Experimental study of mechanical property for prestressed rubber bearing

To overcome the shortages of existing Rubber Bearings (RBs), an innovative type of isolator, named as Prestressed Rubber Bearing (PRB), is presented in this paper. Base on conventional laminated Rubber Bearing (RB), PRB is developed by increasing the thickness of rubber layers, setting vertical ducts and installing prestress tendons. Through the vertical and horizontal monotonic loading test, the vertical and horizontal stiffness of PRBs are investigated. The empirical formulas for stiffness are proposed. Moreover, the hysteresis behavior and the energy dissipation capacity of PRBs are studied by reversed cyclic loading test. The results show that PRBs not only have the horizontal isolating capacity as conventional RBs, but also have the capacity of horizontal displacement-limitation and improved capacity of energy dissipation

AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.Comment: Accepted by CIKM 202

Understanding Translationese in Cross-Lingual Summarization

Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS data, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese. Then we systematically investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Lastly, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.Comment: Accepted to the Findings of EMNLP 202

Analytical and experimental investigation on eigenfrequency-based damage diagnosis of cantilever beam

This paper presents two eigenfrequency-based damage diagnosis methods in a cantilever beam. The analytical relationship has been established between the eigenfrequency and damage parameters, including relative damage location and severity. On the premise that pre-damaged eigenfrequencies are known, a diagnosis algorithm without requirement of material properties is proposed based on change ratios of the first three eigenfrequencies. If pre-damaged eigenfrequencies are unfeasible to be acquired, a three-contour method based on only post-damaged eigenfrequencies is introduced to estimate damage parameters. The uniqueness of solution is discussed. Both the numerical simulation by the finite element method and the experiment on real beams are conducted and result in a good agreement between actual damage parameters and calculated values by using the proposed methods

Rethinking Normalization Methods in Federated Learning

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover external covariate shift problem in FL, which is caused by the independent local training processes on different devices. We demonstrate that external covariate shifts will lead to the obliteration of some devices' contributions to the global model. Further, we show that normalization layers are indispensable in FL since their inherited properties can alleviate the problem of obliterating some devices' contributions. However, recent works have shown that batch normalization, which is one of the standard components in many deep neural networks, will incur accuracy drop of the global model in FL. The essential reason for the failure of batch normalization in FL is poorly studied. We unveil that external covariate shift is the key reason why batch normalization is ineffective in FL. We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model. We conduct experiments on CIFAR10 under non-IID settings. The results demonstrate that models with layer normalization converge fastest and achieve the best or comparable accuracy for three different model architectures.Comment: Submitted to DistributedML'22 worksho

Comparison of joint status using ultrasound assessments and Haemophilia Joint Health Score 2.1 in children with haemophilia

IntroductionUltrasound (US) has gained popularity in the evaluation of haemophilic joint diseases because it enables the imaging of soft-tissue lesions in the joints and bone-cartilage lesions. We aimed to determine the correlation between US evaluations and clinical assessments performed using HJHS 2.1 and to evaluate their respective characteristics in assessing early haemophilic arthropathy.MethodsA total of 178 joints (32 knees, 85 elbows, and 61 ankles) in 45 haemophilia A patients (median age, 10 years; range, 6–15) were assessed using US and HJHS 2.1. Ultrasonographic scoring was performed in consensus assessments by one imager by using the US scores.ResultsThe total HJHS 2.1 and US scores showed a strong correlation (rS=0.651, P=0.000, CI: 0.553–0.763), with an excellent correlation for the elbows (rS=0.867, P=0.000, CI: 0.709–0.941) and a substantial correlation for the knees (rS=0.681, P=0.000, CI: 0.527–0.797). The correlation for the ankles was relatively moderate (rS=0.518, P=0.000, CI: 0.308–0.705). Nine subjects (15.5%) without abnormalities, as indicated by HJHS 2.1, showed haemophilic arthropathy in US scoring. All nine joints showed moderate (1/9) to severe (8/9) synovial thickening in the ankle (5/9) and elbow joints (4/9). In contrast, 50 joints (50.5%) showed normal US scores and abnormal changes as indicated by HJHS 2.1. S scores correlated well with HJHS 2.1 for overall and individual joints.DiscussionUS could identify some early pathological changes in joints showing normal clinical findings, but still cannot replace the HJHS; however, it can serve as an imaging examination complementing HJHS 2

