With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a...With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.展开更多
Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with...Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with exposed{100}-rich facets were synthesized by a glucose-assisted solvent-thermal method,in which alloying W not only can facilitate the formation of such specific nanostructures to expose more active sites for AOR,but also modulate the electronic structure of PtIr to promote the kinetics of AOR.The PtIrW-NCBs featuring the small nanoparticle size of 5.05±0.07 nm exhibit superior AOR performance,wherein the onset potential is down to 0.319 V and the mass activity is 30.15 A g_((PGM=Pt,Ir))^(-1)at 0.50 V vs.RHE,significantly higher than those of reported majority of AOR catalysts and even commercial PtIr/C.Meanwhile,in situ Fourier transform infrared spectroscopy measurement further reveals that AOR on PtIrW-NCBs dominantly undergoes the dimerization path of NH_(x)(1≤x≤2).In addition,the theoretical calculations also identify that alloying W into PtIr can contribute additional electrons to 5d orbitals of PtIr,enabling the d-band center approaching the Femi level,which in turn induces the high-filling of bonding orbitals of N-N bond in^(*)N_(2)H_(4),promoting the dimerization of^(*)NH_(2)to^(*)N_(2)H_(4)and thus leading to high AOR activity of PtIrW.This work provides new insights for designing efficient AOR electrocatalysts.展开更多
Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)a...Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies.Although many efforts are made to improve the resilience to cyber attacks,there are still many unsolved challenges.This paper first identifies some major security attacks on intelligent connected vehicles.Then,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware detection.Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.展开更多
With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions.These investigations ...With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions.These investigations include the fundamental security and privacy problems associated with 6G technologies.Therefore,in order to consolidate and solidify this foundational research as a basis for future investigations,we have prepared a survey on the status quo of 6G security and privacy.The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking.We then discuss four key aspects of 6G networks–real-time intelligent edge computing,distributed artificial intelligence,intelligent radio,and 3D intercoms–and some promising emerging technologies in each area,along with the relevant security and privacy issues.The survey concludes with a report on the potential use of 6G.Some of the references used in this paper along and further details of several points raised can be found at:security-privacyin5g-6g.github.io.展开更多
Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the centra...Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.展开更多
Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,...Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.展开更多
The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of mass...The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space,cost savings.However,the openness of cloud brings challenges for image data security.In this paper,we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment,which takes the weights of participants(i.e.,cloud service providers)into consideration.An extended Mignotte sequence is constructed according to the weights of participants,and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images.Then we store the shadows in every cloud service provider,respectively.At last,we restore the remote sensing image based on the Chinese Remainder Theorem.Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud.The experiment also shows that no matter weight values,each service providers only needs to save one share,which simplifies the management and usage,it also reduces the transmission of secret information,strengthens the security and practicality of this scheme.展开更多
A wealth of evidence has suggested that gastrointestinal dysfunction is associated with the onset and progression of Parkinson’s disease(PD).However,the mechanisms underlying these links remain to be defined.Here,we ...A wealth of evidence has suggested that gastrointestinal dysfunction is associated with the onset and progression of Parkinson’s disease(PD).However,the mechanisms underlying these links remain to be defined.Here,we investigated the impact of deregulation of intestinal dopamine D2 receptor(DRD2)signaling in response to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced dopaminergic neurodegeneration.Dopamine/dopamine signaling in the mouse colon decreased with ageing.Selective ablation of Drd2,but not Drd4,in the intestinal epithelium,caused a more severe loss of dopaminergic neurons in the substantia nigra following MPTP challenge,and this was accompanied by a reduced abundance of succinate-producing Alleoprevotella in the gut microbiota.Administration of succinate markedly attenuated dopaminergic neuronal loss in MPTP-treated mice by elevating the mitochondrial membrane potential.This study suggests that intestinal epithelial DRD2 activity and succinate from the gut microbiome contribute to the maintenance of nigral DA neuron survival.These findings provide a potential strategy targeting neuroinflammation-related neurological disorders such as PD.展开更多
Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from n...Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.展开更多
