Digital earth science data originated from sensors aboard satellites and platforms such as airplane,UAV,and mobile systems are increasingly available with high spectral,spatial,vertical,and temporal resolution data.Wh...Digital earth science data originated from sensors aboard satellites and platforms such as airplane,UAV,and mobile systems are increasingly available with high spectral,spatial,vertical,and temporal resolution data.When such big earth science data are processed and analyzed via geocomputation solutions,or utilized in geospatial simulation or modeling,considerable computing power and resources are necessary to complete the tasks.While classic computer clusters equipped by central processing units(CPUs)and the new computing resources of graphics processing units(GPUs)have been deployed in handling big earth data,coprocessors based on the Intel’s Many Integrated Core(MIC)Architecture are emerging and adopted in many high-performance computer clusters.This paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms,Maximum Likelihood Classification(MLC)and Cellular Automata(CA),on supercomputer Beacon,a cluster of MICs.Four different programming models are examined,including(1)the native model,(2)the offload model,(3)the symmetric model,and(4)the hybrid-offload model.It can be concluded that while different kinds of parallel programming models can enable big data handling efficiently,the hybrid-offload model can achieve the best performance and scalability.These different programming models can be applied and extended to other types of geocomputation to handle big earth data.展开更多
Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to fi...Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.展开更多
A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to bal...A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions.The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities.The scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action spaces.One of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy systems.The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep Q-learning.The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.展开更多
SRAM （Static RAM）-based FPGAs （Field Programmable Gate Arrays （FPGAs） have gained wide acceptance due to their on-line reconfigurable features. The growing demand for FPGAs has motivated semiconductor chip manufa...SRAM （Static RAM）-based FPGAs （Field Programmable Gate Arrays （FPGAs） have gained wide acceptance due to their on-line reconfigurable features. The growing demand for FPGAs has motivated semiconductor chip manufacturers to build more densely packed FPGAs with higher logic capacity. The downside of high density devices is that the probability of errors in such devices tends to increase. This paper proposes an FPGA architecture that is composed of an array of cells with built in error correction capability. Collectively a group of such cells can implement any logic function that is either registered or combinational. A cell is composed of three units： a logic block, a fault-tolerant address generator and a director unit. The logic block uses a look-up table to implement logic functions. The fault-tolerant address generator corrects any single bit error in the incoming data to the functional cell. The director block can transmit output data from the logic block to another cell located at its South, North, East or West, or to cells in all four directions. Thus a functional cell can also be used to route signals to other functional cells, thus avoiding any intricate network of interconnects, switching boxes, or routers commonly found in commercially available FPGAs.展开更多
Background:MicroRNAs(miRNAs)are a significant type of non-coding RNAs,which usually were encoded by endogenous genes with about?22 nt nucleotides.Accumulating biological experiments have shown that miRNAs have close a...Background:MicroRNAs(miRNAs)are a significant type of non-coding RNAs,which usually were encoded by endogenous genes with about?22 nt nucleotides.Accumulating biological experiments have shown that miRNAs have close associations with various human diseases.Although traditional experimental methods achieve great successes in miRNA-disease interaction identification,these methods also have some limitations.Therefore,it is necessary to develop computational method to predict miRNA-disease interactions.Methods:Here,we propose a computational framework(MDVSI)to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity.Firstly,the CosRA index is utilized to measure miRNA similarity based on network topological feature.Then,in order to enhance the reliability of miRNA similarity,the functional similarity and CosRA similarity are integrated based on linear weight method.Further,the potential miRNA-disease associations are predicted by using recommendation method.In addition,in order to overcome limitation of recommendation method,for new disease,a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.Results:To evaluate the performance of different methods,we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods.The experimental result shows that MDVSI achieves an AUC of 0.91,which is at least 0.012 higher than other compared methods.Conclusions:In summary,we propose a computational framework(MDSVI)for miRNA-disease interaction prediction.The experiment results demonstrate that it outperforms other the-state-of^the-art methods.Case study shows that it can effectively identify potential miRNA-disease interactions.展开更多
Attribute-based encryption(ABE)has been a preferred encryption technology to solve the problems of data protection and access control,especially when the cloud storage is provided by third-party service providers.ABE ...Attribute-based encryption(ABE)has been a preferred encryption technology to solve the problems of data protection and access control,especially when the cloud storage is provided by third-party service providers.ABE can put data access under control at each data item level.However,ABE schemes have practical limitations on dynamic attribute revocation.We propose a generic attribute revocation system for ABE with user privacy protection.The attribute revocation ABE(AR-ABE)system can work with any type of ABE scheme to dynamically revoke any number of attributes.展开更多
