Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy ...Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy is proposed to construct adjacent Nb-Nb diatomic pairs with Se/O-coordination(Se4-Nb2-O2)in N-doped carbon-confined amorphous selenide clusters(a-Nb-Se/O@NC).Synergistic carbon confinement and hydrothermal oxygenation induce amorphization of Nb–Se bonds,eliminating crystalline rigidity while creating isotropic dual-ion transport channels and high-density active sites enriched with dangling bonds,thereby enhancing structural integrity and Na+storage capacity.The unique Se/O-coordinated Nb-Nb diatomic configuration establishes an electron-delocalized system,where the low electronegativity of Se counterbalances electron withdrawal from coordinated O at Nb centers.These strengthen d-p orbital hybridization,reduce Na+adsorption energy,and optimize charge transfer pathways and reaction kinetics in the amorphous clusters.Electrochemical tests reveal that the a-Nb-Se/O@NC anode delivers a high reversible capacity of 312.57 mAh g^(−1)and exceptional cyclic stability(103%capacity retention)after 5000 cycles at 10.0 A g^(−1).Assembled SIHCs achieve outstanding energy/power densities(207.1 Wh kg^(−1)/18966 W kg^(−1)),surpassing most amorphous and crystalline counterparts.This work provides methodological insights for the design of electrodes in high-power storage devices through atomic modulation and electronic optimization of amorphous selenides.展开更多
随着互联网技术的迅猛发展,越来越多的非结构化数据涌入到人们的生活中,为这些数据建立高效的索引面临极大的挑战.键值数据库Key-Value以其结构简单和高扩展性而引起人们的广泛关注,已成为海量数据存储系统中的重要组成部分.由于Key-Va...随着互联网技术的迅猛发展,越来越多的非结构化数据涌入到人们的生活中,为这些数据建立高效的索引面临极大的挑战.键值数据库Key-Value以其结构简单和高扩展性而引起人们的广泛关注,已成为海量数据存储系统中的重要组成部分.由于Key-Value系统对吞吐量要求较高,而基于Flash的固态硬盘(solid state drive,SSD)能够提供很高的随机读性能,在SSD上构建Key-Value系统已成为海量数据存储领域的一大研究热点.鉴于Flash具有非定点更新、寿命有限等特性,基于SSD的KeyValue系统必须针对Flash的特性作专门优化.以一种称为SkimpyStash的基于SSD的Key-Value系统为基础,提出了一种新的Key-Value系统低延迟存储系统(low latency store,LLStore).LLStore使用内存文件映射技术来减少针对SSD的IO请求,除此之外,针对SkimpyStash中低效的压缩策略,提出一种改进方法,可以在少量增加内存开销的情况下极大地减少查询时间.通过与原系统的性能比较实验,LLStore在平均查询时间上可以获得至少12%的加速.展开更多
In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.Howev...In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.However,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection.Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key.Additionally,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under LDP.Initially,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods.Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs.Theoretically,we demonstrate that the variance of KS-UE is lower than that of existing methods.These claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.展开更多
The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of ...The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility.However,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were limited.In this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values stably.Specifically,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system.The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly.We also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET operation.Through the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data sets.Furthermore,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.展开更多
The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Neverth...The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.展开更多
As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, ...As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.展开更多
The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial ...The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial to cut the tail latency of these key-value accesses.Recently,the C3 algorithm,which creatively piggybacks the queue-size of waiting keys from replica servers for the replica selection at clients,is proposed in NSDI 2015.Although C3 improves the tail latency a lot,it suffers from the timeliness issue on the feedback information,which directly influences the replica selection.In this paper,we analysis the evaluation of queuesize of waiting keys of C3,and some findings of queue-size variation were made.It motivate us to propose the Prediction-Based Replica Selection(PRS)algorithm,which predicts the queue-size at replica servers under the poor timeliness condition,instead of utilizing the exponentially weighted moving average of the state piggybacked queue-size as in C3.Consequently,PRS can obtain more accurate queue-size at clients than C3,and thus outperforms C3 in terms of cutting the tail latency.Simulation results confirm the advantage of PRS over C3.展开更多
It is very appealing that 5-hydroxymethylfurfural(HMF)is electrocatalytical oxidized as 2,5-furandicarboxylic acid(FDCA)linking to non-classical cathodic hydrogen(H_(2))production.However,the electrocatalysts for elec...It is very appealing that 5-hydroxymethylfurfural(HMF)is electrocatalytical oxidized as 2,5-furandicarboxylic acid(FDCA)linking to non-classical cathodic hydrogen(H_(2))production.However,the electrocatalysts for electrocatalytic HMF oxidative reaction(e-HMFOR)have been facing low Faradaic efficiency(FE)and high water splitting voltage.Herein,we propose a strategy of the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction by constructing a Co-Ni paired site,where the Co site is in charge of adsorbing for HMF while the electrons are transferred to the Ni site,thus giving the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction superior electrocata lytic performances for e-HMFOR and water splitting.By optimizing conditions,the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction has high conversion of 99.7%,high selectivity of 99.9%,and high FE of 98.4%at 1.3 V,as well as low cell voltage of 1.31 V at 10 mA cm^(-2)in 1 M KOH+0.1 M HMF.This study offers a potential insight for e-HMFOR to high value-added FDCA coupling water splitting to produce H_(2)in an economical manner.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-represent...Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52573299)the Natural Science Foundation of Jiangxi province(No.20242BAB25223,20232BCJ23025,20232BCJ25040,20232BAB214024)the Special Funding Program for Graduate Student Innovation of Jiangxi Province(No.YC2024-S594).
