Modeling Sustainability Reporting with Ternary Attractor Neural Networks

International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02

Ensemble of diluted attractor networks with optimized topology for fingerprint retrieval

The present study analyzes the retrieval capacity of an Ensemble of diluted Attractor Neural Networks for real patterns (i.e., non-random ones), as it is the case of human fingerprints. We explore the optimal number of Attractor Neural Networks in the ensemble to achieve a maximum fingerprint storage capacity. The retrieval performance of the ensemble is measured in terms of the network connectivity structure, by comparing 1D ring to 2D cross grid topologies for the random shortcuts ratio. Given the nature of the network ensemble and the different characteristics of patterns, an optimization can be carried out considering how the pattern subsets are assigned to the ensemble modules. The ensemble specialization splitting into several modules of attractor networks is explored with respect to the activities of patterns and also in terms of correlations of the subsets of patterns assigned to each module in the ensemble network.This work was funded by and UDLA-SIS.MGR.20.01. This research was also funded by the Spanish Ministry of Science and Innovation/FEDER, under the \RETOS" Programme, with grant numbers: TIN2017-84452-R and RTI2018-098019-B-I00; and by the CYTED Network \Ibero-American Thematic Network on ICT Applications for Smart Cities", grant number: 518RT0559

Automatic Cardiac MRI Image Segmentation and Mesh Generation

Segmenting and reconstructing cardiac anatomical structures from magnetic resonance (MR) images is essential for the quantitative measurement and automatic diagnosis of cardiovascular diseases [1]. However, manual evaluation of the time-series cardiac MRI (CMRI) obtained during routine clinical care are laborious, inefficient, and tends to produce biased and non-reproducible results [2]. This thesis proposes an end-to-end pipeline for automatically segmenting short-axis (SAX) CMRI images and generating high-quality 2D and 3D meshes suitable for finite element analysis. The main advantage of our approach is that it can not only work as a stand-alone pipeline for the automatic CMR image segmentation and mesh generation but also functions effectively as a post-processing tool for improving the outcomes of deep learning methods. Our results indicate that the segmentation accuracy outperformed the traditional U-Net-based approach by as much as 82.5% (percent increase in Dice score) for 5 patient types. The mesh models generated from our contoured segmentations had minimized mean distance error of less than 1.3 pixels and optimized mesh quality with an average Kupp index greater than 0.8

MACHINE LEARNING AUGMENTATION MICRO-SENSORS FOR SMART DEVICE APPLICATIONS

Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability of MEMS devices for sensing. Furthermore, this dissertation addresses the high-voltage requirements in electrostatically actuated MEMS devices using a passive amplification scheme. The CTRNN architecture is emulated using a network of bistable MEMS devices. This bistable behavior is shown in the pull-in, the snapthrough, and the feedback regimes, when excited around the electrical resonance frequency. In these regimes, MEMS devices exhibit key behaviors found in biological neuronal populations. When coupled, networks of MEMS are shown to be successful at classification and control tasks. Moreover, MEMS accelerometers are shown to be successful at acceleration waveform classification without the need for external processors. MEMS devices are additionally shown to perform computing by utilizing the RC architecture. Here, a delay-based RC scheme is studied, which uses one MEMS device to simulate the behavior of a large neural network through input modulation. We introduce a modulation scheme that enables colocalized sensing-and-computing by modulating the bias signal. The MEMS RC is tested to successfully perform pure computation and colocalized sensing-and-computing for both classification and regression tasks, even in noisy environments. Finally, we address the high-voltage requirements of electrostatically actuated MEMS devices by proposing a passive amplification scheme utilizing the mechanical and electrical resonances of MEMS devices simultaneously. Using this scheme, an order-of-magnitude of amplification is reported. Moreover, when only electrical resonance is used, we show that the MEMS device exhibits a computationally useful bistable response. Adviser: Dr. Fadi Alsalee