The collaboration of at least a threshold number of secret shareholders in a threshold secret sharing scheme is a strict requirement to ensure its intended functionality. Due to its promising characteristics, such a s...The collaboration of at least a threshold number of secret shareholders in a threshold secret sharing scheme is a strict requirement to ensure its intended functionality. Due to its promising characteristics, such a scheme has been proposed to solve a range of security problems in mobile ad hoc networks. However, discovering a sufficient number of secret shareholders in such dynamic and unpredictable networks is not easy. In this paper, we propose a more efficient shareholder discovery mechanism compared to our previous work. The discovery process is performed in a multihop fashion to adapt to the mobile ad hoc network environment. We introduce batch extension that gradually extends the shareholders＇ collaboration boundary by more than one hop at a time around the service requestor, to find at least the threshold number of the unknown shareholders. Through the batch extension, reply aggregation is applicable, hence reducing the redundancy use of reply routes, decreasing the required packet transmission, and lessening the service delay, compared to the previously proposed mechanism. Our simulation results show that, with the appropriate batch size, the latest mechanism is more efficient with an insignificant increase of control overhead.展开更多
Aim: To investigate whether AF1q, overexpressed in metastatic cells compared with the primary tumor cells, plays a pivotal role in breast cancer metastasis. Methods: To investigate whether AF1q has a responsibility in...Aim: To investigate whether AF1q, overexpressed in metastatic cells compared with the primary tumor cells, plays a pivotal role in breast cancer metastasis. Methods: To investigate whether AF1q has a responsibility in the acquisition of a metastatic phenotype, we performed RNA-sequencing (RNA-Seq) to identify the gene signature and applied the Metacore direct interactions network building algorithm with the top 40 amplicons of RNA-Seq. Results: Most genes were directly linked with intercellular adhesion molecule-1 (ICAM-1). Likewise, we identified that ICAM-1 expression is attenuated in metastatic cells compared to primary tumor cells. Moreover, overexpression of AF1q attenuated ICAM-1 expression, whereas suppression of AF1q elicited the opposite effect. AF1q had an effect on ICAM-1 promoter region and regulated its transcription. Decreased ICAM-1 expression ;affected the attachment of T cells to a breast cancer cell monolayer. We confirmed the finding by performing the analysis on Burkitt's lymphoma. Conclusion: Attenuation of ICAM-1 by AF1q on tumor cells disadvantages host anti-tumor defenses through the trafficking of lymphocytes, which affects tumor progression and metastasis.展开更多
基金This research was partially supported by the National Science Foundation through the award SMA-1416509.
文摘Digital earth science data originated from sensors aboard satellites and platforms such as airplane,UAV,and mobile systems are increasingly available with high spectral,spatial,vertical,and temporal resolution data.When such big earth science data are processed and analyzed via geocomputation solutions,or utilized in geospatial simulation or modeling,considerable computing power and resources are necessary to complete the tasks.While classic computer clusters equipped by central processing units(CPUs)and the new computing resources of graphics processing units(GPUs)have been deployed in handling big earth data,coprocessors based on the Intel’s Many Integrated Core(MIC)Architecture are emerging and adopted in many high-performance computer clusters.This paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms,Maximum Likelihood Classification(MLC)and Cellular Automata(CA),on supercomputer Beacon,a cluster of MICs.Four different programming models are examined,including(1)the native model,(2)the offload model,(3)the symmetric model,and(4)the hybrid-offload model.It can be concluded that while different kinds of parallel programming models can enable big data handling efficiently,the hybrid-offload model can achieve the best performance and scalability.These different programming models can be applied and extended to other types of geocomputation to handle big earth data.
基金This work supports in part by National Key R&D Program of China(No.2018YFB2100400)National Science Foundation of China(No.61872100)+2 种基金Industrial Internet Innovation and Development Project of China(2019)PCL Future Regional Network Facilities for Large-scale Experiments and Applications(PCL2018KP001)Guangdong Higher Education Innovation Team(NO.2020KCXTD007).
文摘Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.
基金supported in part by the U.S National Science Foundation(NSF)(No.ECCS-1711087)NSF Center for Infrastructure Trustworthiness in Energy Systems(CITES).
文摘A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions.The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities.The scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action spaces.One of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy systems.The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep Q-learning.The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.
基金Acknowledgement The first author was supported in part by the National Science Foundation, USA under Grant 0925080.
文摘SRAM （Static RAM）-based FPGAs （Field Programmable Gate Arrays （FPGAs） have gained wide acceptance due to their on-line reconfigurable features. The growing demand for FPGAs has motivated semiconductor chip manufacturers to build more densely packed FPGAs with higher logic capacity. The downside of high density devices is that the probability of errors in such devices tends to increase. This paper proposes an FPGA architecture that is composed of an array of cells with built in error correction capability. Collectively a group of such cells can implement any logic function that is either registered or combinational. A cell is composed of three units： a logic block, a fault-tolerant address generator and a director unit. The logic block uses a look-up table to implement logic functions. The fault-tolerant address generator corrects any single bit error in the incoming data to the functional cell. The director block can transmit output data from the logic block to another cell located at its South, North, East or West, or to cells in all four directions. Thus a functional cell can also be used to route signals to other functional cells, thus avoiding any intricate network of interconnects, switching boxes, or routers commonly found in commercially available FPGAs.
