Academic literature on the topic 'Complex temporal data'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Complex temporal data.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Complex temporal data"
1
Käfer, Wolfgang, and Harald Schöning. "Realizing a temporal complex-object data model." ACM SIGMOD Record 21, no. 2 (June 1992): 266–75. http://dx.doi.org/10.1145/141484.130323.
Full text
2
Harada, Lilian. "Detection of complex temporal patterns over data streams." Information Systems 29, no. 6 (September 2004): 439–59. http://dx.doi.org/10.1016/j.is.2003.10.004.
Full text
3
Kvet, Michal, Emil Kršák, and Karol Matiaško. "Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture." Applied Sciences 11, no. 3 (January 20, 2021): 916. http://dx.doi.org/10.3390/app11030916.
Full textAbstract:
Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics.
4
Cappello, C., S. De Iaco, S. Maggio, and D. Posa. "Modeling spatio-temporal complex covariance functions for vectorial data." Spatial Statistics 47 (March 2022): 100562. http://dx.doi.org/10.1016/j.spasta.2021.100562.
Full text
5
Wu, Xing, Shuai Mao, Luolin Xiong, and Yang Tang. "A survey on temporal network dynamics with incomplete data." Electronic Research Archive 30, no. 10 (2022): 3786–810. http://dx.doi.org/10.3934/era.2022193.
Full textAbstract:
<abstract><p>With the development of complex network theory, many phenomena on complex networks, such as infectious disease transmission, information spreading and transportation management, can be explained by temporal network dynamics, to reveal the evolution of the real world. Due to the failure of equipment for collecting data, human subjectivity, and false decisions made by machines when the high accuracy is required, data from temporal networks is usually incomplete, which makes the samples unrepresentative and the model analysis more challenging. This survey concentrates on the pre-processing strategies of incomplete data and overviews two categories of methods on data imputation and prediction, respectively. According to whether each layer in temporal networks has the coupling process, this survey overviews the dynamic modeling approaches in terms of both a single process and coupling processes on complex temporal networks. Moreover, for complex temporal networks with incomplete data, this survey summarizes various characteristic analysis methods, which concentrate on critical nodes identification, network reconstruction, network recoverity, and criticality. Finally, some future directions are discussed for temporal networks dynamics with incomplete data.</p></abstract>
6
Wu, X., R. Zurita-Milla, M. J. Kraak, and E. Izquierdo-Verdiguier. "CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1387–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1387-2017.
Full textAbstract:
As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.
7
Parra, R. Gonzalo, Nikolaos Papadopoulos, Laura Ahumada-Arranz, Jakob El Kholtei, Noah Mottelson, Yehor Horokhovsky, Barbara Treutlein, and Johannes Soeding. "Reconstructing complex lineage trees from scRNA-seq data using MERLoT." Nucleic Acids Research 47, no. 17 (August 20, 2019): 8961–74. http://dx.doi.org/10.1093/nar/gkz706.
Full textAbstract:
Abstract Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.
8
Porch, William, and Daniel Rodriguez. "Spatial Interpolation of Meteorological Data in Complex Terrain Using Temporal Statistics." Journal of Climate and Applied Meteorology 26, no. 12 (December 1987): 1696–708. http://dx.doi.org/10.1175/1520-0450(1987)026<1696:siomdi>2.0.co;2.
Full text
9
Song, Chao, Youfang Lin, Shengnan Guo, and Huaiyu Wan. "Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 914–21. http://dx.doi.org/10.1609/aaai.v34i01.5438.
Full textAbstract:
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.
10
Kosiuczenko, Piotr. "An Interval Temporal Logic for Time Series Specification and Data Integration." Remote Sensing 13, no. 12 (June 8, 2021): 2236. http://dx.doi.org/10.3390/rs13122236.
Full textAbstract:
The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.
Dissertations / Theses on the topic "Complex temporal data"
1
Renz, Matthias. "Enhanced query processing on complex spatial and temporal data." Diss., [S.l.] : [s.n.], 2006. http://edoc.ub.uni-muenchen.de/archive/00006231.
Full text
2
Pacella, Massimo. "High-dimensional statistics for complex data." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3016.
Full textAbstract:
2016 - 2017High dimensional data analysis has become a popular research topic in the recent years, due to the emergence of various new applications in several fields of sciences underscoring the need for analysing massive data sets. One of the main challenge in analysing high dimensional data regards the interpretability of estimated models as well as the computational efficiency of procedures adopted. Such a purpose can be achieved through the identification of relevant variables that really affect the phenomenon of interest, so that effective models can be subsequently constructed and applied to solve practical problems. The first two chapters of the thesis are devoted in studying high dimensional statistics for variable selection. We firstly introduce a short but exhaustive review on the main developed techniques for the general problem of variable selection using nonparametric statistics. Lastly in chapter 3 we will present our proposal regarding a feature screening approach for non additive models developed by using of conditional information in the estimation procedure... [edited by Author]
XXX ciclo
3
Törmänen, Patrik. "Forecasting important disease spreaders from temporal contact data." Thesis, Umeå universitet, Institutionen för fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-56747.
Full text
4
Schaidnagel, Michael. "Automated feature construction for classification of complex, temporal data sequences." Thesis, University of the West of Scotland, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692834.
