Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing head mounted display VR sickness through dynamic field of view constriction

Although virtual reality (VR) head-mounted displays (HMD) have been in use since the mid-1960s, the surge in public awareness and access to VR had spurred an increased interest in all industries to investigate the potential of VR as an interaction modality associated with high subjective presence. Many challenges need to be addressed through the disciplined application of research methods, especially combating VR sickness, if this potential is to be realised. This Engineering Doctorate thesis reports a series of investigations within the context of real-world development with a partner company (BMT Defence Service, a naval engineering consultant). The primary interest of the thesis is in the potential of VR for developing cases and uses for this technology in training. The target modality of training was a portable set-up, i.e. sitting down with a laptop, HMD and a game controller. This set up would prove beneficial for providing axillary training to personnel who are not always able to receive regular on-board training. It would also prepare people for situations which are difficult to simulate in real-world conditions. Example cases included familiarisation, line of sight tests, hazard recognition and evacuation procedures. An initial study of VR HMD experience in training scenario highlighted VR sickness as a key limiting factor for usability thus focusing the research on identifying and reducing the factors which induce VR sickness. Prior research suggest that static field of view restrictions could help but only at the cost of loss of presence. There were no reported studies of the effects of restricting the field of view dynamically thus this thesis presents two investigations of dynamic Field of View (FOV) constriction triggered by movement in a virtual space. It was hypothesised that a reduction in FOV reduced the induction of VR sickness. The problem with doing so however was that it may negatively influence presence as the change in FOV could distract the user. This thesis reports the development of a method for adjusting FOV to reduce simulator VR without loss of presence. Two dynamic FOV constriction studies are reported. The first failed to demonstrate a clear effect but subjective user reports suggested methodological and experiential issues in its design. Meanwhile, research into a similar method was published at the 3DUI Symposium at IEEE VR 2016. Fernandes & Feiner (2016) [1], who demonstrated that dynamic FOV constriction can reduce VR sickness without compromising presence. However, their work used interaction scenarios with normal walking in an unchallenging virtual environment. Users were not subject to the types of motion which literature suggests are most likely to induce sickness. Consequently, the second DFOV constriction study tested VR sickness reduction in
Algorithms for partitioning well-clustered graphs

Graphs occurring in the real world usually exhibit a high level of order and organisation: higher concentration of edges within the same group of vertices, and lower concentration among different groups. A common way to analyse these graphs is to partition the vertex set of a graph into clusters according to some connectivity measure. Graph clustering has been widely applied to many fields of computer science, from machine learning to bioinformatics and social network analysis. The focus of this thesis is to design and analyse algorithms for partitioning graphs presenting a strong cluster-structure, which we call well-clustered. We first study the spectral properties of the Laplacian matrix of such graphs, and prove a structure theorem that relates the eigenvectors corresponding to the smallest eigenvalues of the Laplacian matrix of a graph to the structure of its clusters. We then harness this theorem to analyse Spectral Clustering, arguably the most popular graph clustering algorithm. We give for the first time approximation guarantees on the number of misclassified vertices by Spectral Clustering when applied to well-clustered graphs. Since Spectral Clustering needs to compute as many eigenvectors of the Laplacian matrix as the number of clusters in the graph, its performance deteriorates as this number grows. We present an algorithm that overcomes this issue without compromising its accuracy. This algorithm runs in time nearly linear in the number of the edges and independently of the number of clusters in the input graph. Finally, we tackle the problem of partitioning a graph whose description is distributed among many sites. We present a distributed algorithm that works in a few synchronous rounds, requires limited communication complexity, and achieves the same guarantees of Spectral Clustering as long as the clusters are balanced in size.
Equivalence of the Erlang-Distributed SEIR Epidemic Model and the Renewal Equation

Most compartmental epidemic models can be represented using the renewal equation. The value of the renewal equation is not widely appreciated in the epidemiological modelling community, perhaps because its equivalence to standard models has not been presented rigorously in nontrivial cases. Here, we provide analytical expressions for the intrinsic generation-interval distribution that must be used in the renewal equation in order to yield epidemic dynamics that are identical to those of the susceptible-exposed-infectious-recovered (SEIR) compartmental model with Erlang-distributed latent and infectious periods. This class of models includes the standard (exponentially distributed) SIR and SEIR models as special cases.
Evaluating error when estimating the loss probability in a packet buffer

