Remotely Operated Aerial Vehicles and Their Applications

This project examines relevant designs and applications of unmanned aerial vehicles (UAVs). We propose UAV design solutions, which can be refined and incorporated into emergency medical services. Mathematical and engineering concepts are used to select the design solutions. We believe that the proposed design solutions will enhance the quality of care in emergency medical services

A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.Comment: Accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 202

Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

Accepted by COLING 2020, final camera ready versionPreprin

Symmetric Sparse Boolean Matrix Factorization and Applications

In this work, we study a variant of nonnegative matrix factorization where we wish to find a symmetric factorization of a given input matrix into a sparse, Boolean matrix. Formally speaking, given $\mathbf{M}\in\mathbb{Z}^{m\times m}$, we want to find $\mathbf{W}\in\{0,1\}^{m\times r}$ such that $\| \mathbf{M} - \mathbf{W}\mathbf{W}^\top \|_0$ is minimized among all $\mathbf{W}$ for which each row is $k$-sparse. This question turns out to be closely related to a number of questions like recovering a hypergraph from its line graph, as well as reconstruction attacks for private neural network training. As this problem is hard in the worst-case, we study a natural average-case variant that arises in the context of these reconstruction attacks: $\mathbf{M} = \mathbf{W}\mathbf{W}^{\top}$ for $\mathbf{W}$ a random Boolean matrix with $k$-sparse rows, and the goal is to recover $\mathbf{W}$ up to column permutation. Equivalently, this can be thought of as recovering a uniformly random $k$-uniform hypergraph from its line graph. Our main result is a polynomial-time algorithm for this problem based on bootstrapping higher-order information about $\mathbf{W}$ and then decomposing an appropriate tensor. The key ingredient in our analysis, which may be of independent interest, is to show that such a matrix $\mathbf{W}$ has full column rank with high probability as soon as $m = \widetilde{\Omega}(r)$, which we do using tools from Littlewood-Offord theory and estimates for binary Krawtchouk polynomials.Comment: 33 pages, to appear in Innovations in Theoretical Computer Science (ITCS 2022), v2: updated ref

MIR-GAN : Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition

PreprintPublisher PD

Noise-aware Speech Enhancement using Diffusion Probabilistic Model

5 pages, 2 figuresPreprin

A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification

Acknowledgment This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P011829/1).PreprintPublisher PD

Postnatal ontogenesis of clock genes in mouse suprachiasmatic nucleus and heart

<p>Abstract</p> <p>Background</p> <p>The master clock within the hypothalamic suprachiasmatic nucleus (SCN) synchronizing clocks in peripheral tissues is entrained by the environmental condition, such as the light-dark (LD) cycle. The mechanisms of circadian clockwork are similar in both SCN and peripheral tissues. The aim of the present work was to observe the profiles of clock genes expression in mouse central and peripheral tissues within postnatal day 5 (P5). The daily expression of four clock genes mRNA (Bmal1, Per2, Cry1 and Rev-erb alpha) in mouse SCN and heart was measured at P1, P3 and P5 by real-time PCR.</p> <p>Results</p> <p>All the studied mice clock genes began to express in a circadian rhythms manner in heart and SCN at P3 and P5 respectively. Interestingly, the daily rhythmic phase of some clock genes shifted during the postnatal days. Moreover, the expressions of clock genes in heart were not synchronized with those in SCN until at P5.</p> <p>Conclusion</p> <p>The data showed the gradual development of clock genes in SCN and a peripheral tissue, and suggested that development of clock genes differed between in the SCN and the heart. Judging from the mRNA expression, it was possible that the central clock synchronized the peripheral clock as early as P5.</p

Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition

12 pages, 5 figures, Accepted by IJCAI 2023Preprin