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Deep convolutional neural networks (DCNNs) have achieved impressive performance in image recognition, object detection, etc. Nevertheless, they are susceptible to adversarial attacks and interferential noise. Adversarial attacks can mislead DCNN models by manipulating input data with small perturbations, causing security risks to intelligent system applications. Comparatively, these small perturbations have very limited perceptual impact on humans. Therefore, the research on brain-inspired adversarial robust models has gained increasing attention. Beginning from the adversarial attack concepts and schemes, we present a review of the conventional adversarial attack and defense methods and compare the advantages and differences between brain-inspired robust neural networks and the conventional adversarial defense methods. We further review the existing adversarial robust DCNN models, including methods inspired by the early visual systems and supervised by neural signals. Representative examples have validated the efficacy of brain-inspired methods for designing adversarial robust models, which may benefit the further research and development of brain-inspired robust deep convolutional neural networks and the intelligent system applications. [ABSTRACT FROM AUTHOR]

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models. [ABSTRACT FROM AUTHOR]

Featured Application: A general framework for functional integration evaluation of human brain is proposed, which effectively combines whole-brain functional connectivity analysis with effective connectivity analysis based on Dynamic Causal Modeling. Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain's intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain's intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM. [ABSTRACT FROM AUTHOR]

The visual quality and spatial distribution of architectural styles represent a city's image, influence inhabitants' living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city's image. [ABSTRACT FROM AUTHOR]

Dividing a pre-defined brain region into several heterogenous subregions is crucial for understanding its functional segregation and integration. Due to the high dimensionality of brain functional features, clustering is often postponed until dimensionality reduction in traditional parcellation frameworks occurs. However, under such stepwise parcellation, it is very easy to fall into the dilemma of local optimum since dimensionality reduction could not take into account the requirement of clustering. In this study, we developed a new parcellation framework based on the discriminative embedded clustering (DEC), combining subspace learning and clustering in a common procedure with alternative minimization adopted to approach global optimum. We tested the proposed framework in functional connectivity-based parcellation of the hippocampus. The hippocampus was parcellated into three spatial coherent subregions along the anteroventral–posterodorsal axis; the three subregions exhibited distinct functional connectivity changes in taxi drivers relative to non-driver controls. Moreover, compared with traditional stepwise methods, the proposed DEC-based framework demonstrated higher parcellation consistency across different scans within individuals. The study proposed a new brain parcellation framework with joint dimensionality reduction and clustering; the findings might shed new light on the functional plasticity of hippocampal subregions related to long-term navigation experience. [ABSTRACT FROM AUTHOR]

This study conducted a nationwide specific assessment of soil chromium (Cr) contamination status in 506 of China's industrial regions. The overall soil Cr concentrations were 0.74–37,967.33 mg/kg, and the soil Cr content in 4.15% of the regions exceeded the reference screening value (2500 mg/kg). Geochemical accumulation index (Igeo) and monomial potential ecological risk index (E) revealed Cr salt production and tanning were the primary control industries. The non-carcinogenic risks posed by Cr salt production and tanning industries were higher than the national average values, and children were the most vulnerable groups. The heavily polluted regions were mainly located at the Yangtze River Delta, the Bohai Rim, the Pearl River Delta, the Yangtze River Basin, and the Yellow River Basin. The Yangtze River Delta was further identified as the high priority control area based on the class distribution of Igeo and E. Regression analysis showed the soil Cr concentrations in industrial regions increased during 2002–2009 and then turned into a declining trend in 2009–2021. This paper gives detailed insights into soil Cr pollution status in industrial regions across China and the results may serve as references for formulating tailored control measures for different industries and areas. [ABSTRACT FROM AUTHOR]

A large amount of recent literature has focused on impaired iron homeostasis in the pathophysiology of schizophrenia. Specifically, microarray analysis has illustrated associations between the transferrin locus and schizophrenia. To elaborate on the effects of transferrin on schizophrenia and its psychiatric phenotypes, our study aimed to investigate whether transferrin gene polymorphism was correlated with cognitive deficits and clinical symptoms in schizophrenia. We recruited 564 patients with chronic schizophrenia and 422 healthy controls (HCs) in a Han Chinese population, collected phenotypic data, and genotyped the rs3811655 polymorphism of the transferrin gene. Our results showed that the rs3811655 polymorphism was related to cognitive performance in both patients and HCs, as well as negative symptoms in patients (all p < 0.05), and patients carrying at least one G-allele showed worsened cognition/severe negative symptoms (all p < 0.05). Further analyses also found that the rs3811655 polymorphism in combination with cognition may exert small but significant contributions to the negative (β = −0.10, t = −2.48, p < 0.05) or total psychiatric symptoms (β = −0.08, t = −1.92, p < 0.05) in patients. Our findings indicated that the rs3811655 polymorphism may be implicated in the cognitive deficits of schizophrenia and HCs as well as psychiatric symptoms in patients, which suggested the possible iron regulatory mechanism in the pathology of schizophrenia. [ABSTRACT FROM AUTHOR]

Gadolinium zirconate with excellent high-temperature phase stability and sintering resistance has become a very promising candidate material for a new generation of thermal barrier coatings (TBCs). However, the low fracture toughness of gadolinium zirconate greatly limits its application. In this study, gadolinium zirconate (GZ) and two kinds of toughened gadolinium zirconate (GZ/YSZ prepared by mixed powder of Gd2Zr2O7 and YSZ and GSZC prepared by (Gd0.925Sc0.075)2(Zr0.7Ce0.3)2O7 powder) double-layered TBCs were prepared by atmospheric plasma spraying (APS). The fracture toughness of the GZ/YSZ coating and GSZC coating were 9 times and 3.5 times that of GZ coating, respectively. The results of thermal shock test showed that the three TBCs exhibit different failure mechanisms. During the thermal shock test, cracking occurred at the interfaces between the YSZ layer and the BC or GZ/YSZ layer, while GSZC TBC failed due to premature cracking inside the GSZC layer. The particle erosion rate of the GZ, GZ/YSZ, and GZSC coatings were 1.81, 0.48, and 1.01 mg/g, respectively, indicating that the erosion resistance of coatings is related to their fracture toughness. Furthermore, the superior erosion resistance of the GZ/YSZ and GSZC coatings can be attributed to the conversion of crack propagation path during the erosion test. [ABSTRACT FROM AUTHOR]

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain. [ABSTRACT FROM AUTHOR]