Semi-supervised wildfire smoke detection based on smoke-aware consistency

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smokeaware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.Peer ReviewedPostprint (published version

Periodic elastic nanodomains in ultrathin tetrogonal-like BiFeO3 films

We present a synchrotron grazing incidence x-ray diffraction analysis of the domain structure and polar symmetry of highly strained BiFeO3 thin films grown on LaAlO3 substrate. We revealed the existence of periodic elastic nanodomains in the pure tetragonal-like BFO ultrathin films down to a thickness of 6 nm. A unique shear strain accommodation mechanism is disclosed. We further demonstrated that the periodicity of the nanodomains increases with film thickness but deviates from the classical Kittel's square root law in ultrathin thickness regime (6 - 30 nm). Temperature-dependent experiments also reveal the disappearance of periodic modulation above 90C due to a MC-MA structural phase transition.Comment: Accepted in Phys. Rev.

Sex determines which section of the SLC6A4 gene is linked to obsessive–compulsive symptoms in normal Chinese college students

Previous case-control and family-based association studies have implicated the SLC6A4 gene in obsessive-compulsive disorder (OCD). Little research, however, has examined this gene's role in obsessive-compulsive symptoms (OCS) in community samples. The present study genotyped seven tag SNPs and two common functional tandem repeat polymorphisms (5-HTTLPR and STin2), which together cover the whole SLC6A4 gene, and investigated their associations with OCS in normal Chinese college students (N = 572). The results revealed a significant gender main effect and gender-specific genetic effects of the SLC6A4 gene on OCS. Males scored significantly higher on total OCS and its three dimensions than did females (ps < .01). The 5-HTTLPR in the promoter region showed a female-specific genetic effect, with the l/l and l/s genotypes linked to higher OCS scores than the s/s genotype (ps < .05). In contrast, a conserved haplotype polymorphism (rs1042173| rs4325622| rs3794808| rs140701| rs4583306| rs2020942) covering from intron 3 to the 3' UTR of the SLC6A4 gene showed male-specific genetic effects, with the CGAAGG/CGAAGG genotype associated with lower OCS scores than the other genotypes (ps < .05). These effects remained significant after controlling for OCS-related factors including participants' depressive and anxiety symptoms as well as stressful life events, and correction for multiple tests. These results are discussed in terms of their implications for our understanding of the sex-specific role of the different sections of the SLC6A4 gene in OCD

Overt coordination in additive numerals of minority languages in South China

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Effect of Electron Beam Irradiation on Surface Dielectric Properties of Polymeric Materials

Секция 3. Модификация свойств материалов = Section 3. Modification of Material PropertiesThe polymeric materials could degrade due to the electron irradiation, photo-irradiation and heat effect following with the surface flashover in vacuum. On the other hand, the surface insulating performance could be improved by electron irradiation. In order to study the effect of electron irradiation on the insulating materials, the surface parameters were measured and the flashover experiment was performed in vacuum after electron beam irradiation. First, several typical polymeric samples, such as polytetrafluoroethylene (PTFE), polymethylmethacrylate (PMMA) and polyamide (PA6), were exposed by different energy electron beam from 10 keV to 30 keV and in different radiation times. Then the surface parameters, such as surface topography, chemical group and surface roughness, were measured for insulating samples in different processing conditions. Since the surface trap distribution is closely related to the microscopic structure and defects, the trap parameters of PA6 samples were deduced based on the surface potential data. In this measurement, the polymeric surface was charged by needle-to-plane corona discharge, and then the surface potential was measured by Kelvin electrostatic probe. The surface flashover experiment in vacuum was performed using finger-type plane-electrodes, and the relationship between electron beam energy, irradiation time, and flashover voltage was analyzed. The experiment results indicate that the surface trap energy level is deeper and the trap density is greater after electron irradiation, and the surface flashover voltages for several materials are also improved to different degrees. Combined the macroscopic dielectric parameters with the microscopic one, the effect of electron irradiation on surface insulating property was analyzed

Effect of Electron Beam Irradiation on Surface Dielectric Properties of Polymeric Materials

