285 research outputs found

    Studies of liquidity in the London Stock Exchange

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    The thesis studies liquidity related issues in the London Stock Exchange from 2001 to 2013 from different viewpoints. The first chapter introduces and motivates the study. The second chapter fully discusses the liquidity and liquidity measures from multiple dimensions and examines liquidity using five liquidity measures: relative spread, the Amihud ratio, the Rtotr ratio, zero trading volume days and zero return days. The time-series study shows that liquidity changes over time and largely depends on the financial environment. The analysis compares liquidity measures and finds that Rtotr may not be a reliable liquidity measure during a financial crisis due to the turnover anomaly. Moreover, the empirical results support the prior findings in the literature that relative spread is positively related to volatility, and negatively related to price and trading volume. The Amihud ratio, zero trading days and zero return days are better measures of explaining relative spread. All these findings give a better understanding of liquidity measures and enlighten the following deeper research.The third chapter continues to study liquidity and market characteristics from a panel viewpoint and the chapter extends the fixed effects model to solve the problem that some of the variables are not stationary. The panel results give more powerful explanations of liquidity. In particular, less liquid stocks are associated with higher volatility, lower price and lower trading volume. Market value has differing relationships with the various liquidity measures.The fourth chapter expands the liquidity research field and contains both theoretical and empirical work indicating that more liquid stocks have higher kurtosis and first lag autocorrelation due to higher transaction costs. In addition, the empirical results show skewness is also negatively related to liquidity. The final chapter presents the conclusions of the research

    Energy Flexibility of Building Cluster – Part I: Occupancy Modelling

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    With the growing application of renewable energy, the stability of power systems can be seriously affected due to the fluctuations in the instantaneous generated power. As one of the potential solutions for this upcoming challenge, energy flexibility of buildings has received attention for research and technology development. Demand response and energy flexibility should be implemented at a large scale to have the accumulated energy flexibility to a magnitude, which can be meaningful for energy sectors. Studies have shown that the energy flexibility of a building is greatly influenced by both building physical characteristics and occupancy pattern of residents. To the best knowledge of authors, occupancy has not been considered in the study of building cluster. The aim of this paper is to present the modelling process of occupancy/vacancy of Danish households based on Danish Time Use Survey (DTUS) 2008/09 data. In this paper, we present a data-driven approach to generate occupancy/vacancy models for different types of household and for building cluster of different scales. As the result, vacancy profile and vacancy duration models are developed. The stochasticity of occupancy is also unveiled. The next step is to apply these models to quantify energy flexibility of building cluster and the uncertainty of energy flexibility due to the stochastic occupancy

    Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks

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    Achieving efficient and robust multi-channel data learning is a challenging task in data science. By exploiting low-rankness in the transformed domain, i.e., transformed low-rankness, tensor Singular Value Decomposition (t-SVD) has achieved extensive success in multi-channel data representation and has recently been extended to function representation such as Neural Networks with t-product layers (t-NNs). However, it still remains unclear how t-SVD theoretically affects the learning behavior of t-NNs. This paper is the first to answer this question by deriving the upper bounds of the generalization error of both standard and adversarially trained t-NNs. It reveals that the t-NNs compressed by exact transformed low-rank parameterization can achieve a sharper adversarial generalization bound. In practice, although t-NNs rarely have exactly transformed low-rank weights, our analysis further shows that by adversarial training with gradient flow (GF), the over-parameterized t-NNs with ReLU activations are trained with implicit regularization towards transformed low-rank parameterization under certain conditions. We also establish adversarial generalization bounds for t-NNs with approximately transformed low-rank weights. Our analysis indicates that the transformed low-rank parameterization can promisingly enhance robust generalization for t-NNs.Comment: 46 pages, accepted to NeurIPS 2023. We have corrected several typos in the first version (arXiv:2303.00196

    Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization

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    Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channels for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.Comment: Accepted by ECCV2022; 14 pages, 10 figure

    Chiral metallohelices enantioselectively target hybrid human telomeric G-quadruplex DNA

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    The design and synthesis of metal complexes that can specifically target DNA secondary structure has attracted considerable attention. Chiral metallosupramolecular complexes (e.g. helicates) in particular display unique DNA-binding behavior, however until recently few examples which are both water-compatible and enantiomerically pure have been reported. Herein we report that one metallohelix enantiomer , available from a diastereoselective synthesis with no need for resolution, can enantioselectively stabilize human telomeric hybrid G-quadruplex and strongly inhibit telomerase activity with IC 50 of 600 nM. In contrast, no such a preference is observed for the mirror image complex . More intriguingly, neither of the two enantiomers binds specifically to human telomeric antiparallel G-quadruplex. To the best of our knowledge, this is the first example of one pair of enantiomers with contrasting selectivity for human telomeric hybrid G-quadruplex. Further studies show that can discriminate human telomeric G-quadruplex from other telomeric G-quadruplexes

    Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark

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    Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Under the low illumination condition, the performance of person Re-ID methods usually sharply deteriorates. To address the low illumination challenge in nighttime person Re-ID, this paper proposes an Illumination Distillation Framework (IDF), which utilizes illumination enhancement and illumination distillation schemes to promote the learning of Re-ID models. Specifically, IDF consists of a master branch, an illumination enhancement branch, and an illumination distillation module. The master branch is used to extract the features from a nighttime image. The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features. However, nighttime and enhanced features usually contain data noise due to unstable lighting conditions and enhancement failures. To fully exploit the complementary benefits of nighttime and enhanced features while suppressing data noise, we propose an illumination distillation module. In particular, the illumination distillation module fuses the features from two branches through a bottleneck fusion model and then uses the fused features to guide the learning of both branches in a distillation manner. In addition, we build a real-world nighttime person Re-ID dataset, named Night600, which contains 600 identities captured from different viewpoints and nighttime illumination conditions under complex outdoor environments. Experimental results demonstrate that our IDF can achieve state-of-the-art performance on two nighttime person Re-ID datasets (i.e., Night600 and Knight ). We will release our code and dataset at https://github.com/Alexadlu/IDF.Comment: Accepted by TM

    Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

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    The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

    Fabrication of Flexible Piezoelectric PZT/Fabric Composite

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    Flexible piezoelectric PZT/fabric composite material is pliable and tough in nature which is in a lack of traditional PZT patches. It has great application prospect in improving the sensitivity of sensor/actuator made by piezoelectric materials especially when they are used for curved surfaces or complicated conditions. In this paper, glass fiber cloth was adopted as carrier to grow PZT piezoelectric crystal particles by hydrothermal method, and the optimum conditions were studied. The results showed that the soft glass fiber cloth was an ideal kind of carrier. A large number of cubic-shaped PZT nanocrystallines grew firmly in the carrier with a dense and uniform distribution. The best hydrothermal condition was found to be pH 13, reaction time 24 h, and reaction temperature 200°C
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