The East Asian trough(EAT)profoundly influences the East Asian spring climate.In this study,the relationship of the EATs among the three spring months is investigated.Correlation analysis shows that the variation in M...The East Asian trough(EAT)profoundly influences the East Asian spring climate.In this study,the relationship of the EATs among the three spring months is investigated.Correlation analysis shows that the variation in March EAT is closely related to that of April EAT.Extended empirical orthogonal function(EEOF)analysis also confirms the co-variation of the March and April EATs.The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April.Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature(SST)pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean.The dipole SST pattern over the North Atlantic,with one center east of Newfoundland Island and another east of Bermuda,could trigger a Rossby wave train to influence the EAT in March−April.The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific,subsequently impacting the southern part of the EAT in March−April.Besides the SST factors,the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April.These three impact factors are generally independent of each other,jointly explaining large variations in the EAT EEOF1.Moreover,the signals of the three factors could be traced back to February,consequently providing a potential prediction source for the EAT variation in March and April.展开更多
The unparalleled energy density has granted lithium-sulfur batteries(LSBs)with attractive usages.Unfortunately,LSBs still face some unsurpassed challenges in industrialization,with polysulfides shuttling,dendrite grow...The unparalleled energy density has granted lithium-sulfur batteries(LSBs)with attractive usages.Unfortunately,LSBs still face some unsurpassed challenges in industrialization,with polysulfides shuttling,dendrite growth and thermal hazard as the major problems triggering the cycling instability and low safety.With the merit of convenience,the method of designing functional separator has been adapted.Concretely,the carbon aerogel confined with CoS_(2)(CoS_(2)-NCA)is constructed and coated on Celgard separator surface,acquiring CoS_(2)-NCA modified separator(CoS_(2)-NCA@C),which holds the promoted electrolyte affinity and flame retardance.As revealed,CoS_(2)-NCA@C cell gives a high discharge capacity 1536.9 mAh/g at 1st cycle,much higher than that of Celgard cell(987.1 mAh/g).Moreover,the thermal runaway triggering time is dramatically prolonged by 777.4 min,corroborating the promoted thermal safety of cell.Noticeably,the higher coulombic efficiency stability and lower overpotential jointly confirm the efficacy of CoS_(2)-NCA@C in suppressing the lithium dendrite growth.Overall,this work can provide useful inspirations for designing functional separator,coping with the vexing issues of LSBs.展开更多
As human beings are deep into the information age, we have been witnessing the rapid development of Big Data. Security and privacy are the most concerned issues in Big Data. Big Data definitely desires the security an...As human beings are deep into the information age, we have been witnessing the rapid development of Big Data. Security and privacy are the most concerned issues in Big Data. Big Data definitely desires the security and privacy protection all through the collection, transmission and analysis procedures. The features of Big Data bring unprecedented challenges to security and privacy protection. To protect the confiden- tiality, integrity and availability, traditional security measures such as cryptography, event analysis, intrusion detection, prevention and access control have taken a new dimension. To protect the privacy, new pattern of measures such as privacy-preserved data analysis need to be explored. There is a lot of work to be done in this emerging field.展开更多
B-type natriuretic peptide(BNP)system is critical to cardiovascular physiological and pathological processes,especially in the development and progression of heart failure(HF)caused by dilated cardiomyopathy(DCMHF).1,...B-type natriuretic peptide(BNP)system is critical to cardiovascular physiological and pathological processes,especially in the development and progression of heart failure(HF)caused by dilated cardiomyopathy(DCMHF).1,2 Single nucleotide polymorphism(SNP)in the noncoding region,especially the promoter region,might correlate well with plasma BNP levels,and potentially affect the susceptibility of DCM-HF,through interacting with transcription factor and regulating natriuretic peptide B(NPPB)gene transcription.展开更多
Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big data.It is an algorithm that does not collect users’raw data...Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big data.It is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s privacy.Nonetheless,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server.In addition,some recent studies have shown that attackers can recover information merely from parameters.Hence,there is still lots of room to improve the current federated learning frameworks.In this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning.Several open issues and existing solutions in federated learning are discussed.We also point out the future research directions of federated learning.展开更多
Due to dramatically increasing information published in social networks,privacy issues have given rise to public concerns.Although the presence of differential privacy provides privacy protection with theoretical foun...Due to dramatically increasing information published in social networks,privacy issues have given rise to public concerns.Although the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further improvement.However,most existing studies do not consider the quantitative impact of the adversary when measuring data utility.In this paper,we firstly propose a personalized differential privacy method based on social distance.Then,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other.We formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian game.The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm.The proposed method achieves fast convergence by reducing the cardinality from n to 2.In addition,the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed.Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.展开更多
基金partially supported by the National Natural Science Foundation of China under Grant No.62372121the Ministry of education of Humanities and Social Science project under Grant No.20YJAZH118+1 种基金the National Key Research and Development Program of China under Grant No.2020YFB1005804the MOE Project at Center for Linguistics and Applied Linguistics,Guangdong University of Foreign Studies。
文摘With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.
