18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XII

Visual Representation Learning with Limited Supervision

The quality of a Computer Vision system is proportional to the rigor of data representation it is built upon. Learning expressive representations of images is therefore the centerpiece to almost every computer vision application, including image search, object detection and classification, human re-identification, object tracking, pose understanding, image-to-image translation, and embodied agent navigation to name a few. Deep Neural Networks are most often seen among the modern methods of representation learning. The limitation is, however, that deep representation learning methods require extremely large amounts of manually labeled data for training. Clearly, annotating vast amounts of images for various environments is infeasible due to cost and time constraints. This requirement of obtaining labeled data is a prime restriction regarding pace of the development of visual recognition systems. In order to cope with the exponentially growing amounts of visual data generated daily, machine learning algorithms have to at least strive to scale at a similar rate. The second challenge consists in the learned representations having to generalize to novel objects, classes, environments and tasks in order to accommodate to the diversity of the visual world. Despite the evergrowing number of recent publications tangentially addressing the topic of learning generalizable representations, efficient generalization is yet to be achieved. This dissertation attempts to tackle the problem of learning visual representations that can generalize to novel settings while requiring few labeled examples. In this research, we study the limitations of the existing supervised representation learning approaches and propose a framework that improves the generalization of learned features by exploiting visual similarities between images which are not captured by provided manual annotations. Furthermore, to mitigate the common requirement of large scale manually annotated datasets, we propose several approaches that can learn expressive representations without human-attributed labels, in a self-supervised fashion, by grouping highly-similar samples into surrogate classes based on progressively learned representations. The development of computer vision as science is preconditioned upon the seamless ability of a machine to record and disentangle pictures' attributes that were expected to only be conceived by humans. As such, particular interest was dedicated to the ability to analyze the means of artistic expression and style which depicts a more complex task than merely breaking an image down to colors and pixels. The ultimate test for this ability is the task of style transfer which involves altering the style of an image while keeping its content. An effective solution of style transfer requires learning such image representation which would allow disentangling image style and its content. Moreover, particular artistic styles come with idiosyncrasies that affect which content details should be preserved and which discarded. Another pitfall here is that it is impossible to get pixel-wise annotations of style and how the style should be altered. We address this problem by proposing an unsupervised approach that enables encoding the image content in such a way that is required by a particular style. The proposed approach exchanges the style of an input image by first extracting the content representation in a style-aware way and then rendering it in a new style using a style-specific decoder network, achieving compelling results in image and video stylization. Finally, we combine supervised and self-supervised representation learning techniques for the task of human and animals pose understanding. The proposed method enables transfer of the representation learned for recognition of human poses to proximal mammal species without using labeled animal images. This approach is not limited to dense pose estimation and could potentially enable autonomous agents from robots to self-driving cars to retrain themselves and adapt to novel environments based on learning from previous experiences

Spatio-Temporal Optimization for Control of Infinite Dimensional Systems in Robotics, Fluid Mechanics, and Quantum Mechanics

The majority of systems in nature have a spatio-temporal dependence and can be described by Partial Differential Equations (PDEs). They are ubiquitous in science and engineering, and are of rising interest among the control, robotics, and machine learning communities. Related methods usually treat these infinite dimensional problems in finite dimensions with reduced order models. This leads to committing to specific approximation schemes and the subsequent control laws cannot generalize outside of the approximation schemes. Additionally, related work does not consider spatio-temporal descriptions of noise that realistically represent the stochastic nature of physical systems. This thesis develops a variety of approaches for control optimization and co-design optimization for PDE and stochastic PDE (SPDE) systems from a unified perspective that can be applied to macroscopic systems in robotics and fluid dynamics, as well as microscopic systems in quantum mechanics. These approaches are each developed completely in the infinite dimensional Hilbert spaces where the systems are mathematically described, enabling the frameworks to be agnostic to the discretization scheme used to implement them. The first three developed approaches are applied in simulation to classical systems in fluid dynamics such as the Heat and Burgers equation. The fourth approach is developed for second-order SPDEs that arise in robotic systems, and is applied in simulation to systems in soft-robotics such as the Euler-Bernoulli equation and a biological model of a soft-robotic limb. Finally, several approaches are developed in the context of quantum feedback control of open quantum systems with non-demolition measurement, and one such approach is applied in simulation to perform explicit feedback control of the two qubit open quantum system.Ph.D

