A Dual-Mode Weight Storage Analog Neural Network Platform for On-Chip Applications

Abstract-On-chip trainable neural networks show great promise in enabling various desired features of modern integrated circuits (IC), such as Built-In Self-Test (BIST), security and trust monitoring, self-healing, etc. Cost-efficient implementation of these features imposes strict area and power constraints on the circuits dedicated to neural networks, which, however, should not compromise their ability to learn fast and retain functionality throughout their lifecycle. To this end, we have designed and fabricated a reconfigurable analog neural network (ANN) chip which serves as an expertise acquisition platform for various applications requiring on-chip ANN integration. With this platform, we intend to address the key cost-efficiency issues: a fully analog implementation with strict area and power budgets, a learning ability of the proposed architecture, fast dynamic programming of the weight memory during training, and high precision non-volatile storage of weight coefficients during operation or standby. We explore two learning structures: a multilayer perceptron (MLP) and an ontogenic neural network with their corresponding training algorithms. The core circuits are biased in weak inversion and make use of the translinear principle for multiplication and non-linear conversion operations. The chip is mounted on a custom PCB and connected to a computer for chip-in-the-loop training. We present measured results of the core circuits and the dual-mode weight memory. The learning ability is evaluated on a 3-input XOR classification task

Identification Of Streptococcus Pyogenes Using Raman Spectroscopy

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman spectra requires pre-processing of the raw spectra, in particular, background removing. Various techniques are developed to remove the background of the raw spectra accurately and with or without less expert intervention. Nevertheless, as the background of the spectra varies in the different media, these methods still require expert effort adding complexity and inefficiency to the identification task. This dissertation describes the development of state-of-the-art classification techniques to identify S. pyogenes from other species, including water and other confounding background pathogens. We compared these techniques in terms of their classification accuracy, sensitivity, and specificity in addition to providing a bias-variance insight in selecting the number of principal components in a principal component analysis (PCA). It was observed that Random Forest provided a better result with an accuracy of 94.11%. Next, a novel deep learning technique was developed to remove the background of the Raman spectra and then identify the pathogen. The architecture of the network was discussed and it was found that this method yields an accuracy of 100% in our test samples. This outperforms other traditional machine learning techniques as discussed. In clinical applications of Raman Spectroscopy, the samples have confounding background creates a challenging task for the removal of the spectral background and subsequent identification of the pathogen in real- time. We tested our methodology on datasets composed of confounding background such as throat swabs from patients and discussed the robustness and generalization of the developed method. It was found that the misclassification error of the test dataset was around 3.7%. Also, the realization of the trained model is discussed in detail to provide a better understating and insight into the efficacy of the deep learning architecture. This technique provides a platform for general analysis of other pathogens in confounding environments as well

Bio-physical controls on tidal network geomorphology

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorLooking over a tidal wetland, the tidal network characterised by its intricate system of bifurcating, blind-ended tidal courses clearly stands out from the overall landscape. This tidal landform exerts a fundamental control on the morphology and ecology within the tidal environment. With today’s recognition of the ecological, economical and societal values provided by tidal wetlands, which has been notably reflected in the development of restoration management strategies across Europe and USA, there is a need to fully understand the nature and development of tidal networks as well as their relationships with associated landforms and biotic components (e.g. vegetation), to eventually guarantee the success of current and future restoration practices. Accordingly, this research aims to bring further insights into the bio-physical controls on the geomorphology of tidal networks. To this end, a combination of remote sensing, modelling and field activities was employed. A geo-spatial analysis was performed at Queen Mary, University of London (UK), to address the variability of tidal network patterns. A series of network scale morphometric variables was extracted using airborne LiDAR data among selected tidal networks across the UK depicting different planview morphologies, and supplemented with the collection of corresponding marsh scale environmental variables from published sources. Multivariate statistics were then performed to characterise the variability of tidal network patterns and identify the inherent environmental controls. The analysis has revealed that every network type can be characterised based upon measures of network size and complexity, with each network pattern depicting proper morphometric aspects. Particularly, the stream Strahler order and the median depth of the network main channel have the highest discriminating weight on the patterns investigated. High correlation between the latter variable and network main channel width has revealed that linear, linear-dendritic and dendritic networks followed a transitional gradient in their aspect ratio approximated by a power law and thus are seen to depict similar erosional processes. To the contrary, meandering networks clearly depart from this relationship, and show particular segregation in their aspect ratios with respect to dendritic networks. Globally, differentiation on network morphometric properties has been linked to environmental conditions specific to the marsh physiographic setting within which a tidal network develops. Conceptually, tidal networks seem to adapt to marsh environmental conditions by adopting suitable morphologies to drain their tidal basin effectively. An eco-geomorphic modelling framework was developed at University of Trento (Italy), to address tidal network morphological development. In line with current theories as well as modelling advances and challenges in the field of tidal network ontogeny, emphasis was thus placed on the investigation of tidal channel formation and evolution in progressive marsh accretional context. Under these environmental conditions, tidal network development can be ascribed to the combination of two channel-forming processes: channel initiation results from bottom incisions in regions where topographic depressions occur; channel elaboration results from differential deposition, contributing to the deepening of the tidal channels relative to the adjacent marsh platform. Further evolutionary stages including channel reduction proceed from the horizontal progradation of the marsh platform which may lead eventually to channel infilling. Moreover, both qualitative and quantitative results allude to an acceleration of the morphological development of the synthetic tidal networks with increasing sediment supply. These different observations thus emphasise the prevalence of depositional processes in shaping tidal channels. In a second stage, the investigation was extended to the role of the initial tidal flat morphology as an inherent control on tidal network development, by considering different scenarios of topographic perturbations, which has revealed its legacy on tidal network morphological features. Modelling experiments have also acknowledged salt marsh macrophytes as a potential control on network evolution depending on their biomass distribution within the tidal frame. However, tidal channel morphodynamcis appears to be sensitive to the way biomass growth is mathematically parameterised in the model. In view of the current challenges in transcribing mathematically such a dynamic process and the relevance of bio-physical interactions in driving salt marsh and tidal network evolution, a field survey was conducted in a temperate salt marsh in the Netherlands, as part of the mobility to UNESCO-IHE (Netherlands) in partnership with University of Antwerp (Belgium), to assess vegetation distribution and productivity in the tidal frame. Particularly, emphasis was placed on extending investigations on the possible presence of relationships involving vegetation properties in different climatic and ecological conditions from those characterising these previously documented relationships. Regression analysis has revealed that biomass growth can be expressed as a linear function of marsh relative elevation, providing therefore direct empirical validation for corresponding assumptions reported in the literature and used in the present modelling framework; surprisingly, that increase did not correlate with an increase in species richness and diversity. Analysis of likely associations between vegetation morphometrics and total standing biomass yielded only a single linear relationship linking the latter variable to stem height. In truth, these observations may bear reconsiderations on the global validity of the assumptions used in the formulation of some eco-geomorphic processes which are applied in the study and prediction of wetland resiliency facing climate change

