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Humans gain knowledge about threats not only from their own experiences but also from observing others' behavior. A neutral stimulus is associated with a threat stimulus for several times and the neutral stimulus will evoke fear responses, which is known as fear conditioning. When encountering a new event that is similar to one previously associated with a threat, one may feel afraid and produce fear responses. This is called fear generalization. Previous studies have mostly focused on fear conditioning and generalization based on direct learning, but few have explored how observational fear learning affects fear conditioning and generalization. To the best of our knowledge, no previous study has focused on the neural correlations of fear conditioning and generalization based on observational learning. In the present study, 58 participants performed a differential conditioning paradigm in which they learned the associations between neutral cues (i.e., geometric figures) and threat stimuli (i.e., electric shock). The learning occurred on their own (i.e., direct learning) and by observing other participant's responses (i.e., observational learning); the study used a within-subjects design. After each learning condition, a fear generalization paradigm was conducted by each participant independently while their behavioral responses (i.e., expectation of a shock) and electroencephalography (EEG) recordings or responses were recorded. The shock expectancy ratings showed that observational learning, compared to direct learning, reduced the differentiation between the conditioned threatening stimuli and safety stimuli and the increased shock expectancy to the generalization stimuli. The EEG indicated that in fear learning, threatening conditioned stimuli in observational and direct learning increased early discrimination (P1) and late motivated attention (late positive potential [LPP]), compared with safety conditioned stimuli. In fear generalization, early discrimination, late motivated attention, and orienting attention (alpha-event-related desynchronization [alpha-ERD]) to generalization stimuli were reduced in the observational learning condition. These findings suggest that compared to direct learning, observational learning reduces differential fear learning and increases the generalization of fear, and this might be associated with reduced discrimination and attentional function related to generalization stimuli. • Here, we provide the first evidence of the electrophysiological correlates of fear generalization after observational learning. • Our finding indicates that observational learning reduces the differentiation between threatening and safety stimuli in fear learning and enhances fear generalization compared with direct learning. • Moreover, observational learning is accompanied by a reduction in discrimination and attention to generalization stimuli compared with direct learning. [ABSTRACT FROM AUTHOR]

The application of acidification technology changes the pore structure and microcrystalline structure of coal, which affects the combustion characteristics of coal products. In this study, three inorganic acids (HF, HCl, and HNO 3) were used to treat bituminous coal, and low-temperature nitrogen adsorption tests, X-ray diffraction analysis (XRD), and simultaneous thermal analysis (STA) were carried out. Fractal dimension theory, microcrystalline structure analysis theory, and thermal analysis kinetic theory analyze the pores, crystallites, and combustion characteristics of raw coal and acidified coal, respectively. The correlation between variables can be judged by the Pearson correlation coefficient. The research results show that compared with the microcrystalline structure, the pores determine the acidified coal's synthetical combustion index S , maximum value of differential thermal analysis DTA top and maximum mass loss rate dW/dt max to a greater extent. Hydrofluoric acid increased the average pore size of the raw coal by 38.66%, resulting in an 11.60% increase in dW/dt max. The ignition temperature T 5 is the composite performance of the aromatic structure's thermal stability and the microcrystalline structure's ordering. The aromaticity f a determines the reaction in the oxidation weight gain stage to a greater extent and can be used as a new indicator to characterise the spontaneous combustion tendency of coal. The pore and microcrystalline structures jointly determine the activation energy E 3 in the combustion stage. [Display omitted] • Comparison of the effects of three inorganic acids on coal combustion performance. • Explaining evolution law in terms of pore and microcrystalline structures. • The aromaticity determines the reaction in the oxidation weight gain stage. • The ignition temperature depends on thermal stability and crystallite ordering. [ABSTRACT FROM AUTHOR]