Full textAbstract:
Data collected from internet applications are mainly stored in the form of transactions. All transactions of one user form a sequence, which shows the user´s behaviour on the site. Nowadays, it is important to be able to classify the behaviour in real time for various reasons: e.g. to increase conversion rate of customers while they are in the store or to prevent fraudulent transactions before they are placed. However, this is difficult due to the complex structure of the data sequences (i.e. a mix of categorical and continuous data types, constant data updates) and the large amounts of data that are stored. Therefore, this thesis studies the classification of complex data sequences. It surveys the fields of time series analysis (temporal data mining), sequence data mining or standard classification algorithms. It turns out that these algorithms are either difficult to be applied on data sequences or do not deliver a classification: Time series need a predefined model and are not able to handle complex data types; sequence classification algorithms such as the apriori algorithm family are not able to utilize the time aspect of the data. The strengths and weaknesses of the candidate algorithms are identified and used to build a new approach to solve the problem of classification of complex data sequences. The problem is thereby solved by a two-step process. First, feature construction is used to create and discover suitable features in a training phase. Then, the blueprints of the discovered features are used in a formula during the classification phase to perform the real time classification. The features are constructed by combining and aggregating the original data over the span of the sequence including the elapsed time by using a calculated time axis. Additionally, a combination of features and feature selection are used to simplify complex data types. This allows catching behavioural patterns that occur in the course of time. This new proposed approach combines techniques from several research fields. Part of the algorithm originates from the field of feature construction and is used to reveal behaviour over time and express this behaviour in the form of features. A combination of the features is used to highlight relations between them. The blueprints of these features can then be used to achieve classification in real time on an incoming data stream. An automated framework is presented that allows the features to adapt iteratively to a change in underlying patterns in the data stream. This core feature of the presented work is achieved by separating the feature application step from the computational costly feature construction step and by iteratively restarting the feature construction step on the new incoming data. The algorithm and the corresponding models are described in detail as well as applied to three case studies (customer churn prediction, bot detection in computer games, credit card fraud detection). The case studies show that the proposed algorithm is able to find distinctive information in data sequences and use it effectively for classification tasks. The promising results indicate that the suggested approach can be applied to a wide range of other application areas that incorporate data sequences.
5
Gao, Feng. "Complex medical event detection using temporal constraint reasoning." Thesis, University of Aberdeen, 2010. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=153271.
Full textAbstract:
The Neonatal Intensive Care Unit (NICU) is a hospital ward specializing in looking after premature and ill newborn babies. Working in such a busy and complex environment is not easy and sophisticated equipment is used to help the daily work of the medical staff . Computers are used to analyse the large amount of monitored data and extract hidden information, e.g. to detect interesting events. Unfortunately, one group of important events lacks features that are recognizable by computers. This group includes the actions taken by the medical sta , for example two actions related to the respiratory system: inserting an endotracheal tube into a baby’s trachea (ET Intubating) or sucking out the tube (ET Suctioning). These events are very important building blocks for other computer applications aimed at helping the sta . In this research, a strategy for detecting these medical actions based on contextual knowledge is proposed. This contextual knowledge specifies what other events normally occur with each target event and how they are temporally related to each other. The idea behind this strategy is that all medical actions are taken for di erent purposes hence may have di erent procedures (contextual knowledge) for performing them. This contextual knowledge is modelled using a point based framework with special attention given to various types of uncertainty. Event detection consists in searching for consistent matching between a model based on the contextual knowledge and the observed event instances - a Temporal Constraint Satisfaction Problem (TCSP). The strategy is evaluated by detecting ET Intubating and ET Suctioning events, using a specially collected NICU monitoring dataset. The results of this evaluation are encouraging and show that the strategy is capable of detecting complex events in an NICU.
6
Ahmad, Saif. "A temporal pattern identification and summarization method for complex time serial data." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/843297/.
Full textAbstract:
Most real-world time series data is produced by complex systems. For example, the economy is a social system which produces time series of stocks, bonds, and foreign exchange rates whereas the human body is a biological system which produces time series of heart rate variations, brain activity, and rate of blood circulation. Complex systems exhibit great variety and complexity and so does the time series emanating from these systems. However, universal principles and tools seem to govern our understanding of highly complex phenomena, processes, and dynamics. It has been argued that one of the universal properties of complex systems and time series produced by complex systems is 'scaling'. The multiscale wavelet analysis shows promise to systematically elucidate complex dynamics in time series data at various timescales. In this research we investigate whether the wavelet analysis can be used as a universal tool to study the universal property of scaling in complex systems. We have developed and evaluated a wavelet time series analysis framework for automatically assessing the state and behaviour of complex systems such as the economy and the human body. Our results are good and support the hypothesis that 'scaling' is indeed a universal property of complex systems and that the wavelet analysis can be used as a universal tool to study it. We conclude that a system based on universal principles (e.g. 'scaling') and tools (e.g. wavelet analysis) is not only robust but also renders itself useful in diverse environments. Key words: Complex systems, scaling, time series analysis, wavelet analysis.
7
Jones-Todd, Charlotte M. "Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12009.
Full textAbstract:
Point processes are mechanisms that beget point patterns. Realisations of point processes are observed in many contexts, for example, locations of stars in the sky, or locations of trees in a forest. Inferring the mechanisms that drive point processes relies on the development of models that appropriately account for the dependencies inherent in the data. Fitting models that adequately capture the complex dependency structures in either space, time, or both is often problematic. This is commonly due to—but not restricted to—the intractability of the likelihood function, or computational burden of the required numerical operations. This thesis primarily focuses on developing point process models with some hierarchical structure, and specifically where this is a latent structure that may be considered as one of the following: (i) some unobserved construct assumed to be generating the observed structure, or (ii) some stochastic process describing the structure of the point pattern. Model fitting procedures utilised in this thesis include either (i) approximate-likelihood techniques to circumvent intractable likelihoods, (ii) stochastic partial differential equations to model continuous spatial latent structures, or (iii) improving computational speed in numerical approximations by exploiting automatic differentiation. Moreover, this thesis extends classic point process models by considering multivariate dependencies. This is achieved through considering a general class of joint point process model, which utilise shared stochastic structures. These structures account for the dependencies inherent in multivariate point process data. These models are applied to data originating from various scientific fields; in particular, applications are considered in ecology, medicine, and geology. In addition, point process models that account for the second order behaviour of these assumed stochastic structures are also considered.
