Learning Efficient Convolutional Networks through Irregular Convolutional Kernels

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power devices are designed with very limited memory that can not store large models. Parameters pruning is critical for deep model deployment on low-power devices. Existing efforts mainly focus on designing highly efficient structures or pruning redundant connections for networks. They are usually sensitive to the tasks or relay on dedicated and expensive hashing storage strategies. In this work, we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage. Our approach transforms a traditional square convolution kernel to line segments, and automatically learn a proper strategy for equipping these line segments to model diverse features. The experimental results indicate that our approach can massively reduce the number of parameters (pruned 69% on DenseNet-40) and calculations (pruned 59% on DenseNet-40) while maintaining acceptable performance (only lose less than 2% accuracy)

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature

Relationship between occupational stress, job burnout, and depressive symptoms among workers in an automobile manufacturing enterprise in Guangzhou

BackgroundThe operation mode of automobile manufacturing industry (AMI) makes workers have different degrees of occupational stress and burnout, which may lead to negative emotions and depressive symptoms. ObjectiveTo study the relationship between occupational stress, job burnout, and depressive symptoms in AMI workers. MethodsIn this study, 1300 workers from a Guangzhou AMI company were selected as subjects by cluster random sampling method. Occupational stress, job burnout, and depressive symptoms of the workers were assessed by using the Effort-Reward Imbalance (ERI) questionnaire, the Maslach Burnout Inventory general survey questionnaire, and the Patient Health Questionnaire-9, respectively. Hierarchical regression was used to analyze the effects of occupational stress and job burnout on depressive symptoms in AMI workers. Mediating effect model was used to analyze the mediating effect of job burnout on the relationship between occupational stress and depressive symptoms. ResultsThere were 1300 questionnaires distributed, 1228 valid questionnaires collected, with a 94.5% recovery rate. The ERI ratio of 1228 AMI workers was 1.06±0.72, and the positive rate of occupational stress was 37.3% (458/1228). The score of job burnout was 2.18±1.37, and the positive rate of job burnout was 62.6% (769/1228). The score of depressive symptoms was 10.27±6.42, and the positive rate of depressive symptoms was 47.1% (578/1228). The dimensional scores of effort and over-commitment in occupational stress as well as emotional exhaustion and depersonalization in job burnout of AMI workers were positively correlated with the depressive symptom scores (rs=0.415, 0.571, 0.573, 0.593, P＜0.05). The dimensional scores of reward and personal achievement were negatively correlated (rs=−0.454, −0.339, P＜0.05). The percentages of variance in depressive symptoms score explained by occupational stress and job burnout were 26.7% and 16.6%, respectively. Job burnout had a partial mediating effect between the three dimensions of occupational stress and depressive symptoms, and the mediating effect values were −0.2832 (95%CI: −0.3250– −0.2434), 0.3553 (95%CI: 0.3071–0.4041), and 0.4193 (95%CI: 0.3681–0.4725), respectively. ConclusionAMI workers' occupational stress affects job burnout, but also indirectly affects depressive symptoms. Job burnout partially mediates the association between occupational stress and depressive symptoms. Reducing occupational stress and burnout levels of AMI workers may alleviate depressive symptoms

Biological and genomic analysis of a symbiotic nitrogen fixation defective mutant in Medicago truncatula

Medicago truncatula has been selected as one of the model legume species for gene functional studies. To elucidate the functions of the very large number of genes present in plant genomes, genetic mutant resources are very useful and necessary tools. Fast Neutron (FN) mutagenesis is effective in inducing deletion mutations in genomes of diverse species. Through this method, we have generated a large mutant resource in M. truncatula. This mutant resources have been used to screen for different mutant using a forward genetics methods. We have isolated and identified a large amount of symbiotic nitrogen fixation (SNF) deficiency mutants. Here, we describe the detail procedures that are being used to characterize symbiotic mutants in M. truncatula. In recent years, whole genome sequencing has been used to speed up and scale up the deletion identification in the mutant. Using this method, we have successfully isolated a SNF defective mutant FN007 and identified that it has a large segment deletion on chromosome 3. The causal deletion in the mutant was confirmed by tail PCR amplication and sequencing. Our results illustrate the utility of whole genome sequencing analysis in the characterization of FN induced deletion mutants for gene discovery and functional studies in the M. truncatula. It is expected to improve our understanding of molecular mechanisms underlying symbiotic nitrogen fixation in legume plants to a great extent

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A density functional theory study of the mechanism of formaldehyde oxidation on Mn-doped ceria

Formaldehyde is an indoor pollutant, whose removal under mild conditions is of growing importance. Mn-doped CeO2 is a promising catalyst for the oxidation of formaldehyde to water and carbon dioxide. We have theoretically investigated the origin of the high activity of Mn-doped ceria as compared with ceria. DFT+U calculations were used to identify adsorption modes and compare different reaction mechanisms. The reaction mechanism involves HCHO adsorption, two C-H bond cleavage steps involving reactive O atoms (either structural O atoms of the support or adsorbed O2), H2O formation and H2O and CO2 desorption. On the stoichiometric surface, a Mars-Van Krevelen mechanism occurs, which involves ceria surface O atoms. The lower coordination number of these O atom in the stoichiometric Mn-doped ceria results in decreased barriers for C-H bond cleavage. In the presence of defects which will be ubiquitous in the Mn-doped surface, a Langmuir-Hinshelwood mechanism becomes feasible, as O2 can strongly adsorb on the oxygen vacancy next to Mn where HCHO adsobs. The adsorbed O2 molecule is strongly activated by the reduced ceria surface. The barriers for C-H cleavage are lowest for reactions involved adsorbed O2. We predict that the HCHO oxidation reaction proceeds with lowest overall barrier on the defective Mn-doped CeO2 surface