Brooding is a major breeding investment of parental birds during the early nestling stage, and has important effects on the development and survival of nestlings. Investigating brooding behavior can help to understand...Brooding is a major breeding investment of parental birds during the early nestling stage, and has important effects on the development and survival of nestlings. Investigating brooding behavior can help to understand avian breeding investment strategies. From January to June in 2013 and 2014, we studied the brooding behaviors of long-tailed tits （Aegithalos caudatus glaucogularis） in Dongzhai National Nature Reserve, Henan Province, China. We analyzed the relationships between parental diurnal brooding duration and nestling age, brood size, temperature, relative breeding season, time of day and nestling frequencies during brooding duration. Results showed that female and male long-tailed tit parents had different breeding investment strategies during the early nestling stage. Female parents bore most of the brooding investment, while male parents performed most of the nestling feedings. In addition, helpers were not found to brood nestlings at the two cooperative breeding nests. Parental brooding duration was significantly associated with the food delivered to nestlings （F=86.10, dr=l, 193.94, P〈0.001）, and was longer when the nestlings received more food. We found that parental brooding duration declined significantly as nestlings aged （F=5.99, dr=-1, 50.13, P=0.018）. When nestlings were six days old, daytime parental brooding almost ceased, implying that long- tailed tit nestlings might be able to maintain their own body temperature by this age. In addition, brooding duration was affected by both brood size （F=12.74, dr=-1,32.08, P=0.001） and temperature （F=5.83, df=-l, 39.59, P=-0.021）, with it being shorter in larger broods and when ambient temperature was higher.展开更多
Jinfengopteryx is a newly uncovered Archaeopteryx-like avialan bird outside Germany, which was found from the Jehol Biota of northern Hebei in northeastern China. It shares many characters only with Archaeopteryx by t...Jinfengopteryx is a newly uncovered Archaeopteryx-like avialan bird outside Germany, which was found from the Jehol Biota of northern Hebei in northeastern China. It shares many characters only with Archaeopteryx by the possession of three fenestrae in the antorbital cavity, 23 caudal vertebrae and long tail feathers attached to all the caudal vertebrae. But the former differs from the latter in the relatively short and high preorbital region of skull, more and closely packed teeth, much shorter forelimb compared to hindlimb. Such differences indicate Jinfengopteryx is even slightly more primitive than Archaeopteryx, although both birds can be placed at the root position of the avialan tree based on cladistic analysis. Shenzhouraptor is suggested to be slightly more advanced than Jinfengopteryx ＋ Archaeopteryx, supported by some derived features in teeth, shoulder girdles and forelimbs such as the reduction of tooth number, dorsolaterally directed glenoid facet, very long forelimb and comparatively short manus. Meanwhile, the tail of Shenzhouraptor shows more primitive characters than those of Jinfengopteryx and Archaeopteryx, e.g., the strikingly longer tail composed of more caudal vertebrae and the long tail feathers attached only to distal caudal segments. The mixed primitive and advanced characters reveal the evident mosaic evolution among long-tailed avialan birds.展开更多
Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years.Nevertheless,most of the current studies only consider single-modal medical images,and the numbe...Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years.Nevertheless,most of the current studies only consider single-modal medical images,and the number of ophthalmic diseases that can be classified is relatively small.Moreover,imbalanced data distribution of different ophthalmic diseases is not taken into consideration,which limits the application of deep learning techniques in realistic clinical scenes.In this paper,we propose a Multimodal Multi-disease Long-tailed Classification Network(M^(2)LC-Net)in response to the challenges mentioned above.M^(2)LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography(OCT)images,respectively,and conduct feature fusion to classify 11 common ophthalmic diseases.Moreover,Class Activation Mapping(CAM)is employed to visualize each mode to improve interpretability of M^(2)LC-Net.We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China,including 34,396 images of 11 disease labels.Experimental results demonstrate effectiveness of our proposed model M^(2)LC-Net.Compared with the stateof-the-art,various performance metrics have been improved significantly.Specifically,Cohen’s kappa coefficient κ has been improved by 3.21%,which is a remarkable improvement.展开更多
With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Alth...With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent. Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently. In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution’s wider coverage in the spatial dimension. The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.展开更多
Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weas...Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weasel,steppe polecat and upland buzzard of carnivores as food respectively;adult passerine birds contributed 22.3%,47.7% and 69.1%,with hatchlings contributing 50.9%,25.6% and 1.70% to each respectively.δ 13 C values plotted against δ 15 N indicated significant partitioning in two-dimensional space among the three carnivores.It was reasonable to propose a food resource partitioning among alpine weasel,steppe polecat and upland buzzard,which partially revealed their co-existence mechanisms.展开更多
Federated learning(FL),a cutting-edge distributed machine learning training paradigm,aims to generate a global model by collaborating on the training of client models without revealing local private data.The co-occurr...Federated learning(FL),a cutting-edge distributed machine learning training paradigm,aims to generate a global model by collaborating on the training of client models without revealing local private data.The co-occurrence of non-independent and identically distributed(non-IID)and long-tailed distribution in FL is one challenge that substantially degrades aggregate performance.In this paper,we present a corresponding solution called federated dual-decoupling via model and logit calibration(FedDDC)for non-IID and long-tailed distributions.The model is characterized by three aspects.First,we decouple the global model into the feature extractor and the classifier to fine-tune the components affected by the joint problem.For the biased feature extractor,we propose a client confidence re-weighting scheme to assist calibration,which assigns optimal weights to each client.For the biased classifier,we apply the classifier re-balancing method for fine-tuning.Then,we calibrate and integrate the client confidence re-weighted logits with the re-balanced logits to obtain the unbiased logits.Finally,we use decoupled knowledge distillation for the first time in the joint problem to enhance the accuracy of the global model by extracting the knowledge of the unbiased model.Numerous experiments demonstrate that on non-IID and long-tailed data in FL,our approach outperforms state-of-the-art methods.展开更多
The prevalence of long-tailed distributions in real-world data often results in classification models favoring the dominant classes,neglecting the less frequent ones.Current approaches address the issues in long-taile...The prevalence of long-tailed distributions in real-world data often results in classification models favoring the dominant classes,neglecting the less frequent ones.Current approaches address the issues in long-tailed image classification by rebalancing data,optimizing weights,and augmenting information.However,these methods often struggle to balance the performance between dominant and minority classes because of inadequate representation learning of the latter.To address these problems,we introduce descriptional words into images as cross-modal privileged information and propose a cross-modal enhanced method for long-tailed image classification,referred to as CMLTNet.CMLTNet improves the learning of intraclass similarity of tail-class representations by cross-modal alignment and captures the difference between the head and tail classes in semantic space by cross-modal inference.After fusing the above information,CMLTNet achieved an overall performance that was better than those of benchmark long-tailed and cross-modal learning methods on the long-tailed cross-modal datasets,NUS-WIDE and VireoFood-172.The effectiveness of the proposed modules was further studied through ablation experiments.In a case study of feature distribution,the proposed model was better in learning representations of tail classes,and in the experiments on model attention,CMLTNet has the potential to help learn some rare concepts in the tail class through mapping to the semantic space.展开更多
Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in H...Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.展开更多
Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follo...Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.展开更多
该文基于“以形索义”和“因音求义”的方法对绍兴方言中的“”一词进行了考释。“”一词在形(犭部字,表兽类)和义(实则指一种学名为黄颡鱼的鱼类)之间存在“错配”关系,该文尝试从字形—字义、字音—字义2个角度对出现“错配”现象的...该文基于“以形索义”和“因音求义”的方法对绍兴方言中的“”一词进行了考释。“”一词在形(犭部字,表兽类)和义(实则指一种学名为黄颡鱼的鱼类)之间存在“错配”关系,该文尝试从字形—字义、字音—字义2个角度对出现“错配”现象的原因进行解释。最后发现,绍兴话中“”[5233 s 52]一词的读音和黄鼠狼[5233 s 5255 kuo 52]相近,黄鼠狼亦称“黄鼪(狌)”,“黄狌”极有可能是“”的本字。绍兴话中对“黄颡鱼”和“黄鼠狼”叫法的相近,是“犭”旁的“”指称黄颡鱼的直接原因。展开更多
