Remarks on automorphism and cohomology of finite cyclic coverings of projective spaces

For a smooth finite cyclic covering over a projective space of dimension greater than one, we show that its group of automorphisms faithfully acts on its cohomology except for a few cases. In characteristic zero, we study the equivariant deformation theory and groups of automorphisms for complex cyclic coverings. The proof uses the decomposition of the sheaf of differential forms due to Esnault and Viehweg. In positive characteristic, a lifting criterion of automorphisms reduce the faithfulness problem to characteristic zero. To apply this criterion, we prove the degeneration of the Hodge-de Rham spectral sequences for a family of smooth cfinite yclic coverings, and the infinitesimal Torelli theorem for finite cyclic coverings defined over an arbitrary field

Prediction and analysis of the residual capacity of concrete-filled steel tube stub columns under axial compression subjected to combined freeze-thaw cycles and acid rain corrosion

© 2019 by the authors. This paper presents a theoretical investigation on the safety evaluation, stability evaluation, and service life prediction of concrete-filled steel tube (CFST) structures in a Northern China area with acid rain. The finite element software ABAQUS was used to establish a numerical model, which was used to simulate the axial compression behavior of CFST columns subjected to the combined actions of freeze-thaw cycles and acid rain corrosion. The model performance was validated using the experimental results of the evaluation of mechanical properties, including the failure mode and load-displacement curve. Then, the effects of the section size, material strength, steel ratio, and combined times on the residual capacity were studied. The results show that the section size has a smaller influence on the residual strength than the other parameters and can be neglected in the design procedure. However, the other parameters, including the material strength, steel ratio, and combined times have relatively large influences on the axial compressive performance of CFST stub columns subjected to a combination of freeze-thaw cycles and acid rain corrosion. Finally, design formulae for predicting the residual strength of CFST stub columns that are under axial compression and the combined effect of freeze-thaw cycles and acid rain corrosion are proposed, and their results agree well with the numerical results

Connection stiffness identification of historic timber buildings using Temperature-based sensitivity analysis

© 2016 Elsevier Ltd The beam-column connection, called ‘Que Ti’, is the key component of historic Tibetan timber buildings to transfer shear, compression and bending loads from one structural element to another. This kind of connections can reduce the internal forces and improve the structure's ability to resist earthquakes. Its structure is very complicated and there is little information about the behaviour of this kind of semi-rigid connections. In this paper, a temperature-based response sensitivity method is proposed to identify the connection stiffness of the ‘Que-Ti’ in typical historical Tibetan buildings from temperature and strain response measurements. The semi-rigid connection is modeled as two rotational springs and one compressive spring. The temperature is treated as a measurable input and the thermal loading on the structure can be determined from the temperature variation. The numerical results show the method is effective and reliable to identify both unknown boundary conditions and the connection stiffness of the structure accurately even with 10% noise in measurements. A long-term monitoring system has also been installed in a typical historical Tibetan building and the monitoring data are used to further verify the proposed method. The experimental results show that the identified stiffnesses by the proposed method are consistent with that by finite element model updating from ambient vibration measurements

Condition assessment of heritage timber buildings in operational environments

© 2017, Springer-Verlag GmbH Germany. Due to changing environments and aging, the structural resistance of the heritage buildings has been reduced significantly. It has become crucial to monitor and protect the architectural heritage buildings. The objective of this research is to monitor and assess the performance of the heritage Tibetan timber building in operational environments. A three-storey corridor part of the typical heritage building was chosen in the study. A long-term monitoring system was installed in the building to collect the structural response and temperature. Detailed finite element model was built based on site investigation and existing documents, and updated based on the temperature-based response sensitivity using the field-monitoring data. The updated model was further evaluated using the static and dynamic analysis for condition assessment of the building in operational environments. The results show that the updated model is effective and accurate to predict the structural behaviour of the building in operational environments. Based on temperature-based response sensitivity, it is capable of tracking structure performance throughout the life-cycle allowing for condition-based maintenance and structural protection

Multi-Level Cross Residual Network for Lung Nodule Classification.

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm

Extracted BERT Model Leaks More Information than You Think!

The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing tasks. This allows corporations to provide machine learning as a service (MLaaS) by encapsulating fine-tuned BERT-based models as APIs. Due to significant commercial interest, there has been a surge of attempts to steal remote services via model extraction. Although previous works have made progress in defending against model extraction attacks, there has been little discussion on their performance in preventing privacy leakage. This work bridges this gap by launching an attribute inference attack against the extracted BERT model. Our extensive experiments reveal that model extraction can cause severe privacy leakage even when victim models are facilitated with advanced defensive strategies

TSS-Net: Two-stage with Sample selection and Semi-supervised Net for deep learning with noisy labels

The significant success of Deep Neural Networks (DNNs) relies on the availability of annotated large-scale datasets. However, it is time-consuming and expensive to obtain the available annotated datasets of huge size, which hinders the development of DNNs. In this paper, a novel two-stage framework is proposed for learning with noisy labels, called Two-Stage Sample selection and Semi-supervised learning Network (TSS-Net). It combines sample selection with semi-supervised learning. The first stage divides the noisy samples from the clean samples using cyclic training. The second stage uses the noisy samples as unlabelled data and the clean samples as labelled data for semi-supervised learning. Unlike previous approaches, TSS-Net does not require specifically designed robust loss functions and complex networks. It achieves decoupling of the two stages, which means that each stage can be replaced with a superior method to achieve better results, and this improves the inclusiveness of the network. Our experiments are conducted on several benchmark datasets in different settings. The experimental results demonstrate that TSS-Net outperforms many state-of-the-art methods

A new model for comprehensive service-Learning : a case study in Long-chi Village

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Quantification of oxygen nanobubbles in particulate matters and potential applications in remediation of anaerobic environment

Interfacial nanobubbles can exist on various hydrophobic and hydrophilic material interfaces. There are diverse applications for oxygen nanobubbles, which are closely related to their content and long-term stability. However, it remains challenging to determine the amount of nanobubbles loaded in a porous material. In this study, a novel method was used to quantify the total amount of oxygen nanobubbles loaded onto irregular particulate materials. Different materials were evaluated and their oxygen-loading capacities were found to be as follows: activated carbon (AC) > zeolite > biochar > diatomite > coal ash > clay. Significant differences in oxygen-loading capacities were mainly ascribed to differences in the specific surface area and hydrophobic/hydrophilic properties of the materials. The total oxygen loading on AC achieved using the high pressure loading method was higher than that achieved by the temperature variation method. This new quantitative method provides the possibility for the manipulation of oxygen nanobubble materials in practical applications and it is anticipated to be an important supplement to the existing methods of characterizing interfacial oxygen nanobubbles. Our results demonstrate that materials containing oxygen nanobubbles can significantly increase the dissolved oxygen and oxidation reduction potential in anaerobic systems. With the addition of oxygen-loaded materials (such as AC), the survival time of zebrafish was prolonged up to 20 h in a deoxygenated water system, and the germination rate of Vallisneria spiralis was also increased from 27 to 73% in an anaerobic sediment

Scalable supergraph search in large graph databases

© 2016 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prunes false answers based on features in the pruning phase and performs subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationship to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a verification-free algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the high scalability of our algorithms