Measurement of Inclusive Ds, D0, and J/ Rates and Determination of the B Production Fraction in b Events at the (5S) Resonance

journal articl

Biomedical time series analysis based on bag-of-words model

This research proposes a number of new methods for biomedical time series classification and clustering based on a novel Bag-of-Words (BoW) representation. It is anticipated that the objective and automatic biomedical time series clustering and classification technologies developed in this work will potentially benefit a wide range of applications, such as biomedical data management, archiving, retrieving, and disease diagnosis and prognosis in the future

System Optimisation for Multi-access Edge Computing Based on Deep Reinforcement Learning

Multi-access edge computing (MEC) is an emerging and important distributed computing paradigm that aims to extend cloud service to the network edge to reduce network traffic and service latency. Proper system optimisation and maintenance are crucial to maintaining high Quality-of-service (QoS) for end-users. However, with the increasing complexity of the architecture of MEC and mobile applications, effectively optimising MEC systems is non-trivial. Traditional optimisation methods are generally based on simplified mathematical models and fixed heuristics, which rely heavily on expert knowledge. As a consequence, when facing dynamic MEC scenarios, considerable human efforts and expertise are required to redesign the model and tune the heuristics, which is time-consuming. This thesis aims to develop deep reinforcement learning (DRL) methods to handle system optimisation problems in MEC. Instead of developing fixed heuristic algorithms for the problems, this thesis aims to design DRL-based methods that enable systems to learn optimal solutions on their own. This research demonstrates the effectiveness of DRL-based methods on two crucial system optimisation problems: task offloading and service migration. Specifically, this thesis first investigate the dependent task offloading problem that considers the inner dependencies of tasks. This research builds a DRL-based method combining sequence-to-sequence (seq2seq) neural network to address the problem. Experiment results demonstrate that our method outperforms the existing heuristic algorithms and achieves near-optimal performance. To further enhance the learning efficiency of the DRL-based task offloading method for unseen learning tasks, this thesis then integrates meta reinforcement learning to handle the task offloading problem. Our method can adapt fast to new environments with a small number of gradient updates and samples. Finally, this thesis exploits the DRL-based solution for the service migration problem in MEC considering user mobility. This research models the service migration problem as a Partially Observable Markov Decision Process (POMDP) and propose a tailored actor-critic algorithm combining Long-short Term Memory (LSTM) to solve the POMDP. Results from extensive experiments based on real-world mobility traces demonstrate that our method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms on various MEC scenarios

A critical assessment of work hardening in TWIP steels through micropillar compression

Mechanical twinning and dislocation slip of a TWIP steel were investigated by compression of micropillars in twin-preferred orientations, i.e. [1 1 1] and [4 4 33] , and slip-preferred orientations, i.e. [1 0 0] and [20 2 9] . The individual effects of twinning and slip on work-hardening behaviour were investigated. The orientation that showed the highest work hardening rate was typified by the concurrent activation of multiple slip systems. The specimens with the lowest work hardening rates deformed principally by mechanical twinning, followed by dislocation slip after the twinning strain was exhausted. It has therefore been concluded that dislocation slip, rather than mechanical twinning, was responsible for the high work hardening rates observed in the present specimens. A comparison of these results with macroscopic single crystal and polycrystal behaviour is also discussed

Effect of Stress Change on Water Inflows to Underground Excavations

The change in ground stresses in a rock mass has a significant influence on the aperture of rock joints, and therefore, on the water inflow to mining excavations. In this . paper, the distinct element code UDEC (Itasca Consulting Group, 1993) has been applied to simulate water flow through simplified joint geometries, considering appropriate joint apertures and in-situ stress ratios. For a rectangular excavation, a fully coupled hydro-mechanical analysis has been conducted. where joint conductivity is dependent on the change in apertures under applied stress, and conversely, the domain water pressures affect the deformation behaviour of the rock mass around the excavation. The numerical results verify that the joint aperture depends upon the magnitude of normal stress acting on the joint surface, and consequently, affects the flow through joints. This study also shows that the ratio of in-situ horizontal to vertical stress plays a major role in controlling the volume of total inflow towards the excavation

Shape-VQ-based lossless hybrid ADPCM/DCT coder

The discrete cosine transform (DCT) has been shown as an optimum encoder for sharp edges in an image (Andrew and Ogunbona, 1997). A conventional lossless coder employing differential pulse code modulation (DPCM) suffers from significant deficiencies in regions of discontinuity, because the simple model cannot capture the edge information. This problem can be partially solved by partitioning the image into blocks that are supposedly statistically stationary. A hybrid lossless adaptive DPCM (ADPCM)/DCT coder is presented, in which the edge blocks are encoded with DCT, and ADPCM is used for the non-edge blocks. The proposed scheme divides each input image into small blocks and classifies them, using shape vector quantisation (VQ), as either edge or smooth. The edge blocks are further vector quantised, and the side information of the coefficient matrix is saved through the shape-VQ index. Evaluation of the compression performance of the proposed method reveals its superiority over other lossless coders

Magnetic and electrical response of Co-doped La0.7Ca0.3MnO3 manganites/insulator system

Magnetic and electrical response of Co-doped La0.7Ca0.3MnO3 manganites/insulator syste

Frailty and socioeconomic status: a systematic review

The relationship between frailty and socioeconomic status has been widely explored in the literature. A deeper understanding toward the underlying mechanism is required to further assist policy makers in reducing the inequalities. The objective of this study is to systematically review evidence investigating the direct relationship between frailty and socioeconomic status. The review was conducted following the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Among the included studies, 52.38% explored the pattern of frailty in age and 42.86% explored mediators as the pathway variables. With various measures and methodologies, included studies did not point to the same conclusions. In terms of the pattern of frailty in age, we found evidence for the age as leveller hypothesis, the status maintenance hypothesis and the cumulative advantage hypothesis. The included mediators differed across studies. However, we found that these mediators can be categorised into behaviours, health, social factors, material resources and mental status. These categories indicate the important aspects to consider for policies aiming at reducing the inequalities in frailty. To obtain a full picture of the underlying mechanism, future research should harmonise different measures for frailty and socioeconomic indicators and apply more comprehensive sets of mediators

Probabilistic latent semantic analysis for multichannel biomedical signal clustering

This letter extends probabilistic latent semantic analysis (pLSA) for multichannel biomedical signal clustering. The proposed multichannel pLSA (M-pLSA) models a multichannel signal as a generative process of local segments. It directly represents a biomedical signal as a mixture of latent topics based on the assumption that local segments extracted from each channel are conditionally independent given the topics. The categories of biomedical signals are automatically discovered in an unsupervised way. Experimental results demonstrate that the proposed M-pLSA model outperforms previous state-of-the-art methods and is robust to noise contamination

Magnetocaloric effect in HoMn2Si2 compound with multiple magnetic phase transitions

Magnetocaloric effect in HoMn2Si2 compound with multiple magnetic phase transition