文摘Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy is proposed to construct adjacent Nb-Nb diatomic pairs with Se/O-coordination(Se4-Nb2-O2)in N-doped carbon-confined amorphous selenide clusters(a-Nb-Se/O@NC).Synergistic carbon confinement and hydrothermal oxygenation induce amorphization of Nb–Se bonds,eliminating crystalline rigidity while creating isotropic dual-ion transport channels and high-density active sites enriched with dangling bonds,thereby enhancing structural integrity and Na+storage capacity.The unique Se/O-coordinated Nb-Nb diatomic configuration establishes an electron-delocalized system,where the low electronegativity of Se counterbalances electron withdrawal from coordinated O at Nb centers.These strengthen d-p orbital hybridization,reduce Na+adsorption energy,and optimize charge transfer pathways and reaction kinetics in the amorphous clusters.Electrochemical tests reveal that the a-Nb-Se/O@NC anode delivers a high reversible capacity of 312.57 mAh g^(−1)and exceptional cyclic stability(103%capacity retention)after 5000 cycles at 10.0 A g^(−1).Assembled SIHCs achieve outstanding energy/power densities(207.1 Wh kg^(−1)/18966 W kg^(−1)),surpassing most amorphous and crystalline counterparts.This work provides methodological insights for the design of electrodes in high-power storage devices through atomic modulation and electronic optimization of amorphous selenides.
文摘随着互联网技术的迅猛发展,越来越多的非结构化数据涌入到人们的生活中,为这些数据建立高效的索引面临极大的挑战.键值数据库Key-Value以其结构简单和高扩展性而引起人们的广泛关注,已成为海量数据存储系统中的重要组成部分.由于Key-Value系统对吞吐量要求较高,而基于Flash的固态硬盘(solid state drive,SSD)能够提供很高的随机读性能,在SSD上构建Key-Value系统已成为海量数据存储领域的一大研究热点.鉴于Flash具有非定点更新、寿命有限等特性,基于SSD的KeyValue系统必须针对Flash的特性作专门优化.以一种称为SkimpyStash的基于SSD的Key-Value系统为基础,提出了一种新的Key-Value系统低延迟存储系统(low latency store,LLStore).LLStore使用内存文件映射技术来减少针对SSD的IO请求,除此之外,针对SkimpyStash中低效的压缩策略,提出一种改进方法,可以在少量增加内存开销的情况下极大地减少查询时间.通过与原系统的性能比较实验,LLStore在平均查询时间上可以获得至少12%的加速.
基金supported by a grant fromthe National Key R&DProgram of China.
文摘In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.However,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection.Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key.Additionally,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under LDP.Initially,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods.Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs.Theoretically,we demonstrate that the variance of KS-UE is lower than that of existing methods.These claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.
文摘The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair.Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility.However,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were limited.In this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values stably.Specifically,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system.The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly.We also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET operation.Through the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data sets.Furthermore,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.
文摘The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.
文摘As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.
文摘The tail latency of end-user requests,which directly impacts the user experience and the revenue,is highly related to its corresponding numerous accesses in key-value stores.The replica selection algorithm is crucial to cut the tail latency of these key-value accesses.Recently,the C3 algorithm,which creatively piggybacks the queue-size of waiting keys from replica servers for the replica selection at clients,is proposed in NSDI 2015.Although C3 improves the tail latency a lot,it suffers from the timeliness issue on the feedback information,which directly influences the replica selection.In this paper,we analysis the evaluation of queuesize of waiting keys of C3,and some findings of queue-size variation were made.It motivate us to propose the Prediction-Based Replica Selection(PRS)algorithm,which predicts the queue-size at replica servers under the poor timeliness condition,instead of utilizing the exponentially weighted moving average of the state piggybacked queue-size as in C3.Consequently,PRS can obtain more accurate queue-size at clients than C3,and thus outperforms C3 in terms of cutting the tail latency.Simulation results confirm the advantage of PRS over C3.
基金supported by the National Natural Science Foundation of China(22302019)the Changzhou Sci&Tech Program(CJ20220214).
文摘It is very appealing that 5-hydroxymethylfurfural(HMF)is electrocatalytical oxidized as 2,5-furandicarboxylic acid(FDCA)linking to non-classical cathodic hydrogen(H_(2))production.However,the electrocatalysts for electrocatalytic HMF oxidative reaction(e-HMFOR)have been facing low Faradaic efficiency(FE)and high water splitting voltage.Herein,we propose a strategy of the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction by constructing a Co-Ni paired site,where the Co site is in charge of adsorbing for HMF while the electrons are transferred to the Ni site,thus giving the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction superior electrocata lytic performances for e-HMFOR and water splitting.By optimizing conditions,the NiSeO_(3)@(CoSeO_(3))_(4)heterojunction has high conversion of 99.7%,high selectivity of 99.9%,and high FE of 98.4%at 1.3 V,as well as low cell voltage of 1.31 V at 10 mA cm^(-2)in 1 M KOH+0.1 M HMF.This study offers a potential insight for e-HMFOR to high value-added FDCA coupling water splitting to produce H_(2)in an economical manner.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
文摘Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.
