bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Comprehensive Evaluation Method of Radar
Authors: Duanyang Shi , Qiang Lin , Bing Hu , Xinyu Yan 
Published: June 2022

Abstract
Aiming at the difficult problem of radar equipment evaluation caused by complex structure and many influencing factors, a comprehensive evaluation method of radar is proposed. This paper starts with the concept, elements and steps of comprehensive evaluation, and introduces the process of comprehensive evaluation in detail. The types of index weighting methods and the historical process of evaluation methods at home and abroad are summarized. According to the practical working experience, the radar comprehensive evaluation index system is constructed, and combining the characteristics of subjective and objective weighting methods, the combined subjective and objective weighting model is designed. On the basis of analysing the results of various evaluation methods, a combination model of evaluation results is constructed. The radar equipment is evaluated according to the scoring data of experts on the supportability of radar equipment. The effectiveness of the method is verified by an example.
A Brief Analysis of the Development of Chinese Virtual Idol Industry Empowered by 5G+Motion Capture Technology——Taking the Virtual Idol Group A-SOUL as an Example
Authors: Wang Y. Q. 
Published: June 2022

Abstract
A-SOUL, a virtual idol girl group, debuted in December 2020, and in just one year, it has turned around from a very poor reputation to become the most influential subculture IP in China today. virtual idols are real people performing in virtual spaces on the internet using virtual images, and the superior motion capture technology, visual design, and 5G network are the key reasons why A-SOUL has been able to stand out from the crowd of virtual anchors. A-SOUL’s superior motion capture technology, visual design, and 5G network are what make it stand out from the crowd of virtual hosts. This paper will use A-SOUL as an example to analyze the current situation, dilemmas, and future development trends of virtual anchor technology in China, and explore the possibilities of virtual idols as an opportunity to drive technological and social change in China.
Score-CAM++: Class Discriminative Localization with Feature Map Selection
Authors: Chen Yifan , Zhong Guoqiang 
Published: June 2022

Abstract
In recent years, explaining convolutional neural networks (CNN) has received increasing attention since it helps to build humans’ trust on CNNs by exposing their inference basis. In this field, generating intuitive saliency maps that highlight the input regions most related to model decisions is one of the popular approaches. Building on a state-of-the-art saliency method named Score-CAM, we propose Score-CAM++ to generate better saliency maps with higher efficiency (when compared to Score-CAM). Different from Score-CAM, which adopts all the feature maps in target layer to produce saliency maps, we propose the “feature map selection” operation to select the feature maps that capture the “positive” information (i.e., the features whose increment in intensity leads to an increase in target score). Then the selected feature maps are up-sampled as masks to perturb the input and compute the weights of the corresponding feature maps. Finally, the linear combination of the weighted feature maps forms the saliency map. Compared with Score-CAM, the experiments based on VGG-16 show that our method saves 25%-30% of the time when generating saliency maps. Meanwhile, our conducted evaluations, both subjective and objective, show that our method provides better visual explanations when compared to the previous saliency methods.
Supervised Contrastive Representation Embedding Based on Transformer for Few-Shot Classification
Authors: Wu Jianxin , Tian Xiangtao , Zhong Guoqiang 
Published: June 2022

Abstract
Few-shot learning as an emerging problem has attracted intensive attention in the data-limited situations of Computer Vision. Recently, massive works concentrate on exploiting meta learning to improve performance of recognition. However, most of them overlook the intra-class and inter-class relationships within the support set during episodic training, which are significant and important for the tasks of downstream. To tackle this problem, we present an efficient supervised contrastive representation embedding for few-shot classification. Specially, (1) we employ Swin Transformer as the backbone to replace CNN architecture in order to explore the huge potential of transformer-based backbone for the field of few-shot learning. (2) We introduce supervised contrastive loss to meta learning to take good advantage of extremely limited relations for the first time. Combined the classifier based on metric learning, extensive experiments have demonstrated the efficiency of the representation embedding. We conduct experiments and establish competitive results on two widely-used few-shot classification benchmarks: Fewshot-CIFAR 100 (FC-100) and MiniImageNet.
Classification of Epileptic Brain Signals of College Students Based on Deep Learning
Authors: Zhang Fuchun , Qi Lei , Li Meng , Wang Baiyang , Yang Yue , Zheng Guiyue 
Published: June 2022