In this thesis we explore precision in measurement of buffer overflow and loss probability. We see how buffer overflow probability compares with queuing delay measurements covered in the literature [1]. More specifically, we measure the overflow probability of a packet buffer for various sampling rates to see the effect of sampling rate on the estimation. There are various reasons for measurement in networks; one key context assumed here is Measurement Based Admission Control. We conduct simulation experiments with analytically derived VoIP and bursty traffic parameters, in Matlab, while treating the buffer under consideration as a two-state Markov Chain. We note that estimation error decreases with increase in sampling gap (or in other words precision improves/variance decreases with decrease in sampling rate). We then perform experiments for VoIP and bursty data using NS-2 simulator and record the buffer states generated therein. We see a similar trend of increase in precision with increase in sampling gap. In our simulations, we have mainly considered static traffic passing through the buffer, and we use elastic traffic (TCP) for comparison. We see from our results that that the sampling error becomes constant beyond certain asymptotic level. We thus look into asymptotic error in estimation, for the lowest sampling gap, to establish a lower bound on estimation error for buffer loss probability measurement. We use formulae given in recent literature [2] for computing the experimental and theoretic asymptotic variance of the buffer state traces in our scenarios. We find that the theoretical and experimental asymptotic variance of overflow probability match when sampling a trace of buffer states modelled as a two-state Markov Chain in Matlab. We claim that this is a new approach to computing the lower bound on the measurement of buffer overflow probability, when the buffer states are modelled as a Markov process. Using Markov Chain modelling for buffer overflow we further explore the relationship between sampling rate and accuracy. We find that there is no relationship between sampling gap and bias of estimation. Crucially we go on to show that a more realistic simulation of a packet buffer reveals that the distribution of buffer overflow periods is not always such as to allow simple Markov modelling of the buffer states; while the sojourn periods are exponential for the smaller burst periods, the tail of the distribution does not fit to the same exponential fitting. While our work validates the use of a two-state Markov model for a useful approximation modelling the overflow of a buffer, we have established that earlier work which relies on simple Markovian assumptions will thereby underestimate the error in the measured overflow probabilities.
Advanced techniques for search-based program repair

Debugging and repairing software defects costs the global economy hundreds of billions of dollars annually, and accounts for as much as 50% of programmers' time. To tackle the burgeoning expense of repair, researchers have proposed the use of novel techniques to automatically localise and repair such defects. Collectively, these techniques are referred to as automated program repair. Despite promising, early results, recent studies have demonstrated that existing automated program repair techniques are considerably less effective than previously believed. Current approaches are limited either in terms of the number and kinds of bugs they can fix, the size of patches they can produce, or the programs to which they can be applied. To become economically viable, automated program repair needs to overcome all of these limitations. Search-based repair is the only approach to program repair which may be applied to any bug or program, without assuming the existence of formal specifications. Despite its generality, current search-based techniques are restricted; they are either efficient, or capable of fixing multiple-line bugs---no existing technique is both. Furthermore, most techniques rely on the assumption that the material necessary to craft a repair already exists within the faulty program. By using existing code to craft repairs, the size of the search space is vastly reduced, compared to generating code from scratch. However, recent results, which show that almost all repairs generated by a number of search-based techniques can be explained as deletion, lead us to question whether this assumption is valid. In this thesis, we identify the challenges facing search-based program repair, and demonstrate ways of tackling them. We explore if and how the knowledge of candidate patch evaluations can be used to locate the source of bugs. We use software repository mining techniques to discover the form of a better repair model capable of addressing a greater number of bugs. We conduct a theoretical and empirical analysis of existing search algorithms for repair, before demonstrating a more effective alternative, inspired by greedy algorithms. To ensure reproducibility, we propose and use a methodology for conducting high-quality automated program research. Finally, we assess our progress towards solving the challenges of search-based program repair, and reflect on the future of the field.
Lagrangian Relaxation Based Gate Sizing With Clock Skew Scheduling - A Fast and Effective Approach

Recent work has established Lagrangian relaxation (LR) based gate sizing as state-of-the-art providing the best power reduction with low run time. Gate sizing has limited potential to reduce the power when the timing constraints are tight. By adjusting the arrival times of clock signals (clock skew scheduling), the timing constraints can be relaxed facilitating more power reduction. Previous LR attempts at simultaneous gate sizing and skew scheduling solved a minimum-cost network flow problem for updating the Lagrange multipliers in each LR iteration, and for optimality assumed continuous sizes with convex delay models. We propose an alternative approach, modifying a LR discrete gate sizing formulation with table lookup non-convex delay models, which are more accurate for modern process technologies. For the Lagrange multiplier update, we use a projection heuristic that is much faster than solving the minimum cost network flow problem. On the ISPD 2012 gate sizing contest benchmark suite, our proposed approach outperforms the previous min-cost flow based approach by saving 5.3% more power and is 70x faster. Compared to sizing alone with the state-of-the-art LR gate sizer, skew scheduling with sizing saves 19.7% more power with a small runtime penalty.
A cross-cultural analysis of music structure