Low-power neuromorphic sensor fusion for elderly care

Smart wearable systems have become a necessary part of our daily life with applications ranging from entertainment to healthcare. In the wearable healthcare domain, the development of wearable fall recognition bracelets based on embedded systems is getting considerable attention in the market. However, in embedded low-power scenarios, the sensor’s signal processing has propelled more challenges for the machine learning algorithm. Traditional machine learning method has a huge number of calculations on the data classification, and it is difficult to implement real-time signal processing in low-power embedded systems. In an embedded system, ensuring data classification in a low-power and real-time processing to fuse a variety of sensor signals is a huge challenge. This requires the introduction of neuromorphic computing with software and hardware co-design concept of the system. This thesis is aimed to review various neuromorphic computing algorithms, research hardware circuits feasibility, and then integrate captured sensor data to realise data classification applications. In addition, it has explored a human being benchmark dataset, which is following defined different levels to design the activities classification task. In this study, firstly the data classification algorithm is applied to human movement sensors to validate the neuromorphic computing on human activity recognition tasks. Secondly, a data fusion framework has been presented, it implements multiple-sensing signals to help neuromorphic computing achieve sensor fusion results and improve classification accuracy. Thirdly, an analog circuits module design to carry out a neural network algorithm to achieve low power and real-time processing hardware has been proposed. It shows a hardware/software co-design system to combine the above work. By adopting the multi-sensing signals on the embedded system, the designed software-based feature extraction method will help to fuse various sensors data as an input to help neuromorphic computing hardware. Finally, the results show that the classification accuracy of neuromorphic computing data fusion framework is higher than that of traditional machine learning and deep neural network, which can reach 98.9% accuracy. Moreover, this framework can flexibly combine acquisition hardware signals and is not limited to single sensor data, and can use multi-sensing information to help the algorithm obtain better stability

RUNTIME AUDIT OF NEURAL SEQUENCE MODELS FOR NLP

Neural network sequence models have become a fundamental building block for natural language processing (NLP) applications. However, with the increasing performance and widespread adoption of these models, the social effects caused by errors in these models' outputs are also amplified. This thesis aims to mitigate such adverse effects by studying different methods that generate user-interpretable auxiliary signals along with model predictions, thus enabling efficient audits of the model output at runtime. We will look at two different types of auxiliary signals respectively generated for the input and the output of the model. The first type explains which input tokens are important for a certain prediction (Chapter 3 and 4), while the second estimates the quality of each output token (Chapter 5 and 6). For model explanations, our focus is to establish a comprehensive and quantitative evaluation framework, thus enabling a systematic comparison of different model explanation methods on a diverse set of architectures and configurations. For quality estimations, because there is already a solid evaluation framework in place, we instead focus on improving state of the art by introducing an end-task-oriented pre-training step that is based on a non-autoregressive neural machine translation architecture. Overall, we show that it is possible to generate auxiliary signals of high quality with little to no human supervision, and we also provide some guidance for best practices regarding future applications of these methods to NLP, such as conducting comprehensive quantitative evaluations for the auxiliary signals before deployment, and selecting the appropriate evaluation metric that best suits the user's goal

Development of an Operational System for a Coastal Area on the German North Sea using Artificial Intelligence

This thesis presents a methodology of developing a quasi real time flow model in a coastal area. This model improves the water level conditions along the boundary of the flow model by the implementation of a set of neural network short term forecast

Attending responding becoming : a living-learning inquiry in a naturally inclusional playspace

