GPU implementation of block transforms

Traditionally, intensive floating-point computational ability of Graphics Processing Units (GPUs) has been mainly limited for rendering and visualization application by architecture and programming model. However, with increasing programmability and architecture progress, GPUs inherent massively parallel computational ability have become an essential part of today\u27s mainstream general purpose (non-graphical) high performance computing system. It has been widely reported that adapted GPU-based algorithms outperform significantly their CPU counterpart. The focus of the thesis is to utilize NVIDIA CUDA GPUs to implement orthogonal transforms such as signal dependent Karhunen-Loeve Transform and signal independent Discrete Cosine Transform. GPU architecture and programming model are examined. Mathematical preliminaries of orthogonal transform, eigen-analysis and algorithms are re-visited. Due to highly parallel structure, GPUs are well suited to such computation. Further, the thesis examines multiple implementations schemes and configuration, measurement of performance is provided. A real time processing display application frame is developed to visually exhibit GPU compute capability

Effect of soundscape on emotional response in an urban acoustical environment

The emotional response in soundscapes has long been the centre of the soundscape field. Despite the efforts of researcher and practitioners, the results on emotional response remain fragmented and inconsistent. This thesis aims to systematically explore the emotional responses in soundscape. By adopting theory from emotional study, this thesis aims to view the subjective assessment of the acoustical environment through the lens of emotional processes. The thesis started with a case study (Chapter 4) to identify and explore all acoustical and environmental stimuli that have the potential to influence emotional responses. The case study was conducted on-site and provided a preliminary examination of the identified stimuli. The identified stimuli included weather conditions (daylight difference and thermal conditions) and sound types (street music, shop music, traffic sound, machinery sound and fountain sounds). Six mood states were studied: anger; confusion; depression; fatigue; tension; and vigour. The results showed that lighting and thermal condition do not impact people’s mood states; music from street performances reduces negative mood states; music from shops increases negative mood states, especially tension; and nature sounds have a non-significant influence on mood states, as do monotonous sounds such as traffic and machinery. The second and third studies (Chapters 5 and 6, respectively) further examined the stimuli that were found to have a significant influence on emotional responses in the initial case study in the laboratory setting. With the controlled environment of the laboratory, the results of the two studies eliminated bias from the result of the initial case study. The study in Chapter 5 focused on the effect of sound types on emotional responses and the study in Chapter 6 focused on the environmental context and aimed to identify their effect on emotional responses when perceiving acoustical environments

Revisiting Hybridization Kinetics with Improved Elementary Step Simulation

Nucleic acid strands, which react by forming and breaking Watson-Crick base pairs, can be designed to form complex nanoscale structures or devices. Controlling such systems requires accurate predictions of the reaction rate and of the folding pathways of interacting strands. Simulators such as Multistrand model these kinetic properties using continuous-time Markov chains (CTMCs), whose states and transitions correspond to secondary structures and elementary base pair changes, respectively. The transient dynamics of a CTMC are determined by a kinetic model, which assigns transition rates to pairs of states, and the rate of a reaction can be estimated using the mean first passage time (MFPT) of its CTMC. However, use of Multistrand is limited by its slow runtime, particularly on rare events, and the quality of its rate predictions is compromised by a poorly-calibrated and simplistic kinetic model. The former limitation can be addressed by constructing truncated CTMCs, which only include a small subset of states and transitions, selected either manually or through simulation. As a first step to address the latter limitation, Bayesian posterior inference in an Arrhenius-type kinetic model was performed in earlier work, using a small experimental dataset of DNA reaction rates and a fixed set of manually truncated CTMCs, which we refer to as Assumed Pathway (AP) state spaces. In this work we extend this approach, by introducing a new prior model that is directly motivated by the physical meaning of the parameters and that is compatible with experimental measurements of elementary rates, and by using a larger dataset of 1105 reactions as well as larger truncated state spaces obtained from the recently introduced stochastic Pathway Elaboration (PE) method. We assess the quality of the resulting posterior distribution over kinetic parameters, as well as the quality of the posterior reaction rates predicted using AP and PE state spaces. Finally, we use the newly parameterised PE state spaces and Multistrand simulations to investigate the strong variation of helix hybridization reaction rates in a dataset of Hata et al. While we find strong evidence for the nucleation-zippering model of hybridization, in the classical sense that the rate-limiting phase is composed of elementary steps reaching a small "nucleus" of critical stability, the strongly sequence-dependent structure of the trajectory ensemble up to nucleation appears to be much richer than assumed in the model by Hata et al. In particular, rather than being dominated by the collision probability of nucleation sites, the trajectory segment between first binding and nucleation tends to visit numerous secondary structures involving misnucleation and hairpins, and has a sizeable effect on the probability of overcoming the nucleation barrier

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure

Modulation of Bile Acid Metabolism to Improve Plasma Lipid and Lipoprotein Profiles

New drugs targeting bile acid metabolism are currently being evaluated in clinical studies for their potential to treat cholestatic liver diseases, non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Changes in bile acid metabolism, however, translate into an alteration of plasma cholesterol and triglyceride concentrations, which may also affect cardiovascular outcomes in such patients. This review attempts to gain insight into this matter and improve our understanding of the interactions between bile acid and lipid metabolism. Bile acid sequestrants (BAS), which bind bile acids in the intestine and promote their faecal excretion, have long been used in the clinic to reduce LDL cholesterol and, thereby, atherosclerotic cardiovascular disease (ASCVD) risk. However, BAS modestly but consistently increase plasma triglycerides, which is considered a causal risk factor for ASCVD. Like BAS, inhibitors of the apical sodium-dependent bile acid transporter (ASBTi’s) reduce intestinal bile acid absorption. ASBTi’s show effects that are quite similar to those obtained with BAS, which is anticipated when considering that accelerated faecal loss of bile acids is compensated by an increased hepatic synthesis of bile acids from cholesterol. Oppositely, treatment with farnesoid X receptor agonists, resulting in inhibition of bile acid synthesis, appears to be associated with increased LDL cholesterol. In conclusion, the increasing efforts to employ drugs that intervene in bile acid metabolism and signalling pathways for the treatment of metabolic diseases such as NAFLD warrants reinforcing interactions between the bile acid and lipid and lipoprotein research fields. This review may be considered as the first step in this process

Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design

A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer-Aided Design (TCAD)-augmented ML for semiconductor structural variation identification and inverse design, without using domain expertise. TCAD-augmented ML utilizes TCAD simulations to generate sufficient data for ML model development when experimental data are inadequate. The ML model can then be used to identify semiconductor structural variation for given experimental electrical measurements. In this study, the variation of layer thicknesses in the p-i-n diode is used as a demonstration. An ML model is developed to predict the diode layer thicknesses based on a given Current-Voltage (IV) curve. Although the variations of interest can be incorporated easily in TCAD simulations to generate ML training data, the TCAD-augmented ML model generally is overfitted and cannot predict the variations in experiment well due to hidden variables which also alters the IV curves. We show that by using an autoencoder, this problem can be solved. To verify the effectiveness, another set of TCAD simulation data is generated with hidden variables (dopant concentration variation) to emulate experimental data. Testing on the second set of data shows that the proposed model can avoid overfitting and has up to 15 times improvement in accuracy in thickness prediction. Moreover, this model is used successfully to perform inverse design and can capture an underlying physics that cannot be described by a simple physical parameter