Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202

Case report: A novel 10.8-kb deletion identified in the β-globin gene through the long-read sequencing technology in a Chinese family with abnormal hemoglobin testing results

BackgroundThalassemia is a common inherited hemoglobin disorder caused by a deficiency of one or more globin subunits. Substitution variants and deletions in the HBB gene are the major causes of β-thalassemia, of which large fragment deletions are rare and difficult to be detected by conventional polymerase chain reaction (PCR)-based methods.Case reportIn this study, we reported a 26-year-old Han Chinese man, whose routine blood parameters were found to be abnormal. Hemoglobin testing was performed on the proband and his family members, of whom only the proband's mother had normal parameters. The comprehensive analysis of thalassemia alleles (CATSA, a long-read sequencing-based approach) was performed to identify the causative variants. We finally found a novel 10.8-kb deletion including the β-globin (HBB) gene (Chr11:5216601-5227407, GRch38/hg38) of the proband and his father and brother, which were consistent with their hemoglobin testing results. The copy number and exact breakpoints of the deletion were confirmed by multiplex ligation-dependent probe amplification (MLPA) and gap-polymerase chain reaction (Gap-PCR) as well as Sanger sequencing, respectively.ConclusionWith this novel large deletion found in the HBB gene in China, we expand the genotype spectrum of β-thalassemia and show the advantages of long-read sequencing (LRS) for comprehensive and precise detection of thalassemia variants

Design and operation of a Peucedani Radix weeding device based on YOLOV5 and a parallel manipulator

To avoid excessive use of herbicides in the weeding operations of Peucedani Radix, a common Chinese herb, a precision seedling avoidance and weeding agricultural robot was designed for the targeted spraying of herbicides. The robot uses YOLOV5 combined with ExG feature segmentation to detect Peucedani Radix and weeds and obtain their corresponding morphological centers. Optimal seedling avoidance and precise herbicide spraying trajectories are generated using a PSO-Bezier algorithm based on the morphological characteristics of Peucedani Radix. Seedling avoidance trajectories and spraying operations are executed using a parallel manipulator with spraying devices. The validation experiments showed that the precision and recall of Peucedani Radix detection were 98.7% and 88.2%, respectively, and the weed segmentation rate could reach 95% when the minimum connected domain was 50. In the actual Peucedani Radix field spraying operation, the success rate of field precision seedling avoidance herbicide spraying was 80.5%, the collision rate between the end actuator of the parallel manipulator and Peucedani Radix was 4%, and the average running time of the parallel manipulator for precision herbicide spraying on a single weed was 2 s. This study can enrich the theoretical basis of targeted weed control and provide reference for similar studies

Association of gestational age at birth with subsequent suspected Developmental Coordination Disorder in early childhood

Importance. It remains unknown whether children born at different degrees of prematurity, early-term and post-term might have a higher risk of developing Developmental Coordination Disorder (DCD) compared to completely full-term children (39-40 gestational weeks). Objective. To differentiate between suspected DCD in children with different gestational ages based on a national representative sample in China. DESIGN, SETTING, AND PARTICIPANTS We conducted a retrospective cohort study in China from 2018 to 2019. A total of 152,433 children from 2,403 public kindergartens in 551 cities of China aged 3-5 years old were included in the final analysis. The association between gestational age and motor performance was investigated. A multi-level regression model was developed to determine the strength of association for different gestational ages associated with suspected DCD when considering kindergartens as clusters. Main outcomes and measures. Children’s motor performance was assessed using the Little Developmental Coordination Disorder Questionnaire (LDCDQ), completed by parents. Gestational age was determined according to the mother’s medical records. Results. Of the 152,433 children aged 3-5 years old, 80,370 (52.7%) were male, and 72,063 (47.3%) were female. There were 45,052 children aged 3 years old (29.6%), 59,796 aged 4 years old(39.2%), and 47,585 children aged 5 years old (31.2%). The LDCDQ total scores for very-preterm (β=-1.74, 95%CI: -1.98, 1.50; p<0.001), moderately-preterm (β=-1.24, 95%CI: -1.60, -0.89; p<0.001), late-preterm (β=-0.92, 95%CI: -1.08, -0.76; p<0.001), early-term (β=-0.36, 95%CI: -0.46, -0.25; p<0.001) and post-term children (β=-0.47, 95%CI: -0.67, -0.26; p<0.001) were significantly lower than full-term children when adjusting for child, family and maternal health characteristics. The very-preterm (OR=1.35, 95%CI: 1.23,1.48; p<0.001), moderately-preterm (OR=1.18, 95%CI: 1.02, 1.36; p<0.001), late-preterm (OR =1.24, 95%CI: 1.16,1.32; p<0.001), early-term (OR =1.11, 95%CI: 1.06,1.16; p<0.001) and post-term children (OR =1.167, 95%CI: 1.07, 1.27; p<0.001) were more likely to fall in the suspected Developmental Coordination Disorder (DCD) category on the LDCDQ compared with completely full-term children after adjusting for the same characteristics. The associations between different gestational ages and suspected DCD were stronger in boys and older (5 year old) children (each p<0.05). Conclusions and relevance. We found significant associations between every degree of prematurity at birth, early-term and post-term birth with suspected DCD when compared with full-term birth. Our findings have important implications for understanding motor development in children born at different gestational ages. Long-term follow-up and rehabilitation interventions should be considered for early- and post-term born children

Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy

In this study, Near-infrared (NIR) spectroscopy was adopted for the collection of 1200 spectra of three types of walnut materials after breaking the shells. A detection model of the walnut shell-kernel separation accuracy was established. The preprocessing method of de-trending (DT) was adopted. A classification model based on a support vector machine (SVM) and an extreme learning machine (ELM) was established with the principal component factor as the input variable. The effect of the penalty value (C) and kernel width (g) on the SVM model was discussed. The selection criteria of the number of hidden layer nodes (L) in the ELM model were studied, and a genetic algorithm (GA) was used to optimize the input layer weight (W) and the hidden layer threshold value (B) of the ELM. The results revealed that the classification accuracy of SVM and ELM models for the shell, kernel, and chimera was 97.78% and 97.11%. The proposed method can serve as a reference for the detection of walnut shell-kernel separation accuracy

Tailoring micro/nano-materials with special wettability for biomedical devices

Wettability, as a fundamental property of interface materials, is becoming increasingly prominent in frontier research areas, such as water harvesting, microfluidics, biomedicine, sensors, decontamination, wearables, and micro-electromechanical systems. Taking advantage of biological paradigm inspirations and manufacturing technology advancements, diverse wettability materials with precisely customized micro/nano-structures have been developed. As a result of these achievements, wettability materials have significant technical ramifications in sectors spanning from academics to industry, agriculture, and biomedical engineering. Practical applications of wettability-customized materials in medical device domains have drawn significant scientific interest in recent decades due to the increased emphasis on healthcare. In this review, recent advances of wettability-customized micro/nano-materials for biomedical devices are presented. After briefly introducing the natural wettable/non-wettable phenomena, the fabrication strategies and novel processing techniques are discussed. The study emphasizes the application progress of biomedical devices with customized wettability. The future challenges and opportunities of wettability-customized micro/nano-materials are also provided

Functional microneedles for wearable electronics

Abstract With an ideal comfort level, sensitivity, reliability, and user‐friendliness, wearable sensors are making great contributions to daily health care, nursing care, early disease discovery, and body monitoring. Some wearable sensors are imparted with hierarchical and uneven microstructures, such as microneedle structures, which not only facilitate the access to multiple bio‐analysts in the human body but also improve the abilities to detect feeble body signals. In this paper, we present the promising applications and latest progress of functional microneedles in wearable sensors. We begin by discussing the roles of microneedles as sensing units, including how the signals are captured, converted, and transmitted. We also introduce the microneedle‐like structures as power units, which depend on triboelectric or piezoelectric effects, etc. Finally, we summarize the cutting‐edge applications of microneedle‐based wearable sensors in biophysical signal monitoring and biochemical analyte detection, and provide critical thinking on their future perspectives

Optimization of Roasted Green Tea Winnowing via Fluid&ndash;Solid Interaction Experiments and Simulations

In the tea industry, achieving a high winnowing accuracy to produce high-quality tea is a complex challenge. The complex shape of the tea leaves and the uncertainty of the flow field lead to the difficulty in determining the wind selection parameters. The purpose of this paper was to determine the accurate wind selection parameters of tea through simulation and improve the precision of tea wind selection. This study used three-dimensional modeling to establish a high-precision simulation of dry tea sorting. The simulation environment of the tea material, flow field, and wind field wall were defined using a fluid&ndash;solid interaction method. The validity of the simulation was verified via experiments. The actual test found that the velocity and trajectory of tea particles in the actual and simulated environments were consistent. The numerical simulations identified wind speed, wind speed distribution, and wind direction as the main factors affecting the winnowing efficacy. The weight-to-area ratio was used to define the characteristics of different types of tea materials. The indices of discrete degree, drift limiting velocity, stratification height, and drag force were employed to evaluate the winnowing results. The separation of tea leaves and stems is best in the range of the wind angle of 5&ndash;25 degrees under the same wind speed. Orthogonal and single-factor experiments were conducted to analyze the influence of wind speed, wind speed distribution, and wind direction on wind sorting. The results of these experiments identified the optimal wind-sorting parameters: a wind speed of 12 m s&minus;1, wind speed distribution of 45%, and wind direction angle of 10&deg;. The larger the difference between the weight-to-area ratios of the tea leaves and stems, the more optimized the wind sorting. The proposed model provides a theoretical basis for the design of wind-based tea-sorting structures

A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification

Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 &times; 3 convolution kernels in CNNs with 1 &times; 1 convolution kernels, decomposes the original one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount