Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,...Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.展开更多
Deep convolutional neural networks(DCNNs)are widely used in content-based image retrieval(CBIR)because of the advantages in image feature extraction.However,the training of deep neural networks requires a large number...Deep convolutional neural networks(DCNNs)are widely used in content-based image retrieval(CBIR)because of the advantages in image feature extraction.However,the training of deep neural networks requires a large number of labeled data,which limits the application.Self-supervised learning is a more general approach in unlabeled scenarios.A method of fine-tuning feature extraction networks based on masked learning is proposed.Masked autoencoders(MAE)are used in the fine-tune vision transformer(ViT)model.In addition,the scheme of extracting image descriptors is discussed.The encoder of the MAE uses the ViT to extract global features and performs self-supervised fine-tuning by reconstructing masked area pixels.The method works well on category-level image retrieval datasets with marked improvements in instance-level datasets.For the instance-level datasets Oxford5k and Paris6k,the retrieval accuracy of the base model is improved by 7%and 17%compared to that of the original model,respectively.展开更多
Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of mas...Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of masked autoencoders-self-supervised vision transformers trained on a reconstruction task-to learn in-distribution representations,here,the distribution of healthy colon images.We then perform out-of-distribution reconstruction and inference,with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples.We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution(i.e.,polyp)segmentation.Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets.Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.展开更多
Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of ...Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.展开更多
Zinc-promoted umpolung thiolation of alkyl electrophiles with masked sulfur transfer reagents in the absence of nickel or copper catalysis is described. This protocol proceeds via a SET process of Zn to electrophilic ...Zinc-promoted umpolung thiolation of alkyl electrophiles with masked sulfur transfer reagents in the absence of nickel or copper catalysis is described. This protocol proceeds via a SET process of Zn to electrophilic sulfur reagent followed by insertion of Zn into disulfide and nucleophilic thiolation, providing straightforward access to a wide range of alkyl sulfides with broad substrate scope. A neutral TMEDA-ligated four-coordinated zinc thiolate with tetrahedra geometry was synthesized, isolated and fully characterized by NMR, IR and X-ray analysis. More importantly, the chemical reactivity of this active intermediate has been investigated, enabling the construction of C-Se, C-Te, Sb-S and Bi-S bonds to prepare valuable sulfur-containing molecules and beyond.展开更多
Much like humans focus solely on object movement to understand actions,directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension.In the recent study,V...Much like humans focus solely on object movement to understand actions,directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension.In the recent study,Video Masked Auto-Encoder(VideoMAE)employs a pre-training approach with a high ratio of tube masking and reconstruction,effectively mitigating spatial bias due to temporal redundancy in full video frames.This steers the model’s focus toward detailed temporal contexts.However,as the VideoMAE still relies on full video frames during the action recognition stage,it may exhibit a progressive shift in attention towards spatial contexts,deteriorating its ability to capture the main spatio-temporal contexts.To address this issue,we propose an attention-directing module named Transformer Encoder Attention Module(TEAM).This proposed module effectively directs the model’s attention to the core characteristics within each video,inherently mitigating spatial bias.The TEAM first figures out the core features among the overall extracted features from each video.After that,it discerns the specific parts of the video where those features are located,encouraging the model to focus more on these informative parts.Consequently,during the action recognition stage,the proposed TEAM effectively shifts the VideoMAE’s attention from spatial contexts towards the core spatio-temporal contexts.This attention-shift manner alleviates the spatial bias in the model and simultaneously enhances its ability to capture precise video contexts.We conduct extensive experiments to explore the optimal configuration that enables the TEAM to fulfill its intended design purpose and facilitates its seamless integration with the VideoMAE framework.The integrated model,i.e.,VideoMAE+TEAM,outperforms the existing VideoMAE by a significant margin on Something-Something-V2(71.3%vs.70.3%).Moreover,the qualitative comparisons demonstrate that the TEAM encourages the model to disregard insignificant features and focus more on the essential video展开更多
In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In thi...In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.展开更多