基金supported by the National Natural Science Foundation of China(22379031)the Guangxi Science and Technology Project of China(AB16380030)+1 种基金the National Research Foundation,SingaporeA*STAR(Agency for Science,Technology and Research)under its LCER Phase 2 Programme Hydrogen&Emerging Technologies FI,Directed Hydrogen Programme(U2305D4003)。
文摘Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with exposed{100}-rich facets were synthesized by a glucose-assisted solvent-thermal method,in which alloying W not only can facilitate the formation of such specific nanostructures to expose more active sites for AOR,but also modulate the electronic structure of PtIr to promote the kinetics of AOR.The PtIrW-NCBs featuring the small nanoparticle size of 5.05±0.07 nm exhibit superior AOR performance,wherein the onset potential is down to 0.319 V and the mass activity is 30.15 A g_((PGM=Pt,Ir))^(-1)at 0.50 V vs.RHE,significantly higher than those of reported majority of AOR catalysts and even commercial PtIr/C.Meanwhile,in situ Fourier transform infrared spectroscopy measurement further reveals that AOR on PtIrW-NCBs dominantly undergoes the dimerization path of NH_(x)(1≤x≤2).In addition,the theoretical calculations also identify that alloying W into PtIr can contribute additional electrons to 5d orbitals of PtIr,enabling the d-band center approaching the Femi level,which in turn induces the high-filling of bonding orbitals of N-N bond in^(*)N_(2)H_(4),promoting the dimerization of^(*)NH_(2)to^(*)N_(2)H_(4)and thus leading to high AOR activity of PtIrW.This work provides new insights for designing efficient AOR electrocatalysts.
文摘Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies.Although many efforts are made to improve the resilience to cyber attacks,there are still many unsolved challenges.This paper first identifies some major security attacks on intelligent connected vehicles.Then,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware detection.Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.
基金This work was supported by an ARC Linkage Project(LP180101150)from the Australian Research Council,Australia.
文摘With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions.These investigations include the fundamental security and privacy problems associated with 6G technologies.Therefore,in order to consolidate and solidify this foundational research as a basis for future investigations,we have prepared a survey on the status quo of 6G security and privacy.The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking.We then discuss four key aspects of 6G networks–real-time intelligent edge computing,distributed artificial intelligence,intelligent radio,and 3D intercoms–and some promising emerging technologies in each area,along with the relevant security and privacy issues.The survey concludes with a report on the potential use of 6G.Some of the references used in this paper along and further details of several points raised can be found at:security-privacyin5g-6g.github.io.
基金supported by the National Natural Science Foundation of China(No.62206238)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220562)the Natural Science Research Project of Universities in Jiangsu Province(No.22KJB520010).
文摘Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.
