3,316 research outputs found

    Faster inference from state space models via GPU computing

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    Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

    Brittle-viscous deformation cycles at the base of the seismogenic zone in the continental crust

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    The main goal of the study was to determine the dynamical cycle of ductile-brittle deformation and to characterise the fluid pathways at different scales of a brittle-viscous fault zone active at the base of the seismogenic crust. Object of analysis are samples from the sinistral strike-slip fault zone BFZ045 from Olkiluoto (SW Finland), located at the site of a deep geological repository for nuclear waste. Combined microstructural analysis, electron backscatter diffraction (EBSD), and mineral chemistry were applied to reconstruct the variations in pressure, temperature, fluid pressure, and differential stress that mediated deformation and strain localization along BFZ045 across the BDTZ. Ductile deformation took place at 400-500° C and 3-4 kbar, and recrystallized grain size piezometry for quartz document a progressive increase in differential stress during mylonitization, from ca. 50 MPa to ca. 120 MPa. The increase in differential stress was localised towards the shear zone center, which was eventually overprinted by brittle deformation in a narrowing shear zone. Cataclastic deformation occurred under lower T conditions down to T ≥ 320° C and was not further overprinted by mylonitic creep. Porosity estimates were obtained through the combination of x-ray micro-computed tomography (µCT), mercury intrusion porosimetry, He pycnometry, and microstructural analysis. Low porosity values (0.8-4.4%) for different rock type, 2-20 µm pore size, representative of pore connectivity, and microstructural observation suggest a relationship to a dynamical cycle of fracturing and sealing mechanism, mostly controlled by ductile deformation. Similarly, the observation from fracture orientation analysis indicates that the mylonitic precursor of BFZ045 played an important role in the localization of the brittle deformation. This thesis highlights that the ductile-brittle deformation cycle in BFZ045 was controlled by transient oscillations in fluid pressure in a narrowing shear zone deforming at progressively higher differential stress during cooling

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Spatial frequency domain imaging towards improved detection of gastrointestinal cancers

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    Early detection and treatment of gastrointestinal cancers has been shown to drastically improve patients survival rates. However, wide population based screening for gastrointestinal cancers is not feasible due to its high cost, risk of potential complications, and time consuming nature. This thesis forms the proposal for the development of a cost-effective, minimally invasive device to return quantitative tissue information for gastrointestinal cancer detection in-vivo using spatial frequency domain imaging (SFDI). SFDI is a non-invasive imaging technique which can return close to real time maps of absorption and reduced scattering coefficients by projecting a 2D sinusoidal pattern onto a sample of interest. First a low-cost, conventional bench top system was constructed to characterise tissue mimicking phantoms. Phantoms were fabricated with specific absorption and reduced scattering coefficients, mimicking the variation in optical properties typically seen in healthy, cancerous, and pre-cancerous oesophageal tissue. The system shows accurate retrieval of absorption and reduced scattering coefficients of 19% and 11% error respectively. However, this bench top system consists of a bulky projector and is therefore not feasible for in-vivo imaging. For SFDI systems to be feasible for in-vivo imaging, they are required to be miniaturised. Many conditions must be considered when doing this such as various illumination conditions, lighting conditions and system geometries. Therefore to aid in the miniaturisation of the bench top system, an SFDI system was simulated in the open-source ray tracing software Blender, where the capability to simulate these conditions is possible. A material of tunable absorption and scattering properties was characterised such that the specific absorption and reduced scattering coefficients of the material were known. The simulated system shows capability in detecting optical properties of typical gastrointestinal conditions in an up-close, planar geometry, as well in a non-planar geometry of a tube simulating a lumen. Optical property imaging in the non-planar, tubular geometry was done with the use of a novel illumination pattern, developed for this work. Finally, using the knowledge gained from the simulation model, the bench top system was miniaturised to a 3 mm diameter prototype. The novel use of a fiber array producing the necessary interfering fringe patterns replaced the bulky projector. The system showed capability to image phantoms simulating typical gastrointestinal conditions at two wavelengths (515 and 660 nm), measuring absorption and reduced scattering coefficients with 15% and 6% accuracy in comparison to the bench top system for the fabricated phantoms. It is proposed that this system may be used for cost-effective, minimally invasive, quantitative imaging of the gastrointestinal tract in-vivo, providing enhanced contrast for difficult to detect cancers

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    Modelling Perception of Large-Scale Thematic Structure in Music

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    Large-scale thematic structure—the organisation of material within a musical composition—holds an important position in the Western classical music tradition and has subsequently been incorporated into many influential models of music cognition. Whether, and if so, how, these structures may be perceived provides an interesting psychological problem, combining many aspects of memory, pattern recognition, and similarity judgement. However, strong experimental evidence supporting the perception of large-scale thematic structures remains limited, often arising from difficulties in measuring and disrupting their perception. To provide a basis for experimental research, this thesis develops a probabilistic computational model that characterises the possible cognitive processes underlying the perception of thematic structure. This modelling is founded on the hypothesis that thematic structures are perceptible through the statistical regularities they form, arising from the repetition and learning of material. Through the formalisation of this hypothesis, features were generated characterising compositions’ intra-opus predictability, stylistic predictability, and the amounts of repetition and variation of identified thematic material in both pitch and rhythmic domains. A series of behavioural experiments examined the ability of these modelled features to predict participant responses to important indicators of thematic structure. Namely, similarity between thematic elements, identification of large-scale repetitions, perceived structural unity, sensitivity to thematic continuation, and large-scale ordering. Taken together, the results of these experiments provide converging evidence that the perception of large-scale thematic structures can be accounted for by the dynamic learning of statistical regularities within musical compositions
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