Дисертації з теми "Temporary Network Structure"
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LOSITO, MARIO. "What matters for ideation? A cross-level investigation of individual, group, and network factors." Doctoral thesis, Luiss Guido Carli, 2012. http://hdl.handle.net/11385/200805.
Повний текст джерелаYEGHIKYAN, Gevorg. "Urban Structure and Mobility as Spatio-temporal complex Networks." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/94477.
Повний текст джерелаGallacher, Kelly Marie. "Using river network structure to improve estimation of common temporal patterns." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7208/.
Повний текст джерелаBazzi, Marya. "Community structure in temporal multilayer networks, and its application to financial correlation networks." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:c3f6aa78-904c-4d10-97f3-ae56bb1f574a.
Повний текст джерелаShertil, M. S. "On the induction of temporal structure by recurrent neural networks." Thesis, Nottingham Trent University, 2014. http://irep.ntu.ac.uk/id/eprint/27915/.
Повний текст джерелаHenri, Dominic Charles. "From individuals to ecosystems : a study of the temporal and spatial variation in ecological network structure." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15726.
Повний текст джерелаAlrajebah, Nora. "Investigating cascades in social networks : structural and temporal aspects." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/420625/.
Повний текст джерелаGardner, Brian C. "Learning spatio-temporally encoded pattern transformations in structured spiking neural networks." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/810241/.
Повний текст джерелаCortés, Rudyar. "Scalable location-temporal range query processing for structured peer-to-peer networks." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066106/document.
Повний текст джерелаIndexing and retrieving data by location and time allows people to share and explore massive geotagged datasets observed on social networks such as Facebook, Flickr, and Twitter. This scenario known as a Location Based Social Network (LBSN) is composed of millions of users, sharing and performing location-temporal range queries in order to retrieve geotagged data generated inside a given geographic area and time interval. A key challenge is to provide a scalable architecture that allow to perform insertions and location-temporal range queries from a high number of users. In order to achieve this, Distributed Hash Tables (DHTs) and the Peer-to-Peer (P2P) computing paradigms provide a powerful building block for implementing large scale applications. However, DHTs are ill-suited for supporting range queries because the use of hash functions destroy data locality for the sake of load balance. Existing solutions that use a DHT as a building block allow to perform range queries. Nonetheless, they do not target location-temporal range queries and they exhibit poor performance in terms of query response time and message traffic. This thesis proposes two scalable solutions for indexing and retrieving geotagged data based on location and time
Le, Nhu Dinh. "Statistical analysis of the temporal-spatial structure of pH levels from the MAP3S/PCN monitoring network." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/25884.
Повний текст джерелаScience, Faculty of
Statistics, Department of
Graduate
Logiaco, Laureline. "Temporal modulation of the dynamics of neuronal networks with cognitive function : experimental evidence and theoretical analysis." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066225/document.
Повний текст джерелаWe investigated the putative function of the fine temporal dynamics of neuronal networks for implementing cognitive processes. First, we characterized the coding properties of spike trains recorded from the dorsal Anterior Cingulate Cortex (dACC) of monkeys. dACC is thought to trigger behavioral adaptation. We found evidence for (i) high spike count variability and (ii) temporal reliability (favored by temporal correlations) which respectively hindered and favored information transmission when monkeys were cued to switch the behavioral strategy. Also, we investigated the nature of the neuronal variability that was predictive of behavioral variability. High vs. low firing rates were not robustly associated with different behavioral responses, while deviations from a neuron-specific prototypical spike train predicted slower responses of the monkeys. These deviations could be due to increased or decreased spike count, as well as to jitters in spike times. Our results support the hypothesis of a complex spatiotemporal coding of behavioral adaptation by dACC, and suggest that dACC signals are unlikely to be decoded by a neural integrator. Second, we further investigated the impact of dACC temporal signals on the downstream decoder by developing mean-field equations to analyze network dynamics. We used an adapting single neuron model that mimics the response of cortical neurons to realistic dynamic synaptic-like currents. We approximated the time-dependent population rate for recurrent networks in an asynchronous irregular state. This constitutes an important step towards a theoretical study of the effect of temporal drives on networks which could mediate cognitive functions
Wirsich, Jonathan. "EEG-fMRI and dMRI data fusion in healthy subjects and temporal lobe epilepsy : towards a trimodal structure-function network characterization of the human brain." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM5040.
