4,309 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    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

    Bayesian inference for challenging scientific models

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    Advances in technology and computation have led to ever more complicated scientific models of phenomena across a wide variety of fields. Many of these models present challenges for Bayesian inference, as a result of computationally intensive likelihoods, high-dimensional parameter spaces or large dataset sizes. In this thesis we show how we can apply developments in probabilistic machine learning and statistics to do inference with examples of these types of models. As a demonstration of an applied inference problem involving a non-trivial likelihood computation, we show how a combination of optimisation and MCMC methods along with careful consideration of priors can be used to infer the parameters of an ODE model of the cardiac action potential. We then consider the problem of pileup, a phenomenon that occurs in astronomy when using CCD detectors to observe bright sources. It complicates the fitting of even simple spectral models by introducing an observation model with a large number of continuous and discrete latent variables that scales with the size of the dataset. We develop an MCMC-based method that can work in the presence of pileup by explicitly marginalising out discrete variables and using adaptive HMC on the remaining continuous variables. We show with synthetic experiments that it allows us to fit spectral models in the presence of pileup without biasing the results. We also compare it to neural Simulation- Based Inference approaches, and find that they perform comparably to the MCMC-based approach whilst being able to scale to larger datasets. As an example of a problem where we wish to do inference with extremely large datasets, we consider the Extreme Deconvolution method. The method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as the Gaia catalog containing noisy observations of a billion stars. In this thesis we propose two minibatch variants of extreme deconvolution, based on an online variation of the EM algorithm, and direct gradient-based optimisation of the log-likelihood, both of which can run on GPUs. We demonstrate that these methods provide faster fitting, whilst being able to scale to much larger models for use with larger datasets. We then extend the extreme deconvolution approach to work with non- Gaussian noise, and to use more flexible density estimators such as normalizing flows. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference in order to fit them. We show that for some datasets that flows can outperform Gaussian mixtures for extreme deconvolution, and that fitting with non-Gaussian noise is now possible

    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

    Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

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    In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    In-line quality control for Zero Defect Manufacturing: design, development and uncertainty analysis of vision-based instruments for dimensional measurements at different scales

