Une nouvelle classe d'opérateurs de Teager-Kaiser multidimensionnels basée sur les dérivées directionnelles d'ordre supérieur

This work aims at introducing some energy operators linked to Teager-Kaiser energy operator and its associated higher order versions and expand them to multidimensional signals. These operators are very useful for analyzing oscillatory signals with time-varying amplitude and frequency (AM-FM). We prove that gradient tensors combined with Kronecker powers allow to express these operators by directional derivatives along any n-D vector. In particular, we show that the construction of a large class of non linear operators for AM-FM multidimensional signal demodulation is possible. Also, a new scalar function using the directional derivative along a vector giving the ”sign” of the frequency components is introduced. An application of this model to local n-D AM-FM signal is presented and related demodulation error rates estimates. To show the effectiveness and the robustness of our method in term of envelope and frequency components extraction, results obtained on synthetic and real data are compared to multi-dimensional energy separation algorithm and to our recently introduced n-D operator

Mapping the Dynamic Protein Network of Dividing Cells in Space and Time

Live cell imaging is a powerful tool for studying the distribution and dynamics of proteins. However, due to the difficulties in absolute quantification and standardization of data obtained from individual cells, it has not been used to map large sets of proteins that carry out dynamic cellular functions. Cell division is a good example of this challenge for an essential cellular function, as rapid changes in protein localization and protein interactions result in dramatic changes to subcellular structures and cellular morphology, which in turn influence the behavior of the enclosed proteins. Here, I report an integrated experimental and computational pipeline to map the dynamic protein network of dividing human cells in space and time. Using 3D live confocal microscopy, I imaged human cell lines that stably expressed fluorescently tagged mitotic proteins throughout mitosis. To obtain the absolute quantities of protein abundance with high subcellular resolution over time, the microscopy pipeline was calibrated by fluorescence correlation spectroscopy (FCS). Cell and chromosome volumes were segmented as references of cellular context for temporal and spatial alignment based on fluorescent landmarks. Together with my colleague Julius Hossain, we computationally generated a canonical model of mitotic progression for both kinetics (“mitotic standard time”) and morphology (“mitotic standard space”) by averaging and kinetically and geometrically parametrizing many registered dividing cells. The resulting model enabled us to subdivide the mitotic process into 20 characteristic kinetic steps and integrate our complete proof of concept dataset of 13 mitotic proteins imaged in over 300 dividing cells, represented as the 3D protein localization probability of each protein over time. To measure localization similarities between different proteins and make predictions about their dynamic interactions, the integrated data was then mined using supervised as well as unsupervised machine learning. The power of this approach was demonstrated by our ability to automatically identify the major subcellular localizations of all proteins in the dataset and quantify protein fluxes between subcellular compartments and structures. Due to the quantitative nature of our imaging data, we were able to estimate the abundance of each protein in mitotic structures and complexes such as kinetochores, centrosomes, and the midbody, and determine the order and kinetics of their formation and disassembly. The integrated computational and experimental method I present in my thesis is generic and scalable and makes many dynamic cellular processes amenable to dynamic protein network analysis even for large numbers of components. The pipeline provides a powerful instrument for analyzing large sets of quantitative live imaging data of fluorescently tagged proteins. It allows the systematic mapping and prediction of dynamic protein networks that drive complex cellular processes such as mitosis, thus promoting our understanding of the mechanisms by which many molecules together achieve spatio-temporal regulation

Blind and pointed Sunyaev-Zel'dovich observations with the Arcminute Microkelvin Imager

In this thesis I introduce the Arcminute Microkelvin Imager. I analyse observations towards known galaxy clusters and I present the initial results from a blind survey for galaxy clusters.In this thesis I discuss my work on the Arcminute Microkelvin Imager (AMI). I focus on the detection of Sunyaev-Zel’dovich (SZ) signatures at 14-18GHz. Once the background science and operation of the instrument are described I proceed to present my contribution to the calibration of AMI, including: primary beam measurements; refinements to the known antenna geometry and flagging geostationary satellite interference. This is followed by an outline of the software that I have developed to subtract sources from visibilities, concatenate data from multiple observations, simulate data, and perform jack-knife tests to evaluate the magnitude of systematic errors. The Bayesian analysis that I use to obtain parameter estimates and to quantify the significance of putative SZ detections is described. I perform realistic simulations of clusters and use these to characterise the analysis. I then, for the first time, apply the analysis to data from the AMI blind cluster survey. I identify several previously unknown SZ decrements. Finally, I conduct pointed observations towards a high luminosity subsample of eight clusters from the Local Cluster Substructure Survey (LoCuSS). For each of these I provide probability distributions of parameters such as mass, radius, and temperature. I compare my results to those in the literature and find an overall agreement

