Efficient Multi-Scale Attention Module with Cross-Spatial Learning

Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel dimensionality reduction may bring side effect in extracting deep visual representations. In this paper, a novel efficient multi-scale attention (EMA) module is proposed. Focusing on retaining the information on per channel and decreasing the computational overhead, we reshape the partly channels into the batch dimensions and group the channel dimensions into multiple sub-features which make the spatial semantic features well-distributed inside each feature group. Specifically, apart from encoding the global information to re-calibrate the channel-wise weight in each parallel branch, the output features of the two parallel branches are further aggregated by a cross-dimension interaction for capturing pixel-level pairwise relationship. We conduct extensive ablation studies and experiments on image classification and object detection tasks with popular benchmarks (e.g., CIFAR-100, ImageNet-1k, MS COCO and VisDrone2019) for evaluating its performance.Comment: Accepted to ICASSP202

Level of depression, anxiety and stress in patients with intrauterine adhesions in Hunan Province, China: A cross-sectional study.

BACKGROUND:The incidence of intrauterine adhesions has been increasing in recent years, seriously affecting women's health. This study aimed to investigate the psychological status and identify risk factors associated with high psychological distress in patients with intrauterine adhesions. METHODS:A cross-sectional study was conducted in Hunan Province, China. A total of 258 patients who presented with intrauterine adhesions between February and May 2018 were included. Data were collected by a questionnaire packet that included the Depression Anxiety Stress Scale, the Medical Coping Mode Questionnaire, and demographic and clinical information. Descriptive statistics, t-tests, one-way ANOVA, Pearson's correlations and multiple linear stepwise regression were employed in this study. RESULTS:Among 258 participants, the detection rates of mild depression and moderate to extremely severe depression were 10.1% and 10.5%, respectively; the detection rates of mild anxiety and moderate to extremely severe anxiety were 11.2% and 20.2%, respectively; the detection rates of mild stress and moderate to extremely severe stress were 9.3% and 10.2%, respectively. Avoidance and resignation coping were positively correlated with the overall scores of general distress which represents the total scores of the Depression Anxiety Stress Scale (r = 0.171, 0.475, P < 0.01). Multiple linear stepwise regression results showed that husband-wife relationships and avoidance and resignation coping strategies were the main factors predicting general distress levels. CONCLUSIONS:Patients with intrauterine adhesions have psychological distress in a certain extent. Target interventions should be taken to improve the mental health level of patients

Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network

Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI). A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN). According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model. The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE. The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise

Changes in Vegetation Dynamics and Relations with Extreme Climate on Multiple Time Scales in Guangxi, China

Understanding the responses of vegetation to climate extremes is important for revealing vegetation growth and guiding environmental management. Guangxi was selected as a case region in this study. This study investigated the spatial-temporal variations of the Normalized Difference Vegetation Index (NDVI), and quantitatively explored effects of climate extremes on vegetation on multiple time scales during 1982–2015 by applying the Pearson correlation and time-lag analyses. The annual NDVI significantly increased in most areas with a regional average rate of 0.00144 year−1, and the highest greening rate appeared in spring. On an annual scale, the strengthened vegetation activity was positively correlated with the increased temperature indices, whereas on a seasonal or monthly scale, this was the case only in spring and summer. The influence of precipitation extremes mainly occurred on a monthly scale. The vegetation was negatively correlated with both the decreased precipitation in February and the increased precipitation in summer months. Generally, the vegetation significantly responded to temperature extremes with a time lag of at least one month, whereas it responded to precipitation extremes with a time lag of two months. This study highlights the importance of accounting for vegetation-climate interactions

Genetic Diversity of Two Globally Invasive Snails in Asia and Americas in Relation with Agricultural Habitats and Climate Factors

The successful establishment of invasive populations is closely linked to environmental factors. It is unclear whether coexisting species in the native area follow the same genetic pattern in the invaded continents under the local climate factors. Two coexisting morphologically similar snails (Pomacea&nbsp;canaliculata and P. maculata), native to tropical and sub-tropical South America, have become invasive species for agriculture production and wetland conservation across five continents over 40 years. We analyzed the correlation between the genetic diversity of the two snails and the climate factors or habitat changes. Based on the 962 sequences from the invaded continents and South America, the nucleotide diversity in the agricultural habitat was low for P. canaliculata, whereas it was high for P. maculata, compared with that in the non-agricultural habitat. The two snails showed a divided population structure among the five continents. The P. canaliculata population in the invaded continents has remained stable, whereas the P. maculata population expanded suddenly. Seven main haplotype networks and two ancestral haplotypes (Pc3, Pm1) were found in the P. canaliculata and P. maculata populations. The haplotypes of the two snails were related to local climate factors. The overall fixation index of P. canaliculata and P. maculata was 0.2657 and 0.3097 between the invaded continents and South America. The population expansion of the two snails fitted the isolation-by-distance model. We discovered nine new sequences from the sampling locations. Overall, the genetic diversity and genetic differentiation of the two invasive snails were closely related to geographic separation, agricultural habitat, and climate factors