Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable ...multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.
This article first provides a conceptual and theoretical analysis of international financial centers (IFCs) by focusing on IFCs' main characteristics, categories, and policy regimes. It then reviews ...the policy initiatives driving Shanghai's IFC, coming from the central and local governments, and evaluates their strategic effects. Finally, I emphasize the disadvantages of Shanghai's IFC dynamics by focusing on the level of internationalization, the financial hinterland, the English professionals, and the legal system. The empirical study reveals that the construction of Shanghai's IFC has achieved great advances, motivated by its policy dynamics since 1990, but its global impact is still limited compared to New York and London. This study sheds light on the dynamics of Shanghai's IFC as a government-led model.
Hierarchical Context Modeling for Video Event Recognition Wang, Xiaoyang; Ji, Qiang
IEEE transactions on pattern analysis and machine intelligence,
2017-Sept.-1, 2017-09-00, 2017-9-1, 20170901, Letnik:
39, Številka:
9
Journal Article
Recenzirano
Current video event recognition research remains largely target-centered. For real-world surveillance videos, target-centered event recognition faces great challenges due to large intra-class target ...variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.
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•A new lattice structural optimization framework is developed by innovatively integrating fillet designs, and yield and buckling constraints.•The constraints are developed based on ...modified Hill’s yield criterion as well as Euler and Johnson buckling formulae.•The yield and buckling constraints guarantee the safety of the optimized Messerschmitt-Bolkow-Blohm beams composed of BCC or PC lattices.•For both lattice types, introducing fillets has resulted in reduced compliance and stress concentration for the optimized beams.
To reduce the stress concentration and ensure structural safety for lattice structure designs, in this paper, a new optimization framework is developed for the optimal design of graded lattice structures, innovatively integrating fillet designs as well as yield and buckling constraints. Both relative strut radii and fillet parameters are defined as design variables, for BCC and PC lattices. Numerical homogenization is employed to characterize the effective elastic constants and yield stresses of the lattice metamaterials. Metamaterial models are developed to represent the relationships between the metamaterial effective properties and lattice geometric variables. Yield and buckling constraints, based on modified Hill’s yield criterion as well as Euler and Johnson buckling formulae respectively, are developed as functions of lattice geometric variables. A new optimization framework is proposed with both yield and buckling constraints integrated. A case study on minimizing the compliance of a Messerschmitt-Bolkow-Blohm beam, composed of either BCC or PC lattices, is conducted. The yield and buckling constraints guarantee the structural safety of the optimized lattice beams. The optimized beams composed of filleted lattices, compared with non-filleted lattices in the corresponding type, show reduced proportions subject to high modified Hill’s stress (σHill≥0.95) with 6 ~ 7% reductions in compliance.
Little is known about COVID-19 outside Hubei. The aim of this paper was to describe the clinical characteristics and imaging manifestations of hospitalized patients with confirmed COVID-19 infection ...in Wenzhou, Zhejiang, China.
In this retrospective cohort study, 149 RT-PCR confirmed positive patients were consecutively enrolled from January 17th to February 10th, 2020 in three tertiary hospitals of Wenzhou. Outcomes were followed up until Feb 15th, 2020.
A total of 85 patients had Hubei travel/residence history, while another 49 had contact with people from Hubei and 15 had no traceable exposure history to Hubei. Fever, cough and expectoration were the most common symptoms, 14 patients had decreased oxygen saturation, 33 had leukopenia, 53 had lymphopenia, and 82 had elevated C-reactive protein. On chest computed tomography (CT), lung segments 6 and 10 were mostly involved. A total of 287 segments presented ground glass opacity, 637 presented mixed opacity and 170 presented consolidation. Lesions were more localized in the peripheral lung with a patchy form. No significant difference was found between patients with or without Hubei exposure history. Seventeen patients had normal CT on admission of these, 12 had negative findings even10 days later.
Most patients presented with a mild infection in our study. The imaging pattern of multifocal peripheral ground glass or mixed opacity with predominance in the lower lung is highly suspicious of COVID-19 in the first week of disease onset. Nevetheless, some patients can present with a normal chest finding despite testing positive for COVID-19. Funding: We did not receive any fundings.
To combat the worsening global energy shortage, global photovoltaic (PV) installation capacity has been increasing rapidly every year. Since the instability and intermittence of PV power output have ...great impacts on utility grids, accurate PV power output prediction is crucial. This paper proposes the use of machine learning approaches, combined with a weather type classification method, to predict short-term PV power output. The datasets are collected from a commercial PV power station located in Yangjiang, Guangdong province of China (latitude 21.56 °N, longitude 112.09 °E). Firstly, daytime meteorological data from 07:30 to 18:00 are divided into six 2-h intervals, and then the meteorological conditions of each interval are divided into four categories using an Extremely randomized Trees Classification model according to the PV power generation in each period. Secondly, nine machine learning models are established based on the weather type classification to predict the PV power output. The results show that weather type classification is vital to the selection of appropriate machine learning models and the accurate prediction of PV power output because the characteristic correlation between the meteorological data and PV power output always changes. In general, the Lasso Regressor, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regressor models show better performances than the other models. Furthermore, all the models’ accuracy is relatively high when the local meteorological conditions are relatively stable, such as in October, November, and December, during which time the Mean Relative Error values are 2.07, 1.07, and 1.73, respectively. During the period when the weather is unstable, the performance of the SVR model is better than that of the other models. The prediction accuracy can be significantly improved with integrating the accurate weather classification into the model. With regards to each daytime period, the prediction accuracy in the morning and evening is relatively high and the MREs for these times are small. This study provides a theoretical basis for selecting appropriate machine learning models to predict photovoltaic power generation under different weather conditions.