Music signal analysis is a research field concerning the extraction of meaningful information from musical audio signals. This thesis analyses the music signals from the note-level to the song-level in a bottom-up manner and situates the research in two Music information retrieval (MIR) problems: audio onset detection (AOD) and music structural segmentation (MSS). Most MIR tools are developed for and evaluated on Western music with specific musical knowledge encoded. This thesis approaches the investigated tasks from a cross-cultural perspective by developing audio features and algorithms applicable for both Western and non-Western genres. Two Chinese Jingju databases are collected to facilitate respectively the AOD and MSS tasks investigated. New features and algorithms for AOD are presented relying on fusion techniques. We show that fusion can significantly improve the performance of the constituent baseline AOD algorithms. A large-scale parameter analysis is carried out to identify the relations between system configurations and the musical properties of different music types. Novel audio features are developed to summarise music timbre, harmony and rhythm for its structural description. The new features serve as effective alternatives to commonly used ones, showing comparable performance on existing datasets, and surpass them on the Jingju dataset. A new segmentation algorithm is presented which effectively captures the structural characteristics of Jingju. By evaluating the presented audio features and different segmentation algorithms incorporating different structural principles for the investigated music types, this thesis also identifies the underlying relations between audio features, segmentation methods and music genres in the scenario of music structural analysis.
Prevalence of Molar Incisor Hypomineralization among School Children Aged 9 to 12 Years in Virajpet, Karnataka, India

BACKGROUND: The molar incisor hypomineralization (MIH) is defined as a qualitative defect of the enamel characterised by the progressive and simultaneous hypomineralization of the enamel structure of the first permanent molars which is of systemic origin, which may be associated frequently with incisors. Although the reported prevalence of MIH ranges from 2.4% to 40.2% worldwide, very little data is available from India. AIM: To determine the prevalence of molar incisor hypomineralization among school children aged 9 to 12 years in virajpet, Karnataka. METHODS: This cross-sectional descriptive study consisted of 1600 school children aged 9-12 years selected by stratified cluster sampling procedure. The European Academy of Pediatric Dentistry criteria were followed for MIH diagnosis. Chi-square test was used to analyse the categorical data. P ≤ 0.05 was considered for statistical significance. RESULTS: The prevalence of MIH is 13.12% with no gender predilection. Ten-year-old children showed the highest prevalence (15%) among all the age group. Majority of children with MIH (70.2%) have lesions in both molars and incisors with demarcated opacities and atypical restorations being the most frequent defect type. CONCLUSION: Prevalence of MIH was 13.12% in the 9-12-year child population in Virajpet. There is a need for a proper planned preventive and restorative program about the increasing prevalence of MIH.
Prevalence and Correlation between Diet and Dysmenorrhea among High School and College Students in Saint Vincent and Grenadines

BACKGROUND: Dysmenorrhea occurs as one of the symptoms of menstruation. While not necessarily a condition that plagues every woman, it is known to cause significant distress. Absenteeism from school and work as well as general discomfort are some of its adverse effects. AIM: This study aims to investigate the effects of certain diets on the prevalence and severity of dysmenorrhea. METHODS: Questionnaires was given to 478 women ranging from ages 1-55. The survey was centred around the age of menarche, presence and incidence of dysmenorrhea as well as how it is related to certain diets. RESULTS: Majority of the participants (81.74%) belonged to the age groups of 11-15 and 16-20. 45.5% of the participants attested to dysmenorrhea at each menstrual cycle. statistical correlation between diet and dysmenorrhea was insignificant (p > 0.05). Consumption of caffeinated beverages correlated with dysmenorrhea (p < 0.05). Although not statistically significant (p > 0.05), the study reported dysmenorrhea in a large proportion of participants who consumed high quantities of sugars. CONCLUSION: No relationship was established between diet and the incidence and severity of dysmenorrhea amongst the sample screened in Saint Vincent and the Grenadines. However, it appears that diet high in sugars might benefit from further research.
Autonomic Dysfunction in α-Synucleinopathies

The α-synucleinopathies are a group of neurodegenerative diseases characterized by abnormal accumulation of insoluble α-synuclein in neurons and glial cells, comprising Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Although varying in prevalence, symptom patterns, and severity among disorders, all α-synucleinopathies have in common autonomic nervous system dysfunctions, which reduce quality of life. Frequent symptoms among α-synucleinopathies include constipation, urinary and sexual dysfunction, and cardiovascular autonomic symptoms such as orthostatic hypotension, supine hypertension, and reduced heart rate variability. Symptoms due to autonomic dysfunction can appear before motor symptom onset, particularly in MSA and PD, hence, detection and quantitative analysis of these symptoms can enable early diagnosis and initiation of treatment, as well as identification of at-risk populations. While patients with PD, DLB, and MSA show both central and peripheral nervous system involvement of α-synuclein pathology, pure autonomic failure (PAF) is a condition characterized by generalized dysregulation of the autonomic nervous system with neuronal cytoplasmic α-synuclein inclusions in the peripheral autonomic small nerve fibers. Patients with PAF often present with orthostatic hypotension, reduced heart rate variability, anhydrosis, erectile dysfunction, and constipation, without motor or cognitive impairment. These patients also have an increased risk of developing an α-synucleinopathy with central involvement, such as PD, DLB, or MSA in later life, possibly indicating a pathophysiological disease continuum. Pathophysiological aspects, as well as developments in diagnosing and treating dysautonomic symptoms in patients with α-synucleinopathies are discussed in this review.
Blood Vessels as Regulators of Neural Stem Cell Properties