Optimizing cost of continuous overlapping queries over data streams by filter adaption

The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially, the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in the streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario

<i>PyuARF16/33</i> Are Involved in the Regulation of Lignin Synthesis and Rapid Growth in <i>Populus yunnanensis</i>

(1) Background: Lignin is a unique component of the secondary cell wall, which provides structural support for perennial woody plants. ARFs are the core factors of the auxin-signaling pathway, which plays an important role in promoting plant growth, but the specific relationship between auxin response factors (ARFs) and lignin has not been fully elucidated with regard to rapid plant growth in forest trees. (2) Objectives: This study aimed to investigate the relationship between ARFs and lignin with regard to rapid plant growth in forest trees. (3) Methods: We used bioinformatics analysis to investigate the PyuARF family, find genes homologous to ARF6 and ARF8 in Populus yunnanensis, and explore the changes in gene expression and lignin content under light treatment. (4) Results: We identified and characterized 35 PyuARFs based on chromosome-level genome data from P. yunnanensis. In total, we identified 92 ARF genes in P. yunnanensis, Arabidopsis thaliana, and Populus trichocarpa, which were subsequently divided into three subgroups based on phylogenetic analysis and classified the conserved exon–intron structures and motif compositions of the ARFs into the same subgroups. Collinearity analysis suggested that segmental duplication and whole-genome duplication events were majorly responsible for the expansion of the PyuARF family, and the analysis of Ka/Ks indicated that the majority of the duplicated PyuARFs underwent purifying selection. The analysis of cis-acting elements showed that PyuARFs were sensitive to light, plant hormones, and stress. We analyzed the tissue-specific transcription profiles of PyuARFs with transcriptional activation function and the transcription profiles of PyuARFs with high expression under light in the stem. We also measured the lignin content under light treatment. The data showed that the lignin content was lower, and the gene transcription profiles were more limited under red light than under white light on days 1, 7, and 14 of the light treatments. The results suggest that PyuARF16/33 may be involved in the regulation of lignin synthesis, thereby promoting the rapid growth of P. yunnanensis. (5) Conclusions: Collectively, this study firstly reports that PyuARF16/33 may be involved in the regulation of lignin synthesis and in promoting the rapid growth in P. yunnanensis.</i

PyuARF16/33 Are Involved in the Regulation of Lignin Synthesis and Rapid Growth in Populus yunnanensis

(1) Background: Lignin is a unique component of the secondary cell wall, which provides structural support for perennial woody plants. ARFs are the core factors of the auxin-signaling pathway, which plays an important role in promoting plant growth, but the specific relationship between auxin response factors (ARFs) and lignin has not been fully elucidated with regard to rapid plant growth in forest trees. (2) Objectives: This study aimed to investigate the relationship between ARFs and lignin with regard to rapid plant growth in forest trees. (3) Methods: We used bioinformatics analysis to investigate the PyuARF family, find genes homologous to ARF6 and ARF8 in Populus yunnanensis, and explore the changes in gene expression and lignin content under light treatment. (4) Results: We identified and characterized 35 PyuARFs based on chromosome-level genome data from P. yunnanensis. In total, we identified 92 ARF genes in P. yunnanensis, Arabidopsis thaliana, and Populus trichocarpa, which were subsequently divided into three subgroups based on phylogenetic analysis and classified the conserved exon&ndash;intron structures and motif compositions of the ARFs into the same subgroups. Collinearity analysis suggested that segmental duplication and whole-genome duplication events were majorly responsible for the expansion of the PyuARF family, and the analysis of Ka/Ks indicated that the majority of the duplicated PyuARFs underwent purifying selection. The analysis of cis-acting elements showed that PyuARFs were sensitive to light, plant hormones, and stress. We analyzed the tissue-specific transcription profiles of PyuARFs with transcriptional activation function and the transcription profiles of PyuARFs with high expression under light in the stem. We also measured the lignin content under light treatment. The data showed that the lignin content was lower, and the gene transcription profiles were more limited under red light than under white light on days 1, 7, and 14 of the light treatments. The results suggest that PyuARF16/33 may be involved in the regulation of lignin synthesis, thereby promoting the rapid growth of P. yunnanensis. (5) Conclusions: Collectively, this study firstly reports that PyuARF16/33 may be involved in the regulation of lignin synthesis and in promoting the rapid growth in P. yunnanensis