8
IACOBELLO, GIOVANNI. "Spatio-temporal analysis of wall-bounded turbulence: A multidisciplinary perspective via complex networks." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2829683.
Full text
9
El, Ouassouli Amine. "Discovering complex quantitative dependencies between interval-based state streams." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI061.
Full textAbstract:
Les avancées significatives qu’ont connu les technologies de capteurs, leur utilisation croissante ainsi que leur intégration dans les systèmes d’information permettent d’obtenir des descriptions temporelles riches d’environnements réels. L’information générée par de telles sources de données peut être qualifiée d’hétérogène sur plusieurs plans: types de mesures physiques, domaines et primitives temporelles, modèles de données etc. Dans ce contexte, l’application de méthodes de fouille de motifs constitue une opportunité pour la découverte de relations temporelles non-triviales, directement utilisables et facilement interprétables décrivant des phénomènes complexes. Nous proposons d’utiliser un ensemble d’abstraction temporelles pour construire une représentation unifiée, sous forme des flux d’intervalles (ou états), de l’information générée par un système hétérogène. Cette approche permet d’obtenir une description temporelle de l’environnent étudié à travers des attributs (ou états), dits de haut niveau, pouvant être utilisés dans la construction des motifs temporelles. A partir de cette représentation, nous nous intéressons à la découverte de dépendances temporelles quantitatives (avec information de délais) entre plusieurs flux d’intervalles. Nous introduisons le modèle de dépendances Complex Temporal Dependency (CTD) défini de manière similaire à une forme normale conjonctive. Ce modèle permets d’exprimer un ensemble riche de relations temporelles complexes. Pour ce modèle de dépendances nous proposons des algorithmes efficaces de découverte : CTD-Miner et ITLD - Interval Time Lag Discovery. Finalement, nous évaluons les performances de notre proposition ainsi que la qualité des résultats obtenus à travers des données issues de simulations ainsi que des données réelles collectées à partir de caméras et d’analyse vidéoThe increasing utilization of sensor devices in addition to human-given data make it possible to capture real world systems complexity through rich temporal descriptions. More precisely, the usage of a multitude of data sources types allows to monitor an environment by describing the evolution of several of its dimensions through data streams. One core characteristic of such configurations is heterogeneity that appears at different levels of the data generation process: data sources, time models and data models. In such context, one challenging task for monitoring systems is to discover non-trivial temporal knowledge that is directly actionable and suitable for human interpretation. In this thesis, we firstly propose to use a Temporal Abstraction (TA) approach to express information given by heterogeneous raw data streams with a unified interval-based representation, called state streams. A state reports on a high level environment configuration that is of interest for an application domain. Such approach solves problems introduced by heterogeneity, provides a high level pattern vocabulary and also permits also to integrate expert(s) knowledge into the discovery process. Second, we introduced the Complex Temporal Dependencies (CTD) that is a quantitative interval-based pattern model. It is defined similarly to a conjunctive normal form and allows to express complex temporal relations between states. Contrary to the majority of existing pattern models, a CTD is evaluated with automatic statistical assessment of streams intersection avoiding the use of any significance user-given parameter. Third, we proposed CTD-Miner a first efficient CTD mining framework. CTD-Miner performs an incremental dependency construction. CTD-Miner benefits from pruning techniques based on a statistical correspondence relationship that aims to accelerate the exploration search space by reducing redundant information and provide a more usable result set. Finally, we proposed the Interval Time Lag Discovery (ITLD) algorithm. ITLD is based on a confidence variation heuristic that permits to reduce the complexity of the pairwise dependency discovery process from quadratic to linear w.r.t a temporal constraint Δ on time lags. Experiments on simulated and real world data showed that ITLD provides efficiently more accurate results in comparison with existing approaches. Hence, ITLD enhances significantly the accuracy, performances and scalability of CTD-Miner. The encouraging results given by CTD-Miner on our real world motion data set suggests that it is possible to integrate insights given by real time video processing approaches in a knowledge discovery process opening interesting perspectives for monitoring smart environments
10
Sherwin, Jason. "A computational approach to achieve situational awareness from limited observations of a complex system." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33955.
Full textAbstract:
At the start of the 21st century, the topic of complexity remains a formidable challenge in engineering, science and other aspects of our world. It seems that when disaster strikes it is because some complex and unforeseen interaction causes the unfortunate outcome. Why did the financial system of the world meltdown in 2008-2009? Why are global temperatures on the rise? These questions and other ones like them are difficult to answer because they pertain to contexts that require lengthy descriptions. In other words, these contexts are complex.
But we as human beings are able to observe and recognize this thing we call 'complexity'. Furthermore, we recognize that there are certain elements of a context that form a system of complex interactions - i.e., a complex system. Many researchers have even noted similarities between seemingly disparate complex systems. Do sub-atomic systems bear resemblance to weather patterns? Or do human-based economic systems bear resemblance to macroscopic flows? Where do we draw the line in their resemblance? These are the kinds of questions that are asked in complex systems research.
