The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information m...The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.展开更多
Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformati...Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive AMen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.展开更多
The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of ...The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of neutrinos from many terrestrial and non-terrestrial sources. The JUNO Event Data Model（EDM） plays a central role in the offline software system. It describes the event data entities through all processing stages for both simulated and collected data, and provides persistency via the input/output system. Also, the EDM is designed to enable flexible event handling such as event navigation, as well as the splitting of MC IBD signals and mixing of MC backgrounds. This paper describes the design, implementation and performance of the JUNO EDM.展开更多
Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first pr...Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.展开更多
Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by t...Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins). Methods: After the deletion of duplicated submissions and the revision of arbitrary drug names, the reports involving pravastatin, simvastatin, atorvastatin, rosuvastatin, and cerivastatin were analyzed. Data mining algorithms were applied for the quantitative detection of signals, where a signal means a drug-associated adverse event, including the proportional reporting ratio, the reporting odds ratio, the information component, and the empirical Bayes geometric mean. Myopathy, myalgia, myositis, rhabdomyolysis, and an increase in creatine phosphokinase level were focused on as the muscular adverse events. Results: The total number of reports was 3,472,494. The signal scores suggested that all 5 statins were associated with 5 muscular adverse events in both male and female patients. The scores varied among statins, but were more noteworthy for cerivastatin. Conclusion: The data strongly suggested the necessity of well-organized clinical studies on statin-associated muscular adverse events.展开更多
The attenuation of prestack internal multiples based on virtual seismic events is computationally costly and hinders seismic data processing. We propose a multiples attenuation method for poststack seismic data by app...The attenuation of prestack internal multiples based on virtual seismic events is computationally costly and hinders seismic data processing. We propose a multiples attenuation method for poststack seismic data by approximating conventional virtual events. The proposed method is iterative. The proposed method is tested using 2D synthetic and the field poststack seismic datasets. Compared with the conventional virtual events method, the proposed method does not require data regularization and offers higher computation efficiency. The method requires to know the travel time of the primary reflection waves. The results of the application to 2D field datasets suggest that the proposed method attenuates the internal multiples while highlighting the deep primaries.展开更多
This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which...This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.展开更多
In order to obtain diagnostic data with physical meaning,the acquired raw data must be processed through a series of physical formulas or processing algorithms.Some diagnostic data are acquired and processed by the di...In order to obtain diagnostic data with physical meaning,the acquired raw data must be processed through a series of physical formulas or processing algorithms.Some diagnostic data are acquired and processed by the diagnostic systems themselves.The data processing programs are specific and usually run manually,and the processed results of the analytical data are stored in their local disk,which is unshared and unsafe.Thus,it is necessary to integrate all the specific process programs and build an automatic and unified data analysis system with shareable data storage.This paper introduces the design and implementation of the online analysis system.Based on the MDSplus event mechanism,this system deploys synchronous operations for different processing programs.According to the computational complexity and real-time requirements,combined with the programmability of parallel algorithms and hardware costs,the OpenMP parallel processing technology is applied to the EAST analysis system,and significantly enhances the processing efficiency.展开更多
Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter ...Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter for high level event selection. This paper describes the implementation of the Sub Farm Interface. Furthermore, this paper introduces some issues on SFI（Sub Farm Interface） optimization and the monitoring service inside SFI.展开更多
In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energ...In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energy will be wasted and thus the network energy will be depleted quickly. Data aggregation is an important paradigm for compressing data so that the energy of the network is spent efficiently. In this paper, a novel data aggregation algorithm called Redundancy Elimination for Accurate Data Aggregation (READA) has been proposed. By exploiting the range of spatial correlations of data in the network, READA applies a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without largely losing the accuracy of the final aggregated data. One peculiarity of READA is that it uses a prediction model derived from cached values to confirm whether any outlier is actually an event which has occurred. From the various simulations conducted, it was observed that in READA the accuracy of data has been highly preserved taking into consideration the energy dissipated for aggregating the展开更多
In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper pro...In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.展开更多
Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could ...Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue truck<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.</span>展开更多
This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system model...This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency. The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.展开更多
Fisher-Tippet-Gnedenko classical theory shows that the normalized maximum of n iid random variables with distribution F belonging to a very wide class of functions, converges in law to an extremal distribution H, that...Fisher-Tippet-Gnedenko classical theory shows that the normalized maximum of n iid random variables with distribution F belonging to a very wide class of functions, converges in law to an extremal distribution H, that is determined by the tail of F. Extensions of this theory from the iid case to stationary and weak dependent sequences are well known from the work of Leadbetter, Lindgreen and Rootzén. In this paper, we present a very simple class of random processes that runs from iid sequences to non-stationary and strongly dependent processes, and we study the asymptotic behavior of its normalized maximum. More interesting, we show that when the process is strongly dependent, the asymptotic distribution is no longer an extremal one, but a mixture of extremal distributions. We present very simple theoretical and simulated examples of this result. This provides a simple framework to asymptotic approximations of extremes values not covered by classical extremal theory and its well-known extensions.展开更多
Due to continuous decreasing feature size and increasing device density, on-chip caches have been becoming susceptible to single event upsets, which will result in multi-bit soft errors. The increasing rate of multi-b...Due to continuous decreasing feature size and increasing device density, on-chip caches have been becoming susceptible to single event upsets, which will result in multi-bit soft errors. The increasing rate of multi-bit errors could result in high risk of data corruption and even application program crashing. Traditionally, L1 D-caches have been protected from soft errors using simple parity to detect errors, and recover errors by reading correct data from L2 cache, which will induce performance penalty. This work proposes to exploit the redundancy based on the characteristic of data values. In the case of a small data value, the replica is stored in the upper half of the word. The replica of a big data value is stored in a dedicated cache line, which will sacrifice some capacity of the data cache. Experiment results show that the reliability of L1 D-cache has been improved by 65% at the cost of 1% in performance.展开更多
文摘The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.
基金supported by National Natural Science Foundation of China (Grant Nos. 11301545, 11501578 and 11501579)
文摘Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive AMen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.
基金Supported by Joint Large-Scale Scientific Facility Funds of the NSFC and CAS(U1532258)the Program for New Century Excellent Talents in University(NCET-13-0342)+1 种基金the Shandong Natural Science Funds for Distinguished Young Scholar(JQ201402)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA10010900)
文摘The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of neutrinos from many terrestrial and non-terrestrial sources. The JUNO Event Data Model（EDM） plays a central role in the offline software system. It describes the event data entities through all processing stages for both simulated and collected data, and provides persistency via the input/output system. Also, the EDM is designed to enable flexible event handling such as event navigation, as well as the splitting of MC IBD signals and mixing of MC backgrounds. This paper describes the design, implementation and performance of the JUNO EDM.
文摘Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
文摘Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins). Methods: After the deletion of duplicated submissions and the revision of arbitrary drug names, the reports involving pravastatin, simvastatin, atorvastatin, rosuvastatin, and cerivastatin were analyzed. Data mining algorithms were applied for the quantitative detection of signals, where a signal means a drug-associated adverse event, including the proportional reporting ratio, the reporting odds ratio, the information component, and the empirical Bayes geometric mean. Myopathy, myalgia, myositis, rhabdomyolysis, and an increase in creatine phosphokinase level were focused on as the muscular adverse events. Results: The total number of reports was 3,472,494. The signal scores suggested that all 5 statins were associated with 5 muscular adverse events in both male and female patients. The scores varied among statins, but were more noteworthy for cerivastatin. Conclusion: The data strongly suggested the necessity of well-organized clinical studies on statin-associated muscular adverse events.
基金supported by the National Natural Science Foundation of China(No.41674122)National Science and Technology Major Project of China(No.2016ZX05004003)National Basic Research Program of China(No.2013CB228602)
文摘The attenuation of prestack internal multiples based on virtual seismic events is computationally costly and hinders seismic data processing. We propose a multiples attenuation method for poststack seismic data by approximating conventional virtual events. The proposed method is iterative. The proposed method is tested using 2D synthetic and the field poststack seismic datasets. Compared with the conventional virtual events method, the proposed method does not require data regularization and offers higher computation efficiency. The method requires to know the travel time of the primary reflection waves. The results of the application to 2D field datasets suggest that the proposed method attenuates the internal multiples while highlighting the deep primaries.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61203147,61374047,and 61403168)
文摘This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.