We present a masked vision-language transformer(MVLT)for fashion-specific multi-modal representation.Technically,we simply utilize the vision transformer architecture for replacing the bidirectional encoder representa...We present a masked vision-language transformer(MVLT)for fashion-specific multi-modal representation.Technically,we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers(BERT)in the pre-training model,making MVLT the first end-to-end framework for the fashion domain.Besides,we designed masked image reconstruction(MIR)for a fine-grained understanding of fashion.MVLT is an extensible and convenient architecture that admits raw multimodal inputs without extra pre-processing models(e.g.,ResNet),implicitly modeling the vision-language alignments.More importantly,MVLT can easily generalize to various matching and generative tasks.Experimental results show obvious improvements in retrieval(rank@5:17%)and recognition(accuracy:3%)tasks over the Fashion-Gen 2018 winner,Kaleido-BERT.The code is available at https://github.com/GewelsJI/MVLT.展开更多
Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering....Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.展开更多
Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve sa...Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.展开更多
Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed tha...Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.展开更多
We consider a series system of two independent and non-identical components which have different BurrⅫ distributed lifetime.The maximum likelihood and Bayes estimators of the parameters of the system＇s components ar...We consider a series system of two independent and non-identical components which have different BurrⅫ distributed lifetime.The maximum likelihood and Bayes estimators of the parameters of the system＇s components are obtained based on masked system life test data.The conclusion is that the Bayes estimates are better than the maximum likelihood estimates in the sense of having smaller mean squared errors.展开更多
This paper is to determine the contribution of the ambulatory measure of blood pressure (AMBP) to the detection of hypertension in type 2 diabetic black African in Benin. Hypertension can stay unknown in diabetic pati...This paper is to determine the contribution of the ambulatory measure of blood pressure (AMBP) to the detection of hypertension in type 2 diabetic black African in Benin. Hypertension can stay unknown in diabetic patients. Patients and Methods: We conducted a cross-sectional, prospective, descriptive and analytical study at “Banqued’ insuline” of Cotonou, Polyclinique Atinkanmey and CHUD-Ouémé-Plateau. The study took place over a period of 6 months from March 01 to August 30 2014. The study included patients with type 1 or 2 diabetes who agreed to participate in the study and who made ambulatory measure of blood pressure (AMBP). Statistical analysis was done by using the software Excel 2013 and SPSS versus 18.0. Results: Sixty six patients were included. Forty one (62.1%) among them were female;sex-ratio was 0.61. The mean of age was 48.9 ± 8.8 years with range from 30 to 68 years. The prevalence of masked hypertension in type 2 diabetics was 37.9% (25/66). Abdominal obesity was the significative factor related to masked high blood pressure (HBP) in the type 2 diabetics (p = 0.005). Among diabetic with masked hypertension, 14 (56%) had “no dipper” profile and 11 (44%) had “dipper” profile. Conclusion: The ambulatory measure of blood pressure (AMBP) may take an important place in the detection of Hypertension in black type 2 diabetic subjects.展开更多
The fat production rate in adult healthy masked civet(Paguma lavata) and nutria (Myocaster coypus) oil were measured. The values of iodine. saponification and acid PH, composition of fstty acids of grease were analyze...The fat production rate in adult healthy masked civet(Paguma lavata) and nutria (Myocaster coypus) oil were measured. The values of iodine. saponification and acid PH, composition of fstty acids of grease were analyzed both chemically and by apparatus. The results showed that acid PH, iodine value, saponification value,and unsaturation point are 1.887 and 0.784, 53.90 and 48.32, 98.80 and 100.23. and 60.05% and 58.85% are respectively for masked civet's fat and nutria's oil. Both of masked civet's fat and nutria's oil contain a little of Eicosatetraenoic acid (C-20;4), which is of great significance in nutrition and metabolism for human body. The analysis results indicate that masked civet's oil is similar to nutria's oil in iodine value, saponification value and unsaturation point. Both masked civet's fat and nutria's oil are steady and have highly nutrition. They can be widely exploited and utilized in health protection and cosmetics made industry.展开更多
Management of hypertension (HTN) largely relies on proper and accurate measurement of blood pressure (BP). Even following the criteria for HTN diagnosis defined in the Fourth report on high BP in children and adolesce...Management of hypertension (HTN) largely relies on proper and accurate measurement of blood pressure (BP). Even following the criteria for HTN diagnosis defined in the Fourth report on high BP in children and adolescents, inaccurate diagnosis and misdiagnosis can occur with white coat effect and masked HTN. The use of Ambulatory Blood Pressure Monitoring (ABPM) has been increasing in pediatrics in the last 20 years. The main use of ABPM is to differentiate between sustained HTN and white coat HTN in patients who have elevated casual BP measurements and to detect masked HTN in high risk patients. ABPM is most useful in patients with casual BP within 20% of the 95th percentile for age, gender, and height. This report will highlight the use of ABPM in the evaluation of elevated BP and management of HTN in pediatrics. The discussion includes a review of various non-invasive BP measuring techniques, a description of ABPM and ABPM-unique data and diagnoses, updated ABPM clinical data more specific to pediatrics, its use in HTN clinical trials, and future outlook and direction of ABPM in pediatrics.展开更多
