Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in ...Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.展开更多
Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential t...Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.展开更多
With the increasingly pervasive deployment of fog servers,fog computing extends data processing and analysis to network edges.At the same time,as the next-generation power grid,the smart grid should meet the requireme...With the increasingly pervasive deployment of fog servers,fog computing extends data processing and analysis to network edges.At the same time,as the next-generation power grid,the smart grid should meet the requirements of security,efficiency,and real-time monitoring of user energy consumption.By utilizing the low-latency and distributed properties of fog computing,it can improve communication efficiency and user service satisfaction in smart grids.For the sake of providing adequate functionality for the power grid,various schemes have been proposed.Whereas,many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats.In this paper,we propose the EPri-MDAS:an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem,which achieves both data integrity verification and data source authentication with the most efficient block cipherbased authenticated encryption algorithm.It performs well in energy efficiency with strong security.Especially,the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses;it also allows for fault tolerance while protecting personal privacy.The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.展开更多
Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased im-pulsivity.In recent years,a large number of genetic studies ...Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased im-pulsivity.In recent years,a large number of genetic studies for ADHD have been published and related ge-netic data has been accumulated dramatically.To pro-vide researchers a comprehensive ADHD genetic re-source,we previously developed the first genetic data-base for ADHD(ADHDgene).The abundant genetic data provides novel candidates for further study.Meanwhile,it also brings new challenge for selecting promising candidate genes for replication and verification research.In this study,we surveyed the computational tools for candidate gene prioritization and selected five tools,which integrate multiple data sources for gene prioritiza-tion,to prioritize ADHD candidate genes in ADHDgene.The prioritization analysis resulted in 16 prioritized can-didate genes,which are mainly involved in several major neurotransmitter systems or in nervous system development pathways.Among these genes,nervous system development related genes,especially SNAP25,STX1A and the gene-gene interactions related with each of them deserve further investigations.Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.展开更多
Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning mode...Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning model(LSTM)with two ensemble models(RF and XGboost)using multiple data.Data is gathered from four stocks of financial sector in China A-share market,and the accuracy and F1-measure are used as performance measure.The data of the past three days is applied to classify the rise and fall trend of price on the next day.The models’performance are tested under different market styles(bull or bear market)and different market activities.The results indicate that under the same conditions,LSTM is the top algorithm followed by RF and XGBoost.For all models applied in this study,prediction performance in bull markets is much better than in bear markets,and the result in active period is better than inactive period by average.It is also found that adding data sources is not always effective in improving forecasting performance,and valuable data sources and proper processing may be more essential than providing a large quantity of data source.展开更多
The housing price has been paid close attention by people in all walks of life,and the development of big data provides a new data environment for the study of urban housing price.Housing price data of four national c...The housing price has been paid close attention by people in all walks of life,and the development of big data provides a new data environment for the study of urban housing price.Housing price data of four national central cities (Beijing,Shanghai,Guangzhou and Wuhan) are taken as research samples.With the help of software GIS,exploratory spatial data analysis method is used to depict the spatial distribution pattern of urban housing price,and commonness and difference of spatial distribution of housing price are explored.The conclusions are as below:①regional imbalance of housing price in national central cities is significant.②Spatial distribution of urban housing price in Beijing,Shanghai,Guangzhou and Wuhan presents a polycentric pattern,and there is obvious spatial agglomeration.③The internal change of housing price in different cities has significant spatial difference.Beijing,Shanghai,Guangzhou and Wuhan are taken as typical city samples for research,with reference and practical significance,which could help to effectively predict spatial development trend of housing prices in other first and second tier cities.The research aims to provide certain reference for the government implementing real estate control policies according to local conditions,project location and reasonable pricing of real estate developers.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, para...Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.展开更多
Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, ther...Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.展开更多
As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and effic...As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.展开更多
Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale a...Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.展开更多
