A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.Comment: accepted at IEEE TM

Analyzing complex data using domain constraints

Data-driven research approaches are becoming increasingly popular in a growing number of scientific disciplines. While a data-driven research approach can yield superior results, generating the required data can be very costly. This frequently leads to small and complex data sets, in which it is impossible to rely on volume alone to compensate for all shortcomings of the data. To counter this problem, other reliable sources of information must be incorporated. In this work, domain knowledge, as a particularly reliable type of additional information, is used to inform data-driven analysis methods. This domain knowledge is represented as constraints on the possible solutions, which the presented methods can use to inform their analysis. It focusses on spatial constraints as a particularly common type of constraint, but the proposed techniques are general enough to be applied to other types of constraints. In this thesis, new methods using domain constraints for data-driven science applications are discussed. These methods have applications in feature evaluation, route database repair, and Gaussian Mixture modeling of spatial data. The first application focuses on feature evaluation. The presented method receives two representations of the same data: one as the intended target and the other for investigation. It calculates a score indicating how much the two representations agree. A presented application uses this technique to compare a reference attribute set with different subsets to determine the importance and relevance of individual attributes. A second technique analyzes route data for constraint compliance. The presented framework allows the user to specify constraints and possible actions to modify the data. The presented method then uses these inputs to generate a version of the data, which agrees with the constraints, while otherwise reducing the impact of the modifications as much as possible. Two extensions of this schema are presented: an extension to continuously valued costs, which are minimized, and an extension to constraints involving more than one moving object. Another addressed application area is modeling of multivariate measurement data, which was measured at spatially distributed locations. The spatial information recorded with the data can be used as the basis for constraints. This thesis presents multiple approaches to building a model of this kind of data while complying with spatial constraints. The first approach is an interactive tool, which allows domain scientists to generate a model of the data, which complies with their knowledge about the data. The second is a Monte Carlo approach, which generates a large number of possible models, tests them for compliance with the constraints, and returns the best one. The final two approaches are based on the EM algorithm and use different ways of incorporating the information into their models. At the end of the thesis, two applications of the models, which have been generated in the previous chapter, are presented. The first is prediction of the origin of samples and the other is the visual representation of the extracted models on a map. These tools can be used by domain scientists to augment their tried and tested tools. The developed techniques are applied to a real-world data set collected in the archaeobiological research project FOR 1670 (Transalpine mobility and cultural transfer) of the German Science Foundation. The data set contains isotope ratio measurements of samples, which were discovered at archaeological sites in the Alps region of central Europe. Using the presented data analysis methods, the data is analyzed to answer relevant domain questions. In a first application, the attributes of the measurements are analyzed for their relative importance and their ability to predict the spatial location of samples. Another presented application is the reconstruction of potential migration routes between the investigated sites. Then spatial models are built using the presented modeling approaches. Univariate outliers are determined and used to predict locations based on the generated models. These are cross-referenced with the recorded origins. Finally, maps of the isotope distribution in the investigated regions are presented. The described methods and demonstrated analyses show that domain knowledge can be used to formulate constraints that inform the data analysis process to yield valid models from relatively small data sets and support domain scientists in their analyses.Datengetriebene Forschungsansätze werden für eine wachsende Anzahl von wissenschaftlichen Disziplinen immer wichtiger. Obwohl ein datengetriebener Forschungsansatz bessere Ergebnisse erzielen kann, kann es sehr teuer sein die notwendigen Daten