Sequential Learning for Adaptive Critic Design: An Industrial Control Application

This paper investigates the feasibility of applying reinforcement learning (RL) concepts to industrial process optimisation. A model-free action-dependent adaptive critic design (ADAC), coupled with sequential learning neural network training, is proposed as an online RL strategy suitable for both modelling and controller optimisation. The proposed strategy is evaluated on data from an industrial grinding process used in the manufacture of disk drives. Comparison with a proprietary control system shows that the proposed RL technique is able to achieve comparable performance without any manual intervention

Receptive fields optimization in deep learning for enhanced interpretability, diversity, and resource efficiency.

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and the excessive number of weights are often deliberately built in into their design. This flexibility and performance usually come with high computational and memory demands both during training and inference. In addition, insight into the mappings DNN models perform and human ability to understand them still remain very limited. This dissertation addresses some of these limitations by balancing three conflicting objectives: computational/ memory demands, interpretability, and accuracy. This dissertation first introduces some unsupervised feature learning methods in a broader context of dictionary learning. It also sets the tone for deep autoencoder learning and constraints for data representations in light of removing some of the aforementioned bottlenecks such as the feature interpretability of deep learning models with nonnegativity constraints on receptive fields. In addition, the two main classes of solution to the drawbacks associated with overparameterization/ over-complete representation in deep learning models are also presented. Subsequently, two novel methods, one for each solution class, are presented to address the problems resulting from over-complete representation exhibited by most deep learning models. The first method is developed to achieve inference-cost-efficient models via elimination of redundant features with negligible deterioration of prediction accuracy. This is important especially for deploying deep learning models into resource-limited portable devices. The second method aims at diversifying the features of DNNs in the learning phase to improve their performance without undermining their size and capacity. Lastly, feature diversification is considered to stabilize adversarial learning and extensive experimental outcomes show that these methods have the potential of advancing the current state-of-the-art on different learning tasks and benchmark datasets

THE PERCEPTIONS, PRACTICES, AND POLICIES THAT GOVERN FOOD, ENERGY, AND WATER CONSUMPTION IN THE U.S. SUBURBAN HOME: “MORE THAN MY FAIR SHARE”

This dissertation addresses household consumption by advancing understandings of complex material, social, and regulatory structures that have a bearing on the future of sustainable and resilient practices at the residential scale in the United States. Interviews with 44 households in the U.S. were conducted to learn about perceptions of food, energy, and water consumption. Chapters two, three, and four utilize grounded theory and theories of practice. This inquiry yielded insights into social dynamics of household consumption, how human and more-than-human actors influence each other’s consumption, and how infrastructure and social class are interrelated. Chapter five is a policy maker’s guidebook, inspired by the interviews, to help small municipalities adopt ordinances that encourage sustainable practices on residential properties and to improve household consumption. The results of this research have implications for recognizing, shifting, and developing new sustainable and resilient practices in households

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

Models, Algorithms, and Architectures for Scalable Packet Classification

The growth and diversification of the Internet imposes increasing demands on the performance and functionality of network infrastructure. Routers, the devices responsible for the switch-ing and directing of traffic in the Internet, are being called upon to not only handle increased volumes of traffic at higher speeds, but also impose tighter security policies and provide support for a richer set of network services. This dissertation addresses the searching tasks performed by Internet routers in order to forward packets and apply network services to packets belonging to defined traffic flows. As these searching tasks must be performed for each packet traversing the router, the speed and scalability of the solutions to the route lookup and packet classification problems largely determine the realizable performance of the router, and hence the Internet as a whole. Despite the energetic attention of the academic and corporate research communities, there remains a need for search engines that scale to support faster communication links, larger route tables and filter sets and increasingly complex filters. The major contributions of this work include the design and analysis of a scalable hardware implementation of a Longest Prefix Matching (LPM) search engine for route lookup, a survey and taxonomy of packet classification techniques, a thorough analysis of packet classification filter sets, the design and analysis of a suite of performance evaluation tools for packet classification algorithms and devices, and a new packet classification algorithm that scales to support high-speed links and large filter sets classifying on additional packet fields

Effective Approaches for Improving the Efficiency of Deep Convolutional Neural Networks for Image Classification

