Self-paced balance learning for clinical skin disease recognition

Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of instances among categories by resampling the majority and minority classes accordingly. However, the imbalanced level of difficulty of recognizing different categories is also crucial, especially for distinguishing samples with many classes. For example, in the task of clinical skin disease recognition, several rare diseases have a small number of training samples, but they are easy to diagnose because of their distinct visual properties. On the other hand, some common skin diseases, e.g., eczema, are hard to recognize due to the lack of special symptoms. To address this problem, we propose a self-paced balance learning (SPBL) algorithm in this paper. Specifically, we introduce a comprehensive metric termed the complexity of image category that is a combination of both sample number and recognition difficulty. First, the complexity is initialized using the model of the first pace, where the pace indicates one iteration in the self-paced learning paradigm. We then assign each class a penalty weight that is larger for more complex categories and smaller for easier ones, after which the curriculum is reconstructed by rearranging the training samples. Consequently, the model can iteratively learn discriminative representations via balancing the complexity in each pace. Experimental results on the SD-198 and SD-260 benchmark data sets demonstrate that the proposed SPBL algorithm performs favorably against the state-of-the-art methods. We also demonstrate the effectiveness of the SPBL algorithm's generalization capacity on various tasks, such as indoor scene image recognition and object classification

Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning

Tesis por compendio[ES] En la última década, el aprendizaje profundo (DL) se ha convertido en la principal herramienta para las tareas de visión por ordenador (CV). Bajo el paradigma de aprendizaje supervisado, y gracias a la recopilación de grandes conjuntos de datos, el DL ha alcanzado resultados impresionantes utilizando redes neuronales convolucionales (CNNs). Sin embargo, el rendimiento de las CNNs disminuye cuando no se dispone de suficientes datos, lo cual dificulta su uso en aplicaciones de CV en las que sólo se dispone de unas pocas muestras de entrenamiento, o cuando el etiquetado de imágenes es una tarea costosa. Estos escenarios motivan la investigación de estrategias de aprendizaje menos supervisadas. En esta tesis, hemos explorado diferentes paradigmas de aprendizaje menos supervisados. Concretamente, proponemos novedosas estrategias de aprendizaje autosupervisado en la clasificación débilmente supervisada de imágenes histológicas gigapixel. Por otro lado, estudiamos el uso del aprendizaje por contraste en escenarios de aprendizaje de pocos disparos para la vigilancia automática de cruces de ferrocarril. Por último, se estudia la localización de lesiones cerebrales en el contexto de la segmentación no supervisada de anomalías. Asimismo, prestamos especial atención a la incorporación de conocimiento previo durante el entrenamiento que pueda mejorar los resultados en escenarios menos supervisados. En particular, introducimos proporciones de clase en el aprendizaje débilmente supervisado en forma de restricciones de desigualdad. Además, se incorpora la homogeneización de la atención para la localización de anomalías mediante términos de regularización de tamaño y entropía. A lo largo de esta tesis se presentan diferentes métodos menos supervisados de DL para CV, con aportaciones sustanciales que promueven el uso de DL en escenarios con datos limitados. Los resultados obtenidos son prometedores y proporcionan a los investigadores nuevas herramientas que podrían evitar la anotación de cantidades masivas de datos de forma totalmente supervisada.[CA] En l'última dècada, l'aprenentatge profund (DL) s'ha convertit en la principal eina per a les tasques de visió per ordinador (CV). Sota el paradigma d'aprenentatge supervisat, i gràcies a la recopilació de grans conjunts de dades, el DL ha aconseguit resultats impressionants utilitzant xarxes neuronals convolucionals (CNNs). No obstant això, el rendiment de les CNNs disminueix quan no es disposa de suficients dades, la qual cosa dificulta el seu ús en aplicacions de CV en les quals només es disposa d'unes poques mostres d'entrenament, o quan l'etiquetatge d'imatges és una tasca costosa. Aquests escenaris motiven la investigació d'estratègies d'aprenentatge menys supervisades. En aquesta tesi, hem explorat diferents paradigmes d'aprenentatge menys supervisats. Concretament, proposem noves estratègies d'aprenentatge autosupervisat en la classificació feblement supervisada d'imatges histològiques gigapixel. D'altra banda, estudiem l'ús de l'aprenentatge per contrast en escenaris d'aprenentatge de pocs trets per a la vigilància automàtica d'encreuaments de ferrocarril. Finalment, s'estudia la localització de lesions cerebrals en el context de la segmentació no supervisada d'anomalies. Així mateix, prestem especial atenció a la incorporació de coneixement previ durant l'entrenament que puga millorar els resultats en escenaris menys supervisats. En particular, introduïm proporcions de classe en l'aprenentatge feblement supervisat en forma de restriccions de desigualtat. A més, s'incorpora l'homogeneïtzació de l'atenció per a la localització d'anomalies mitjançant termes de regularització de grandària i entropia. Al llarg d'aquesta tesi es presenten diferents mètodes menys supervisats de DL per a CV, amb aportacions substancials que promouen l'ús de DL en escenaris amb dades limitades. Els resultats obtinguts són prometedors i proporcionen als investigadors noves eines que podrien evitar l'anotació de quantitats massives de dades de forma totalment supervisada.[EN] In the last decade, deep learning (DL) has become the main tool for computer vision (CV) tasks. Under the standard supervised learnng paradigm, and thanks to the progressive collection of large datasets, DL has reached impressive results on different CV applications using convolutional neural networks (CNNs). Nevertheless, CNNs performance drops when sufficient data is unavailable, which creates challenging scenarios in CV applications where only few training samples are available, or when labeling images is a costly task, that require expert knowledge. Those scenarios motivate the research of not-so-supervised learning strategies to develop DL solutions on CV. In this thesis, we have explored different less-supervised learning paradigms on different applications. Concretely, we first propose novel self-supervised learning strategies on weakly supervised classification of gigapixel histology images. Then, we study the use of contrastive learning on few-shot learning scenarios for automatic railway crossing surveying. Finally, brain lesion segmentation is studied in the context of unsupervised anomaly segmentation, using only healthy samples during training. Along this thesis, we pay special attention to the incorporation of tasks-specific prior knowledge during model training, which may be easily obtained, but which can substantially improve the results in less-supervised scenarios. In particular, we introduce relative class proportions in weakly supervised learning in the form of inequality constraints. Also, attention homogenization in VAEs for anomaly localization is incorporated using size and entropy regularization terms, to make the CNN to focus on all patterns for normal samples. The different methods are compared, when possible, with their supervised counterparts. In short, different not-so-supervised DL methods for CV are presented along this thesis, with substantial contributions that promote the use of DL in data-limited scenarios. The obtained results are promising, and provide researchers with new tools that could avoid annotating massive amounts of data in a fully supervised manner.The work of Julio Silva Rodríguez to carry out this research and to elaborate this dissertation has been supported by the Spanish Government under the FPI Grant PRE2018-083443.Silva Rodríguez, JJ. (2022). Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/190633Compendi