Thirty Siberian weasels (Mustela Sibirica) (15 males and 15 females)were sampled from Longkou Forest Farm of Tonghe in Xiaoxing’an Mountains in winter. For each individual, 5 guard hairs from the mid-back and 5 upper...Thirty Siberian weasels (Mustela Sibirica) (15 males and 15 females)were sampled from Longkou Forest Farm of Tonghe in Xiaoxing’an Mountains in winter. For each individual, 5 guard hairs from the mid-back and 5 upper-hairs from the hind-claw were collected and subjected to morphological examination of scale pattern using electron scanning microscopy. All the hairs were measured for indices including hair length, medulla length, hair follicle length, hair diameter, medulla diameter,and hair root diameter using biological microscope system H6303i, and then medulla length index (ratio of medulla length to hair length) and medulla index (ratio of medulla diameter to hair diameter) were calculated. The statistical results showed that the length of guard hairs from the mid-back was 33.50±0.52 mm in males and 28.85±0.28 mm in females, the average of medulla length index was 95.36% in males and 95.16% in females, and the average of medulla index was 79.41% in male and 80.68% in females. All these indices were significantly larger than those of upper-hairs from hind-claw (P<0.05). Such morphological structure characters of guard hairs from mid-back favor heat insulation properties and those of upper-hair from hind-claw enhance the function of protection.The for the upper-hair from the hind-claw, the hair follicle length was 0.91±0.05 mm in male and 0.79±0.10 mm in female, hair root diameter was 86.0±3.7μm in male and 71.9±3.1μm in female, the ratio of the length with irregular-wave scales and regular imbricate scales to the hair length is 100% in both male and female. All these indices were significantly larger than those of guard hairs from the mid-back (P<0.05) and such morphological structure characters enhance the function of protection further. The functional differentiation between guard hairs from mid-back and upper-hairs from hind-claw make the weasels more adaptable to a cold environment with complex vegetation form.展开更多
Coat characteristics of seasonal molting mammals reveal significant seasonal variation as an adaptive strategy to cope with seasonal climate changes. However, the adaptive significance of such morphological variation ...Coat characteristics of seasonal molting mammals reveal significant seasonal variation as an adaptive strategy to cope with seasonal climate changes. However, the adaptive significance of such morphological variation has not yet been addressed. We analyzed seasonal variation of microscopic indices of hair and skin of adult Siberian weasels (Mustela sibirica manchurica Brass) from the Tonghe forest area of the Xiaoxing’anling Mountains, Heilongjiang. Skins from 8 males and 8 females were collected from summer (July to September), and an additional 8 male and 8 females skins were collected from winter (November to December )(i.e., n=32). Morphological indexes included length and width of guard hair, cuticular scale patterns of guard hair, external and cross-section form of guard hair, and medullary characteristics. We found significant differences between winter and summer coat hair density, hair length, and proportion of medulla-absent part of guard hair. We discuss the adaptive mechanism of this seasonal variation.展开更多
基金Foundation item： This study was supported by the National Natural Science Foundation of China （31472011）ACKNOWLEDGEMENTS We are grateful to Peng ZHANG, Zheng CHEN, Jia-Hui WANG, and Hui-Jia YUAN of Beijing Normal University for field assistance, and staff from Henan Dongzhai National Nature Reserve for help during field work. We also thank editor for revising the English, and the two reviewers for their constructive comments, which have helped to improve the manuscript.
文摘Brooding is a major breeding investment of parental birds during the early nestling stage, and has important effects on the development and survival of nestlings. Investigating brooding behavior can help to understand avian breeding investment strategies. From January to June in 2013 and 2014, we studied the brooding behaviors of long-tailed tits （Aegithalos caudatus glaucogularis） in Dongzhai National Nature Reserve, Henan Province, China. We analyzed the relationships between parental diurnal brooding duration and nestling age, brood size, temperature, relative breeding season, time of day and nestling frequencies during brooding duration. Results showed that female and male long-tailed tit parents had different breeding investment strategies during the early nestling stage. Female parents bore most of the brooding investment, while male parents performed most of the nestling feedings. In addition, helpers were not found to brood nestlings at the two cooperative breeding nests. Parental brooding duration was significantly associated with the food delivered to nestlings （F=86.10, dr=l, 193.94, P〈0.001）, and was longer when the nestlings received more food. We found that parental brooding duration declined significantly as nestlings aged （F=5.99, dr=-1, 50.13, P=0.018）. When nestlings were six days old, daytime parental brooding almost ceased, implying that long- tailed tit nestlings might be able to maintain their own body temperature by this age. In addition, brooding duration was affected by both brood size （F=12.74, dr=-1,32.08, P=0.001） and temperature （F=5.83, df=-l, 39.59, P=-0.021）, with it being shorter in larger broods and when ambient temperature was higher.
基金financially supported by the National Basic Research Program of China(973 Project,Grant No.2006CB701405)the China Geological Survey,and the National Natural Science Foundation of China(Grant No.40272008).