Повний текст джерелаThe understanding human brain structure and the function patterns arising from it is a central challenge to better characterize brain network pathologies such as temporal lobe epilepsies, which could help to improve the clinical predictability of epileptic surgery outcome.Brain functioning can be accessed by both electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), while brain structure can be measured with diffusion MRI (dMRI). We use these modalities to measure brain functioning during a face recognition task and in rest in order to link the different modalities in an optimal temporal and spatial manner. We discovered disruption of the network processing famous faces as well a disruption of the structure-function relation during rest in epileptic patients.This work broadened the understanding of epilepsy as a network disease that changes the brain on a large scale not limited to a local epileptic focus. In the future these results could be used to guide clinical intervention during epilepsy surgery but also they provide new approaches to evaluate pharmacological treatment on its functional implications on a whole brain scale
KC, Rabi. "Study of Some Biologically Relevant Dynamical System Models: (In)stability Regions of Cyclic Solutions in Cell Cycle Population Structure Model Under Negative Feedback and Random Connectivities in Multitype Neuronal Network Models." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou16049254273607.
Повний текст джерелаFeja, Malte [Verfasser], Michael [Akademischer Betreuer] Koch, and Ursula [Akademischer Betreuer] Dicke. "Investigation of neuronal structures and networks on the modulation of decision-making and impulse control by temporary inactivation via local microinfusion of the GABAA receptor agonist muscimol in rats / Malte Feja. Gutachter: Michael Koch ; Ursula Dicke. Betreuer: Michael Koch." Bremen : Staats- und Universitätsbibliothek Bremen, 2014. http://d-nb.info/1072225972/34.
Повний текст джерелаPajkert, Anna Ewa. "Behavioural and Structural Adaptation to Hippocampal Dysfunction in Humans." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21757.
Повний текст джерелаApplying knowledge flexibly to new situations is a cognitive faculty that is necessary in every-day life. Previous findings emphasise the crucial role the hippocampus plays in learning and linking new information with pre-existing knowledge. However, the functional integrity of the hippocampus changes over the lifespan and is frequently affected by neuropsychiatric disorders. The affected subjects must, therefore, develop adaptive strategies to achieve behavioural goals. Thus, my doctoral thesis deals with adaptation processes in the developing brain and in adult brains with a hippocampal dysfunction. This synopsis encompasses three studies on: (1) behavioural strategies in the developing brain, (2) behavioural strategies in the lesioned fully developed brain, and (3) structural changes in the lesioned fully developed brain. Study 1 suggests an age-related shift in the associative memory: Children, adolescents, and young adults use different memory strategies when integrating information. Study 2 suggests that the memory deficits observed in patients with right-sided hippocampal lesions are not merely a consequence of an impaired associative memory but rather result from an additional hippocampal contribution to the memory integration. Study 3 suggests that postoperative structural adaptation processes occur on a surprisingly short time-scale, and this structural reorganisation happens not only in the hippocampus but also in distant brain areas connected to the hippocampus. In conclusion, findings from these three studies show that adaptation processes in the developing brain and in adult brains with hippocampal dysfunction occur on both the behavioural and the structural level.
Pommellet, Adrien. "On model-checking pushdown systems models." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC207/document.