Abstract
Epilepsy, as a common nervous system disease in the world, urgently needs effective methods for diagnosis and treatment. Scalp electroencephalogram (EEG) contains a lot of physiological and pathological information, which plays a very important role in the diagnosis of brain diseases such as epilepsy. At present, the clinical analysis of EEG signal is mainly based on the clinician’s visual analysis, which makes the clinician’s task heavy, and there is no quantitative standard for the analysis results. Therefore, the automatic classification of epileptic EEG signals has great potential in current clinical application. Based on the deep learning VGG16 convolutional neural network model, a method for automatic detection of seizure signals is proposed in this paper. Firstly, the samples are preprocessed, then enter the VGG16 convolutional neural network model for training, and finally input the validation set for validation. The experimental results indicate that this model can distinguish the pre-ictal EEG, interictal EEG and ictal EEG.
Deep Face-Swap Model Combining Attention Mechanism and CycleGAN
Authors: Liu Kai , Li Bicheng , Li Jiale 
Published: June 2022

Abstract
Deep face swapping is a hot field in computer vision, which uses deep learning technology to replace the face in the original image while keeping other contents unchanged. To solve the problem of discontinuities across the blending boundaries in existing mainstream deepfake methods, we propose a face swapping model combining attention mechanism and CycleGAN. Firstly, a generator incorporating the attention module is used to obtain the generated graph. Then, the discriminator based on PatchGAN is used to discriminate if the graph is synthesized. Finally, the least square loss and SmoothL1 loss are used to improve the loss function. Experimental results manifest that our method reduces the difference by 0.43 in facial region and increases the similarity by 0.56 in background region. The model we proposed can improve the quality of face swapping images effectively, and enrich the existing face swapping methods, which has great significance for both generation and detection of deep face swapping research.
Image Segmentation of Endoscopic Cataract Based on Semantic Segmentation
Authors: Chen Jingshi , Ye Haili , Yin Huayi , Wang Da-Han , Li Jianmin 
Published: June 2022

Abstract
Medical imaging technology has become one of the indispensable computer-assisted intervention methods in clinical disease diagnosis and treatment, including identifying and locating lesion areas, detecting and segmenting tissue and organ lesions in different modalities. The wide application of medical image analysis in clinical examination and medical auxiliary diagnosis has effectively improved the diagnostic efficiency of doctors. In this paper, we propose a novel and effective model to achieve semantic segmentation of cataract datasets. This model uses the self-supervised method BYOL for parameter pre-training, which improves the model’s ability to extract image consistency features. In addition, we have added a lightweight Coordinate attention mechanism to the backbone network to enable the model to independently learn the correlation between the channel and the space and enhance the ability of network feature expression. Experiments are conducted on the cataract endoscopy fine-grained segmentation data set, showing the effectiveness of the proposed method for segment the organs and surgical instruments in the cataract surgical microscope image, which demonstrates the accuracy and robustness of the proposed method.
Branching pattern of flexible trees for environmental load mitigation
Authors: Ojo Oluwafemi , Shoele Kourosh 
Published: June 2022