Deep Grassmann Manifold Optimization for Computer Vision

In this work, we propose methods that advance four areas in the field of computer vision: dimensionality reduction, deep feature embeddings, visual domain adaptation, and deep neural network compression. We combine concepts from the fields of manifold geometry and deep learning to develop cutting edge methods in each of these areas. Each of the methods proposed in this work achieves state-of-the-art results in our experiments. We propose the Proxy Matrix Optimization (PMO) method for optimization over orthogonal matrix manifolds, such as the Grassmann manifold. This optimization technique is designed to be highly flexible enabling it to be leveraged in many situations where traditional manifold optimization methods cannot be used. We first use PMO in the field of dimensionality reduction, where we propose an iterative optimization approach to Principal Component Analysis (PCA) in a framework called Proxy Matrix optimization based PCA (PM-PCA). We also demonstrate how PM-PCA can be used to solve the general $L_p$-PCA problem, a variant of PCA that uses arbitrary fractional norms, which can be more robust to outliers. We then present Cascaded Projection (CaP), a method which uses tensor compression based on PMO, to reduce the number of filters in deep neural networks. This, in turn, reduces the number of computational operations required to process each image with the network. Cascaded Projection is the first end-to-end trainable method for network compression that uses standard backpropagation to learn the optimal tensor compression. In the area of deep feature embeddings, we introduce Deep Euclidean Feature Representations through Adaptation on the Grassmann manifold (DEFRAG), that leverages PMO. The DEFRAG method improves the feature embeddings learned by deep neural networks through the use of auxiliary loss functions and Grassmann manifold optimization. Lastly, in the area of visual domain adaptation, we propose the Manifold-Aligned Label Transfer for Domain Adaptation (MALT-DA) to transfer knowledge from samples in a known domain to an unknown domain based on cross-domain cluster correspondences