And the ability to recognize complexity is not only limited to analytic research. Rather, there are many known examples of humans who, not only observe and recognize but also, operate complex systems. How do they do it? Is there something superhuman about these people or is there something common to human anatomy that makes it possible to fly a plane? - Or to drive a bus? Or to operate a nuclear power plant? Or to play Chopin's etudes on the piano? In each of these examples, a human being operates a complex system of machinery, whether it is a plane, a bus, a nuclear power plant or a piano. What is the common thread running through these abilities?
The study of situational awareness (SA) examines how people do these types of remarkable feats. It is not a bottom-up science though because it relies on finding general principles running through a host of varied human activities. Nevertheless, since it is not constrained by computational details, the study of situational awareness provides a unique opportunity to approach complex tasks of operation from an analytical perspective. In other words, with SA, we get to see how humans observe, recognize and react to complex systems on which they exert some control.
Reconciling this perspective on complexity with complex systems research, it might be possible to further our understanding of complex phenomena if we can probe the anatomical mechanisms by which we, as humans, do it naturally. At this unique intersection of two disciplines, a hybrid approach is needed. So in this work, we propose just such an approach.
In particular, this research proposes a computational approach to the situational awareness (SA) of complex systems. Here we propose to implement certain aspects of situational awareness via a biologically-inspired machine-learning technique called Hierarchical Temporal Memory (HTM). In doing so, we will use either simulated or actual data to create and to test computational implementations of situational awareness. This will be tested in two example contexts, one being more complex than the other. The ultimate goal of this research is to demonstrate a possible approach to analyzing and understanding complex systems. By using HTM and carefully developing techniques to analyze the SA formed from data, it is believed that this goal can be obtained.
Books on the topic "Complex temporal data"
1
Pernet, Bruno, ed. Larval Feeding: Mechanisms, Rates, and Performance in Nature. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0007.
Full textAbstract:
Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.
2
Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Full textAbstract:
The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
3
Hammond, Christopher J., Marc N. Potenza, and Linda C. Mayes. Development of Impulse Control, Inhibition, and Self-Regulatory Behaviors in Normative Populations across the Lifespan. Edited by Jon E. Grant and Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0082.
Full textAbstract:
Impulsivity represents a complex multidimensional construct that may change across the lifespan and is associated with numerous neuropsychiatric disorders including substance use disorders, conduct disorder/antisocial personality disorder, and traumatic brain injury. Multiple psychological theories have considered impulsivity and the development of impulse control, inhibition, and self-regulatory behaviors during childhood. Some psychoanalytic theorists have viewed impulse control and self-regulatory behaviors as developing ego functions emerging in the context of id-based impulses and inhibitory pressures from the superego. Object relationists added to this framework but placed more emphasis on mother–child dyadic relationships and the process of separation and individuation within the infant. Cognitive and developmental theorists have viewed impulse control and self-regulation as a series of additive cognitive functions emerging at different temporal points during childhood and with an emphasis on attentional systems and the ability to inhibit a prepotent response. Commonalities exist across all of these developmental theories, and they all are consistent with the idea that the development of impulse control appears cumulative and emergent in early life, with the age range of 24–36 months being a formative period. Impulsivity is part of normal development in the healthy child, and emerging empirical data on normative populations (as measured by neuropsychological testing batteries, self-report measures, and behavioral observation) suggest that impulse control, self-regulation, and other impulsivity-related phenomena may follow different temporal trajectories, with impulsivity decreasing linearly over time and sensation seeking and reward responsiveness following an inverted U-shaped trajectory across the lifespan. These different trajectories coincide with developmental brain changes, including early maturation of subcortical regions in relation to the later maturation of the frontal lobes, and may underlie the frequent risk-taking behavior often observed during adolescence.
4
Komlos, John, and Inas R. Kelly, eds. The Oxford Handbook of Economics and Human Biology. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199389292.001.0001.
Full textAbstract:
The Oxford Handbook of Economics and Human Biology provides an extensive and insightful overview of how economic conditions affect human well-being and how human health influences economic outcomes. The book addresses both macro and micro factors, as well as their interaction, providing new understanding of complex relationships and developments in economic history and economic dynamics. Among the topics explored is how variation in height, whether over time, among different socioeconomic groups, or in different locations, is an important indicator of changes in economic growth and economic development, levels of economic inequality, and economic opportunities for individuals. The book covers a broad geographic range: Africa, Latin and North America, Asia, and Europe. Its temporal scope ranges from the late Iron Age to the present. Taking advantage of recent improvements in data collection and economic methods, the book also explores how humans’ biological conditions influence and are influenced by their economic circumstances, including poverty. Among the issues addressed are how height, body mass index (BMI), and obesity can affect and are affected by productivity, wages, and wealth. How family environment affects health and well-being is examined, as is the importance of both pre-birth and early-childhood conditions for subsequent economic outcomes. The volume shows that well-being is a salient aspect of economics, and the new toolkit of evidence from biological living standards enhances understanding of how industrialization, commercialization, income distribution, the organization of health care, social status, and the redistributive state affect such human attributes as physical stature, weight, and the obesity epidemic in historical and contemporary populations.
5
Holdaway, Simon, and Patricia Fanning. Geoarchaeology of Aboriginal Landscapes in Semi-arid Australia. CSIRO Publishing, 2014. http://dx.doi.org/10.1071/9780643108950.
Full textAbstract:
This book provides readers with a unique understanding of the ways in which Aboriginal people interacted with their environment in the past at one particular location in western New South Wales. It also provides a statement showing how geoarchaeology should be conducted in a wide range of locations throughout Australia.
