Balanced Audiovisual Dataset for Imbalance Analysis

The imbalance problem is widespread in the field of machine learning, which also exists in multimodal learning areas caused by the intrinsic discrepancy between modalities of samples. Recent works have attempted to solve the modality imbalance problem from algorithm perspective, however, they do not fully analyze the influence of modality bias in datasets. Concretely, existing multimodal datasets are usually collected under specific tasks, where one modality tends to perform better than other ones in most conditions. In this work, to comprehensively explore the influence of modality bias, we first split existing datasets into different subsets by estimating sample-wise modality discrepancy. We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias. To further explore the influence of modality bias and analyze the effectiveness of existing imbalance algorithms, we build a balanced audiovisual dataset, with uniformly distributed modality discrepancy over the whole dataset. We then conduct extensive experiments to re-evaluate existing imbalance algorithms and draw some interesting findings: existing algorithms only provide a compromise between modalities and suffer from the large modality discrepancy of samples. We hope that these findings could facilitate future research on the modality imbalance problem.Comment: website:https://gewu-lab.github.io/Balanced-Audiovisual-Dataset

Heat extraction capacity and its attenuation of deep borehole heat exchanger array

A model is proposed to analyze the heat transfer of deep borehole heat exchanger (DBHE)arrays. Based on this, a dimension reduction algorithm is proposed for the numerical simulation of heat transfer of DBHE arrays, which can improve calculation speed by several orders of magnitude compared with that by the CFD software. An index of heat extraction capacity (HECI) is adopted to evaluate the heat extraction capacity of DBHE arrays. The influence of borehole spacing, operation time, annual heating duration, terrestrial heat flow rate, borehole depth, soil thermal parameters, pipe diameter and circulating fluid flow rate on DBHE array heat extraction capacity and its attenuation are analyzed. The results show that the borehole spacing, operation time, and annual heating duration all have apparent influence on DBHE array heat extraction capacity and its attenuation rate, while the others only have apparent influence on the heat extraction capacity. According to the calculation results, when the DBHE arrays have a service lifetime of 20–50 years, the recommended borehole spacing range is 40–70 m

A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation

Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity. As a result, there exists an emerging research interest in the problem of achieving high operation accuracy while enhancing adaptability and compliance. However, previous works relied on an inefficient algorithm framework that separates motion planning from compliant control. Additionally, the compliant controller lacks robustness due to manually adjusted parameters. This paper proposes a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation. Specifically, first, the algorithm framework combines desired trajectory generation with impedance-parameter adjustment to improve efficiency and robustness. Second, we introduce a centralized Actor-Critic framework with LSTM networks, enhancing the synchronization of bi-manual manipulation. LSTM networks pre-process the force states obtained by the agents, further ameliorating the performance of compliance operations. When evaluated in the dual-arm cooperative handling and peg-in-hole assembly experiments, our method outperforms baseline algorithms in terms of optimality and robustness.Comment: 12 pages, 10 figure

Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts

Relationship Between Dairy Products Intake and Risk of Endometriosis: A Systematic Review and Dose-Response Meta-Analysis

Objective: Diet lifestyle can influence the risk of endometriosis. Therefore, we conducted a systematicmeta-analysis to investigate the association between dairy products and the risk of endometriosis. Besides, we performed a dose-responsemeta-analysis to evaluate the amount of dairy intake affecting the risk of endometriosis. Methods: Relevant studies were searched from Pubmed, Embase databases, Cochrane Library, and Web of Science from the inception to November 6th, 2020. Also, the dose-response meta-analysis was conducted. All the pooled results were performed by risk ratios (RRs). Results: Finally, seven high-quality studies were included in the present meta-analysis. Total dairy intake was inversely associated with the risk of endometriosis, and the risk of endometriosis tended to decrease with a decrease in the risk of endometriosis when dairy products intake was over 21 servings/week (RR 0.87, 95% CI 0.76–1.00; pnon−linearity = 0.04). Similarly, people who consumed more than 18 servings of high-fat dairy products per week had a reduced risk of endometriosis (RR 0.86, 95% CI 0.76–0.96). When stratified-analyses were conducted based on specific dairy product categories, it indicated that people with high cheese intake might have a reduced risk of endometriosis (RR 0.86, 95%CI 0.74–1.00). Other specific dairy products such as whole milk (RR 0.90, 95% CI 0.72–1.12), reduced-fat/skim milk (RR 0.83, 95% CI 0.50–1.73), ice cream (RR 0.83, 95% CI 0.50–1.73), and yogurt (RR 0.83, 95% CI 0.62–1.11) have not shown significant evidence of an association with the risk of endometriosis. However, there is a higher risk of endometriosis in the females with high butter intake compared to females with low butter intake (1.27, 95% CI 1.03–1.55). Conclusions: Overall, dairy products intake was associated with a reduction in endometriosis, with significant effects when the average daily intake 3 servings. When analyzed according to the specific type of dairy product, it was shown that females with higher high-fat dairy and cheese intake might have a reduced risk of endometriosis. However, high butter intake might be associated to the increased risk of endometriosis. More future studies are needed to validate and add to this finding

Association of Maternal Body Mass Index With Risk of Infant Mortality: A Dose-Response Meta-Analysis

Objective: This study presumed that a high or low bodymass index (BMI)might increase the risk of infant mortality. Therefore, a meta-analysis was performed to systematically assess the association between maternal BMI and the risk of infant mortality. Methods: The electronic databases, including Pubmed, Embase database, and Cochrane Library, were systemically searched by two investigators from inception to November 26th, 2020, with no language restriction. In parallel, a dose-response was assessed. Results: Finally, 22 cohort studies involving 13,532,293 participants were included into this paper, which showed that compared with normal BMI, maternal overweight significantly increased the risks of infant mortality [risk ratio (RR), 1.16; 95% confidence interval (CI), 1.13–1.19], neonatal mortality (RR, 1.23; 95% CI, 1.08–1.39), early neonatal mortality (RR, 1.55; 95% CI, 1.26–1.92) and post-neonatal mortality (RR, 1.18; 95% CI, 1.07–1.29). Similarly, maternal obesity significantly increased the risk of infant mortality (RR, 1.55; 95% CI, 1.41–1.70), neonatal mortality (RR, 1.55; 95% CI, 1.28–1.67), early neonatal mortality (RR, 1.37; 95% CI, 1.13–1.67), and post-neonatal mortality (RR, 1.30; 95% CI, 1.03–1.65), whereas maternal underweight potentially decreased the risk of infant mortality (RR, 0.93; 95% CI, 0.88–0.98). In the dose-response analysis, the risk of infant mortality significantly increased when the maternal BMI was >25 kg/m2. Conclusions: Maternal overweight or obesity significantly increases the risks of infant mortality, neonatal mortality, early neonatal mortality, and post-neonatal mortality compared with normal BMI in a dose-dependentmanner. Besides,maternal underweight will not increase the risk of infant mortality, neonatal mortality, early neonatal mortality, or postneonatal mortality; instead, it tends to decrease the risk of infant mortality. Early weight management may provide potential benefits to infants, and more large-scale prospective studies are needed to verify this finding in the future