A fundamental problem of fault-tolerant distributed computing is for the reliable processes to reach a consensus. For a synchronous distributed system of n processes with up to t crash failures and f failures actually occur, we prove using a straightforward bivalency argument that the lower bound for reaching uniform consensus is (f + 2)-rounds in the case of 0 < f ≤ t −2, and a new lower bound for early-stopping consensus is min (t + 1, f + 2)-rounds where 0 ≤ f ≤ t. Both proofs are simpler and more intuitive than the traditional methods such as backward induction. Our main contribution is that we solve the open problem of proving that bivalency can be applied to show the (f + 2)-rounds lower bound for synchronous uniform consensus. / Singapore-MIT Alliance (SMA)

<p>In the 3G standards, wireless communication system can support 2 Mb/s. With this data rate, multimedia communication is realized on handset. However, it is expected that new applications will require even higher data rates in future. In order to fulfil the growing requirement of high data rate, 100 Mb/s is considered as the aim of 4G standards. Such high data rate will result in very large power consumption, which is unacceptable considering the current battery capability. Therefore, reducing the power consumption of turbo decoders becomes a major issue to be solved. This report explores new techniques for implementing low power, small area and high throughput turbo decoders.</p>

In this paper, we discuss the consensus problem for synchronous distributed systems with orderly crash failures. For a synchronous distributed system of n processes with up to t crash failures and f failures actually occur, first, we present a bivalency argument proof to solve the open problem of proving the lower bound, min (t + 1, f + 2) rounds, for early-stopping synchronous consensus with orderly crash failures, where t < n - 1. Then, we extend the system model with orderly crash failures to a new model in which a process is allowed to send multiple messages to the same destination process in a round and the failing processes still respect the order specified by the protocol in sending messages. For this new model, we present a uniform consensus protocol, in which all non-faulty processes always decide and stop immediately by the end of f + 1 rounds. We prove that the lower bound of early stopping protocols for both consensus and uniform consensus are f + 1 rounds under the new model, and our proposed protocol is optimal. / Singapore-MIT Alliance (SMA)

In the 3G standards, wireless communication system can support 2 Mb/s. With this data rate, multimedia communication is realized on handset. However, it is expected that new applications will require even higher data rates in future. In order to fulfil the growing requirement of high data rate, 100 Mb/s is considered as the aim of 4G standards. Such high data rate will result in very large power consumption, which is unacceptable considering the current battery capability. Therefore, reducing the power consumption of turbo decoders becomes a major issue to be solved. This report explores new techniques for implementing low power, small area and high throughput turbo decoders.

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Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.

Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.

This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training process. In order to reduce learning costs involving the imbalanced data set of the uneven class distribution, random sampling and k-means clustering are implemented to draw a smaller subset of representatives from the original training data set. Finally, the confidence interval for the AUC is estimated in a non-parametric approach.

Artificial Intelligence (AI) and Computer Vision (CV) have brought the promise of many applications along with many challenges to solve. The majority of current AI research has been dedicated to single-modal data processing meaning they use only one modality such as visual recognition or text recognition. However, real-world challenges are often a combination of different modalities of data such as text, audio and images. This thesis focuses on solving the Visual Question Answering (VQA) problem which is a significant multi-modal challenge. VQA is defined as a computer vision system that when given a question about an image will answer based on an understanding of both the question and image. The goal is improving the training time of VQA models. In this thesis, Look, Read, Reason and Answer (LoRRA), which is a state-of-the-art architecture, is used as the base model. Then, Reduce Uni-modal Biases (RUBi) is applied to this model to reduce the importance of uni- modal biases in training. Finally, an early stopping strategy is employed to stop the training process once the model accuracy has converged to prevent the model from overfitting. Numerical results are presented which show that training LoRRA with RUBi and early stopping can converge in less than 5 hours. The impact of batch size, learning rate and warm up hyper parameters is also investigated and experimental results are presented. / Graduate