Acid fracturing technology is applied to increase the permeability of coal seams to promote gas extraction, but the effects of acid solutions on coal combustion cannot be ignored. This paper focuses on HF, HCl, and HNO 3. The Bagchi method determines the mechanism function of each nonisothermal TG stage of acidified coal. Combining FTIR, TG-DTA, and KTA, this paper studies the combustion performance of acidified coal from the perspectives of the coal molecular structure evolution, combustion phenomenon, and kinetics. The results show that inorganic acids can destroy C=O in coal, resulting in the carbonyl groups in the HF, HCl, and HNO 3 samples decreasing to 21%, 35%, and 0% of that in the RAW samples, respectively. Inorganic acid significantly enhances the reaction rate and thermal effect of combustion stage and improves the comprehensive combustion performance of coal, and the effect is in the order of HF > HNO 3 >HCl. Inorganic acids change the most likely mechanism function of initial weight loss stage. The oxidation increase of the HNO 3 sample is greater than the corrosion weight loss, resulting in a corrosion ratio of −1.74%. HF and HCl reduce the activation energy of oxidation weight gain stage and increase the spontaneous combustion tendency of coal samples. • The most likely mechanism functions of acidified coals are determined. • The combustion mechanism is revealed from the evolution of functional groups. • Inorganic acids reduce carbonyl groups in coal by more than 65%. • The corrosion ratio of the HNO 3 coal sample due to oxidation is negative. • HF and HCl increase the spontaneous combustion tendency of coal. [ABSTRACT FROM AUTHOR]

• In the microporous stage, hydrofluoric acid is mainly used for pore formation. • Exceeding a certain pressure, acid will no longer play a role in CO 2 adsorption. • The initial desorption amount depends on the coal's pore volume. In order to cooperate with the application of acid fracturing enhanced permeability technology, this paper proposes using CO 2 adsorption and desorption system and low-temperature nitrogen adsorption method to conduct experimental research on the changes in the CO 2 adsorption and desorption characteristics of coal after treatment with HF solution and HNO 3 solution. In addition, Combined with X-ray diffraction experiments, the indirect relationship between mineral dissolution and adsorption and desorption characteristics is analyzed. The research results show that: The acid solution transforms the pore structure of coal through the dissolution of mineral impurities. In the microporous stage, hydrofluoric acid is mainly used for pore formation and nitric acid for pore expansion. Acid treatment changed the adsorption characteristics of the coal sample, reduced the saturated adsorption capacity a , and increased the adsorption constant b. And a and b were linearly related to the specific surface area and pore volume, respectively. This paper has determined that adsorption equilibrium pressure 1.91 MPa and 2.57 MPa are the enhanced adsorption thresholds of hydrofluoric acid and nitric acid, respectively. After exceeding the threshold, acid treatment will no longer play a role in increasing CO 2 adsorption. In addition, the acid treatment also changed the desorption characteristics of the coal sample, increased the initial desorption amount and the final desorption amount, shortened the desorption time. The effect of hydrofluoric acid was more evident than that of nitric acid. [ABSTRACT FROM AUTHOR]

• Supervised training of deep learning models requires large labeled datasets. • Label noise can significantly impact the performance of deep learning models. • We critically review recent progress in handling label noise in deep learning. • We experimentally study this problem in medical image analysis and draw useful conclusions. Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter- and intra-observer variability, and erroneous predictions may influence decisions that directly impact human health. In this paper, we first review the state-of-the-art in handling label noise in deep learning. Then, we review studies that have dealt with label noise in deep learning for medical image analysis. Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community. To help achieve a better understanding of the extent of the problem and its potential remedies, we conducted experiments with three medical imaging datasets with different types of label noise, where we investigated several existing strategies and developed new methods to combat the negative effect of label noise. Based on the results of these experiments and our review of the literature, we have made recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis. We hope that this article helps the medical image analysis researchers and developers in choosing and devising new techniques that effectively handle label noise in deep learning. [ABSTRACT FROM AUTHOR]