An eco-geomorphic model of tidal channel initiation and elaboration in progressive marsh accretional contexts

The formation and evolution of tidal networks have been described through various theories which mostly assume that tidal network development results from erosional processes, therefore emphasizing the chief role of external forcing triggering channel net erosion such as tidal currents. In contrast, in the present contribution we explore the influence of sediment supply in governing tidal channel initiation and further elaboration using an ecogeomorphic modeling framework. This deliberate choice of environmental conditions allows for the investigation of tidal network growth and development in different sedimentary contexts and provides evidences for the occurrence of both erosional and depositional channel-forming processes. Results show that these two mechanisms in reality coexist but act at different time scales: channel initiation stems from erosional processes, while channel elaboration mostly results from depositional processes. Furthermore, analyses suggest that tidal network ontogeny is accelerated as the marsh accretional activity increases, revealing the high magnitude and prevalence of the depositional processes in governing the morphodynamic evolution of the tidal network. On a second stage, we analyze the role of different initial topographic configurations in driving the development of tidal networks. Results point out an increase in network complexity over highly perturbed initial topographic surfaces, highlighting the legacy of initial conditions on channel morphological properties. Lastly, the consideration that landscape evolution depends significantly on the parameterization of the vegetation biomass distribution suggests that the claim to use uncalibrated models for vegetation dynamics is still questionable when studying real cases

Resolving Cell Fate: Experimental, computational and mathematical methods in single cell transcriptomic analysis

Deeper understanding of the embryological origins of tissues and organs is likely to provide insights into novel clinically relevant preventative and therapeutic strategies. To do so effectively and at a large scale so as to have clinical significance requires an exhaustive and meticulously accurate knowledge of normal morphological development and the underlying molecular pathways. A variety of lineage tracing and classical molecular biology techniques have led to key insights but emerging technologies now offer the possibility of more fine grained and precise measurements at the level of the single cell rather than ensembles of heterogeneous cells. In this study the rapidly developing technology of single cell RNA sequencing was combined with the development of state of the art computational methods to study murine gastrulation and early organogenesis at a hitherto unprecedented granularity. A comprehensive analysis of FLK1+ cells harvested from gastrulating murine embryos was performed. In-silico reconstruction of pseudo-temporal and pseudo-spacial relations were demonstrated. Using prior knowledge of known driver genes deeper substructure was revealed in clusters that were initially defined by unsupervised algorithms, illustrating that careful implementation of supervised approaches can outperform naïve unsupervised methods. In this way multiple members of the leukotriene branch of the arachidonic acid pathway were found to be enriched within a subset of the endothelial cluster that has a molecular signature consistent with yolk sac derived definitive wave haematopoiesis. This was subsequently validated in an in-vitro embryonic stem cell differentiation colony assay. By developing a novel adaptation to tSNE dimensionality reduction that now allows new data points to be mapped backed to previously calculated embeddings, the FLK1+ data set became a reference to which cells from other experiments were mapped. The Tal1-/- knockout mutant was characterised, reaffirming the known phenotype with complete failure of embryonic haematopoiesis. Analysing the Tal1-/- endothelial cluster shows definitively in the in-vivo organism no activation of an alternative cardiac fate programme as was previously postulated from an in-vitro model system. Additionally tSNE mapped Brachyury cells into the void in the FLK1+ dataset and identified a node like population. Loss of spacial context remains an Achilles’ heel of single cell protocols. Generation of the single cell suspensions leads to disruption of cellular contacts and loss of any spacial information. A novel method is described which uses tSNE of bulk spacial data to pre- condition a tSNE map upon which single cells can then be computationally positioned reconstructing spacial context. To model gene interactions and perturbations from single-cell data, a hybrid feed-forward deep neural network was trained on branching pseudo-temporally arranged single cell qPCR data of in-vivo wild-type murine developmental haematopoiesis. Strikingly despite the model never having ‘seen’ a mutant, in-silico gene perturbations in the deep neural network are able to faithfully reproduce the Tal1-/- phenotype. In summary the use of single cell transcriptomics to probe early murine embryology com- bined with development of new methods has uncovered a novel pathway in embryonic haematopoietic development and allowed in-silico reconstruction of a short period of early embryonic haematopoiesis. Critically these methods have broad application within the fields of developmental and stem cell biology.Wellcome Trust GRANT_NUMBER: 103392/Z/13/A GRANT_NUMBER: 103392/Z/13/