Searching and mining in enriched geo-spatial data

The emergence of new data collection mechanisms in geo-spatial applications paired with a heightened tendency of users to volunteer information provides an ever-increasing flow of data of high volume, complex nature, and often associated with inherent uncertainty. Such mechanisms include crowdsourcing, automated knowledge inference, tracking, and social media data repositories. Such data bearing additional information from multiple sources like probability distributions, text or numerical attributes, social context, or multimedia content can be called multi-enriched. Searching and mining this abundance of information holds many challenges, if all of the data's potential is to be released. This thesis addresses several major issues arising in that field, namely path queries using multi-enriched data, trend mining in social media data, and handling uncertainty in geo-spatial data. In all cases, the developed methods have made significant contributions and have appeared in or were accepted into various renowned international peer-reviewed venues. A common use of geo-spatial data is path queries in road networks where traditional methods optimise results based on absolute and ofttimes singular metrics, i.e., finding the shortest paths based on distance or the best trade-off between distance and travel time. Integrating additional aspects like qualitative or social data by enriching the data model with knowledge derived from sources as mentioned above allows for queries that can be issued to fit a broader scope of needs or preferences. This thesis presents two implementations of incorporating multi-enriched data into road networks. In one case, a range of qualitative data sources is evaluated to gain knowledge about user preferences which is subsequently matched with locations represented in a road network and integrated into its components. Several methods are presented for highly customisable path queries that incorporate a wide spectrum of data. In a second case, a framework is described for resource distribution with reappearance in road networks to serve one or more clients, resulting in paths that provide maximum gain based on a probabilistic evaluation of available resources. Applications for this include finding parking spots. Social media trends are an emerging research area giving insight in user sentiment and important topics. Such trends consist of bursts of messages concerning a certain topic within a time frame, significantly deviating from the average appearance frequency of the same topic. By investigating the dissemination of such trends in space and time, this thesis presents methods to classify trend archetypes to predict future dissemination of a trend. Processing and querying uncertain data is particularly demanding given the additional knowledge required to yield results with probabilistic guarantees. Since such knowledge is not always available and queries are not easily scaled to larger datasets due to the #P-complete nature of the problem, many existing approaches reduce the data to a deterministic representation of its underlying model to eliminate uncertainty. However, data uncertainty can also provide valuable insight into the nature of the data that cannot be represented in a deterministic manner. This thesis presents techniques for clustering uncertain data as well as query processing, that take the additional information from uncertainty models into account while preserving scalability using a sampling-based approach, while previous approaches could only provide one of the two. The given solutions enable the application of various existing clustering techniques or query types to a framework that manages the uncertainty.Das Erscheinen neuer Methoden zur Datenerhebung in räumlichen Applikationen gepaart mit einer erhöhten Bereitschaft der Nutzer, Daten über sich preiszugeben, generiert einen stetig steigenden Fluss von Daten in großer Menge, komplexer Natur, und oft gepaart mit inhärenter Unsicherheit. Beispiele für solche Mechanismen sind Crowdsourcing, automatisierte Wissensinferenz, Tracking, und Daten aus sozialen Medien. Derartige Daten, angereichert mit mit zusätzlichen Informationen aus verschiedenen Quellen wie Wahrscheinlichkeitsverteilungen, Text- oder numerische Attribute, sozialem Kontext, oder Multimediainhalten, werden als multi-enriched bezeichnet. Suche und Datamining in dieser weiten Datenmenge hält viele Herausforderungen bereit, wenn das gesamte Potenzial der Daten genutzt werden soll. Diese Arbeit geht auf mehrere große Fragestellungen in diesem Feld ein, insbesondere Pfadanfragen in multi-enriched Daten, Trend-mining in Daten aus sozialen Netzwerken, und die Beherrschung von Unsicherheit in räumlichen Daten. In all diesen Fällen haben die entwickelten Methoden signifikante Forschungsbeiträge geleistet und wurden veröffentlicht oder angenommen zu diversen renommierten internationalen, von Experten begutachteten Konferenzen und Journals. Ein gängiges Anwendungsgebiet räumlicher Daten sind Pfadanfragen in Straßennetzwerken, wo traditionelle Methoden die Resultate anhand absoluter und oft auch singulärer Maße optimieren, d.h., der kürzeste Pfad in Bezug auf die Distanz oder der beste Kompromiss zwischen Distanz und Reisezeit. Durch die Integration zusätzlicher Aspekte wie qualitativer Daten oder Daten aus sozialen Netzwerken als Anreicherung des Datenmodells mit aus diesen Quellen abgeleitetem Wissen werden Anfragen möglich, die ein breiteres Spektrum an Anforderungen oder Präferenzen erfüllen. Diese Arbeit präsentiert zwei Ansätze, solche multi-enriched Daten in Straßennetze einzufügen. Zum einen wird eine Reihe qualitativer Datenquellen ausgewertet, um Wissen über Nutzerpräferenzen zu generieren, welches darauf mit Örtlichkeiten im Straßennetz abgeglichen und in das Netz integriert wird. Diverse Methoden werden präsentiert, die stark personalisierbare Pfadanfragen ermöglichen, die ein weites Spektrum an Daten mit einbeziehen. Im zweiten Fall wird ein Framework präsentiert, das eine Ressourcenverteilung im Straßennetzwerk modelliert, bei der einmal verbrauchte Ressourcen erneut auftauchen können. Resultierende Pfade ergeben einen maximalen Ertrag basieren auf einer probabilistischen Evaluation der verfügbaren Ressourcen. Eine Anwendung ist die Suche nach Parkplätzen. Trends in sozialen Medien sind ein entstehendes Forscchungsgebiet, das Einblicke in Benutzerverhalten und wichtige Themen zulässt. Solche Trends bestehen aus großen Mengen an Nachrichten zu einem bestimmten Thema innerhalb eines Zeitfensters, so dass die Auftrittsfrequenz signifikant über den durchschnittlichen Level liegt. Durch die Untersuchung der Fortpflanzung solcher Trends in Raum und Zeit präsentiert diese Arbeit Methoden, um Trends nach Archetypen zu klassifizieren und ihren zukünftigen Weg vorherzusagen. Die Anfragebearbeitung und Datamining in unsicheren Daten ist besonders herausfordernd, insbesondere im Hinblick auf das notwendige Zusatzwissen, um Resultate mit probabilistischen Garantien zu erzielen. Solches Wissen ist nicht immer verfügbar und Anfragen lassen sich aufgrund der \P-Vollständigkeit des Problems nicht ohne Weiteres auf größere Datensätze skalieren. Dennoch kann Datenunsicherheit wertvollen Einblick in die Struktur der Daten liefern, der mit deterministischen Methoden nicht erreichbar wäre. Diese Arbeit präsentiert Techniken zum Clustering unsicherer Daten sowie zur Anfragebearbeitung, die die Zusatzinformation aus dem Unsicherheitsmodell in Betracht ziehen, jedoch gleichzeitig die Skalierbarkeit des Ansatzes auf große Datenmengen sicherstellen