基金The work reported in this paper was partially supported by the National Natural Science Foundation of China(Nos.61702122,61751314 and 31560317)the Natural Science Foundation of Guangxi(Nos.2017GXNSFDA198033 and 2018GXNSFBA281193)+3 种基金the Key Research and Development Plan of Guangxi(No.AB 17195055)the Bossco Project of Guangxi University(No.20190240)the Hunan Provincial Science and Technology Program(No.2018WK4001)111 Project(No.Bl8059).
文摘Background:MicroRNAs(miRNAs)are a significant type of non-coding RNAs,which usually were encoded by endogenous genes with about?22 nt nucleotides.Accumulating biological experiments have shown that miRNAs have close associations with various human diseases.Although traditional experimental methods achieve great successes in miRNA-disease interaction identification,these methods also have some limitations.Therefore,it is necessary to develop computational method to predict miRNA-disease interactions.Methods:Here,we propose a computational framework(MDVSI)to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity.Firstly,the CosRA index is utilized to measure miRNA similarity based on network topological feature.Then,in order to enhance the reliability of miRNA similarity,the functional similarity and CosRA similarity are integrated based on linear weight method.Further,the potential miRNA-disease associations are predicted by using recommendation method.In addition,in order to overcome limitation of recommendation method,for new disease,a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.Results:To evaluate the performance of different methods,we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods.The experimental result shows that MDVSI achieves an AUC of 0.91,which is at least 0.012 higher than other compared methods.Conclusions:In summary,we propose a computational framework(MDSVI)for miRNA-disease interaction prediction.The experiment results demonstrate that it outperforms other the-state-of^the-art methods.Case study shows that it can effectively identify potential miRNA-disease interactions.
基金Project supported by the Ningbo eHealth Project,China(No.2016C11024)
文摘Attribute-based encryption(ABE)has been a preferred encryption technology to solve the problems of data protection and access control,especially when the cloud storage is provided by third-party service providers.ABE can put data access under control at each data item level.However,ABE schemes have practical limitations on dynamic attribute revocation.We propose a generic attribute revocation system for ABE with user privacy protection.The attribute revocation ABE(AR-ABE)system can work with any type of ABE scheme to dynamically revoke any number of attributes.
文摘The collaboration of at least a threshold number of secret shareholders in a threshold secret sharing scheme is a strict requirement to ensure its intended functionality. Due to its promising characteristics, such a scheme has been proposed to solve a range of security problems in mobile ad hoc networks. However, discovering a sufficient number of secret shareholders in such dynamic and unpredictable networks is not easy. In this paper, we propose a more efficient shareholder discovery mechanism compared to our previous work. The discovery process is performed in a multihop fashion to adapt to the mobile ad hoc network environment. We introduce batch extension that gradually extends the shareholders＇ collaboration boundary by more than one hop at a time around the service requestor, to find at least the threshold number of the unknown shareholders. Through the batch extension, reply aggregation is applicable, hence reducing the redundancy use of reply routes, decreasing the required packet transmission, and lessening the service delay, compared to the previously proposed mechanism. Our simulation results show that, with the appropriate batch size, the latest mechanism is more efficient with an insignificant increase of control overhead.
基金The work was supported by the start-up funds from James Graham Brown Cancer Center,University of Louisville,and an award from the Kentucky Lung Cancer Research Foundation to Tse W.Part of this work was performed with assistance of the UofL Genomics Facility,which is supported by NIH/NIGMS KY-INB(P20GM103436)the James Graham Brown Foundation,and user fees
文摘Aim: To investigate whether AF1q, overexpressed in metastatic cells compared with the primary tumor cells, plays a pivotal role in breast cancer metastasis. Methods: To investigate whether AF1q has a responsibility in the acquisition of a metastatic phenotype, we performed RNA-sequencing (RNA-Seq) to identify the gene signature and applied the Metacore direct interactions network building algorithm with the top 40 amplicons of RNA-Seq. Results: Most genes were directly linked with intercellular adhesion molecule-1 (ICAM-1). Likewise, we identified that ICAM-1 expression is attenuated in metastatic cells compared to primary tumor cells. Moreover, overexpression of AF1q attenuated ICAM-1 expression, whereas suppression of AF1q elicited the opposite effect. AF1q had an effect on ICAM-1 promoter region and regulated its transcription. Decreased ICAM-1 expression ;affected the attachment of T cells to a breast cancer cell monolayer. We confirmed the finding by performing the analysis on Burkitt's lymphoma. Conclusion: Attenuation of ICAM-1 by AF1q on tumor cells disadvantages host anti-tumor defenses through the trafficking of lymphocytes, which affects tumor progression and metastasis.