In the central nervous system (CNS), a precise communication between the vascular and neural compartments is essential for proper development and function. Recent studies demonstrate that certain neuronal populations secrete various molecular cues to regulate blood vessel growth and patterning in the spinal cord and brain during development. Interestingly, the vasculature is now emerging as a critical component that regulates stem cell niches during neocortical development, as well as during adulthood. In this review article, we will first provide an overview of blood vessel development and maintenance in embryonic and adult neurogenic niches. We will also summarize the current understanding of how blood vessel-derived signals influence the behavior of neural stem cells (NSCs) during early development as well as in adulthood, with a focus on their metabolism.
A Velocity Map Imaging Study of the Reactions of O+ (4S) With CH4

We present a velocity map imaging study of the key ion-molecule reactions occurring in the O+(4S3/2) + CH4 (X 1A1) system at collision energies of 1.84 and 2.14 eV. In addition to charge transfer to form CH4+ (X 2B2), we also present data on formation of CH3+ (X 1A1'), for which the experimentally determined images provide clear confirmation that the products arise from dissociative charge transfer rather than hydride transfer. Experimental data are also presented on the formation of HCO+ through a transient [OCH4]+ complex living many rotational periods. Plausible reaction pathways and intermediate structures are presented to give insight into the routes for formation of these reaction products.
Trait Mindfulness and Functional Connectivity in Cognitive and Attentional Resting State Networks

Mindfulness has been described as an orienting of attention to the present moment, with openness and compassion. Individuals displaying high trait mindfulness exhibit this tendency as a more permanent personality attribute. Given the numerous physical and mental health benefits associated with mindfulness, there is a great interest in understanding the neural substrates of this trait. The purpose of the current research was to examine how individual differences in trait mindfulness associated with functional connectivity in five resting-state networks related to cognition and attention: the default mode network (DMN), the salience network (SN), the central executive network (CEN), and the dorsal and ventral attention networks (DAN and VAN). Twenty-eight undergraduate participants completed the Five-Facet Mindfulness Questionnaire (FFMQ), a self-report measure of trait mindfulness which also provides scores on five of its sub-categories (Observing, Describing, Acting with Awareness, Non-judging of Inner Experience, and Non-reactivity to Inner Experience). Participants then underwent a structural MRI scan and a 7-min resting state functional MRI scan. Resting-state data were analyzed using independent-component analyses. An analysis of covariance (ANCOVA) was performed to determine the relationship between each resting state network and each FFMQ score. These analyses indicated that: (1) trait mindfulness and its facets showed increased functional connectivity with neural regions related to attentional control, interoception, and executive function; and (2) trait mindfulness and its facets showed decreased functional connectivity with neural regions related to self-referential processing and mind wandering. These patterns of functional connectivity are consistent with some of the benefits of mindfulness—enhanced attention, self-regulation, and focus on present experience. This study provides support for the notion that non-judgmental attention to the present moment facilitates the integration of regions in neural networks that are related to cognition, attention, and sensation.
Ranking Hits From Saturation Transfer Difference Nuclear Magnetic Resonance–Based Fragment Screening

Fragment-based screening is an established route to identify low-molecular-weight molecules to generate high-affinity inhibitors in drug discovery. The affinities of these early hits from fragment screenings require a highly sensitive biophysical screening technique. Saturation transfer difference (STD) nuclear magnetic resonance (NMR) is one of the most popular methods owing to its high sensitivity for low-affinity ligands. It would be highly beneficial if rank-ordering of hits according to their affinity from an initial or counter-screen could be performed—a selection criterion found in the literature. We applied Complete Relaxation and Conformational Exchange Matrix (CORCEMA) theory adapted for saturation transfer (ST) measurements (CORCEMA-ST) calculations to predict STD NMR results from a large set of fragment/receptor pairs to investigate the boundaries under which the assumption holds true that a high STD effect can be applied to select for higher-affinity fragments. Overall, we come to the conclusion that this assumption is invalid.