Traditional scientific paradigms emphasise writing in the third person, effectively marginalising the subjective perspective of the researcher. Many systems thinking, cybernetics and complexity approaches are better in this regard, as they involve systemic interventions where the relationships between the researcher and other participants really matter. Writing in the first person therefore becomes acceptable.In this Thesis (and a partner document coupled with it), I have explored how to reincorporate subjective empiricism into my systemic intervention practice. This has brought forth many unanticipated contributions. These take the form of new frameworks, concepts and approaches for systems and complexity practice, emerging from my engagements with myself and others, as well as from reflections upon those engagements.However, the content of my reflections and ‘becomings’ are not all that represent my doctoral contribution; there is also the form of my representation(s), as well as the emergent nature of the process through which they have come to be. I have drawn from Gregory Bateson’s use of metalogues: where the nature of a conversation mirrors its content – e.g. getting into a muddle whilst talking about muddles! Intuitively, I grasped the importance of metalogue in what I was attempting, and found myself coining the term metalogic coherence. Without fully appreciating what this might mean in practice, I groped my way into undertaking and documenting my research in ways that I believed would be metalogically coherent with the complexity-attuned principles to which I was committing. In sum, and key to appreciating what unfolds in the narrative, is recognising this Thesis and its partner document as metalogically coherent artefacts of naturally inclusional, complexity-attuned, evolutionary research.To fully acknowledge the different ways of knowing that have flowed into my inquiry, I have written in multiple voices (called statewaves, for reasons to be explained in the thesis). I found myself shifting from one voice to another as I explored and expressed different dimensions of what I was experiencing and discovering.In addition, I have made liberal use of hyperlinks, so both documents are far from linear. They are more akin to a mycorrhizal network, interlinking flows of ideas and sensemaking, all of which can be accessed and experienced differently, depending on each reader’s engagement with and through it.The thesis and its partner document are part of a composite submission that contains both poetry and artwork (visual depictions and animations of the ideas). These elements, along with the more conventional academic text, are augmented by penetrating reflections on my personal motivations, guided by a narrator signposting the streams as they flow into and between each other. All of my being has been implicated and impacted by this endeavour. When insights and new ‘becomings’ emerged flowfully during my practice, my joy was reflected in my narrative; as indeed were my pain, doubts and reinterpretations associated with ideas that were difficult to birth. I present all this in my submission, without retrospective sanitisation or simplification. In so doing, I am keeping faith with the principle that I remain at the heart of my research, and cannot be extracted from it without doing violence to the metalogical coherence that gives it meaning

Using context-sensitive criteria to evaluate local and regional transportation policy : a case study on cordon pricing

As traffic congestion grows but existing roadway capacity remains fixed or limited, downtown congestion pricing offers potential as a tool to manage the transportation system. Though the idea is not new, congestion pricing has received a resurgence of attention in the United States in recent years because it could offer both congestion relief and transportation revenue. However, in order for a modern congestion pricing proposal to be politically feasible and publicly acceptable today it must be designed to offer more, such as equitable or progressive distribution of impacts, greenhouse gas emissions, and encouragement or support for alternate modes, including new mobility services. In Seattle, serious consideration of the implementation of congestion pricing by 2021 is underway, and numerous policy questions remain open. One which many anticipate, particularly the public, is the question of where congestion pricing revenue would be spent. It is likely that at least some of the revenue will be allocated for transit, but where should service improvements be targeted, both geographically and demographically, so that mobility and access are not impaired, particularly for the already transportation-disadvantaged, and so that multimodal travel is not just possible but preferable to driving? Could a regional partnership between transit agencies like Sound Transit and King County Metro and the City of Seattle secure transportation outcomes that align with both transit agencies’ ambitious service expansion goals and Seattle’s core equity, multimodal mobility, and climate goals? This thesis seeks to answer these questions by using a mix of statistical models of transportation system level of service and individual-level mode choice. These models are used to predict how travelers across the region would change their travel behavior in response to cordon pricing in Center City Seattle under two investment scenarios. It is projected that investing in transit broadly across the region by decreasing transit service times produces transportation system outcomes that advance both local and regional strategic goals more than concentrated investment on downtown Seattle roadways and transit could advance Seattle’s goals alone. Regional transit investment would decrease congestion more in Center City Seattle by improving transit access from outside Seattle into Center City, especially among neighborhoods with the lowest housing and transportation affordability, highest automobility, and highest transportation-related greenhouse gas emissions. Therefore, the findings strongly motivate that congestion pricing revenue in Seattle be spent on regional transit service improvement and expansion. Furthermore, the findings suggest that even regional transit investments that may not be directly linked to Center City will help to produce a mix of better transportation outcomes in Center City than concentrated investment would.Civil, Architectural, and Environmental Engineerin