Abstract
Wind-induced stress is the primary mechanical cause of tree failures. Among different factors, the branching mechanism plays a central role in the stress distribution and stability of trees in windstorms. A recent study showed that Leonardo da Vinci’s original observation, stating that the total cross section of branches conserved across branching nodes is the optimal configuration for resisting wind-induced damage in rigid trees, is correct. However, the breaking risk and the optimal branching pattern of trees are also a function of their reconfiguration capabilities and the processes they employ to mitigate high wind-induced stress hotspots. In this study, using a numerical model of rigid and flexible branched trees, we explore the role of flexibility and branching patterns of trees in their reconfiguration and stress mitigation capabilities. We identify the robust optimal branching mechanism for an extensive range of tree flexibility. Our results show that the probability of a tree breaking at each branching level from the stem to terminal foliage strongly depends on the cross section changes in the branching nodes, the overall tree geometry, and the level of tree flexibility. Three response categories have been identified: the stress concentration in the main trunk, the uniform stress level through the tree’s height, and substantial stress localization in the terminal branches. The reconfigurability of the tree determines the dominant response mode. The results suggest a very similar optimal branching law for both flexible and rigid trees wherein uniform stress distribution occurs throughout the tree’s height. An exception is the very flexible branched plants in which the optimal branching pattern deviates from this prediction and is strongly affected by the reconfigurability of the tree.
Detailed microwave absorption performance of BaFe12O19 nano-hexaplates with a large variety of thicknesses
Authors: Thi Kim Yen Nguyen , Tuan Nguyen Quy , Trinh Ngoc Dat , Son Le Vu Truong , Dang Ngoc Toan , Thi Le Anh , Tran Nguyen Tien , Tran Ngo 
Published: June 2022

Abstract
We successfully prepared BaFe12O19 (BaM) nano-hexaplates using the co-precipitation method followed by heat treatment. The hexaplate-like grain size varied in the range of 50–200 nm, while its crystalline size was ∼46 nm. Before investigating microwave absorption, structural and magnetic properties were studied by x-ray diffraction, Raman scattering, and a vibrating sample magnetometer. The structural studies showed a pure phase of BaM, while the magnetic studies revealed a ferro/ferrimagnetic behaviour with M
s = 59.93 emu g−1, M
r = 32.49 emu g−1, and H
c = 5.16 kOe. Microwave dissipation features were systemically investigated for device thickness of 0.25–10 mm with a measured frequency range of 2–16 GHz. The study showed that the BaFe12O19 sample could absorb more than 99.9% of the incident microwave in the 12–14 GHz frequency range for 6.5 mm thickness. The effective absorption bandwidth was also fairly large, as ∼3 GHz in this frequency range. The microwave dissipation features of BaM in this work were better than pure BaM ones in other studies and relatively comparable with BaM-based microwave absorbers. The magnetic loss originated from natural resonance, while the dielectric loss resulted from multi-interface, interface, and dipolar polarization. Overall, the BaFe12O19 sample in this study could be used as a promising microwave absorber in the Ku waveband.
Differences in Fine Particle Exposure and Estimated Pulmonary Ventilation Rate with Respect to Work Tasks of Wildland Firefighters at Prescribed Burns: A Repeated Measures Study
Authors: Adetona Anna M , Adetona Olorunfemi , Chartier Ryan T , Paulsen Michael H , Simpson Christopher D , Rathbun Stephen L , Naeher Luke P 
Published: June 2022

AbstractWildland firefighters (WLFFs) are exposed to a mixture of chemicals found in wildland fire smoke and emissions from nonwildland-fuel smoke sources such as diesel. We investigated compositional differences in exposure to particulate matter and explored differences in ventilation rate and potential inhaled dose relative to the work tasks of WLFFs. Repeated measures on ten professional and two volunteer firefighters were collected on prescribed burn and nonburn days. Personal monitoring consisted of real-time and gravimetric fine particulate matter (PM2.5), carbon monoxide (CO), and accelerometer measurements to estimate ventilation rate and potential dose of PM2.5. The fine particulate matter was analyzed for levoglucosan (LG) and light absorbing carbon as a surrogate for black carbon (BC). Breathing zone personal exposure concentrations of PM2.5, LG, BC, and CO were higher on burn days (P &lt; 0.05). Differences in exposure concentrations were observed between burn day tasks (P &lt; 0.05) with firefighters managing fire boundaries (holders) being exposed to higher CO and LG concentrations and less BC concentrations than those conducting lighting (lighters). While no statistical difference in PM2.5 exposure measures was observed between the two tasks, holders in the study tended to be exposed to higher PM2.5 concentrations (~1.4×), while lighters tended to have more inhaled amounts of PM2.5 (~1.3×). Our findings demonstrate possible diversity in the sources of particulate matter exposure at the fireline and suggest the potential importance of using dose as a metric of inhalation exposure in occupational or other settings.
Genetic control of the operculum and capsule morphology of Eucalyptus globulus
Authors: Hernández Mariano A , Butler Jakob B , Ammitzboll Hans , Weller James L , Vaillancourt René E , Potts Brad M 
Published: June 2022