文摘Jinfengopteryx is a newly uncovered Archaeopteryx-like avialan bird outside Germany, which was found from the Jehol Biota of northern Hebei in northeastern China. It shares many characters only with Archaeopteryx by the possession of three fenestrae in the antorbital cavity, 23 caudal vertebrae and long tail feathers attached to all the caudal vertebrae. But the former differs from the latter in the relatively short and high preorbital region of skull, more and closely packed teeth, much shorter forelimb compared to hindlimb. Such differences indicate Jinfengopteryx is even slightly more primitive than Archaeopteryx, although both birds can be placed at the root position of the avialan tree based on cladistic analysis. Shenzhouraptor is suggested to be slightly more advanced than Jinfengopteryx ＋ Archaeopteryx, supported by some derived features in teeth, shoulder girdles and forelimbs such as the reduction of tooth number, dorsolaterally directed glenoid facet, very long forelimb and comparatively short manus. Meanwhile, the tail of Shenzhouraptor shows more primitive characters than those of Jinfengopteryx and Archaeopteryx, e.g., the strikingly longer tail composed of more caudal vertebrae and the long tail feathers attached only to distal caudal segments. The mixed primitive and advanced characters reveal the evident mosaic evolution among long-tailed avialan birds.
基金the National Natural Science Foundation of China(No.62076035)。
文摘Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years.Nevertheless,most of the current studies only consider single-modal medical images,and the number of ophthalmic diseases that can be classified is relatively small.Moreover,imbalanced data distribution of different ophthalmic diseases is not taken into consideration,which limits the application of deep learning techniques in realistic clinical scenes.In this paper,we propose a Multimodal Multi-disease Long-tailed Classification Network(M^(2)LC-Net)in response to the challenges mentioned above.M^(2)LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography(OCT)images,respectively,and conduct feature fusion to classify 11 common ophthalmic diseases.Moreover,Class Activation Mapping(CAM)is employed to visualize each mode to improve interpretability of M^(2)LC-Net.We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China,including 34,396 images of 11 disease labels.Experimental results demonstrate effectiveness of our proposed model M^(2)LC-Net.Compared with the stateof-the-art,various performance metrics have been improved significantly.Specifically,Cohen’s kappa coefficient κ has been improved by 3.21%,which is a remarkable improvement.
文摘With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent. Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently. In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution’s wider coverage in the spatial dimension. The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.
文摘Based on mass balance theory and IsoSource program,stable carbon and nitrogen isotopic ratios revealed that small mammals (plateau pika,root vole and plateau zokor) contributed 26.8% and 27.0% and 29.2% to alpine weasel,steppe polecat and upland buzzard of carnivores as food respectively;adult passerine birds contributed 22.3%,47.7% and 69.1%,with hatchlings contributing 50.9%,25.6% and 1.70% to each respectively.δ 13 C values plotted against δ 15 N indicated significant partitioning in two-dimensional space among the three carnivores.It was reasonable to propose a food resource partitioning among alpine weasel,steppe polecat and upland buzzard,which partially revealed their co-existence mechanisms.
基金supported by the National Natural Science Foundation of China(No.61702321)。
文摘Federated learning(FL),a cutting-edge distributed machine learning training paradigm,aims to generate a global model by collaborating on the training of client models without revealing local private data.The co-occurrence of non-independent and identically distributed(non-IID)and long-tailed distribution in FL is one challenge that substantially degrades aggregate performance.In this paper,we present a corresponding solution called federated dual-decoupling via model and logit calibration(FedDDC)for non-IID and long-tailed distributions.The model is characterized by three aspects.First,we decouple the global model into the feature extractor and the classifier to fine-tune the components affected by the joint problem.For the biased feature extractor,we propose a client confidence re-weighting scheme to assist calibration,which assigns optimal weights to each client.For the biased classifier,we apply the classifier re-balancing method for fine-tuning.Then,we calibrate and integrate the client confidence re-weighted logits with the re-balanced logits to obtain the unbiased logits.Finally,we use decoupled knowledge distillation for the first time in the joint problem to enhance the accuracy of the global model by extracting the knowledge of the unbiased model.Numerous experiments demonstrate that on non-IID and long-tailed data in FL,our approach outperforms state-of-the-art methods.
基金supported in part by the National Natural Science Foundation of China(62006141)the National Key R&D Program of China(2021YFC3300203)+1 种基金the Overseas Innovation Team Project of the“20 Regulations for New Universities”Funding Program of Jinan(2021GXRC073)the Excellent Youth Scholars Program of Shandong Province(2022HWYQ-048).