Trauma experience not only could predict long-term physical and mental health problems, but also could have impact on the cognitive processes. Modified Stroop task and subliminal masked priming task were used to exami...Trauma experience not only could predict long-term physical and mental health problems, but also could have impact on the cognitive processes. Modified Stroop task and subliminal masked priming task were used to examine the automatic cognitive processing of earthquake-related stimulus (disaster-related, rescue-related, and earthquake-unrelated words) of healthy undergraduates at one month and two years since the Wenchuan earthquake happened, who came from the worst-hit areas of the Wenchuan earthquake. The results showed that the earthquake interference effects were showed in modified Stroop task and reversed priming effects were found in subliminal masked priming task at one month after the Wenchuan earthquake. However, two years later, earthquake interference effects and reversed priming effects were not found in the same experiments. The results showed the automatic cognitive processing of healthy subjects experienced trauma was affected by the earthquake episodic memory, and these interference effects were weakened with the passage of time.展开更多
An efficient chlorination reaction of in situ generated(β-diazo-α,α-difluoroethyl)phosphonates has been achieved with hydrochloric acid as a chlorine source under mild and operationally convenient conditions.The re...An efficient chlorination reaction of in situ generated(β-diazo-α,α-difluoroethyl)phosphonates has been achieved with hydrochloric acid as a chlorine source under mild and operationally convenient conditions.The reaction does not need any catalyst and tolerates a wide scope of substrates,which affords the(β-chlorodifluoroethyl)phosphonate products in good to excellent yields.This reaction represents the first example of the halogenation of difluoroalkyl diazo compounds,and also provides an easy way for the synthesis of difluoromethylenephosphonate-containing compounds.展开更多
基金supported by the project “The demonstration system of rich semantic search application in scientific literature” (Grant No. 1734) from the Chinese Academy of Sciences
文摘Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.
基金the Project of Introducing Urgently Needed Talents in Key Supporting Regions of Shandong Province,China(No.SDJQP20221805)。
文摘Deep convolutional neural networks(DCNNs)are widely used in content-based image retrieval(CBIR)because of the advantages in image feature extraction.However,the training of deep neural networks requires a large number of labeled data,which limits the application.Self-supervised learning is a more general approach in unlabeled scenarios.A method of fine-tuning feature extraction networks based on masked learning is proposed.Masked autoencoders(MAE)are used in the fine-tune vision transformer(ViT)model.In addition,the scheme of extracting image descriptors is discussed.The encoder of the MAE uses the ViT to extract global features and performs self-supervised fine-tuning by reconstructing masked area pixels.The method works well on category-level image retrieval datasets with marked improvements in instance-level datasets.For the instance-level datasets Oxford5k and Paris6k,the retrieval accuracy of the base model is improved by 7%and 17%compared to that of the original model,respectively.
文摘Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.We leverage the ability of masked autoencoders-self-supervised vision transformers trained on a reconstruction task-to learn in-distribution representations,here,the distribution of healthy colon images.We then perform out-of-distribution reconstruction and inference,with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples.We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution(i.e.,polyp)segmentation.Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets.Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.
基金supported in part by National Natural Science Foundation of China(No.62176041)in part by Excellent Science and Technique Talent Foundation of Dalian(No.2022RY21).
文摘Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.
基金the National Science Foundation of China(22001051)Zhejiang Provincial Natural Science Foundation of China(LY23B020002)for financial supportfunding from the STU Scientific Research Foundation for Talents(NTF20022)。
文摘Zinc-promoted umpolung thiolation of alkyl electrophiles with masked sulfur transfer reagents in the absence of nickel or copper catalysis is described. This protocol proceeds via a SET process of Zn to electrophilic sulfur reagent followed by insertion of Zn into disulfide and nucleophilic thiolation, providing straightforward access to a wide range of alkyl sulfides with broad substrate scope. A neutral TMEDA-ligated four-coordinated zinc thiolate with tetrahedra geometry was synthesized, isolated and fully characterized by NMR, IR and X-ray analysis. More importantly, the chemical reactivity of this active intermediate has been investigated, enabling the construction of C-Se, C-Te, Sb-S and Bi-S bonds to prepare valuable sulfur-containing molecules and beyond.