Повний текст джерелаIn this thesis, we propose different model-checking techniques for pushdown system models. Pushdown systems (PDSs) are indeed known to be a natural model for sequential programs, as they feature an unbounded stack that can simulate the assembly stack of an actual program. Our first contribution consists in model-checking the logic HyperLTL that adds existential and universal quantifiers on path variables to LTL against pushdown systems (PDSs). The model-checking problem of HyperLTL has been shown to be decidable for finite state systems. We prove that this result does not hold for pushdown systems nor for the subclass of visibly pushdown systems. Therefore, we introduce approximation algorithms for the model-checking problem, and show how these can be used to check security policies. In the second part of this thesis, as pushdown systems can fail to accurately represent the way an assembly stack actually operates, we introduce pushdown systems with an upper stack (UPDSs), a model where symbols popped from the stack are not destroyed but instead remain just above its top, and may be overwritten by later push rules. We prove that the sets of successors post* and predecessors pre* of a regular set of configurations of such a system are not always regular, but that post* is context-sensitive, hence, we can decide whether a single configuration is forward reachable or not. We then present methods to overapproximate post* and under-approximate pre*. Finally, we show how these approximations can be used to detect stack overflows and stack pointer manipulations with malicious intent. Finally, in order to analyse multi-threaded programs, we introduce in this thesis a model called synchronized dynamic pushdown networks (SDPNs) that can be seen as a network of pushdown processes executing synchronized transitions, spawning new pushdown processes, and performing internal pushdown actions. The reachability problem for this model is obviously undecidable. Therefore, we compute an abstraction of the execution paths between two regular sets of configurations. We then apply this abstraction framework to a iterative abstraction refinement scheme
Kinuthia, Wanyee. "“Accumulation by Dispossession” by the Global Extractive Industry: The Case of Canada." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30170.
Повний текст джерелаWu, Tsunghan, and 吳宗翰. "Tracking Dynamics of Temporal Social Networks andApplications in Structural Network Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s488ab.
Повний текст джерела國立臺灣大學
電機工程學研究所
106
Structural network analysis for temporal social networks is an essential discipline for comprehending human behaviors and interactions on social networks. For systematically interpreting the temporal networks, we raise two fundamental questions and propose a general framework to track, model, and predict the structures of time-varying networks. In this dissertation, both temporal user-item (bipartite) networks and temporal social (unipartite) networks are scrutinized respectively. We introduce temporal bipartite projection (TBP) to socially aggregate the temporal information among users and represent the item transition tendencies within an item transition graph (ITG). Based on the ITG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair which is for performing the new link prediction task. Furthermore, we introduce temporal Laplacian eigenmaps (TLE) for determining the sequence of latent feature vectors for each node from temporal networks. A general prediction framework is proposed based on the results of TLE, which use the Finite Impulse Response (FIR) filter to learn the dynamics of evolving latent feature vectors of users. Then, the predicted feature vectors are used for various network analysis applications, including community detection, link prediction, and node ranking. Besides, we also use the recurrent neural networks (RNNs) to model the temporal latent feature vectors for better accuracy. To validate the effectiveness of our frameworks, we conduct various experiments based on our synthetic datasets and real-world datasets such as DBLP, Flickr, Delicious for temporal user-item networks and Infectious, Haggle, Reality Mining for temporal social networks. Our experimental results show that our framework is very effective in tracking latent feature vectors and predicting future network structures.
Rallapalli, Swati. "Exploiting temporal stability and low-rank structure for localization in mobile networks." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-08-1794.
Повний текст джерелаtext
Lee, Chien-Ping, and 李健平. "A Study of the Seismicity and Subsurface Structures using a Temporary Seismic Network in Northwestern Taiwan." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/73706308606008227151.
Повний текст джерела國立中央大學
地球物理研究所
93
The seismicity is low in northwestern Taiwan for a long time. However, recent studies indicated the geological characteristics in this area are special. Besides that, many important facilities, industrial and economic cities are in this area. Significant intensity was recorded at seismic stations in Hsinchu area during the 1999 Chi-Chi earthquake. And the disastrous Hsinchu-Taichung earthquake in 1935 occurred in the southern part of this area. Thus, potential recurrence of large earthquakes in this area becomes important topics. In response, the seismicity and subsurface structures are analyzed in this study. A temporary seismic network including ten seismic stations was deployed in Taoyuan, Hsinchu and Miaoli counties in northwestern Taiwan since January 2001. Each station has one triaxial accelerograph, three external one-component velocity sensors, global positioning system, and data storage device. Seismic records with absolute timing are critical to obtain accurate earthquake locations. Dense station distribution is necessary to get earthquakes with