•The data is collected from a 50 MW commercial power station.•A 2-h meteorological classification based on ETC model is proposed.•Explore nine machine learning models to predict the PV power output.•The best performing models are the ETC-RFR, ETC-LAR, ETC-GBR, ETC-SVR, and ETC-GPR.•Compared with similar studies, our method performs better.
Camalexin is a major phytoalexin that plays a crucial role in disease resistance in Arabidopsis (
). We previously characterized the regulation of camalexin biosynthesis by the mitogen-activated ...protein kinases MPK3 and MPK6 and their downstream transcription factor WRKY33. Here, we report that the pathogen-responsive CALCIUM-DEPENDENT PROTEIN KINASE5 (CPK5) and CPK6 also regulate camalexin biosynthesis in Arabidopsis. Chemically induced expression of constitutively active CPK5 or CPK6 variants was sufficient to induce camalexin biosynthesis in transgenic Arabidopsis plants. Consistently, the simultaneous mutation of
and
compromised camalexin production in Arabidopsis induced by the fungal pathogen
Moreover, we identified that WRKY33 functions downstream of CPK5/CPK6 to activate camalexin biosynthetic genes, thereby inducing camalexin biosynthesis. CPK5 and CPK6 interact with WRKY33 and phosphorylate its Thr-229 residue, leading to an increase in the DNA binding ability of WRKY33. By contrast, the MPK3/MPK6-mediated phosphorylation of WRKY33 on its N-terminal Ser residues enhances the transactivation activity of WRKY33. Furthermore, both gain- and loss-of-function genetic analyses demonstrated the cooperative regulation of camalexin biosynthesis by CPK5/CPK6 and MPK3/MPK6. Taken together, these findings indicate that WRKY33 functions as a convergent substrate of CPK5/CPK6 and MPK3/MPK6, which cooperatively regulate camalexin biosynthesis via the differential phospho-regulation of WRKY33 activity.
Summary Preterm birth and infectious diseases are the most common causes of neonatal and early childhood deaths worldwide. The rates of preterm birth have increased over recent decades and account ...for 11% of all births worldwide. Preterm infants are at significant risk of severe infection in early life and throughout childhood. Bacteraemia, inflammation, or both during the neonatal period in preterm infants is associated with adverse outcomes, including death, chronic lung disease, and neurodevelopmental impairment. Recent studies suggest that bacteraemia could trigger cerebral injury even without penetration of viable bacteria into the CNS. Here we review available evidence that supports the concept of a strong association between bacteraemia, inflammation, and cerebral injury in preterm infants, with an emphasis on the underlying biological mechanisms, clinical correlates, and translational opportunities.
A general and facile synthetic method for C(sp2)–H difluoroalkylations and perfluoroalkylations of alkenes and (hetero)arenes with commercially available fluoroalkyl halides has been developed with ...a copper-amine catalyst system. This method is characterized by high yields, mild reaction conditions, low-cost catalyst, broad substrate scope, and excellent functional group compatibility, therefore providing a convenient synthetic strategy toward various difluoroalkyl- and perfluoroalkyl-substituted alkenes and (hetero)arenes.
Given a social network and a positive integer k, the influence maximization problem aims to identify a set of k nodes in that can maximize the influence spread under a certain propagation model. As ...the proliferation of geo-social networks, location-aware promotion is becoming more necessary in real applications. In this paper, we study the distance-aware influence maximization (DAIM) problem, which advocates the importance of the distance between users and the promoted location. Unlike the traditional influence maximization problem, DAIM treats users differently based on their distances from the promoted location. In this situation, the k nodes selected are different when the promoted location varies. In order to handle the large number of queries and meet the online requirement, we develop two novel index-based approaches, MIA-DA and RIS-DA, by utilizing the information over some pre-sampled query locations. MIA-DA is a heuristic method which adopts the maximum influence arborescence (MIA) model to approximate the influence calculation. In addition, different pruning strategies as well as a priority-based algorithm are proposed to significantly reduce the searching space. To improve the effectiveness, in RIS-DA, we extend the reverse influence sampling (RIS) model and come up with an unbiased estimator for the DAIM problem. Through carefully analyzing the sample size needed for indexing, RIS-DA is able to return a 1 - 1=e - ε approximate solution with at least 1 - δ probability for any given query. Finally, we demonstrate the efficiency and effectiveness of proposed methods over real geo-social networks.
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