Composing in English: a study of the effects of L1 or L2 planning and topic choice by Japanese learners of English

Often when teaching oral communication, great emphasis is placed on the use of target English language only in the classroom. Reasons often given to defend this policy include the use of L1 in learning English causes unwanted language interference and extended “thinking-time” slowing down a conversation. However this may not be the best policy when producing L2 writing, particularly in the early planning stage where the use of L1 might in fact reduce cognitive loads on L2 writers especially if the topic of the writing is linked to a writer’s L1 and may be best recalled in L1. This PhD study explores the questions and reservations regarding the optimum methods of planning an English essay by Japanese writers of L2 English, both in the UK and in Japan, at intermediate and advanced proficiency levels, with particular focus on the variables of language of planning and topic choice The overarching aims of this PhD study are * To investigate whether planning in L1 about an L1 related topic or planning in L2 about an L2 related topic (language and topic match conditions) enhances L1 Japanese writers’ final essay texts in L2 English. * To investigate whether topic choice independent of planning language, or planning language independent of topic choice (language and topic mismatch conditions) have any impact on plans or resulting L2 English final essay texts. This investigation takes place in three common contexts in which L1 Japanese writers of L2 English operate. The design of the study and methods used to collect, analyse, discuss and compare data are done both quantitatively and qualitatively, that is empirically and also hermeneutically

Category Independent Object Proposals Using Quantum Superposition

A vast amount of digital images and videos are continually being generated and shared across the Internet. An important step towards utilizing this ‘big data’ and deducing meaningful information from its visual contents, is to detect the presence of objects belonging to a particular class in digital images. Earlier computer vision algorithms devised for this purpose exhaustively search the entire image space for detecting objects belonging to a particular class. Object proposals aim to reduce this search space by proposing probable locations of objects in the image beforehand. This paves the way for efficiently using more computationally expensive and sophisticated detection algorithms. Conventional approaches to generating object proposals have revolved around learning a scoring function from the characteristics of objects in ground truth annotations of images. In this thesis, we propose a novel category independent proposal generation framework that is unsupervised and inspired by the psycho-visual analysis of human visual system where the search for objects gradually transitions from the most salient parts of a scene to comparatively non-salient regions. We use a state-of-the-art visual saliency estimation technique which proposes a unique relationship between spectral clustering and quantum mechanics. We improve this method by exploiting for the first time, the quantum superposition principle, to extend the search of objects beyond the salient ones. We also propose an unsupervised scoring strategy that does not incorporate any prior information about the spatial, color or textural features of objects. Experimental results have proved that our proposed methodology achieves comparable results with the contemporary state-of-the-art methods. Our unsupervised scoring strategy is shown to outperform, in some cases, the supervised frameworks employed by other methods. Moreover, it also enables us to achieve a three-fold decrease in the number of proposals while keeping the loss of recall to less than 3%. The success of our proposed methodology opens the door to a research direction where quantum mechanical principles can be utilized to enable computer vision algorithms to find objects in digital images without having any prior knowledge about them

Self-Organized Operational Neural Networks for Severe Image Restoration Problems

Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image restoration and has outperformed the traditional non-local class of methods. However, the top-performing networks are generally composed of many convolutional layers and hundreds of neurons, with trainable parameters in excess of several millions. We claim that this is due to the inherent linear nature of convolution-based transformation, which is inadequate for handling severe restoration problems. Recently, a non-linear generalization of CNNs, called the operational neural networks (ONN), has been shown to outperform CNN on AWGN denoising. However, its formulation is burdened by a fixed collection of well-known nonlinear operators and an exhaustive search to find the best possible configuration for a given architecture, whose efficacy is further limited by a fixed output layer operator assignment. In this study, we leverage the Taylor series-based function approximation to propose a self-organizing variant of ONNs, Self-ONNs, for image restoration, which synthesizes novel nodal transformations onthe-fly as part of the learning process, thus eliminating the need for redundant training runs for operator search. In addition, it enables a finer level of operator heterogeneity by diversifying individual connections of the receptive fields and weights. We perform a series of extensive ablation experiments across three severe image restoration tasks. Even when a strict equivalence of learnable parameters is imposed, Self-ONNs surpass CNNs by a considerable margin across all problems, improving the generalization performance by up to 3 dB in terms of PSNR

Effects of calibration uncertainties on the detection and parameter estimation of isotropic gravitational-wave backgrounds

Gravitational-wave backgrounds are expected to arise from the superposition of gravitational wave signals from a large number of unresolved sources and also from the stochastic processes that occurred in the Early universe. So far, we have not detected any gravitational wave background, but with the improvements in the detectors' sensitivities, such detection is expected in the near future. The detection and inferences we draw from the search for a gravitational-wave background will depend on the source model, the type of search pipeline used, and the data generation in the gravitational-wave detectors. In this work, we focus on the effect of the data generation process, specifically the calibration of the detectors' digital output into strain data used by the search pipelines. Using the calibration model of the current LIGO detectors as an example, we show that for power-law source models and calibration uncertainties $\lesssim 10 \%$, the detection of isotropic gravitational wave background is not significantly affected. We also show that the source parameter estimation and upper limits calculations get biased. For calibration uncertainties of $\lesssim 5 \%$, the biases are not significant ($\lesssim 2 \%$), but for larger calibration uncertainties, they might become significant, especially when trying to differentiate between different models of isotropic gravitational-wave backgrounds.Comment: 11 pages, 7 figure

Diluted Federalism, the Cause of Spoiled Nationalism: a study of historical facts of weak federalism in Pakistan

The study covers the issues that Pakistan faced and is facing owing to the absence of federalism. This absence is becoming the cause of nonappearance of nationalism among the people of small provinces. The reasons for the obvious segmentation of the society have been explored. People are disintegrated and are indulged in pursing their regional status and goals. Their preference is tobe recognized by their region rather by their country. The study has also discovered the major causes of tremulous federalism in Pakistan and how they affected the community as a whole. The role of the regional/local politicians has also been observed throughout the study especially with reference to Balochistan. Some recommendations to bring federalism back in the country have been put forth. The terms ‘federal system’ and ‘federalism’ have been used interchangeably

Road traffic injuries in Rawalpindi city, Pakistan.

Data on road traffic accident (RTA) injuries and their outcome are scarce in Pakistan. This study assessed patterns of RTA injuries reported in Rawalpindi city using standard surveillance methods. All RTA injury patients presenting to emergency departments of 3 tertiary care facilities from July 2007 to June 2008 were included. RTA injuries (n = 19 828) accounted for 31.7% of all injuries. Among children aged 0-14 years females suffered twice as many RTA injuries as males (21.3% versus 11.4%), whereas this trend reversed for the age group 15-24 years (41.9% versus 21.7%). One-fifth of injuries were either fractures or concussion. Severity and outcome of injuries were worse for the age group 45 years and older. For every road traffic death in Rawalpindi city, 29 more people were hospitalized and 177 more received emergency department care. These results suggest the need for better RTA injury surveillance to identify preventive and control measures for the increasingly high road disease burden in this city