• Evidence from the questionnaire, behavioral and cognitive neural levels showed that adolescents with high autistic traits had significantly lower empathy ability than those with low autistic traits. • The N2 and LPP visualized the time course of cognitive and emotional processes. Based on the mind-blindness hypothesis, a large number of studies have shown that individuals with autism-spectrum disorder (ASD) and autistic traits have empathy deficits. However, the recent double empathy theory contradicts the mind-blindness hypothesis and suggests that individuals with ASD and autistic traits do not necessarily lack empathy. Thus, the presence of empathy deficits in individuals with ASD and autistic traits is still controversial. We recruited 56 adolescents (28 high autistic traits, 28 low autistic traits, 14–17 years old) in this study to explore the relationship between empathy and autistic traits. The study participants were required to undertake the pain empathy task, during which the electroencephalograph (EEG) activities were recorded. Our results show that empathy was negatively associated with autistic traits at the questionnaire, behavioral, and EEG levels. Our results also suggest ed that empathy deficits in adolescents with autistic traits may be manifested mainly in the late stages of cognitive control processing. [ABSTRACT FROM AUTHOR]

• A specialized segmentation model that can address blurry or occluded edges in ultrasound images. • A differentiable boundary selection module that can automatically focus on the marginal area. • A GCN-based boundary rendering module that can incorporate global contour information. • A unified framework that can perform segmentation and classification simultaneously. Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models. [Display omitted] [ABSTRACT FROM AUTHOR]

• Jointly employing four modalities of US image, namely B-mode, Doppler, SWE and SE. • A self-supervised consistent loss is proposed to extract multi-modal information. • Dynamically weighting different modalities by a Reinforcement Learning strategy. • Handling the possible missing data issue during testing by a novel recovery block. [Display omitted] Recently, more clinicians have realized the diagnostic value of multi-modal ultrasound in breast cancer identification and began to incorporate Doppler imaging and Elastography in the routine examination. However, accurately recognizing patterns of malignancy in different types of sonography requires expertise. Furthermore, an accurate and robust diagnosis requires proper weights of multi-modal information as well as the ability to process missing data in practice. These two aspects are often overlooked by existing computer-aided diagnosis (CAD) approaches. To overcome these challenges, we propose a novel framework (called AW3M) that utilizes four types of sonography (i.e. B-mode, Doppler, Shear-wave Elastography, and Strain Elastography) jointly to assist breast cancer diagnosis. It can extract both modality-specific and modality-invariant features using a multi-stream CNN model equipped with self-supervised consistency loss. Instead of assigning the weights of different streams empirically, AW3M automatically learns the optimal weights using reinforcement learning techniques. Furthermore, we design a light-weight recovery block that can be inserted to a trained model to handle different modality-missing scenarios. Experimental results on a large multi-modal dataset demonstrate that our method can achieve promising performance compared with state-of-the-art methods. The AW3M framework is also tested on another independent B-mode dataset to prove its efficacy in general settings. Results show that the proposed recovery block can learn from the joint distribution of multi-modal features to further boost the classification accuracy given single modality input during the test. [ABSTRACT FROM AUTHOR]

• We propose a novel Multi-Agent Reinforcement Learning framework for detecting multiple planes simultaneously in challenging 3D US datasets. • We propose an RNN based collaborative module to enhance the communication among agents effectively. • We adopt Neural Architecture Search method to obtain the optimal agents and the collaborative RNN module automatically. • Our proposed method performs well on both normal and abnormal cases in pelvic US dataset. [Display omitted] 3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one shot. Thus, automatically localizing SPs in 3D US has the potential to improve user-independence and scanning-efficiency. However, manual SP localization in 3D US is challenging because of the low image quality, huge search space and large anatomical variability. In this work, we propose a novel multi-agent reinforcement learning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our contribution is four-fold. First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets. Second, we equip the MARL system with a recurrent neural network (RNN) based collaborative module, which can strengthen the communication among agents and learn the spatial relationship among planes effectively. Third, we explore to adopt the neural architecture search (NAS) to automatically design the network architecture of both the agents and the collaborative module. Last, we believe we are the first to realize automatic SP localization in pelvic US volumes, and note that our approach can handle both normal and abnormal uterus cases. Extensively validated on two challenging datasets of the uterus and fetal brain, our proposed method achieves the average localization accuracy of 7.03 ∘ /1.59 mm and 9.75 ∘ /1.19 mm. Experimental results show that our light-weight MARL model has higher accuracy than state-of-the-art methods. [ABSTRACT FROM AUTHOR]