基金This work is partially supported by the National Natural Science Foundation of China under Grant Nos.61876205 and 61877013the Ministry of Education of Humanities and Social Science project under Grant Nos.19YJAZH128 and 20YJAZH118+1 种基金the Science and Technology Plan Project of Guangzhou under Grant No.201804010433the Bidding Project of Laboratory of Language Engineering and Computing under Grant No.LEC2017ZBKT001.
文摘Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.
基金This research was partly supported by(National Natural Science Foundation of China under 41671431,61572421and Shanghai Science and Technology Commission Project 15590501900.
文摘The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space,cost savings.However,the openness of cloud brings challenges for image data security.In this paper,we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment,which takes the weights of participants(i.e.,cloud service providers)into consideration.An extended Mignotte sequence is constructed according to the weights of participants,and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images.Then we store the shadows in every cloud service provider,respectively.At last,we restore the remote sensing image based on the Chinese Remainder Theorem.Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud.The experiment also shows that no matter weight values,each service providers only needs to save one share,which simplifies the management and usage,it also reduces the transmission of secret information,strengthens the security and practicality of this scheme.
基金This work was supported by grants from the Ministry of Science and Technology of China(2020YFC2002800)the Natural Science Foundation of China(U1801681)+3 种基金Strategic Priority Research Program of Chinese Academy of Science(XDB32020100)Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)Key Realm R&D Program of Guangdong Province(2018B030337001)Innovative Research Team of High-Level Local Universities in Shanghai.
文摘A wealth of evidence has suggested that gastrointestinal dysfunction is associated with the onset and progression of Parkinson’s disease(PD).However,the mechanisms underlying these links remain to be defined.Here,we investigated the impact of deregulation of intestinal dopamine D2 receptor(DRD2)signaling in response to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced dopaminergic neurodegeneration.Dopamine/dopamine signaling in the mouse colon decreased with ageing.Selective ablation of Drd2,but not Drd4,in the intestinal epithelium,caused a more severe loss of dopaminergic neurons in the substantia nigra following MPTP challenge,and this was accompanied by a reduced abundance of succinate-producing Alleoprevotella in the gut microbiota.Administration of succinate markedly attenuated dopaminergic neuronal loss in MPTP-treated mice by elevating the mitochondrial membrane potential.This study suggests that intestinal epithelial DRD2 activity and succinate from the gut microbiome contribute to the maintenance of nigral DA neuron survival.These findings provide a potential strategy targeting neuroinflammation-related neurological disorders such as PD.
文摘Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.
基金the National Natural Science Foundation of China(Grant Nos.41825010 and 42005024).
文摘The East Asian trough(EAT)profoundly influences the East Asian spring climate.In this study,the relationship of the EATs among the three spring months is investigated.Correlation analysis shows that the variation in March EAT is closely related to that of April EAT.Extended empirical orthogonal function(EEOF)analysis also confirms the co-variation of the March and April EATs.The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April.Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature(SST)pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean.The dipole SST pattern over the North Atlantic,with one center east of Newfoundland Island and another east of Bermuda,could trigger a Rossby wave train to influence the EAT in March−April.The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific,subsequently impacting the southern part of the EAT in March−April.Besides the SST factors,the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April.These three impact factors are generally independent of each other,jointly explaining large variations in the EAT EEOF1.Moreover,the signals of the three factors could be traced back to February,consequently providing a potential prediction source for the EAT variation in March and April.