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Early stopping by correlating online indicators in neural networks

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85160-C2-2-R/ES/AVANCES EN NUEVOS SISTEMAS DE EXTRACCION DE RESPUESTAS CON ANALISIS SEMANTICO Y APRENDIZAJE PROFUNDOinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113230RB-C22/ES/SEQUENCE LABELING MULTITASK MODELS FOR LINGUISTICALLY ENRICHED NER: SEMANTICS AND DOMAIN ADAPTATION (SCANNER-UVIGO)In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.Agencia Estatal de Investigación | Ref. TIN2017-85160-C2-2-RAgencia Estatal de Investigación | Ref. PID2020-113230RB-C22Xunta de Galicia | Ref. ED431C 2018/5

On Patching Learning Discrepancies in Neural Network Training

Neural network\u27s ability to model data patterns proved to be immensely useful in a plethora of practical applications. However, using the physical world\u27s data can be problematic since it is often cluttered, crowded with scattered insignificant patterns, contain unusual compositions, and widely infiltrated with biases and imbalances. Consequently, training a neural network to find meaningful patterns in seas of chaotic data points becomes virtually as hard as finding a needle in a haystack. Specifically, attempting to simulate real-world multi-modal noisy distributions with high precision leads the network to learn an ill-informed inference distribution. In this work, we discuss four techniques to mitigate common discrepancies between real-world representations and the training distribution learned by the network. Namely, we address the techniques of Diverse sampling, objective generalization, domain, and task adaptation being introduced as priors in learning the primary objective. For each of these techniques, we contrast the basic training where no prior is applied to the learning with our proposed method and show the advantage of guiding the training distribution to the critical patterns in real-world data using our suggested approaches. We examine those discrepancy-mitigation techniques on a variety of vision tasks ranging from image generation and retrieval to video summarization and actionness ranking

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting valuable knowledge. Explainable AI (XAI) research aims to increase the transparency of DNN model behaviour to improve interpretability. Feature importance explanations are the most popular interpretability approaches. They show the importance of each input feature (e.g., pixel, patch, word vector) to the model’s prediction. However, we hypothesise that feature importance explanations have two main shortcomings concerning their inability to describe the complexity of a DNN behaviour with sufficient (1) fidelity and (2) richness. Fidelity and richness are essential because different tasks, users, and data types require specific levels of trust and understanding. The goal of this thesis is to showcase the shortcomings of feature importance explanations and to develop explanation techniques that describe the DNN behaviour with greater richness. We design an adversarial explanation attack to highlight the infidelity and inadequacy of feature importance explanations. Our attack modifies the parameters of a pre-trained model. It uses fairness as a proxy measure for the fidelity of an explanation method to demonstrate that the apparent importance of a feature does not reveal anything reliable about the fairness of a model. Hence, regulators or auditors should not rely on feature importance explanations to measure or enforce standards of fairness. As one solution, we formulate five different levels of the semantic richness of explanations to evaluate explanations and propose two function decomposition frameworks (DGINN and CME) to extract explanations from DNNs at a semantically higher level than feature importance explanations. Concept-based approaches provide explanations in terms of atomic human-understandable units (e.g., wheel or door) rather than individual raw features (e.g., pixels or characters). Our function decomposition frameworks can extract specific class representations from 5% of the network parameters and concept representations with an average-per-concept F1 score of 86%. Finally, the CME framework makes it possible to compare concept-based explanations, contributing to the scientific rigour of evaluating interpretability methods.The author would like to appreciate the generous sponsorship of the Engineering and Physical Sciences Research Council (EPSRC), The Department of Computer Science and Technology at the University of Cambridge, and Tenyks, Inc

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions

This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation