773 research outputs found

    Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization

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    The recently proposed tensor robust principal component analysis (TRPCA) methods based on tensor singular value decomposition (t-SVD) have achieved numerous successes in many fields. However, most of these methods are only applicable to third-order tensors, whereas the data obtained in practice are often of higher order, such as fourth-order color videos, fourth-order hyperspectral videos, and fifth-order light-field images. Additionally, in the t-SVD framework, the multi-rank of a tensor can describe more fine-grained low-rank structure in the tensor compared with the tubal rank. However, determining the multi-rank of a tensor is a much more difficult problem than determining the tubal rank. Moreover, most of the existing TRPCA methods do not explicitly model the noises except the sparse noise, which may compromise the accuracy of estimating the low-rank tensor. In this work, we propose a novel high-order TRPCA method, named as Low-Multi-rank High-order Bayesian Robust Tensor Factorization (LMH-BRTF), within the Bayesian framework. Specifically, we decompose the observed corrupted tensor into three parts, i.e., the low-rank component, the sparse component, and the noise component. By constructing a low-rank model for the low-rank component based on the order-dd t-SVD and introducing a proper prior for the model, LMH-BRTF can automatically determine the tensor multi-rank. Meanwhile, benefiting from the explicit modeling of both the sparse and noise components, the proposed method can leverage information from the noises more effectivly, leading to an improved performance of TRPCA. Then, an efficient variational inference algorithm is established for parameters estimation. Empirical studies on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative results

    Elementary Mode Analysis for the Rational Design of Efficient Succinate Conversion from Glycerol by Escherichia coli

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    By integrating the restriction of oxygen and redox sensing/regulatory system, elementary mode analysis was used to predict the metabolic potential of glycerol for succinate production by E. coli under either anaerobic or aerobic conditions. It was found that although the theoretical maximum succinate yields under both anaerobic and aerobic conditions are 1.0 mol/mol glycerol, the aerobic condition was considered to be more favorable for succinate production. Although increase of the oxygen concentration would reduce the succinate yield, the calculation suggests that controlling the molar fraction of oxygen to be under 0.65 mol/mol would be beneficial for increasing the succinate productivity. Based on the elementary mode analysis, the rational genetic modification strategies for efficient succinate production under aerobic and anaerobic conditions were obtained, respectively. Overexpressing the phosphoenolpyruvate carboxylase or heterogonous pyruvate carboxylase is considered to be the most efficient strategy to increase the succinate yield

    The Promising Fuel-Biobutanol

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    Essays In Market Efficiency And Empirical Asset Pricing

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    This dissertation consists of two chapters that address question about market efficiency in asset pricing. In the first chapter, ``Comovement in Arbitrage Limits , I document that estimates of mispricing, such as deviations from no-arbitrage relations, strongly comove across five financial markets. In particular, I find that one common component---the arbitrage gap---explains the majority of variability in mispricing estimates for futures, Treasury securities, foreign exchange, and options. Prominent equity anomalies also comove significantly with the arbitrage gap. Existing theories propose that funding constraints faced by arbitrageurs can impair market efficiency. Consistent with these theories, I find that variables affecting arbitrage capital availability, such as the TED spread and hedge-fund flows and returns, explain two-thirds of the arbitrage gap’s variation. During periods of tighter capital constraints, the comovement in mispricings becomes stronger. In the second chapter, ``Size and Value in China, joint with Robert F. Stambaugh and Yu Yuan, we construct size and value factors in China. The size factor excludes the smallest 30\% of firms, which are companies valued significantly as potential shells in reverse mergers that circumvent tight IPO constraints. The value factor is based on the earnings-price ratio, which subsumes the book-to-market ratio in capturing all Chinese value effects. Our three-factor model strongly dominates a model formed by just replicating the Fama and French (1993) procedure in China. Unlike that model, which leaves a 17\% annual alpha on the earnings-price factor, our model explains most reported Chinese anomalies, including profitability and volatility anomalies

    O modo conjuntivo em português europeu

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    O objetivo do presente trabalho é tentar estudar a morfologia e a utilização do modo conjuntivo em PE. Centrar-nos-emos nas características do modo conjuntivo, começando por uma abordagem gramatical para de seguida nos concertarmos na sua utilização em PE. Este tópico foi escolhido devido às dificuldades que os estudantes chineses enfrentam na utilização da conjugação verbal portuguesa e na aprendizagem do emprego deste modo em PE. Espera-se que a presente dissertação possa proporcionar uma melhor compreensão da morfologia e do emprego do modo conjuntivo aos alunos estrangeiros, nomeadamente chineses, que estudam Português como Língua Estrangeira ou Língua Segunda.The objective of this work is to try to study the morphology and the use of the conjunctive mode in PE. We will focus on the characteristics of the conjunctive mode, starting with a grammatical approach and then agreeing on its use in PE. This topic was chosen because of the difficulties that Chinese students face in using Portuguese verbal conjugation and learning how to work this way in PE. It is hoped that this dissertation will provide a better understanding of the morphology and employment of the conjunctive mode to foreign students, namely Chinese, who study Portuguese as a Foreign Language or Second LanguageMestrado em Português Língua Estrangeira/Língua Segund

    Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation

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    Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a broad spectrum of potential applications in digital pathology. Recently, unsupervised domain adaptation (UDA) methods have been proposed to mitigate the distributional gap between different imaging modalities for unsupervised nuclei segmentation in histopathology images. However, existing UDA methods are built upon the assumption that data distributions within each domain should be uniform. Based on the over-simplified supposition, they propose to align the histopathology target domain with the source domain integrally, neglecting severe intra-domain discrepancy over subpartitions incurred by mixed cancer types and sampling organs. In this paper, for the first time, we propose to explicitly consider the heterogeneity within the histopathology domain and introduce open compound domain adaptation (OCDA) to resolve the crux. In specific, a two-stage disentanglement framework is proposed to acquire domain-invariant feature representations at both image and instance levels. The holistic design addresses the limitations of existing OCDA approaches which struggle to capture instance-wise variations. Two regularization strategies are specifically devised herein to leverage the rich subpartition-specific characteristics in histopathology images and facilitate subdomain decomposition. Moreover, we propose a dual-branch nucleus shape and structure preserving module to prevent nucleus over-generation and deformation in the synthesized images. Experimental results on both cross-modality and cross-stain scenarios over a broad range of diverse datasets demonstrate the superiority of our method compared with state-of-the-art UDA and OCDA methods
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