基金This work was supported by the National Research Foundation of Korea(NRF)Grant(Nos.2018R1A5A7059549,2020R1A2C1014037)by Institute of Information&Communications Technology Planning&Evaluation(IITP)Grant(No.2020-0-01373)funded by the Korea government(*MSIT).*Ministry of Science and Information&Communication Technology.
文摘Much like humans focus solely on object movement to understand actions,directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension.In the recent study,Video Masked Auto-Encoder(VideoMAE)employs a pre-training approach with a high ratio of tube masking and reconstruction,effectively mitigating spatial bias due to temporal redundancy in full video frames.This steers the model’s focus toward detailed temporal contexts.However,as the VideoMAE still relies on full video frames during the action recognition stage,it may exhibit a progressive shift in attention towards spatial contexts,deteriorating its ability to capture the main spatio-temporal contexts.To address this issue,we propose an attention-directing module named Transformer Encoder Attention Module(TEAM).This proposed module effectively directs the model’s attention to the core characteristics within each video,inherently mitigating spatial bias.The TEAM first figures out the core features among the overall extracted features from each video.After that,it discerns the specific parts of the video where those features are located,encouraging the model to focus more on these informative parts.Consequently,during the action recognition stage,the proposed TEAM effectively shifts the VideoMAE’s attention from spatial contexts towards the core spatio-temporal contexts.This attention-shift manner alleviates the spatial bias in the model and simultaneously enhances its ability to capture precise video contexts.We conduct extensive experiments to explore the optimal configuration that enables the TEAM to fulfill its intended design purpose and facilitates its seamless integration with the VideoMAE framework.The integrated model,i.e.,VideoMAE+TEAM,outperforms the existing VideoMAE by a significant margin on Something-Something-V2(71.3%vs.70.3%).Moreover,the qualitative comparisons demonstrate that the TEAM encourages the model to disregard insignificant features and focus more on the essential video
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601part by the National Natural Science Foundation of China(Grant No.U22A2002,61941104,62201605)part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.
文摘We present a masked vision-language transformer(MVLT)for fashion-specific multi-modal representation.Technically,we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers(BERT)in the pre-training model,making MVLT the first end-to-end framework for the fashion domain.Besides,we designed masked image reconstruction(MIR)for a fine-grained understanding of fashion.MVLT is an extensible and convenient architecture that admits raw multimodal inputs without extra pre-processing models(e.g.,ResNet),implicitly modeling the vision-language alignments.More importantly,MVLT can easily generalize to various matching and generative tasks.Experimental results show obvious improvements in retrieval(rank@5:17%)and recognition(accuracy:3%)tasks over the Fashion-Gen 2018 winner,Kaleido-BERT.The code is available at https://github.com/GewelsJI/MVLT.
基金Technology Foundation of Guizhou Province,China(No.QianKeHeJZi[2015]2064)Scientific Research Foundation for Advanced Talents in Guizhou Institue of Technology and Science,China(No.XJGC20150106)Joint Foundation of Guizhou Province,China(No.QianKeHeLHZi[2015]7105)
文摘Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.
基金Supported by the Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(XCXJH20220318)。
文摘Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.
基金supported by the National Natural Science Foundation of China(71401134 71571144+1 种基金 71171164)the Program of International Cooperation and Exchanges in Science and Technology Funded by Shaanxi Province(2016KW-033)
文摘Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.
基金Supported by the National Natural Science Foundation of China（70471057）
文摘We consider a series system of two independent and non-identical components which have different BurrⅫ distributed lifetime.The maximum likelihood and Bayes estimators of the parameters of the system＇s components are obtained based on masked system life test data.The conclusion is that the Bayes estimates are better than the maximum likelihood estimates in the sense of having smaller mean squared errors.