One of the key difficulties faced by all those interested in the interaction between humans and their environment in the past is the complex array of processes acting over different spatial and temporal scales. The authors take account of this complexity by integrating three key areas of study – geomorphology, geochronology and archaeology – applied at a landscape scale, with the intention of understanding the record of how Australian Aboriginal people interacted with the environment through time and across space.
This analysis is based on the results of archaeological research conducted at the University of New South Wales Fowlers Gap Arid Zone Research Station between 1999 and 2002 as part of the Western New South Wales Archaeology Program. The interdisciplinary geoarchaeological program was targeted at expanding the potential offered by archaeological deposits in western New South Wales, Australia.
The book contains six chapters: the first two introduce the study area, then three data analysis chapters deal in turn with the geomorphology, geochronology and archaeology of Fowlers Gap Station. A final chapter considers the results in relation to the history of Aboriginal occupation of Fowlers Gap Station, as well as the insights they provide into Aboriginal ways of life more generally. Analyses are well illustrated through the tabulation of results and the use of figures created through Geographic Information System software.
Winner of the 2015 Australian Archaeology Association John Mulvaney Book Award
6
El-Bushra, Judy. How Should We Explain the Recurrence of Violent Conflict, and What Might Gender Have to Do with It? Edited by Fionnuala Ní Aoláin, Naomi Cahn, Dina Francesca Haynes, and Nahla Valji. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199300983.013.5.
Full textAbstract:
This chapter examines the factors behind the lack of progress in minimizing conflict, building peace, and improving security for women in conflict-affected environments. It reviews how cycles of conflict have been described in mainstream conflict analysis, which often include ill-conceived and temporary approaches to conflict management. The chapter explores where gender has been situated in these analyses, as well as the impact of adding gender data in operationalizing conflict responses, as opposed to engaging in a more thorough feminist analysis. This chapter then offers suggestions for broadening the mainstream approach by integrating a more fruitful gender analysis that addresses integrating holistic understandings of gendered relationships within society as a whole. The chapter ends with a call to conceptualize both conflict and gender as complex and fluid in order to create a more accurate analysis and more nuanced responses.
7
Maher, Garret. Highly Skilled Lebanese Transnational Migrants. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608873.003.0009.
Full textAbstract:
This chapter provides new information relating to aspects of transnational migration among high-skilled Lebanese migrants from a dual country perspective; that of the sending country, Lebanon, and of the receiving country, Kuwait. By using a dual, home and host country perspective, the chapter shows a more complete picture of some specific aspects of transnational migration, in particular, the motivations and drivers of migration, and why migrants chose Kuwait as a destination, as opposed to other GCC countries. It then explores aspects of integration and socialization to first identify the Lebanese in Kuwait who, according to this research sample, are integrated into Kuwaiti society, and to see if a transnational community was formed among and between other Lebanese in Kuwait. The chapter proceeds to explore temporal aspects of migration to discover how long migrants plan on staying in Kuwait as well as presenting data on returned migrants and the reason for their return to Lebanon. Finally, it explores remittances, which form a key feature of transnationalism.
8
Teitelbaum, Michael S. High-Skilled Migration Policy Challenges from a US Perspective. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815273.003.0007.
Full textAbstract:
This chapter addresses the arguments and available evidence about the complex intersections among basic research, claimed STEM (Science, Technology, Engineering, Mathematics) talent shortages, migration policy, and US economic growth. ‘Technical progress’ is a critical factor in economic growth, especially in the modern world of the ‘knowledge economy’. On the basis of this, should the US and other governments seek to increase their nations’ economic growth by expanding investments in basic research, or does basic research produce ‘global public goods’ that can readily be exploited economically by other countries? Should governments expand the number of domestic students pursuing higher education in science and engineering while also facilitating global recruitment by expanding temporary visas in these fields, or do these two approaches involve mutual contradictions? To what extent does the US government make available the migration data needed to assess such questions or support objective research and analysis on these issues?
9
Sime, Stuart. 30. Striking Out, Discontinuance, and Stays. Oxford University Press, 2018. http://dx.doi.org/10.1093/he/9780198823100.003.3500.
Full textAbstract:
This chapter discusses striking-out orders, discontinuance, and stays in civil proceedings. Rule 3.4(2) of the Civil Procedure Rules 1998 (CPR) allows the court to strike out a statement of case if it appears to the court: that the statement of case discloses no reasonable grounds for bringing or defending the claim; that the statement of case is an abuse of the court’s process or is otherwise likely to obstruct the just disposal of the proceedings; or that there has been a failure to comply with a rule, practice direction, or court order. A party who realizes their case is doomed is often best advised to discontinue to prevent the accumulation of further costs, but often has to pay the costs of the other parties to date. Stays are temporary halts in proceedings, and can be granted for a range of reasons. A stay is normally lifted once the reason no longer applies.
10
Sime, Stuart. 30. Striking out, discontinuance, and stays. Oxford University Press, 2017. http://dx.doi.org/10.1093/he/9780198787570.003.3500.
Full textAbstract:
This chapter discusses striking-out orders, discontinuance, and stays in civil proceedings. Rule 3.4(2) of the Civil Procedure Rules 1998 (CPR) allows the court to strike out a statement of case if it appears to the court: that the statement of case discloses no reasonable grounds for bringing or defending the claim; that the statement of case is an abuse of the court’s process or is otherwise likely to obstruct the just disposal of the proceedings; or that there has been a failure to comply with a rule, practice direction, or court order. A party who realizes their case is doomed is often best advised to discontinue to prevent the accumulation of further costs, but often has to pay the costs of the other parties to date. Stays are temporary halts in proceedings, and can be granted for a range of reasons. A stay is normally lifted once the reason no longer applies.