lower magnitude from clear seismic signals recorded in local area. Based on considerations of available instruments and recording sites, the stations were deployed uniformly in the study area. In this study, the arrival time data of the temporary seismic network and CWBSN are combined to locate earthquake. Two dense earthquake clusters were relocated to compare several geological cross sections. In order to study the stress patterns, numerous focal mechanisms were determined by waveform inversion. Finally, the thicknesses of alluvium were estimated by using the P-wave travel-time residuals from earthquake location and the dominant frequencies identified from spectra of ground acceleration. By combining the temporary seismic network and CWBSN data, the results of earthquake location show significant convergent in focal depths due to adding of the near source arrival time data. Most of the hypocenters are located shallower than 15 km at depth. Relocation of two dense clusters using the JHD and DD methods removed systematic bias due to one-dimensional velocity model. The events were shifted toward northwest in the horizontal direction and became clustered at depth from 5 to 10 km. The station corrections of JHD reflected the difference in geology of the northwest and southeast parts in the study area. It is also consistent with distinct topographic features. By comparing the relocated events with several geological cross sections, we found that the seismicity and subsurface structures are related. To determine the focal mechanisms using waveform inversion, the acceleration records were used. The acceleration records of a single station with three-component sensors are doubly integrated to get displacement waveforms. Then the focal mechanisms are determined by waveform inversion. In total, 88 focal mechanisms were determined with local magnitudes from 1.35 to 3.33. The widespread presences of complex fault types of focal mechanisms imply that the microearthquakes might be triggered by subfaults. Finally, the thicknesses of alluvium were estimated by correlating the velocity and acceleration data. Results from the averaged P-wave travel-time residuals from earthquake location and from the dominant frequency from Fourier spectra are not consistent to each other. The thicknesses of alluvium obtained by these two methods are different. However, the averaged P-wave travel-time residuals can be correlated with geology and topography. There is good relation between travel-time residuals and geological cross-sections. The results imply that the averaged P-wave travel-time residuals can be used to prospect the subsurface structures.
Raghavan, Mohan. "First-Spike-Latency Codes : Significance, Relation to Neuronal Network Structure and Application to Physiological Recordings." Thesis, 2013. http://etd.iisc.ernet.in/2005/3393.
Повний текст джерелаCastellano, Marta. "Computational Principles of Neural Processing: modulating neural systems through temporally structured stimuli." Doctoral thesis, 2014. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2014121112959.
Повний текст джерелаNouri, Golmaei Sara. "Improving the Performance of Clinical Prediction Tasks by using Structured and Unstructured Data combined with a Patient Network." Thesis, 2021. http://dx.doi.org/10.7912/C2/41.
Повний текст джерелаWith the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.
Finger, Holger Ewald. "Information Processing in Neural Networks: Learning of Structural Connectivity and Dynamics of Functional Activation." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017031615634.
Повний текст джерела(11189856), Vibha Viswanathan. "Neurophysiological Mechanisms of Speech Intelligibility under Masking and Distortion." Thesis, 2021.
Знайти повний текст джерелаDifficulty understanding speech in background noise is the most common hearing complaint. Elucidating the neurophysiological mechanisms underlying speech intelligibility in everyday environments with multiple sound sources and distortions is hence important for any technology that aims to improve real-world listening. Using a combination of behavioral, electroencephalography (EEG), and computational modeling experiments, this dissertation provides insight into how the brain analyzes such complex scenes, and what roles different acoustic cues play in facilitating this process and in conveying phonetic content. Experiment #1 showed that brain oscillations selectively track the temporal envelopes (i.e., modulations) of attended speech in a mixture of competing talkers, and that the strength and pattern of this attention effect differs between individuals. Experiment #2 showed that the fidelity of neural tracking of attended-speech envelopes is strongly shaped by the modulations in interfering sounds as well as the temporal fine structure (TFS) conveyed by the cochlea, and predicts speech intelligibility in diverse listening environments. Results from Experiments #1 and #2 support the theory that temporal coherence of sound elements across envelopes and/or TFS shapes scene analysis and speech intelligibility. Experiment #3 tested this theory further by measuring and computationally modeling consonant categorization behavior in a range of background noises and distortions. We found that a physiologically plausible model that incorporated temporal-coherence effects predicted consonant confusions better than conventional speech-intelligibility models, providing independent evidence that temporal coherence influences scene analysis. Finally, results from Experiment #3 also showed that TFS is used to extract speech content (voicing) for consonant categorization even when intact envelope cues are available. Together, the novel insights provided by our results can guide future models of speech intelligibility and scene analysis, clinical diagnostics, improved assistive listening devices, and other audio technologies.