Data Driven Techniques for Modeling Coupled Dynamics in Transient Processes

We study the problem of modeling coupled dynamics in transient processes that happen in a network. The problem is considered at two levels. At the node level, the coupling between underlying sub-processes of a node in a network is considered. At the network level, the direct influence among the nodes is considered. After the model is constructed, we develop a network-based approach for change detection in high dimension transient processes. The overall contribution of our work is a more accurate model to describe the underlying transient dynamics either for each individual node or for the whole network and a new statistic for change detection in multi-dimensional time series. Specifically, at the node level, we developed a model to represent the coupled dynamics between the two processes. We provide closed form formulas on the conditions for the existence of periodic trajectory and the stability of solutions. Numerical studies suggest that our model can capture the nonlinear characteristics of empirical data while reducing computation time by about 25% on average, compared to a benchmark modeling approach. In the last two problems, we provide a closed form formula for the bound in the sparse regression formulation, which helps to reduce the effort of trial and error to find an appropriate bound. Compared to other benchmark methods in inferring network structure from time series, our method reduces inference error by up to 5 orders of magnitudes and maintain better sparsity. We also develop a new method to infer dynamic network structure from a single time series. This method is the basis for introducing a new spectral graph statistic for change detection. This statistic can detect changes in simulation scenario with modified area under curve (mAUC) of 0.96. When applying to the problem of detecting seizure from EEG signal, our statistic can capture the physiology of the process while maintaining a detection rate of 40% by itself. Therefore, it can serve as an effective feature to detect change and can be added to the current set of features for detecting seizures from EEG signal

Data Driven Techniques for Modeling Coupled Dynamics in Transient Processes

We study the problem of modeling coupled dynamics in transient processes that happen in a network. The problem is considered at two levels. At the node level, the coupling between underlying sub-processes of a node in a network is considered. At the network level, the direct influence among the nodes is considered. After the model is constructed, we develop a network-based approach for change detection in high dimension transient processes. The overall contribution of our work is a more accurate model to describe the underlying transient dynamics either for each individual node or for the whole network and a new statistic for change detection in multi-dimensional time series. Specifically, at the node level, we developed a model to represent the coupled dynamics between the two processes. We provide closed form formulas on the conditions for the existence of periodic trajectory and the stability of solutions. Numerical studies suggest that our model can capture the nonlinear characteristics of empirical data while reducing computation time by about 25% on average, compared to a benchmark modeling approach. In the last two problems, we provide a closed form formula for the bound in the sparse regression formulation, which helps to reduce the effort of trial and error to find an appropriate bound. Compared to other benchmark methods in inferring network structure from time series, our method reduces inference error by up to 5 orders of magnitudes and maintain better sparsity. We also develop a new method to infer dynamic network structure from a single time series. This method is the basis for introducing a new spectral graph statistic for change detection. This statistic can detect changes in simulation scenario with modified area under curve (mAUC) of 0.96. When applying to the problem of detecting seizure from EEG signal, our statistic can capture the physiology of the process while maintaining a detection rate of 40% by itself. Therefore, it can serve as an effective feature to detect change and can be added to the current set of features for detecting seizures from EEG signal

Sound Event Detection by Exploring Audio Sequence Modelling

Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Regional power systems planning: a state of the art assessment. Final report

The purpose of this report was to define regional power systems planning problems, the tools available and their shortcomings, and to document all of the above in a concise readable form. The approach consisted of a survey and literature search. The survey determined the tools being used by utilities, the tools they had rejected, and the tools they planned to try out. The literature search was conducted for the purpose of documenting the tools available, and performing a comparative analysis of these tools. The project included a mix of utility, university, and consulting organizations. Several organizations were consulted in the selection of the participants. A non-profit organization, The University of Oklahoma, was selected to manage the project. The results were reviewed in a series of four one day meetings by known authorities in each field. This report consists of the results of this project. Perhaps its major finding is that several aspects of the regional planning problem are not well defined, the roles of the various participants in regional planning is not clear, and certainly research is needed for the development of new methodology