Book chapters on the topic "Complex temporal data"
1
Kamps, Oliver, and Joachim Peinke. "Analysis of Noisy Spatio-Temporal Data." In Understanding Complex Systems, 319–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27635-9_22.
Full text
2
Friedrich, R., V. K. Jirsa, H. Haken, and C. Uhl. "Analyzing Spatio-Temporal Patterns of Complex Systems." In Nonlinear Analysis of Physiological Data, 101–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71949-3_7.
Full text
3
Eckardt, Matthias. "Reviewing Graphical Modelling of Multivariate Temporal Processes." In Analysis of Large and Complex Data, 221–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25226-1_19.
Full text
4
Morik, Katharina. "Some Machine Learning Approaches to the Analysis of Temporal Data." In Robustness and Complex Data Structures, 279–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35494-6_17.
Full text
5
Pray, Keith A., and Carolina Ruiz. "Mining Expressive Temporal Associations from Complex Data." In Machine Learning and Data Mining in Pattern Recognition, 384–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11510888_38.
Full text
6
Sacchi, Lucia, Arianna Dagliati, and Riccardo Bellazzi. "Analyzing Complex Patients’ Temporal Histories: New Frontiers in Temporal Data Mining." In Methods in Molecular Biology, 89–105. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1985-7_6.
Full text
7
Harada, Lilian. "Complex Temporal Patterns Detection over Continuous Data Streams." In Advances in Databases and Information Systems, 401–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45710-0_32.
Full text
8
Bueno, Renato, Daniel S. Kaster, Agma Juci Machado Traina, and Caetano Traina. "Time-Aware Similarity Search: A Metric-Temporal Representation for Complex Data." In Advances in Spatial and Temporal Databases, 302–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02982-0_20.
Full text
9
Loglisci, Corrado, Michelangelo Ceci, Angelo Impedovo, and Donato Malerba. "Mining Spatio-Temporal Patterns of Periodic Changes in Climate Data." In New Frontiers in Mining Complex Patterns, 198–212. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61461-8_13.
Full text
10
Lima Graf, Jeniffer, Srđan Krstić, and Joshua Schneider. "Metric First-Order Temporal Logic with Complex Data Types." In Runtime Verification, 126–47. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44267-4_7.
Full textConference papers on the topic "Complex temporal data"
1
Fogaça, Isis Caroline Oliveira de Sousa, and Renato Bueno. "Temporal Evolution of Complex Data." In XXXV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbbd.2020.13622.
Full textAbstract:
Monitoring the temporal evolution of data is essential in many areas of application of databases, such as medicine, agriculture and meteorology. Complex data are usually represented in metric spaces, where only the elements and the distances between them are available, which makes it impossible to represent trajectories considering a temporal dimension. In this paper we propose to map the metric data to multidimensional spaces so that we can estimate the element's status at a given time, based on known states of the same element. As it is not possible to create the complex data equivalent to its estimated position, we propose to apply similarity queries using this position as query center. We evaluated three types of similarity queries: k-NN, kAndRange and kAndRev.
2
Hu, Xiao, Stavros Sintos, Junyang Gao, Pankaj K. Agarwal, and Jun Yang. "Computing Complex Temporal Join Queries Efficiently." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3517893.
Full text
3
Verhein, Florian. "Mining Complex Spatio-Temporal Sequence Patterns." In Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.52.
Full text
4
Käfer, Wolfgang, and Harald Schöning. "Realizing a temporal complex-object data model." In the 1992 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/130283.130323.
Full text
5
Zheng, Yang, Annies Ductan, Devin Thomas, and Mohamed Y. Eltabakh. "Complex Patten Processing in Spatio-temporal Databases." In 3rd International Conference on Data Management Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004992401570169.
Full text
6
Feng, Xin, and Odilon K. Senyana. "Mining Multiple Temporal Patterns of complex dynamic data systems." In 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938679.
Full text
7
Ouassouli, Amine El, Lionel Robinault, and Vasile-Marian Scuturici. "Mining complex temporal dependencies from heterogeneous sensor data streams." In the 23rd International Database Applications & Engineering Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3331076.3331112.
Full text
8
Chen, Yueguo, Shouxu Jiang, Beng Chin Ooi, and Anthony K. H. Tung. "Querying Complex Spatio-Temporal Sequences in Human Motion Databases." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497417.
Full text
9
Gal, Avigdor, Arik Senderovich, and Matthias Weidlich. "Online Temporal Analysis of Complex Systems Using IoT Data Sensing." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00224.
Full text
10
Nielsen, Allan A., Henning Skriver, and Knut Conradsen. "Complex Wishart Distribution Based Analysis of Polarimetric Synthetic Aperture Radar Data." In 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. IEEE, 2007. http://dx.doi.org/10.1109/multitemp.2007.4293078.
Full textReports on the topic "Complex temporal data"
1
Koopmann, Patrick. Ontology-Mediated Query Answering for Probabilistic Temporal Data with EL Ontologies (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.242.
Full textAbstract:
Especially in the field of stream reasoning, there is an increased interest in reasoning about temporal data in order to detect situations of interest or complex events. Ontologies have been proved a useful way to infer missing information from incomplete data, or simply to allow for a higher order vocabulary to be used in the event descriptions. Motivated by this, ontology-based temporal query answering has been proposed as a means for the recognition of situations and complex events. But often, the data to be processed do not only contain temporal information, but also probabilistic information, for example because of uncertain sensor measurements. While there has been a plethora of research on ontologybased temporal query answering, only little is known so far about querying temporal probabilistic data using ontologies. This work addresses this problem by introducing a temporal query language that extends a well-investigated temporal query language with probability operators, and investigating the complexity of answering queries using this query language together with ontologies formulated in the description logic EL.