基金supported by the Chinese National Fusion Project for ITER(No.2012GB105000)Anhui Provincial Natural Science University Research Project(No.KJ2012A144)+2 种基金The Grants for Scientific Research of BSKY(No.XJ201125)from Anhui Medical University,ChinaAnhui Provincial Science Foundation for Outstanding Young Talent(No.2012SQRL265)Young and Middle-Aged Academic Backbone Finance Fund from Anhui Medical University,China
文摘In order to obtain diagnostic data with physical meaning,the acquired raw data must be processed through a series of physical formulas or processing algorithms.Some diagnostic data are acquired and processed by the diagnostic systems themselves.The data processing programs are specific and usually run manually,and the processed results of the analytical data are stored in their local disk,which is unshared and unsafe.Thus,it is necessary to integrate all the specific process programs and build an automatic and unified data analysis system with shareable data storage.This paper introduces the design and implementation of the online analysis system.Based on the MDSplus event mechanism,this system deploys synchronous operations for different processing programs.According to the computational complexity and real-time requirements,combined with the programmability of parallel algorithms and hardware costs,the OpenMP parallel processing technology is applied to the EAST analysis system,and significantly enhances the processing efficiency.
文摘Sub Farm Interface is the event builder of the ATLAS（A Toroidal LHC ApparatuS） Dataflow System. It receives event fragments from the Read Out System, builds full events and sends complete events to the Event Filter for high level event selection. This paper describes the implementation of the Sub Farm Interface. Furthermore, this paper introduces some issues on SFI（Sub Farm Interface） optimization and the monitoring service inside SFI.
文摘In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energy will be wasted and thus the network energy will be depleted quickly. Data aggregation is an important paradigm for compressing data so that the energy of the network is spent efficiently. In this paper, a novel data aggregation algorithm called Redundancy Elimination for Accurate Data Aggregation (READA) has been proposed. By exploiting the range of spatial correlations of data in the network, READA applies a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without largely losing the accuracy of the final aggregated data. One peculiarity of READA is that it uses a prediction model derived from cached values to confirm whether any outlier is actually an event which has occurred. From the various simulations conducted, it was observed that in READA the accuracy of data has been highly preserved taking into consideration the energy dissipated for aggregating the
基金Supported by the National Pre-research Foundation Project of China (513150402)
文摘In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.
文摘Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue truck<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.</span>
文摘This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency. The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.
文摘Fisher-Tippet-Gnedenko classical theory shows that the normalized maximum of n iid random variables with distribution F belonging to a very wide class of functions, converges in law to an extremal distribution H, that is determined by the tail of F. Extensions of this theory from the iid case to stationary and weak dependent sequences are well known from the work of Leadbetter, Lindgreen and Rootzén. In this paper, we present a very simple class of random processes that runs from iid sequences to non-stationary and strongly dependent processes, and we study the asymptotic behavior of its normalized maximum. More interesting, we show that when the process is strongly dependent, the asymptotic distribution is no longer an extremal one, but a mixture of extremal distributions. We present very simple theoretical and simulated examples of this result. This provides a simple framework to asymptotic approximations of extremes values not covered by classical extremal theory and its well-known extensions.
基金Projects(61472322,61272122)supported by the National Natural Science Foundation of ChinaProject(3102014JSJ0001)supported by the Fundamental Research Funds for the Central Universities,China+1 种基金Project(2013JQ8034)supported by the Natural Science Foundation of Shaanxi Province,ChinaProject(JC20120239)supported by the Basic Research Foundation of NWPU,China
文摘Due to continuous decreasing feature size and increasing device density, on-chip caches have been becoming susceptible to single event upsets, which will result in multi-bit soft errors. The increasing rate of multi-bit errors could result in high risk of data corruption and even application program crashing. Traditionally, L1 D-caches have been protected from soft errors using simple parity to detect errors, and recover errors by reading correct data from L2 cache, which will induce performance penalty. This work proposes to exploit the redundancy based on the characteristic of data values. In the case of a small data value, the replica is stored in the upper half of the word. The replica of a big data value is stored in a dedicated cache line, which will sacrifice some capacity of the data cache. Experiment results show that the reliability of L1 D-cache has been improved by 65% at the cost of 1% in performance.
