High performance latent dirichlet allocation for text mining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space

Thermodynamical properties of dark energy with the equation of state ω=ω0+ω1z% \omega =\omega_{0}+\omega_{1}z

The thermodynamical properties of dark energy are usually investigated with the equation of state $\omega =\omega_{0}+\omega_{1}z$. Recent observations show that our universe is accelerating, and the apparent horizon and the event horizon vary with redshift $z$. When definitions of the temperature and entropy of a black hole are used to the two horizons of the universe, we examine the thermodynamical properties of the universe which is enveloped by the apparent horizon and the event horizon respectively. We show that the first and the second laws of thermodynamics inside the apparent horizon in any redshift are satisfied, while they are broken down inside the event horizon in some redshift. Therefore, the apparent horizon for the universe may be the boundary of thermodynamical equilibrium for the universe like the event horizon for a black hole.Comment: 6 pages, 5 figures, Accepted for publication in Physical Review

Label Mask for Multi-Label Text Classification

One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train language models. On the basis, we assign a different token to each potential label, and randomly mask the token with a certain probability to build a label based Masked Language Model (MLM). We train the MTC and MLM together, further improving the generalization ability of the model. A large number of experiments on multiple datasets demonstrate the effectiveness of our method

Effects of Coronal Magnetic Field Configuration on Particle Acceleration and Release during the Ground Level Enhancement Events in Solar Cycle 24

Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, by a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic field configuration near active regions on particle acceleration and release. Although the active regions both located near the western limb, temporal variations of SEP intensities and energy spectra measured in-situ display different behaviors at early stages. By combining a potential field model, we find the CME in GLE 71 originated below the streamer belt, while in GLE 72 near the edge of the streamer belt. We reconstruct the CME shock fronts with an ellipsoid model based on nearly simultaneous coronagraph images from multi-viewpoints, and further derive the 3D shock geometry at the GLE onset. The highest-energy particles are primarily accelerated in the shock-streamer interaction regions, i.e., likely at the nose of the shock in GLE 71 and the eastern flank in GLE 72, due to quasi-perpendicular shock geometry and confinement of closed fields. Subsequently, they are released to the field lines connecting to near-Earth spacecraft when the shocks move through the streamer cusp region. This suggests that magnetic structures in the corona, especially shock-streamer interactions, may have played an important role in the acceleration and release of the highest-energy particles in the two events.Comment: Accepted for publication in Ap

Urban Feature Extraction within a Complex Urban Area with an Improved 3D-CNN Using Airborne Hyperspectral Data

Article describes how airborne hyperspectral data has high spectral-spatial information, but mining and using this information effectively is still a great challenge. Therefore, a 3D-1D-CNN model was proposed for feature extraction in complex urban with hyperspectral images affected by cloud shadows

Studying the distribution patterns, dynamics and influencing factors of city functional components by gradient analysis

Understanding the spatial distribution characteristics and formation mechanism of urban facilities (city functional components) constitutes the basis of urban layout optimization. Currently, research on the overall distribution of the various types of city functional components is lacking. In this study, by applying the gradient analysis method common in ecology, we considered 13 types of city functional components (80,214 individuals in total) in large, medium and small Chinese cities (9 cities in total) to carry out quantitative analysis of the distribution of components along urban–rural gradients through density distribution curves. The results indicated that: (1) a higher density of city functional components near the city centre revealed an obvious aggregated distribution; (2) the spatial distribution dynamics of city functional components were related to the city size, providing a reference for the rational distribution of components in cities of different sizes; (3) the distribution of city functional components was affected by their ecosystem services. This study offers a new perspective for the application of ecological methods in the examination of the distribution of city functional components

Capsaicin Protects Cardiomyocytes against Anoxia/Reoxygenation Injury via Preventing Mitochondrial Dysfunction Mediated by SIRT1

Capsaicin (Cap) has been reported to have beneficial effects on cardiovascular system, but the mechanisms underlying these effects are still poorly understood. Apoptosis has been shown to be involved in mitochondrial dysfunction, and upregulating expression of SIRT1 can inhibit the apoptosis of cardiomyocytes induced by anoxia/reoxygenation (A/R). Therefore, the aim of this study was to test whether the protective effects of Cap against the injury to the cardiomyocytes are mediated by SIRT1. The effects of Cap with or without coadministration of sirtinol, a SIRT1 inhibitor, on changes induced by A/R in the cell viability, activities of lactate dehydrogenase (LDH), creatine phosphokinase (CPK), levels of intracellular reactive oxygen species (ROS), and mitochondrial membrane potential (MMP), related protein expression, mitochondrial permeability transition pore (mPTP) opening, and apoptosis rate in the primary neonatal rat cardiomyocytes were tested. Cap significantly increased the cell viability, upregulated expression of SIRT1 and Bcl-2, and decreased the LDH and CPK release, generation of ROS, loss of MMP, mPTP openness, activities of caspase-3, release of the cytochrome c, and apoptosis of the cardiomyocytes. Sirtinol significantly blocked the cardioprotective effects of Cap. The results suggest that the protective effects of Cap against A/R-induced injury to the cardiomyocytes are involved with SIRT1

Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits

Qubit initialization is a critical task in quantum computation and communication. Extensive efforts have been made to achieve this with high speed, efficiency and scalability. However, previous approaches have either been measurement-based and required fast feedback, suffered from crosstalk or required sophisticated calibration. Here, we report a fast and high-fidelity reset scheme, avoiding the issues above without any additional chip architecture. By modulating the flux through a transmon qubit, we realize a swap between the qubit and its readout resonator that suppresses the excited state population to 0.08% $\pm$ 0.08% within 34 ns (284 ns if photon depletion of the resonator is required). Furthermore, our approach (i) can achieve effective second excited state depletion, (ii) has negligible effects on neighbouring qubits, and (iii) offers a way to entangle the qubit with an itinerant single photon, useful in quantum communication applications.Comment: 38 pages, 15 figure

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features