Секция 3. Модификация свойств материалов = Section 3. Modification of Material PropertiesThe polymeric materials could degrade due to the electron irradiation, photo-irradiation and heat effect following with the surface flashover in vacuum. On the other hand, the surface insulating performance could be improved by electron irradiation. In order to study the effect of electron irradiation on the insulating materials, the surface parameters were measured and the flashover experiment was performed in vacuum after electron beam irradiation. First, several typical polymeric samples, such as polytetrafluoroethylene (PTFE), polymethylmethacrylate (PMMA) and polyamide (PA6), were exposed by different energy electron beam from 10 keV to 30 keV and in different radiation times. Then the surface parameters, such as surface topography, chemical group and surface roughness, were measured for insulating samples in different processing conditions. Since the surface trap distribution is closely related to the microscopic structure and defects, the trap parameters of PA6 samples were deduced based on the surface potential data. In this measurement, the polymeric surface was charged by needle-to-plane corona discharge, and then the surface potential was measured by Kelvin electrostatic probe. The surface flashover experiment in vacuum was performed using finger-type plane-electrodes, and the relationship between electron beam energy, irradiation time, and flashover voltage was analyzed. The experiment results indicate that the surface trap energy level is deeper and the trap density is greater after electron irradiation, and the surface flashover voltages for several materials are also improved to different degrees. Combined the macroscopic dielectric parameters with the microscopic one, the effect of electron irradiation on surface insulating property was analyzed

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin

Language distance in orthographic transparency affects cross-language pattern similarity between native and non-native languages.

How native and non-native languages are represented in the brain is one of the most important questions in neurolinguistics. Much research has found that the similarity in neural activity of native and non-native languages are influenced by factors such as age of acquisition, language proficiency, and language exposure in the non-native language. Nevertheless, it is still unclear how the similarity between native and non-native languages in orthographic transparency, a key factor that affects the cognitive and neural mechanisms of phonological access, modulates the cross-language similarity in neural activation and which brain regions show the modulatory effects of language distance in orthographic transparency. To address these questions, the present study used representational similarity analysis (RSA) to precisely estimate the neural pattern similarity between native language and two non-native languages in Uyghur-Chinese-English trilinguals, whose third language (i.e., English) was more similar to the native language (i.e., Uyghur) in orthography than to their second language (i.e., Chinese). Behavioral results revealed that subjects responded faster to words in the non-native language with more similar orthography to their native language in the word naming task. More importantly, RSA revealed greater neural pattern similarity between Uyghur and English than between Uyghur and Chinese in select brain areas for phonological processing, especially in the left hemisphere. Further analysis confirmed that those brain regions represented phonological information. These results provide direct neuroimaging evidence for the modulatory effect of language distance in orthographic transparency on cross-language pattern similarity between native and non-native languages during word reading

Language distance in orthographic transparency affects cross-language pattern similarity between native and non-native languages.

How native and non-native languages are represented in the brain is one of the most important questions in neurolinguistics. Much research has found that the similarity in neural activity of native and non-native languages are influenced by factors such as age of acquisition, language proficiency, and language exposure in the non-native language. Nevertheless, it is still unclear how the similarity between native and non-native languages in orthographic transparency, a key factor that affects the cognitive and neural mechanisms of phonological access, modulates the cross-language similarity in neural activation and which brain regions show the modulatory effects of language distance in orthographic transparency. To address these questions, the present study used representational similarity analysis (RSA) to precisely estimate the neural pattern similarity between native language and two non-native languages in Uyghur-Chinese-English trilinguals, whose third language (i.e., English) was more similar to the native language (i.e., Uyghur) in orthography than to their second language (i.e., Chinese). Behavioral results revealed that subjects responded faster to words in the non-native language with more similar orthography to their native language in the word naming task. More importantly, RSA revealed greater neural pattern similarity between Uyghur and English than between Uyghur and Chinese in select brain areas for phonological processing, especially in the left hemisphere. Further analysis confirmed that those brain regions represented phonological information. These results provide direct neuroimaging evidence for the modulatory effect of language distance in orthographic transparency on cross-language pattern similarity between native and non-native languages during word reading