08 seasonal influenza outbreaks. Since the 2007-08 outbreak featured three antigenically distinct strains of influenza, it is possible to identify which antigenic strains may have been responsible for infecting each transmission hub. These strains are identified using a probabilistic model that is joined with the geographic transmission model, providing a link between population dynamics and molecular surveillance.

The brain processes information sensed from the environment and guides behaviour. A fundamental component in this process is the storage and retrieval of past experiences as memories, which relies on the hippocampal formation. Although there has been a great progress in understanding the underlying neural code by which neurons communicate information, there are still open questions. Neural activity can be measured extracellularly as either spikes or field potentials. Isolated spikes and bursts of high-frequency spikes followed by silent periods can transmit messages to distant networks. The local field potential (LFP) reflects synaptic activity within a local network. The interplay between the two has been linked to cognitive functions, such as memory, attention and decision making. However, the code by which this neural communication is achieved is not well understood. We investigated a mechanism by which local network information contained in LFP rhythms can be transmitted to distant networks in the formof spike patterns fired by bursting neurons. Since rhythms within different frequency bands are prevalent during behavioural states, we studied this encoding during different states within the hippocampal formation. In the first paper, using a computational model we show that bursts of different size preferentially lock to the phase of the dominant rhythm within the LFP.We also present examples showing that bursting activity in the subiculum of an anaesthetised rat was phase-locked to delta or theta rhythms as predicted by the model. In the second paper, we explored possible neural codes by which bursting neurons can encode features of the LFP.We used the computational model reported in the first paper and analysed recordings from the subiculum of anaesthetised rats and the medial entorhinal cortex of an awake behaving rat. We show that bursting neurons encoded information about the instantaneous voltage, phase, slope and/or amplitude of the dominant LFP rhythm (delta or theta) in their firing rate. In addition, some neurons encoded about 10-15% of this information in intra-burst spike counts. We subsequently studied how the interactions between delta or theta rhythms can transfer information between different areas within the hippocampal formation. In the third paper, we show that delta and theta rhythms can act as separate routes for simultaneously transferring segregate information between the hippocampus and the subiculum of anaesthetised mice. We found that the phase of the rhythms conveyed more information than amplitude. We next investigated whether neurodegenerative pathology affects this information exchange. We compared information transfer within the hippocampal formation of young transgenic mice exhibiting Alzheimer’s disease-like pathology and healthy aged-matched control mice and show that at early stages of the disease the information transmission by LFP rhythm interactions appears to be intact but with some differences. The outcome of this project supports a burst code for relaying information about local network activity to downstream neurons and underscores the importance of LFP phase, which provides a reference time frame for coordinating neural activity, in information exchange between neural networks.