Longwall mining-induced fracture characterisation based on seismic monitoring

Despite several technological advancements, mining-induced fractures are still critical for the safety of underground coal mines. Rocking fracturing as a natural response to mining activities can pose a potential hazard to mine operators, equipment, and infrastructures. The fractures occur not only around the working face that can be visually measured but also above and in front of the working face and where geological structures are affected by mining activities. Therefore, it is of importance to detect and investigate the properties of mining-induced fractures. Mining-induced seismicity has been generated due to rock fracturing during progressive mining activities and can provide critical fracture information. Currently, the application of using seismic monitoring to characterise fractures has remained relatively challenged in mining because mining-induced fractures are initiated by stress change and strata movement after mineral extraction. Compared to seismic monitoring in the oil and gas industry, the fractures and seismic responses may show different characteristics. Therefore, seismic monitoring in mines lacks a comprehensive investigation of received seismic signals to the properties of induced fractures and the effect on mine workings by these fractures. Additionally, constraints such as the quality of seismic signals and the deficiency of correlation analysis of seismic events in underground mining pose great challenges in using seismic data for hazard prediction. This thesis aims to address these challenges in using seismic monitoring to understand and characterise mining-induced fractures by (1) calculating fracture properties related to seismic source location, magnitude and mechanism based on uniaxial seismic data, (2) spatial and temporal correlation analysis of seismic events, and (3) inspecting fracture distributions and simulation of the fractured zone in longwall coal mines. Firstly, since cheap and easily removable uniaxial geophones close to production areas are preferable in coal mines, a novel method to use uniaxial signal and moment tensor inversion to generate synthetic triaxial waves is designed for a comprehensive description of the fracture properties, including location, radius, aperture and orientation. Secondly, to apply seismic data for advanced analysis, such as rockburst prediction and caving assessment, the correlation of seismic events is proved to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process. The spatial and temporal correlation of seismic event energy is quantitatively analysed using various statistical methods, including autocorrelation function (ACF), semivariogram and Moran's I analysis. In addition, based on the integrated spatial-temporal (ST) correlation assessment, seismic events are further classified into seven clusters to assess the correlations within individual clusters. Finally, several source parameters such as seismic moment (M0), seismic source radius (R), fracture aperture (τ), failure type and fracture orientation were used to characterise fractures induced by longwall mining. This thesis also presents the fracture patterns induced caused progressive longwall mining for the first time. Besides, a discrete element method (DEM) model with seismic-derived fractures is generated and proves the impact of mining-induced fractures on altering stress conditions during mineral extraction. In addition, with the analysis of the seismic source mechanism and a synthetic triaxial method, a discrete fracture network (DFN) is generated from monitored seismic events to restore complete induced fractures. Overall, the outcomes of this study lead to a comprehensive assessment of mining-induced fracture properties based on real-time seismic monitoring, demonstrating its significant potential for hazard prediction and improving the safety of resource recovery

Matching algorithms : fundamentals, applications and challenges

Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record*