Diurnal brooding behavior of long-tailed tits （Aegithalos caudatus glaucogularis）

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.

Jinfengopteryx Compared to Archaeopteryx,with Comments on the Mosaic Evolution of Long-tailed Avialan Birds

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.

Dual Channel with Involution for Long-Tailed Visual Recognition

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.

M^(2)LC-Net: A Multi-Modal Multi-Disease Long-Tailed Classification Network for Real Clinical Scenes

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.

Federated learning on non-IID and long-tailed data viadual-decoupling

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.

Long-tailed object detection of kitchen waste with class-instance balanced detector

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.

Semi-supervised Long-tail Endoscopic Image Classification

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.

基于开集识别的恶意代码家族同源性分析

目前,恶意代码家族同源性分析方法多侧重于闭集分类问题的研究,即假定待测样本一定属于某个已知家族类别.然而真实环境中的恶意代码家族众多,未知类别的家族通常占大多数,采用闭集识别的方法,无法准确识别真实环境中的恶意代码家族.针对上述问题,提出了一种基于开集识别的恶意代码家族同源性分析方法.通过N-Gram滑动窗口和Doc2vec句嵌入方法将恶意代码可执行文件转换成灰度图像,基于卷积神经网络模型MobileNet获取灰度图像数据的特征,利用Open Long-tailed Recognition模型实现恶意代码家族的开集识别.在9个已知类别和9个未知类别恶意代码家族上进行识别,实验结果表明,所提出的方法能够识别出未知类别恶意代码家族,同时在已知类别和未知类别家族上都能保持较高的准确率.

The finite-time ruin probability in the presence of Sarmanov dependent financial and insurance risks

Consider a discrete-time insurance risk model. Within period i, i≥ 1, Xi and Yi denote the net insurance loss and the stochastic discount factor of an insurer, respectively. Assume that {（Xi, Yi）, i≥1） form a sequence of independent and identically distributed random vectors following a common bivariate Sarmanov distribution. In the presence of heavy-tailed net insurance losses, an asymptotic formula is derived for the finite-time ruin probability.

Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

Does scatter-hoarding of seeds benefit cache owners or pilferers?

The scatter-hoarding behavior of granivorous rodents plays an important role in seed dispersal and seedling re­generation of trees,as well as the evolution of several well-known mutualisms between trees and rodents in for­est ecosystems.Because it is difficult to identify seed hoarders and pilferers under field conditions by traditional methods,the full costs incurred and benefits accrued by scatter-hoarding have not been fully evaluated in most systems.By using infrared radiation camera tracking and seed tagging,we investigated the benefits and losses of scatter-hoarded seeds(Camellia oleifera)for 3 sympatric rodent species(Apodemus draco,Niviventer confu­cianus and Leopoldamys edwardsi)in a subtropical forest of Southwest China during 2013 to 2015.We estab­lished the relationships between the rodents and the seeds at the individual level.For each rodent species,we calculated the cache recovery rate of cache owners,as well as conspecific and interspecific pilferage rates.We found that all 3 sympatric rodent species had a cache recovery advantage with rates that far exceeded average pilferage rates over a 30-day tracking period.The smallest species(A.draco)showed the highest rate of scat­ter-hoarding and the highest recovery advantage compared with the other 2 larger species(N.confucianus and L.edwardsi).Our results suggest that scatter-hoarding benefits cache owners in food competition,supporting the pilferage avoidance hypothesis.Therefore,scatter-hoarding behavior should be favored by natural selection,and plays a significant role in species coexistence of rodent community and in the formation of mutualism between seeds and rodents in forest ecosystems.

Local Asymptotics of a Markov Modulated Random Walk with Heavy-tailed Increments

In this paper, we obtain sufficient and necessary conditions for local asymptotics for the maximum of a Markov modulated random walk with long-tailed increments and negative drifts, where the local asymptotics means asymptotic behaviour of P（ ∈ （x,x ＋ z]） for each z 〉 0, as x→∞ Our results extend and improve the existing ones in the literature.

Past climate change and recent anthropogenic activities affect genetic structure and population demography of the greater longtailed hamster in northern China

The genetic diversity and the spatial structure of a species are likely consequences of both past and recent evolutionary processes,but relevant studies are still rare in East Asia where the Pleistocene climate has unique influences.In this study,we examined the impact of past climate change and recent anthropogenic activities on the genetic structure and population size of the greater long-tailed hamster(Tscherskia triton),an agricultural rodent pest species in northern China.DNA sequence data of 2 mitochondrial genes and genotypic data of 11 microsatellite DNA loci from 41 populations(545 individuals)were gathered.Phylogenetic and population genetic analyses,as well as species distribution modeling and coalescent simulations,were conducted to infer its historical and demographic patterns and processes.Two deeply diverged mitochondrial clades were recovered.A small one was restricted to the Shandong Peninsula while the main clade was further divided into 3 geographic clusters by their microsatellite DNA genotypes:Northwest,North-center and Northeast.Divergence dating indicated a Middle-to-Late Pleistocene divergence between the 2 clades.Demographic analysis indicated that all 3 and pooled populations showed consistent long-period expansions during last glacial period;but not during the Holocene,probably due to the impact of climate warming and human disturbances.Conflicting patterns between mtDNA and microsatellite markers imply an anthropogenic impact on North-center populations due to intensified agricultural cultivation in this region.Our study demonstrated that the impact of past glaciation on organisms in East Asia significantly differs from that of Europe and North America,and human activity is an important factor in determining the genetic diversity of a species,as well as its spatial structure.