文摘This paper is to determine the contribution of the ambulatory measure of blood pressure (AMBP) to the detection of hypertension in type 2 diabetic black African in Benin. Hypertension can stay unknown in diabetic patients. Patients and Methods: We conducted a cross-sectional, prospective, descriptive and analytical study at “Banqued’ insuline” of Cotonou, Polyclinique Atinkanmey and CHUD-Ouémé-Plateau. The study took place over a period of 6 months from March 01 to August 30 2014. The study included patients with type 1 or 2 diabetes who agreed to participate in the study and who made ambulatory measure of blood pressure (AMBP). Statistical analysis was done by using the software Excel 2013 and SPSS versus 18.0. Results: Sixty six patients were included. Forty one (62.1%) among them were female;sex-ratio was 0.61. The mean of age was 48.9 ± 8.8 years with range from 30 to 68 years. The prevalence of masked hypertension in type 2 diabetics was 37.9% (25/66). Abdominal obesity was the significative factor related to masked high blood pressure (HBP) in the type 2 diabetics (p = 0.005). Among diabetic with masked hypertension, 14 (56%) had “no dipper” profile and 11 (44%) had “dipper” profile. Conclusion: The ambulatory measure of blood pressure (AMBP) may take an important place in the detection of Hypertension in black type 2 diabetic subjects.
文摘The fat production rate in adult healthy masked civet(Paguma lavata) and nutria (Myocaster coypus) oil were measured. The values of iodine. saponification and acid PH, composition of fstty acids of grease were analyzed both chemically and by apparatus. The results showed that acid PH, iodine value, saponification value,and unsaturation point are 1.887 and 0.784, 53.90 and 48.32, 98.80 and 100.23. and 60.05% and 58.85% are respectively for masked civet's fat and nutria's oil. Both of masked civet's fat and nutria's oil contain a little of Eicosatetraenoic acid (C-20;4), which is of great significance in nutrition and metabolism for human body. The analysis results indicate that masked civet's oil is similar to nutria's oil in iodine value, saponification value and unsaturation point. Both masked civet's fat and nutria's oil are steady and have highly nutrition. They can be widely exploited and utilized in health protection and cosmetics made industry.
文摘Management of hypertension (HTN) largely relies on proper and accurate measurement of blood pressure (BP). Even following the criteria for HTN diagnosis defined in the Fourth report on high BP in children and adolescents, inaccurate diagnosis and misdiagnosis can occur with white coat effect and masked HTN. The use of Ambulatory Blood Pressure Monitoring (ABPM) has been increasing in pediatrics in the last 20 years. The main use of ABPM is to differentiate between sustained HTN and white coat HTN in patients who have elevated casual BP measurements and to detect masked HTN in high risk patients. ABPM is most useful in patients with casual BP within 20% of the 95th percentile for age, gender, and height. This report will highlight the use of ABPM in the evaluation of elevated BP and management of HTN in pediatrics. The discussion includes a review of various non-invasive BP measuring techniques, a description of ABPM and ABPM-unique data and diagnoses, updated ABPM clinical data more specific to pediatrics, its use in HTN clinical trials, and future outlook and direction of ABPM in pediatrics.
文摘Trauma experience not only could predict long-term physical and mental health problems, but also could have impact on the cognitive processes. Modified Stroop task and subliminal masked priming task were used to examine the automatic cognitive processing of earthquake-related stimulus (disaster-related, rescue-related, and earthquake-unrelated words) of healthy undergraduates at one month and two years since the Wenchuan earthquake happened, who came from the worst-hit areas of the Wenchuan earthquake. The results showed that the earthquake interference effects were showed in modified Stroop task and reversed priming effects were found in subliminal masked priming task at one month after the Wenchuan earthquake. However, two years later, earthquake interference effects and reversed priming effects were not found in the same experiments. The results showed the automatic cognitive processing of healthy subjects experienced trauma was affected by the earthquake episodic memory, and these interference effects were weakened with the passage of time.
基金supports from the National Natural Science Foundation of China (No. 21761132021)German Research Foundation (No. RO362/74–1)Qin Lan project from Jiangsu Province for JLH is also acknowledged
文摘An efficient chlorination reaction of in situ generated(β-diazo-α,α-difluoroethyl)phosphonates has been achieved with hydrochloric acid as a chlorine source under mild and operationally convenient conditions.The reaction does not need any catalyst and tolerates a wide scope of substrates,which affords the(β-chlorodifluoroethyl)phosphonate products in good to excellent yields.This reaction represents the first example of the halogenation of difluoroalkyl diazo compounds,and also provides an easy way for the synthesis of difluoromethylenephosphonate-containing compounds.