基金financially supported by the National Natural Science Foundation of China(52104197)Hongkong Scholar Program(XJ2022022)+5 种基金National Science Foundation for Post-doctoral Scientists of China(2021M691549,2021M703082)National Natural Science Foundation of China(52272396,52306090)Jiangsu Provincial Double-Innovation Doctor Program(JSSCBS20210402)Natural Science Foundation of the Jiangsu Higher Education Institutions(21KJB620001)The Open Fund of the State Key Laboratory of Fire Science(SKLFS)Program(HZ2022-KF04)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX22-0457)。
文摘The unparalleled energy density has granted lithium-sulfur batteries(LSBs)with attractive usages.Unfortunately,LSBs still face some unsurpassed challenges in industrialization,with polysulfides shuttling,dendrite growth and thermal hazard as the major problems triggering the cycling instability and low safety.With the merit of convenience,the method of designing functional separator has been adapted.Concretely,the carbon aerogel confined with CoS_(2)(CoS_(2)-NCA)is constructed and coated on Celgard separator surface,acquiring CoS_(2)-NCA modified separator(CoS_(2)-NCA@C),which holds the promoted electrolyte affinity and flame retardance.As revealed,CoS_(2)-NCA@C cell gives a high discharge capacity 1536.9 mAh/g at 1st cycle,much higher than that of Celgard cell(987.1 mAh/g).Moreover,the thermal runaway triggering time is dramatically prolonged by 777.4 min,corroborating the promoted thermal safety of cell.Noticeably,the higher coulombic efficiency stability and lower overpotential jointly confirm the efficacy of CoS_(2)-NCA@C in suppressing the lithium dendrite growth.Overall,this work can provide useful inspirations for designing functional separator,coping with the vexing issues of LSBs.
文摘As human beings are deep into the information age, we have been witnessing the rapid development of Big Data. Security and privacy are the most concerned issues in Big Data. Big Data definitely desires the security and privacy protection all through the collection, transmission and analysis procedures. The features of Big Data bring unprecedented challenges to security and privacy protection. To protect the confiden- tiality, integrity and availability, traditional security measures such as cryptography, event analysis, intrusion detection, prevention and access control have taken a new dimension. To protect the privacy, new pattern of measures such as privacy-preserved data analysis need to be explored. There is a lot of work to be done in this emerging field.
基金supported by grants from the Excellent Youth Incubation Program of Chinese People's Liberation Army General Hospital (No.2020-YQPY-007)the Natural Science Foundation of Hainan Province,China (No.821QN389,821MS117,823MS161,820MS124,821MS112,822MS198,820MS126,820QN383,822MS193)+8 种基金the Military Medical Science and Technology Youth Incubation Program (China) (No.20QNPY110,19QNPY060)the Key R&D Program of Hainan Province,China (No.ZDYF2023SHFZ145)the Health Care Project of PLA (China) (No.22BJZ30)the Clinical Medical Research Center Project of Hainan Province,China (LCYX202106,LCYX202201,LCYX202303)the National Key R&D Program of China (No.2018YFC2000400)the National S&T Resource Sharing Service Platform Project of China (No.YCZYPT[2018]07)the Specific Research Fund of Innovation Platform for Academicians of Hainan Province,China (No.YSPTZX202216)the Heatstroke Treatment and Research Center of PLA (China) (No.413EGZ1D10)the Major Science and Technology Programme of Hainan Province,China (No.ZDKJ2019012).
文摘B-type natriuretic peptide(BNP)system is critical to cardiovascular physiological and pathological processes,especially in the development and progression of heart failure(HF)caused by dilated cardiomyopathy(DCMHF).1,2 Single nucleotide polymorphism(SNP)in the noncoding region,especially the promoter region,might correlate well with plasma BNP levels,and potentially affect the susceptibility of DCM-HF,through interacting with transcription factor and regulating natriuretic peptide B(NPPB)gene transcription.
基金This work was supported by Guangdong Provincial Key Laboratory(2020B121201001).
文摘Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big data.It is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s privacy.Nonetheless,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server.In addition,some recent studies have shown that attackers can recover information merely from parameters.Hence,there is still lots of room to improve the current federated learning frameworks.In this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning.Several open issues and existing solutions in federated learning are discussed.We also point out the future research directions of federated learning.
文摘Due to dramatically increasing information published in social networks,privacy issues have given rise to public concerns.Although the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further improvement.However,most existing studies do not consider the quantitative impact of the adversary when measuring data utility.In this paper,we firstly propose a personalized differential privacy method based on social distance.Then,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other.We formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian game.The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm.The proposed method achieves fast convergence by reducing the cardinality from n to 2.In addition,the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed.Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.
