8,826 research outputs found
Applicability of Phase-Function Normalization Techniques for Radiation Transfer Computation
No full textThe applicability of recently-developed four phase-function (PF) normalization techniques for modeling radiation transfer in strongly anisotropic scattering media is intensively examined using the discrete-ordinate method. The three simple techniques via normalization of only the forward- and/or backward-scattering directions were shown to reduce normalization complexity whilst retaining diffuse radiation computation accuracy for Henyey-Greenstein (HG) PFs. For Legendre PFs, however, such simple techniques are found to result in unphysical negative PF value at one or few correction direction in some cases. Additionally, negative PF values can occur for these simple techniques for ballistic radiation transfer for both HG and Legendre PF types. If negative-intensity correction is applied, however, radiative heat transfer calculation can still converge regardless of the appearance of negative PF values. The relatively complex Hunter and Guo 2012 technique, in which normalization is realized through a correction matrix covering all discrete directions, is shown to be applicable for diffuse and ballistic radiation for both PF types.Peer reviewed
Quantifying high-order interdependencies on individual patterns via the local O-information : theory and applications to music analysis
No full textHigh-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such systems. This paper presents a framework to measure high-order interdependence that disentangles their effect on each individual pattern exhibited by a multivariate system. The approach is centered on the local O-information, a new measure that assesses the balance between synergistic and redundant interdependencies at each pattern. To illustrate the potential of this framework, we present a detailed analysis of music scores from J. S. Bach, which reveals how high-order interdependence is deeply connected with highly nontrivial aspects of the musical discourse. Our results place the local O-information as a promising tool of wide applicability, which opens other perspectives for analyzing high-order relationships in the patterns exhibited by complex systems
Flow and heat transfer inside a new diversion-type gas heating device
No full textThe present paper characterizes ethylene glycol flow and heat transfer inside a new diversion-type gas heating device. A 2-D natural convection heat transfer model was built and solved by the finite volume method with unstructured body-fitted grids. The numerical model was first validated through temperature comparison with experimental measurements in a conventional device structure. Then analyses and comparisons of the flow fields and temperature distributions with use of different guide plate structures were carried out. The numerical results show that using the guide plate structures can form better organized flow patterns that augment heat transfer. The heat required for heating up the gas passing through the heating device can be reduced by 3% via installing two guide plates.Peer reviewed
Analysing datafied life
No full textOur life is being increasingly quantified by data. To obtain information from quantitative data, we need to develop various analysis methods, which can be drawn from diverse fields, such as computer science, information theory and statistics. This thesis focuses on investigating methods for analysing data generated for medical research. Its focus is on the purpose of using various data to quantify patients for personalized treatment.
From the perspective of data type, this thesis proposes analysis methods for the data from the fields of Bioinformatics and medical imaging. We will discuss the need of using data from molecular level to pathway level and also incorporating medical imaging data. Different preprocessing methods should be developed for different data types, while some post-processing steps for various data types, such as classification and network analysis, can be done by a generalized approach. From the perspective of research questions, this thesis studies methods for answering five typical questions from simple to complex. These questions are detecting associations, identifying groups, constructing classifiers, deriving connectivity and building dynamic models. Each research question is studied in a specific field. For example, detecting associations is investigated for fMRI signals. However, the proposed methods can be naturally extended to solve questions in other fields.
This thesis has successfully demonstrated that applying a method traditionally used in one field to a new field can bring lots of new insights. Five main research contributions for different research questions have been made in this thesis. First, to detect active brain regions associated to tasks using fMRI signals, a new significance index, CR-value, has been proposed. It is originated from the idea of using sparse modelling in gene association study. Secondly, in quantitative Proteomics analysis, a clustering based method has been developed to extract more information from large scale datasets than traditional methods. Clustering methods, which are usually used in finding subgroups of samples or features, are used to match similar identities across samples. Thirdly, a pipeline originally proposed in the field of Bioinformatics has been adapted to multivariate analysis of fMRI signals. Fourthly, the concept of elastic computing in computer science has been used to develop a new method for generating functional connectivity from fMRI data. Finally, sparse signal recovery methods from the domain of signal processing are suggested to solve the underdetermined problem of network model inference.Open Acces
Reborn Translated: Xiaolu Guo as a World Author
No full textThis paper introduces the concept of “world author,” taking as its exemplar the Chinese British writer and filmmaker Xiaolu Guo. It investigates how Guo utilizes her bilingualism to construct and negotiate her creative agency, especially when dealing with the political and commercial forces imposed on diasporic authors. Through engaging with Rebecca Walkowitz’s idea of world literature as being “born translated,” I point out that the translational should not be limited to the thematic and representational arrangements internal to a given text. Instead, translation as movements between linguistic systems and media forms can generate multipleversions of a text, to the point that such translational multiplicity fundamentally challenges its supposed singularity. This argument is demonstrated with Guo’s self-translation of the stories of Fenfang and her filmic adaptation of the novel UFO in Her Eyes. Through these examples of what I call “translational rebirths,” I demonstrate the importance of paratextual details and intertextual connections between clusters of an author’s creative output for the interpretation and appreciation of l’oeuvre d’un auteur instead of une oeuvre d’art. This case study also shows the need for the academic debates on world literature to go beyond the singularity of texts and evaluative criteria of worldliness based on this assumption, so that the discipline can realize its full potential in accommodating multilingual transnational authors like Guo
Investigations into elasticity in cloud computing
No full textThe pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of the most important features in cloud computing. This elasticity enables real-time acquisition/release of compute resources to meet application performance demands. In this thesis we investigate the problem of delivering cost-effective elasticity services for cloud applications.
Traditionally, the application level elasticity addresses the question of how to scale applications up and down to meet their performance requirements, but does not adequately address issues relating to minimising the costs of using the service. With this current limitation in mind, we propose a scaling approach that makes use of cost-aware criteria to detect the bottlenecks within multi-tier cloud applications, and scale these applications only at bottleneck tiers to reduce the costs incurred by consuming cloud infrastructure resources. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness by studying the behaviour of an example electronic commerce site application.
Furthermore, we consider the characteristics of the algorithm for implementing the business logic of cloud applications, and investigate the elasticity at the algorithm level: when dealing with large-scale data under resource and time constraints, the algorithm’s output should be elastic with respect to the resource consumed. We propose a novel framework to guide the development of elastic algorithms that adapt to the available budget while guaranteeing the quality of output result, e.g. prediction accuracy for classification tasks, improves monotonically with the used budget. We demonstrate the application of the framework by developing two elastic data mining algorithms as examples. Experimental evaluations have been performed using prediction accuracy as the quality measure on real datasets. The results show that both algorithms indeed exhibit consistent increase in quality.Open Acces
Adversarial Learning for Image-to-Image Generative Creativity
No full textAchieving generative creativity in the context of visual data, i.e. the generation of novel and valuable images, is a long-standing goal in computer vision and artificial intelligence. Generative adversarial networks (GANs) are prominent deep generative models that can successfully generate visually-appealing images. However, the generated images are mostly simple memorisation or imitation of training samples, which exhibits limited generative creativity. To obtain higher-degree generative creativity, we focus on more challenging image-to-image generation tasks, in which the generated images are not only more practically valuable, but also more distinct from existing data. The challenges of achieving image-to-image generative creativity lie in three aspects: whether the generated images 1) are truly useful, especially for critical applications (e.g. in the field of medical imaging), and 2) can demonstrate a clear difference from training samples, and 3) are varied and diverse for one input image, which is a natural requirement for many image generation tasks. In this thesis, we aim to develop deep conditional adversarial networks for challenging image-to-image generation tasks, each of which respectively exhibits one type of image-to-image generative creativity. We make the following contributions. First, we propose EnrichGAN for fast compressed sensing magnetic resonance imaging (CS-MRI) reconstruction that exhibits enrichment creativity. We demonstrate that EnrichGAN qualitatively and quantitatively outperforms various conventional and state-of-the-art methods, with a much faster processing time that enables real-time applications. Second, we propose SimGAN for semantic image manipulation. It requires learning good mappings between visual and text features. We show that SimGAN achieves superior results on this challenging image-to-image generation task that demonstrates high-level transformative creativity. Finally, we propose DesignGAN for automating the process of shape-oriented bionic design. It requires learning to combine features of images from different domains, in an unsupervised fashion. We demonstrate that Design- GAN learns to achieve image-to-image combinatorial creativity.Open Acces
3-D simulation of gases transport under condition of inert gas injection into goaf
No full textTo prevent coal spontaneous combustion in mines, it is paramount to understand O2 gas distribution under condition of inert gas injection into goaf. In this study, the goaf was modeled as a 3-D porous medium based on stress distribution. The variation of O2 distribution influenced by CO2 or N2 injection was simulated based on the multi-component gases transport and the Navier-Stokes equations using Fluent. The numerical results without inert gas injection were compared with field measurements to validate the simulation model. Simulations with inert gas injection show that CO2 gas mainly accumulates at the goaf floor level; however, a notable portion of N2 gas moves upward. The evolution of the spontaneous combustion risky zone with continuous inert gas injection can be classified into three phases: slow inerting phase, rapid accelerating inerting phase, and stable inerting phase. The asphyxia zone with CO2 injection is about 1.25~2.4 times larger than that with N2 injection. The efficacy of preventing and putting out mine fires is strongly related with the inert gas injecting position. Ideal injections are located in the oxidation zone or the transitional zone between oxidation zone and heat dissipation zone.Peer reviewed
Heat Transfer and Thermodynamic Processes in Coal-Bearing Strata Under Spontaneous Combustion Condition
No full textSimulations and experiments have been carried out to investigate heat transfer and thermodynamic processes in coal-bearing strata in order to quantitatively understand the development of underground coal fires under spontaneous combustion condition. With controlled temperature and under lean oxygen conditions, the thermodynamic parameters for coal oxidation at different stages are experimentally determined in combination with simultaneous thermal analysis. A combined heat transfer model of conduction, convection and radiation with finite reactions is developed for the porous coal and rocks. The temperature distributions in the coal and roof strata at different times are simulated based on the single- and two-stage kinetic models, respectively, and compared with field geophysical prospecting. Effects of oxidation kinetic properties due to coal metamorphism on propagation of coal fires are examined. It reveals that a significant step change exists during the thermal process of coal fire caused by two-stage oxidation, and the coal rank of occurrence directly determines the spontaneous combustion period of underground coal fire.Peer reviewe
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