2
Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski, and Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47261.
Full textAbstract:
Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.
3
Lauth, Timothy, David Biedenharn, Travis Dahl, Casey Mayne, Keaton Jones, Charles Little, Joseph Dunbar, Samantha Lucker, and Nalini Torres. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers : geomorphic assessment. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45143.
Full textAbstract:
This report documents the geomorphic assessment component of the Old River, Mississippi River, Atchafalaya River, and Red River System Technical Assessment. The overall objectives of the geomorphic assessment are to utilize all available data to document the historic trends in hydrology, sedimentation, and channel geometry for the rivers in the vicinity of the Old River Control Complex and to summarize the changes observed at locations where repetitive datasets exist and at key reaches that are determined during the study. The geomorphic assessment tasks include data compilation, geometric data analysis, gage and discharge analysis, dredge record analysis, sediment data analysis, development of an events timeline, and integration of results. Geomorphic reaches were developed, and the morphological trends during different time periods were identified. The geomorphic assessment highlighted the importance of considering spatial and temporal variability when assessing morphological trends.
4
Taucher, Jan, and Markus Schartau. Report on parameterizing seasonal response patterns in primary- and net community production to ocean alkalinization. OceanNETs, November 2021. http://dx.doi.org/10.3289/oceannets_d5.2.
Full textAbstract:
We applied a 1-D plankton ecosystem-biogeochemical model to assess the impacts of ocean alkalinity enhancement (OAE) on seasonal changes in biogeochemistry and plankton dynamics. Depending on deployment scenarios, OAE should theoretically have variable effects on pH and seawater pCO2, which might in turn affect (a) plankton growth conditions and (b) the efficiency of carbon dioxide removal (CDR) via OAE. Thus, a major focus of our work is how different magnitudes and temporal frequencies of OAE might affect seasonal response patterns of net primary productivity (NPP), ecosystem functioning and biogeochemical cycling. With our study we aimed at identifying a parameterization of how magnitude and frequency of OAE affect net growth rates, so that these effects could be employed for Earth System Modell applications. So far we learned that a meaningful response parameterization has to resolve positive and negative anomalies that covary with temporal shifts. As to the intricacy of the response patterns, the derivation of such parameterization is work in progress. However, our study readily provides valuable insights to how OAE can alter plankton dynamics and biogeochemistry. Our modelling study first focuses at a local site where time series data are available (European Station for Time series in the Ocean Canary Islands ESTOC), including measurements of pH, concentrations of total alkalinity, dissolved inorganic carbon (DIC), chlorophyll-a and dissolved inorganic nitrogen (DIN). These observational data were made available by Andres Cianca (personal communication, PLOCAN, Spain), Melchor Gonzalez and Magdalena Santana Casiano (personal communication, Universidad de Las Palmas de Gran Canaria). The choice of this location was underpinned by the fact that the first OAE mesocosm experiment was conducted on the Canary Island Gran Canaria, which will facilitate synthesizing our modelling approach with experimental findings. For our simulations at the ESTOC site in the Subtropical North Atlantic we found distinct, non-linear responses of NPP to different temporal modes of alkalinity deployment. In particular, phytoplankton bloom patterns displayed pronounced temporal phase shifts and changes in their amplitude. Notably, our simulations suggest that OAE can have a slightly stimulating effect on NPP, which is however variable, depending on the magnitude of OAE and the temporal mode of alkalinity addition. Furthermore, we find that increasing alkalinity perturbations can lead to a shift in phytoplankton community composition (towards coccolithophores), which even persists after OAE has stopped. In terms of CDR, we found that a decrease in efficiency with increasing magnitude of alkalinity addition, as well as substantial differences related to the timing of addition. Altogether, our results suggest that annual OAE during the right season (i.e. physical and biological conditions), could be a reasonable compromise in terms of logistical feasibility, efficiency of CDR and side-effects on marine biota. With respect to transferability to global models, the complex, non-linear responses of biological processes to OAE identified in our simulations do not allow for simple parameterizations that can easily adapted. Dedicated future work is required to transfer the observed responses at small spatiotemporal scales to the coarser resolution of global models.
5
Borgwardt, Stefan, and Veronika Thost. Temporal Query Answering in EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.214.
Full textAbstract:
Context-aware systems use data about their environment for adaptation at runtime, e.g., for optimization of power consumption or user experience. Ontology-based data access (OBDA) can be used to support the interpretation of the usually large amounts of data. OBDA augments query answering in databases by dropping the closed-world assumption (i.e., the data is not assumed to be complete any more) and by including domain knowledge provided by an ontology. We focus on a recently proposed temporalized query language that allows to combine conjunctive queries with the operators of the well-known propositional temporal logic LTL. In particular, we investigate temporalized OBDA w.r.t. ontologies in the DL EL, which allows for efficient reasoning and has been successfully applied in practice. We study both data and combined complexity of the query entailment problem.
6
Wilson, D., Matthew Kamrath, Caitlin Haedrich, Daniel Breton, and Carl Hart. Urban noise distributions and the influence of geometric spreading on skewness. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42483.
Full textAbstract:
Statistical distributions of urban noise levels are influenced by many complex phenomena, including spatial and temporal variations in the source level, multisource mixtures, propagation losses, and random fading from multipath reflections. This article provides a broad perspective on the varying impacts of these phenomena. Distributions incorporating random fading and averaging (e.g., gamma and noncentral Erlang) tend to be negatively skewed on logarithmic (decibel) axes but can be positively skewed if the fading process is strongly modulated by source power variations (e.g., compound gamma). In contrast, distributions incorporating randomly positioned sources and explicit geometric spreading [e.g., exponentially modified Gaussian (EMG)] tend to be positively skewed with exponential tails on logarithmic axes. To evaluate the suitability of the various distributions, one-third octave band sound-level data were measured at 37 locations in the North End of Boston, MA. Based on the Kullback-Leibler divergence as calculated across all of the locations and frequencies, the EMG provides the most consistently good agreement with the data, which were generally positively skewed. The compound gamma also fits the data well and even outperforms the EMG for the small minority of cases exhibiting negative skew. The lognormal provides a suitable fit in cases in which particular non-traffic noise sources dominate.
7
Suir, Glenn, Molly Reif, and Christina Saltus. Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45241.
Full textAbstract:
Engineering With Nature (EWN®) is a US Army Corps of Engineers (USACE) Initiative and Program that promotes more sustainable practices for delivering economic, environmental, and social benefits through collaborative processes. As the number and variety of EWN® projects continue to grow and evolve, there is an increasing opportunity to improve how to quantify their benefits and communicate them to the public. Recent advancements in remote sensing technologies are significant for EWN® because they can provide project-relevant detail across a large areal extent, in which traditional survey methods may be complex due to site access limitations. These technologies encompass a suite of spatial and temporal data collection and processing techniques used to characterize Earth's surface properties and conditions that would otherwise be difficult to assess. This document aims to describe the general underpinnings and utility of remote sensing technologies and applications for use: (1) in specific phases of the EWN® project life cycle; (2) with specific EWN® project types; and (3) in the quantification and assessment of project implementation, performance, and benefits.
8
Bourgaux, Camille, and Anni-Yasmin Turhan. Temporal Query Answering in DL-Lite over Inconsistent Data. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2022.236.
Full textAbstract:
In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic and extend to this setting three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for DL-LiteR temporal knowledge bases, and furthermore complete the picture for the consistent case.
9
Savaldi-Goldstein, Sigal, and Todd C. Mockler. Precise Mapping of Growth Hormone Effects by Cell-Specific Gene Activation Response. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7699849.bard.
Full textAbstract:
Plant yield largely depends on a complex interplay and feedback mechanisms of distinct hormonal pathways. Over the past decade great progress has been made in elucidating the global molecular mechanisms by which each hormone is produced and perceived. However, our knowledge of how interactions between hormonal pathways are spatially and temporally regulated remains rudimentary. For example, we have demonstrated that although the BR receptor BRI1 is widely expressed, the perception of BRs in epidermal cells is sufficient to control whole-organ growth. Supported by additional recent works, it is apparent that hormones are acting in selected cells of the plant body to regulate organ growth, and furthermore, that local cell-cell communication is an important mechanism. In this proposal our goals were to identify the global profile of translated genes in response to BR stimulation and depletion in specific tissues in Arabidopsis; determine the spatio-temporal dependency of BR response on auxin transport and signaling and construct an interactive public website that will provide an integrated analysis of the data set. Our technology incorporated cell-specific polysome isolation and sequencing using the Solexa technology. In the first aim, we generated and confirmed the specificity of novel transgenic lines expressing tagged ribosomal protein in various cell types in the Arabidopsis primary root. We next crossed these lines to lines with targeted expression of BRI1 in the bri1 background. All lines were treated with BRs for two time points. The RNA-seq of their corresponding immunopurified polysomal RNA is nearly completed and the bioinformatic analysis of the data set will be completed this year. Followed, we will construct an interactive public website (our third aim). In the second aim we started revealing how spatio-temporalBR activity impinges on auxin transport in the Arabidopsis primary root. We discovered the unexpected role of BRs in controlling the expression of specific auxin efflux carriers, post-transcriptionally (Hacham et al, 2012). We also showed that this regulation depends on the specific expression of BRI1 in the epidermis. This complex and long term effect of BRs on auxin transport led us to focus on high resolution analysis of the BR signaling per se. Taking together, our ongoing collaboration and synergistic expertise (hormone action and plant development (IL) and whole-genome scale data analysis (US)) enabled the establishment of a powerful system that will tell us how distinct cell types respond to local and systemic BR signal. BR research is of special agriculture importance since BR application and BR genetic modification have been shown to significantly increase crop yield and to play an important role in plant thermotolerance. Hence, our integrated dataset is valuable for improving crop traits without unwanted impairment of unrelated pathways, for example, establishing semi-dwarf stature to allow increased yield in high planting density, inducing erect leaves for better light capture and consequent biomass increase and plant resistance to abiotic stresses.
10
Baader, Franz, Stefan Borgwardt, and Marcel Lippmann. On the Complexity of Temporal Query Answering. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.191.
Full textAbstract:
Ontology-based data access (OBDA) generalizes query answering in databases towards deduction since (i) the fact base is not assumed to contain complete knowledge (i.e., there is no closed world assumption), and (ii) the interpretation of the predicates occurring in the queries is constrained by axioms of an ontology. OBDA has been investigated in detail for the case where the ontology is expressed by an appropriate Description Logic (DL) and the queries are conjunctive queries. Motivated by situation awareness applications, we investigate an extension of OBDA to the temporal case. As query language we consider an extension of the well-known propositional temporal logic LTL where conjunctive queries can occur in place of propositional variables, and as ontology language we use the prototypical expressive DL ALC. For the resulting instance of temporalized OBDA, we investigate both data complexity and combined complexity of the query entailment problem.