文摘The prevalence of long-tailed distributions in real-world data often results in classification models favoring the dominant classes,neglecting the less frequent ones.Current approaches address the issues in long-tailed image classification by rebalancing data,optimizing weights,and augmenting information.However,these methods often struggle to balance the performance between dominant and minority classes because of inadequate representation learning of the latter.To address these problems,we introduce descriptional words into images as cross-modal privileged information and propose a cross-modal enhanced method for long-tailed image classification,referred to as CMLTNet.CMLTNet improves the learning of intraclass similarity of tail-class representations by cross-modal alignment and captures the difference between the head and tail classes in semantic space by cross-modal inference.After fusing the above information,CMLTNet achieved an overall performance that was better than those of benchmark long-tailed and cross-modal learning methods on the long-tailed cross-modal datasets,NUS-WIDE and VireoFood-172.The effectiveness of the proposed modules was further studied through ablation experiments.In a case study of feature distribution,the proposed model was better in learning representations of tail classes,and in the experiments on model attention,CMLTNet has the potential to help learn some rare concepts in the tail class through mapping to the semantic space.
文摘Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.
基金supported by the National Key Research and Development Program of China (Grant No. 2021YFC1910402)。
文摘Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.
文摘该文基于“以形索义”和“因音求义”的方法对绍兴方言中的“”一词进行了考释。“”一词在形(犭部字,表兽类)和义(实则指一种学名为黄颡鱼的鱼类)之间存在“错配”关系,该文尝试从字形—字义、字音—字义2个角度对出现“错配”现象的原因进行解释。最后发现,绍兴话中“”[5233 s 52]一词的读音和黄鼠狼[5233 s 5255 kuo 52]相近,黄鼠狼亦称“黄鼪(狌)”,“黄狌”极有可能是“”的本字。绍兴话中对“黄颡鱼”和“黄鼠狼”叫法的相近,是“犭”旁的“”指称黄颡鱼的直接原因。
文摘Thirty Siberian weasels (Mustela Sibirica) (15 males and 15 females)were sampled from Longkou Forest Farm of Tonghe in Xiaoxing’an Mountains in winter. For each individual, 5 guard hairs from the mid-back and 5 upper-hairs from the hind-claw were collected and subjected to morphological examination of scale pattern using electron scanning microscopy. All the hairs were measured for indices including hair length, medulla length, hair follicle length, hair diameter, medulla diameter,and hair root diameter using biological microscope system H6303i, and then medulla length index (ratio of medulla length to hair length) and medulla index (ratio of medulla diameter to hair diameter) were calculated. The statistical results showed that the length of guard hairs from the mid-back was 33.50±0.52 mm in males and 28.85±0.28 mm in females, the average of medulla length index was 95.36% in males and 95.16% in females, and the average of medulla index was 79.41% in male and 80.68% in females. All these indices were significantly larger than those of upper-hairs from hind-claw (P<0.05). Such morphological structure characters of guard hairs from mid-back favor heat insulation properties and those of upper-hair from hind-claw enhance the function of protection.The for the upper-hair from the hind-claw, the hair follicle length was 0.91±0.05 mm in male and 0.79±0.10 mm in female, hair root diameter was 86.0±3.7μm in male and 71.9±3.1μm in female, the ratio of the length with irregular-wave scales and regular imbricate scales to the hair length is 100% in both male and female. All these indices were significantly larger than those of guard hairs from the mid-back (P<0.05) and such morphological structure characters enhance the function of protection further. The functional differentiation between guard hairs from mid-back and upper-hairs from hind-claw make the weasels more adaptable to a cold environment with complex vegetation form.
文摘Coat characteristics of seasonal molting mammals reveal significant seasonal variation as an adaptive strategy to cope with seasonal climate changes. However, the adaptive significance of such morphological variation has not yet been addressed. We analyzed seasonal variation of microscopic indices of hair and skin of adult Siberian weasels (Mustela sibirica manchurica Brass) from the Tonghe forest area of the Xiaoxing’anling Mountains, Heilongjiang. Skins from 8 males and 8 females were collected from summer (July to September), and an additional 8 male and 8 females skins were collected from winter (November to December )(i.e., n=32). Morphological indexes included length and width of guard hair, cuticular scale patterns of guard hair, external and cross-section form of guard hair, and medullary characteristics. We found significant differences between winter and summer coat hair density, hair length, and proportion of medulla-absent part of guard hair. We discuss the adaptive mechanism of this seasonal variation.