Abstract

Background and aims
The petaline operculum that covers the inner whorls until anthesis and the woody capsule that develops after fertilization are reproductive structures of eucalypts that protect the flower and seeds. Although they are distinct organs, they both develop from flower buds and this common ontogeny suggests shared genetic control. In Eucalyptus globulus their morphology is variable and we aimed to identify the quantitative trait loci (QTL) underlying this variation and determine whether there is common genetic control of these ecologically and taxonomically important reproductive structures.

Methods
Samples of opercula and capsules were collected from 206 trees that belong to a large outcrossed F2E. globulus mapping population. The morphological variation in these structures was characterized by measuring six operculum and five capsule traits. QTL analysis was performed using these data and a linkage map consisting of 480 markers.

Key results
A total of 27 QTL were detected for operculum traits and 28 for capsule traits, with the logarithm of odds ranging from 2.8 to 11.8. There were many co-located QTL associated with operculum or capsule traits, generally reflecting allometric relationships. A key finding was five genomic regions where co-located QTL affected both operculum and capsule morphology, and the overall trend for these QTL was to affect elongation of both organs. Some of these QTL appear to have a significant effect on the phenotype, with the strongest QTL explaining 26.4 % of the variation in operculum shape and 16.4 % in capsule shape. Flower bud measurements suggest the expression of these QTL starts during bud development. Several candidate genes were found associated with the QTL and their putative function is discussed.

Conclusions
Variation in both operculum and capsule traits in E. globulus is under strong genetic control. Our results suggest that these reproductive structures share a common genetic pathway during flower bud development.
Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation
Authors: Wang Qin , Wei Jun , Zhou Yuzhe , Lin Mingzhi , Ren Ruobing , Wang Sheng , Cui Shuguang , Li Zhen 
Published: June 2022

AbstractMotivationProtein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignment (MSA). Unfortunately, many proteins lack homologous sequences, which results in the low quality of MSA and poor performance. In this article, we propose the novel dynamic scoring matrix (DSM)-Distil to tackle this issue, which takes advantage of the pretrained BERT and exploits the knowledge distillation on the newly designed DSM features. Specifically, we propose the DSM to replace the widely used profile and PSSM (position-specific scoring matrix) features. DSM could automatically dig for the suitable feature for each residue, based on the original profile. Namely, DSM-Distil not only could adapt to the low homologous proteins but also is compatible with high homologous ones. Thanks to the dynamic property, DSM could adapt to the input data much better and achieve higher performance. Moreover, to compensate for low-quality MSA, we propose to generate the pseudo-DSM from a pretrained BERT model and aggregate it with the original DSM by adaptive residue-wise fusion, which helps to build richer and more complete input features. In addition, we propose to supervise the learning of low-quality DSM features using high-quality ones. To achieve this, a novel teacher–student model is designed to distill the knowledge from proteins with high homologous sequences to that of low ones. Combining all the proposed methods, our model achieves the new state-of-the-art performance for low homologous proteins.ResultsCompared with the previous state-of-the-art method ‘Bagging’, DSM-Distil achieves an improvement about 5% and 7.3% improvement for proteins with MSA count ≤30 and extremely low homologous cases, respectively. We also compare DSM-Distil with Alphafold2 which is a state-of-the-art framework for protein structure prediction. DSM-Distil outperforms Alphafold2 by 4.1% on extremely low-quality MSA on 8-state secondary structure prediction. Moreover, we release a large-scale up-to-date test dataset BC40 for low-quality MSA structure prediction evaluation.Availability and implementationBC40 dataset: https://drive.google.com/drive/folders/15vwRoOjAkhhwfjDk6-YoKGf4JzZXIMC. HardCase dataset: https://drive.google.com/drive/folders/1BvduOr2b7cObUHy6GuEWk-aUkKJgzTUv. Code: https://github.com/qinwang-ai/DSM-Distil.
Effective drug–target interaction prediction with mutual interaction neural network
Authors: Li Fei , Zhang Ziqiao , Guan Jihong , Zhou Shuigeng 
Published: June 2022