zu gewinnen. Dies hat häufig zur Folge, dass kleine und komplexe Datensätze entstehen, bei denen es nicht möglich ist sich auf die Menge der Datenpunkte zu verlassen um Probleme bei der Analyse auszugleichen. Um diesem Problem zu begegnen müssen andere Informationsquellen verwendet werden. Fachwissen als eine besonders zuverlässige Quelle solcher Informationen kann herangezogen werden, um die datengetriebenen Analysemethoden zu unterstützen. Dieses Fachwissen wird ausgedrückt als Constraints (Nebenbedingungen) der möglichen Lösungen, die die vorgestellten Methoden benutzen können um ihre Analyse zu steuern. Der Fokus liegt dabei auf räumlichen Constraints als eine besonders häufige Art von Constraints, aber die vorgeschlagenen Methoden sind allgemein genug um auf andere Arte von Constraints angewendet zu werden. Es werden neue Methoden diskutiert, die Fachwissen für datengetriebene wissenschaftliche Anwendungen verwenden. Diese Methoden haben Anwendungen auf Feature-Evaluation, die Reparatur von Bewegungsdatenbanken und auf Gaussian-Mixture-Modelle von räumlichen Daten. Die erste Anwendung betrifft Feature-Evaluation. Die vorgestellte Methode erhält zwei Repräsentationen der selben Daten: eine als Zielrepräsentation und eine zur Untersuchung. Sie berechnet einen Wert, der aussagt, wie einig sich die beiden Repräsentationen sind. Eine vorgestellte Anwendung benutzt diese Technik um eine Referenzmenge von Attributen mit verschiedenen Untermengen zu vergleichen, um die Wichtigkeit und Relevanz einzelner Attribute zu bestimmen. Eine zweite Technik analysiert die Einhaltung von Constraints in Bewegungsdaten. Das präsentierte Framework erlaubt dem Benutzer Constraints zu definieren und mögliche Aktionen zur Veränderung der Daten anzuwenden. Die präsentierte Methode benutzt diese Eingaben dann um eine neue Variante der Daten zu erstellen, die die Constraints erfüllt ohne die Datenbank mehr als notwendig zu verändern. Zwei Erweiterungen dieser Grundidee werden vorgestellt: eine Erweiterung auf stetige Kostenfunktionen, die minimiert werden, und eine Erweiterung auf Bedingungen, die mehr als ein bewegliches Objekt betreffen. Ein weiteres behandeltes Anwendungsgebiet ist die Modellierung von multivariaten Messungen, die an räumlich verteilten Orten gemessen wurden. Die räumliche Information, die zusammen mit diesen Daten erhoben wurde, kann als Grundlage genutzt werden um Constraints zu formulieren. Mehrere Ansätze zum Erstellen von Modellen auf dieser Art von Daten werden vorgestellt, die räumliche Constraints einhalten. Der erste dieser Ansätze ist ein interaktives Werkzeug, das Fachwissenschaftlern dabei hilft, Modelle der Daten zu erstellen, die mit ihrem Wissen über die Daten übereinstimmen. Der zweite ist eine Monte-Carlo-Simulation, die eine große Menge möglicher Modelle erstellt, testet ob sie mit den Constraints übereinstimmen und das beste Modell zurückgeben. Zwei letzte Ansätze basieren auf dem EM-Algorithmus und benutzen verschiedene Arten diese Information in das Modell zu integrieren. Am Ende werden zwei Anwendungen der gerade vorgestellten Modelle vorgestellt. Die erste ist die Vorhersage der Herkunft von Proben und die andere ist die grafische Darstellung der erstellten Modelle auf einer Karte. Diese Werkzeuge können von Fachwissenschaftlern benutzt werden um ihre bewährten Methoden zu unterstützen. Die entwickelten Methoden werden auf einen realen Datensatz angewendet, der von dem archäo-biologischen Forschungsprojekt FOR 1670 (Transalpine Mobilität und Kulturtransfer der Deutschen Forschungsgemeinschaft erhoben worden ist. Der Datensatz enthält Messungen von Isotopenverhältnissen von Proben, die in archäologischen Fundstellen in den zentraleuropäischen Alpen gefunden wurden. Die präsentierten Datenanalyse-Methoden werden verwendet um diese Daten zu analysieren und relevante Forschungsfragen zu klären. In einer ersten Anwendung werden die Attribute der Messungen analysiert um ihre relative Wichtigkeit und ihre Fähigkeit zu bewerten, die räumliche Herkunft der Proben vorherzusagen. Eine weitere vorgestellte Anwendung ist die Wiederherstellung von möglichen Migrationsrouten zwischen den untersuchten Fundstellen. Danach werden räumliche Modelle der Daten unter Verwendung der vorgestellten Methoden erstellt. Univariate Outlier werden bestimmt und ihre möglich Herkunft basierend auf der erstellten Karte wird bestimmt. Die vorhergesagte Herkunft wird mit der tatsächlichen Fundstelle verglichen. Zuletzt werden Karten der Isotopenverteilung der untersuchten Region vorgestellt. Die beschriebenen Methoden und vorgestellten Analysen zeigen, dass Fachwissen verwendet werden kann um Constraints zu formulieren, die den Datenanalyseprozess unterstützen, um gültige Modelle aus relativ kleinen Datensätzen zu erstellen und Fachwissenschaftler bei ihren Analysen zu unterstützen

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013