Aquesta tesi presenta dos mètodes per reduir el nombre de paràmetres i càlculs de punt flotant a arquitectures DCNN utilitzades amb classificació d'imatges. El primer mètode és una modificació de les primeres capes d‟una DCNN que divideix els canals d‟una imatge codificada amb l‟espai de color CIE Lab en dos camins separats, un per al canal acromàtic i un altre per a la resta de canals cromàtics. Modifiquem una arquitectura Inception V3 per incloure una branca específica per a dades acromàtiques (canal L) i una altra branca específica per a dades cromàtiques (canals AB). Aquesta modificació aprofita el desacoblament de la informació cromàtica i acromàtica. A més, la divisió de branques redueix el nombre de paràmetres entrenables i la càrrega de càlcul fins a un 50% de les xifres originals a les capes modificades. Vam aconseguir una state-of-the-art precisió classificació de 99,48% a Plant Village. També trobem una millor fiabilitat en la classificació d'imatges quan les imatges d'entrada contenen soroll. A les DCNNs, el recompte de paràmetres en convolucions puntuals creix ràpidament a causa de la multiplicació dels filtres i canals dentrada de la capa anterior. Per gestionar aquest creixement, el segon mètode d'optimització fa que les convolucions puntuals tinguin pocs paràmetres mitjançant l'ús de branques paral·leles, on cada branca conté un grup de filtres i processa una fracció dels canals d'entrada. Per evitar degradar la capacitat daprenentatge de les DCNN, proposem intercalar la sortida dels filtres de branques paral·leles en capes intermèdies de convolucions puntuals successives. Provem la nostra optimització en un EfficientNet-B0 com a arquitectura de referència i realitzem proves de classificació als conjunts de dades CIFAR-10, Histologia del càncer colorectal i Malària. Per a cada conjunt de dades, la nostra optimització aconsegueix un estalvi del 76%, 89% i 91% de la quantitat de paràmetres entrenables de EfficientNet-B0, mantenint la precisió de classificació.Esta tesis presenta dos métodos para reducir el número de parámetros y cálculos de punto flotante en arquitecturas DCNN utilizadas con clasificación de imágenes. El primer método es una modificación de las primeras capas de una DCNN que divide los canales de una imagen codificada con el espacio de color CIE Lab en dos caminos separados, uno para el canal acromático y otro para el resto de canales cromáticos. Modificamos una arquitectura Inception V3 para incluir una rama específica para datos acromáticos (canal L) y otra rama específica para datos cromáticos (canales AB). Esta modificación aprovecha el desacoplamiento de la información cromática y acromática. Además, la división de ramas reduce el número de parámetros entrenables y la carga de cálculo hasta en un 50% de las cifras originales en las capas modificadas. Logramos una state-of-the-art precisión clasificación de 99,48% en Plant Village. También encontramos una mejor confiabilidad en la clasificación de imágenes cuando las imágenes de entrada contienen ruido. En las DCNNs, el conteo de parámetros en convoluciones puntuales crece rápidamente debido a la multiplicación de los filtros y canales de entrada de la capa anterior. Para manejar este crecimiento, el segundo método de optimización hace que las convoluciones puntuales tengan pocos parametros mediante el empleo de ramas paralelas, donde cada rama contiene un grupo de filtros y procesa una fracción de los canales de entrada. Para evitar degradar la capacidad de aprendizaje de las DCNN, proponemos intercalar la salida de los filtros de ramas paralelas en capas intermedias de convoluciones puntuales sucesivas. Probamos nuestra optimización en un EfficientNet-B0 como arquitectura de referencia y realizamos pruebas de clasificación en los conjuntos de datos CIFAR-10, Histología del cáncer colorrectal y Malaria. Para cada conjunto de datos, nuestra optimización logra un ahorro del 76 %, 89 % y 91 % de la cantidad de parámetros entrenables de EfficientNet-B0, manteniendo la precisión de clasificación.This thesis presents two methods for reducing the number of parameters and floating-point computations in existing DCNN architectures used with image classification. The first method is a modification of the first layers of a DCNN that splits the channels of an image encoded with CIE Lab color space in two separate paths, one for the achromatic channel and another for the remaining chromatic channels. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures in the modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset. This two-branch method improves image classification reliability when the input images contain noise. In DCNNs, the parameter count in pointwise convolutions quickly grows due to the multiplication of the filters and input channels from the preceding layer. To handle this growth, the second optimization method makes pointwise convolutions parameter-efficient via parallel branching. Each branch contains a group of filters and processes a fraction of the input channels. To avoid degrading the learning capability of DCNNs, we propose interleaving the filters' output from separate branches at intermediate layers of successive pointwise convolutions. We tested our optimization on an EfficientNet-B0 as a baseline architecture and made classification tests on the CIFAR-10, Colorectal Cancer Histology, and Malaria datasets. For each dataset, our optimization saves 76%, 89%, and 91% of the number of trainable parameters of EfficientNet-B0, while keeping its test classification accuracy

Test time cost sensitivity in machine learning

The use of deep neural networks has enabled machines to classify images, translate between languages and compete with humans in games. These achievements have been enabled by the large and expensive computational resources that are now available for training and running such networks. However, such a computational burden is highly undesirable in some settings. In this thesis we demonstrate how the computational expense of a machine learning algorithm may be reduced. This is possible because, until recently, most research in deep learning has focused on achieving better statistical results on benchmarks, rather than targeting efficiency. However, the learning process is flexible enough for us to control for the test-time computational expense that will be paid when the model is run in an application. To achieve this test-time computation sensitivity, a budget can be incorporated as part of the model. This budget expresses what costs we are willing to incur when we allocate resources at test time. Alternatively we can prescribe the size or computational resources we expect and use that to decide on the appropriate classification model. In either case, considering the resources available when building the model allows us to use it more effectively. In this thesis, we demonstrate methods to reduce the stored size, or floating point operations, of state-of-the-art classification models by an order of magnitude with little effect on their performance. Finally, we find that such compression can even be performed by simply changing the parameterisation of linear transforms used in the network. These results indicate that the design of learning systems can benefit from taking resource efficiency into account