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    Lo scopo di questo progetto di dottorato industriale finanziato attraverso una borsa di studio della Regione Marche è stato quello di sviluppare ricerca con potenziale impatto su un settore industriale, promuovere il coinvolgimento delle fabbriche e delle imprese locali nella ricerca e innovazione svolta in collaborazione con l'università e produrre ricerca in linea con le esigenze dell'ambiente industriale, non solo a livello regionale. Quindi, attraverso la collaborazione con una torneria locale (Zannini SpA) e una piccola azienda high-tech focalizzata sull'introduzione dell'innovazione meccatronica nel settore della tornitura (Z4Tec srl), e anche grazie a una collaborazione internazionale con l'Università di Anversa, abbiamo progettato e sviluppato nuovi strumenti per il controllo qualità in linea, basati su tecnologie senza contatto, in particolare tecnologie elettro-ottiche. Portando anche l'attenzione sull'importanza di prendere in considerazione l'incertezza, poiché è fondamentale nel processo decisionale basato sui dati che sono alla base di una strategia di Zero Defect Manufacturing. Infatti, la scarsa qualità delle misure può pregiudicare la qualità dei dati. In particolare, questo lavoro presenta due strumenti di misura che sono stati progettati e sviluppati con lo scopo di effettuare controllo qualità in linea di produzione e l’incertezza di misura di ogni strumento è stata analizzata in confronto ad altri strumenti presenti sul mercato. Nella parte finale di questo lavoro si è valutata l’incertezza di un profilometro a triangolazione di linea laser. Pertanto, la ricerca condotta in questa tesi può essere organizzata in due obiettivi principali: lo sviluppo di nuovi sistemi di misura dimensionale basati sulla visione da implementare in linea di produzione e l'analisi dell'incertezza di questi strumenti di misura. Per il primo obiettivo ci siamo concentrati su due tipi di misure dimensionali imposte dall'industria manifatturiera: macroscopiche (misure in mm) e microscopiche (misure in µm). Per le misure macroscopiche l'obiettivo era il controllo in linea della qualità dimensionale di pezzi torniti attraverso la profilometria ottica telecentrica. Il campione da ispezionare è stato posto tra l'illuminatore e l'obiettivo per ottenere la proiezione dell'ombra del campione. Le misure sono state eseguite mediante analisi grafica dell'immagine. Abbiamo discusso le disposizioni meccaniche mirate a ottimizzare le immagini acquisite e i problemi che eventuali disallineamenti meccanici dei componenti potrebbero introdurre nella qualità delle immagini. Per le misure microscopiche abbiamo progettato un sistema di misurazione della rugosità superficiale basato sulla visione retroilluminata, con l'obiettivo di determinare le condizioni ottimali di imaging utilizzando la modulation transfer function e l'uso di una electrically tunable lens. Un campione tornito (un cilindro) è posto di fronte a una telecamera ed è retroilluminato da una sorgente di luce collimata; tale configurazione ottica fornisce l'immagine del bordo del campione. Per testare la sensibilità del sistema di misura è stata utilizzata una serie di campioni di acciaio torniti con diverse rugosità superficiali. Per il secondo obiettivo, le tecniche di valutazione dell'incertezza di misura utilizzate in questo lavoro sono state un'analisi dell'incertezza statistica di tipo A e un'analisi Gage R&R. Nel caso del profilometro telecentrico, l'analisi è stata eseguita in confronto con altri dispositivi presenti sul mercato con un'analisi di tipo A e una Gage R&R. L'incertezza di misura del profilometro si è rivelata sufficiente per ottenere risultati nell'intervallo di tolleranza richiesto. Per il sistema di visione retroilluminato, il confronto dei risultati è stato effettuato con altri strumenti allo stato dell'arte, con un'analisi di Tipo A. Il confronto ha mostrato che le prestazioni dello strumento retroilluminato dipendono dai valori di rugosità superficiale considerati; mentre a valori maggiori di rugosità l'offset aumenta, per valori inferiori di rugosità i risultati sono compatibili con quelli dello strumento di riferimento (a stilo). Infine, sono state valutate la ripetibilità e la riproducibilità di un profilometro a triangolazione di linea laser, attraverso uno studio Gage R&R. Ogni punto di misura è stato ispezionato da tre operatori e l'insieme dei dati è stato elaborato con un'analisi dell'incertezza di Tipo A. Successivamente, uno studio Gage R&R ha contribuito a indagare la ripetibilità, la riproducibilità e la variabilità del sistema. Questa analisi ha dimostrato un'incertezza accettabile.The purpose of this industrial PhD project financed through a scholarship from the Regione Marche was to develop research with potential impact on an industrial sector, to promote the involvement of local factories and companies in research and innovation performed jointly with the university and to produce research in line with the needs of the industrial environment, not only at regional level. Hence, through collaborating with a local turning factory (Zannini SpA) and a small high-tech company focused on introducing mechatronic innovation in the turning sector (Z4Tec srl), and also thanks to an international collaboration with the University of Antwerp, we designed and developed new instruments for in-line quality control, based on non-contact technologies, specifically electro-optical technologies. While also bringing attention to the importance of taking uncertainty into consideration, since it is pivotal in data-based decision making which are at the base of a Zero Defect Manufacturing strategy. This means that poor quality of measurements can prejudice the quality of the data. In particular, this work presents two measurement instruments that were designed and developed for the purpose of in-line quality control and the uncertainty of each of the two instruments was evaluated and analyzed in comparison with instruments already present on the market. In the last part of this work, the uncertainty of a hand-held laser-line triangulation profilometer is estimated. Hence, the research conducted in this thesis can be organized in two main objectives: the development of new vision-based dimensional measurement systems to be implemented in production line and the uncertainty analysis of these measurement instruments. For the first objective we focused on two types of dimensional measurements imposed by the manufacturing industry: macroscopic (measuring dimensions in mm) and microscopic (measuring roughness in µm). For macroscopic measurements the target was the in-production dimensional quality control of turned parts through telecentric optical profilometry. The sample to be inspected was placed between illuminator and objective in order to obtain the projection of the shadow of the sample over a white background. Dimensional measurements were then performed by means of image processing over the image obtained. We discussed the mechanical arrangements targeted to optimize images acquired as well as the main issues that eventual mechanical misalignments of components might introduce in the quality of images. For microscopic measurements we designed a backlit vision-based surface roughness measurement system with a focus on smart behaviors such as determining the optimal imaging conditions using the modulation transfer function and the use of an electrically tunable lens. A turned sample (a cylinder) is placed in front of a camera and it is backlit by a collimated source of light; such optical configuration provides the image of the edge of the sample. A set of turned steel samples with different surface roughness was used to test the sensitivity of the measurement system. For the second objective, the measurement uncertainty evaluation techniques used in this work were a Type A statistical uncertainty analysis and a Gage R&R analysis. In the case of the telecentric profilometer, the analysis was performed in comparison with other on-the-market devices with a Type A analysis and a Gage R&R analysis. The measurement uncertainty of the profilometer proved to be sufficient to obtain results within the tolerance interval required. For the backlit vision system, the comparison of the results was made with other state-of-the-art instruments, with a Type A analysis. The comparison showed that the performance of the backlit instrument depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the reference instrument (stylus-based) at lower values of roughness. Lastly, the repeatability and reproducibility of a laser-line triangulation profilometer were assessed, through a Gage R&R study. Each measuring point was inspected by three different operators and the data set has been, at first, processed by a Type A uncertainty analysis. Then, a Gage R&R study helped investigate repeatability, reproducibility and the system variability. This analysis showed that the presented laser-line triangulation system has an acceptable uncertainty

    Enhancing the Structural Stability of α-phase Hybrid Perovskite Films through Defect Engineering Approaches under Ambient Conditions

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    This thesis investigates methods whereby perovskite solar cell power conversion efficiency and material stability may be improved. Hybrid perovskites have gained increased attention for optoelectronic applications due to favourable properties such as strong absorption, facile processing, and changeable band-gap. Despite excellent improvements in power conversion efficiency of devices, perovskite films are unstable, degrading with relative ease in the presence of moisture, oxygen, light, heat, and electric fields. The focus of this thesis is on ambient atmosphere stability, concerned with the influence of moisture in particular on perovskite film fabrication, degradation, and device functionality. In order to shed light on the impact of ambient atmosphere on perovskite films, experiments are designed to investigate films during fabrication and degradation. The influences firstly of stoichiometry during ambient fabrication, and then ionic substitution (with caesium and formadinium) upon moisture-induced degradation are investigated. Finally, films and devices with a novel composition incorporating Zn are fabricated under ambient conditions to investigate the effect of Zn addition on perovskite film stability

    Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

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    Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure
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