AbstractMotivationAccurately predicting drug–target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in building deep learning models for DTI prediction is how to appropriately represent drugs and targets. Target distance map and molecular graph are low dimensional and informative representations, which however have not been jointly used in DTI prediction. Another challenge is how to effectively model the mutual impact between drugs and targets. Though attention mechanism has been used to capture the one-way impact of targets on drugs or vice versa, the mutual impact between drugs and targets has not yet been explored, which is very important in predicting their interactions.ResultsTherefore, in this article we propose MINN-DTI, a new model for DTI prediction. MINN-DTI combines an interacting-transformer module (called Interformer) with an improved Communicative Message Passing Neural Network (CMPNN) (called Inter-CMPNN) to better capture the two-way impact between drugs and targets, which are represented by molecular graph and distance map, respectively. The proposed method obtains better performance than the state-of-the-art methods on three benchmark datasets: DUD-E, human and BindingDB. MINN-DTI also provides good interpretability by assigning larger weights to the amino acids and atoms that contribute more to the interactions between drugs and targets.Availability and implementationThe data and code of this study are available at https://github.com/admislf/MINN-DTI.
Subsampling sparse graphons under minimal assumptions
Authors: Lunde Robert , Sarkar Purnamrita 
Published: June 2022

Summary
We study the properties of two subsampling procedures for networks, vertex subsampling and $p$-subsampling, under the sparse graphon model. The consistency of network subsampling is demonstrated under the minimal assumptions of weak convergence of the corresponding network statistics and an expected subsample size growing to infinity more slowly than the number of vertices in the network. Furthermore, under appropriate sparsity conditions, we derive limiting distributions for the nonzero eigenvalues of an adjacency matrix under the sparse graphon model. Our weak convergence result implies the consistency of our subsampling procedures for eigenvalues under appropriate conditions.
Sensory experience modulates the reorganization of auditory regions for executive processing
Authors: Manini Barbara , Vinogradova Valeria , Woll Bencie , Cameron Donnie , Eimer Martin , Cardin Velia 
Published: June 2022

Abstract
Crossmodal plasticity refers to the reorganization of sensory cortices in the absence of their typical main sensory input. Understanding this phenomenon provides insights into brain function and its potential for change and enhancement. Using functional MRI, we investigated how early deafness influences crossmodal plasticity and the organization of executive functions in the adult human brain. Deaf (n = 25; age: mean = 41.68, range = 19–66, SD = 14.38; 16 female, 9 male) and hearing (n = 20; age: mean = 37.50, range = 18–66, SD = 16.85; 15 female, 5 male) participants performed four visual tasks tapping into different components of executive processing: task switching, working memory, planning and inhibition. Our results show that deaf individuals specifically recruit ‘auditory’ regions during task switching. Neural activity in superior temporal regions, most significantly in the right hemisphere, are good predictors of behavioural performance during task switching in the group of deaf individuals, highlighting the functional relevance of the observed cortical reorganization. Our results show executive processing in typically sensory regions, suggesting that the development and ultimate role of brain regions are influenced by perceptual environmental experience.