A new species of the Asian leaf litter toad genus Leptobrachella is described from Sichuan Province, China. Molecular phylogenetic analyses based on mitochondrial and nuclear gene sequences clustered the new species a...A new species of the Asian leaf litter toad genus Leptobrachella is described from Sichuan Province, China. Molecular phylogenetic analyses based on mitochondrial and nuclear gene sequences clustered the new species as an independent clade nested into L. oshanensis species group. The new species could be distinguished from its congeners by a combination of following characters: body size moderate(25.8–32.6 mm in male, 33.7–34.1 mm in female);distinct black spots present on flanks;toes rudimentary webbed, with narrow lateral fringes, dermal ridges under toes interrupted at articulations;ventral belly cream white with variable brown specking;skin on dorsum relatively smooth with fine tiny granules or short ridges;iris copper above, silver bellow;greyish black patches on posterior thigh absent or small;spines on surface of chest absent in male during breeding season;nasals entirely or partially separated from sphenethmoid in male;dorsal surface of tadpoles semitransparent light brown, spots on tail absent, keratodont row formula I: 3+3(2+2)/2+2: I;calls simple, call series basically consist of repeated long calls, at dominant frequency(4831.9 ± 155.8) Hz and call duration(544.5 ± 146.8) ms. In addition, we made supplementary description on L. oshanensis including holotype, variations, tadpoles, skull and bioacoustics. Besides, this paper reports cases of femoral adipose glands in the genus Leptobrachella as first known sexual dimorphism skin glands for males of Megophryidae.展开更多
Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable t...Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable technique is taken. In this paper, a dynamic power consumption model by using a lookup table as a unit is proposed. Then, we establish a system-level task scheduling model according to the task type. Based on single instruction multiple data （SIMD） architecture which contains a processing system and a control system with a Nios II processor, a practical dynamic reconfigurable system is built. The approach is evaluated on a hardware platform. The test results show that the system can automatically adjust the power consumption in case of external energy input changing. The utilization of the system dynamic power of their portion is from 80.05% to 91.75% during the first task assignment. During the entire processing cycle, the total energy efficiency is 97.67%.展开更多
Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple...Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.展开更多
This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three ...This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three coprocessors that are used for synchronization, FFT and channel decoder. In this MIMO OFDM system, the Zero Correlation Zone （ZCZ） code is used as the synchronization word preamble of packet in the physical layer in order to avoid the interference from other transmitting antennas. Furthermore, a simple channel estimation algorithm is proposed which is appropriate tbr the SIMD DSP computation.展开更多
We present novel vector permutation and branch reduction methods to minimize the number of execution cycles for bit reversal algorithms.The new methods are applied to single instruction multiple data(SIMD) parallel im...We present novel vector permutation and branch reduction methods to minimize the number of execution cycles for bit reversal algorithms.The new methods are applied to single instruction multiple data(SIMD) parallel implementation of complex data floating-point fast Fourier transform(FFT).The number of operational clock cycles can be reduced by an average factor of 3.5 by using our vector permutation methods and by 1.1 by using our branch reduction methods,compared with conventional im-plementations.Experiments on MPC7448(a well-known SIMD reduced instruction set computing processor) demonstrate that our optimal bit-reversal algorithm consistently takes fewer than two cycles per element in complex array operations.展开更多
To efficiently exploit the performance of single instruction multiple data （SIMD） architectures for video coding, a parallel memory architecture with power-of-two memory modules is proposed. It employs two novel ske...To efficiently exploit the performance of single instruction multiple data （SIMD） architectures for video coding, a parallel memory architecture with power-of-two memory modules is proposed. It employs two novel skewing schemes to provide conflict-free access to adjacent elements （8-bit and 16-bit data types） or with power-of-two intervals in both horizontal and vertical directions, which were not possible in previous parallel memory architectures. Area consumptions and delay estimations are given respectively with 4, 8 and 16 memory modules. Under a 0.18-pm CMOS technology, the synthesis results show that the proposed system can achieve 230 MHz clock frequency with 16 memory modules at the cost of 19k gates when read and write latencies are 3 and 2 clock cycles, respectively. We implement the proposed parallel memory architecture on a video signal processor （VSP）. The results show that VSP enhanced with the proposed architecture achieves 1.28× speedups for H.264 real-time decoding.展开更多
With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW...With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW codesign method to implement the H.264 decoder in an SoC with an ARM core, a multimedia processor and a deblocking filter coprocessor. For the parallel processing features of the multimedia processor, clock cycles of decoding process can be dramatically reduced. And the hardware dedicated deblocking filter coprocessor can improve the efficiency a lot. With maximum clock frequency of 150 MHz, the whole system can achieve real time processing speed and flexibility.展开更多
Single instruction multiple data （SIMD） instructions are often implemented in modem media processors. Although SIMD instructions are useful in multimedia applications, most compilers do not have good support for SIM...Single instruction multiple data （SIMD） instructions are often implemented in modem media processors. Although SIMD instructions are useful in multimedia applications, most compilers do not have good support for SIMD instructions. This paper focuses on SIMD instructions generation for media processors. We present an efficient code optimization approach that is integrated into a retargetable C compiler. SIMD instructions are generated by finding and combining the same operations in programs. Experimental results for the UltraSPARC VIS instruction set show that a speedup factor up to 2.639 is obtained.展开更多
A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set process...A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.展开更多
基金Supported by Zhejiang Provincial Key Research and Development Program(Grant No.2021C04015)。
文摘Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.
基金partially supported by the National Natural Science Foundation of China(Grant No.12171079)the National Key R&D Program of China(Grant No.2020YFA0714102)+1 种基金partially supported by the National Natural Science Foundation of China(Grant No.12101116)the National Key Research and Development Program of China(Grant No.2022YFA1003701)。
文摘Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.
基金supported by the National Natural Science Foundation of China(62272282).
文摘With the increasingly pervasive deployment of fog servers,fog computing extends data processing and analysis to network edges.At the same time,as the next-generation power grid,the smart grid should meet the requirements of security,efficiency,and real-time monitoring of user energy consumption.By utilizing the low-latency and distributed properties of fog computing,it can improve communication efficiency and user service satisfaction in smart grids.For the sake of providing adequate functionality for the power grid,various schemes have been proposed.Whereas,many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats.In this paper,we propose the EPri-MDAS:an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem,which achieves both data integrity verification and data source authentication with the most efficient block cipherbased authenticated encryption algorithm.It performs well in energy efficiency with strong security.Especially,the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses;it also allows for fault tolerance while protecting personal privacy.The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.
基金supported by Key Laboratory of Mental Health,Insti-tute of Psychology,Chinese Academy of Sciences.
文摘Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased im-pulsivity.In recent years,a large number of genetic studies for ADHD have been published and related ge-netic data has been accumulated dramatically.To pro-vide researchers a comprehensive ADHD genetic re-source,we previously developed the first genetic data-base for ADHD(ADHDgene).The abundant genetic data provides novel candidates for further study.Meanwhile,it also brings new challenge for selecting promising candidate genes for replication and verification research.In this study,we surveyed the computational tools for candidate gene prioritization and selected five tools,which integrate multiple data sources for gene prioritiza-tion,to prioritize ADHD candidate genes in ADHDgene.The prioritization analysis resulted in 16 prioritized can-didate genes,which are mainly involved in several major neurotransmitter systems or in nervous system development pathways.Among these genes,nervous system development related genes,especially SNAP25,STX1A and the gene-gene interactions related with each of them deserve further investigations.Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.
基金This work is supported by:Engineering Research Center of State Financial Security,Ministry of Education,Central University of Finance and Economics,Beijing,102206,ChinaProgram for Innovation Research in Central University of Finance and Economics.
文摘Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning model(LSTM)with two ensemble models(RF and XGboost)using multiple data.Data is gathered from four stocks of financial sector in China A-share market,and the accuracy and F1-measure are used as performance measure.The data of the past three days is applied to classify the rise and fall trend of price on the next day.The models’performance are tested under different market styles(bull or bear market)and different market activities.The results indicate that under the same conditions,LSTM is the top algorithm followed by RF and XGBoost.For all models applied in this study,prediction performance in bull markets is much better than in bear markets,and the result in active period is better than inactive period by average.It is also found that adding data sources is not always effective in improving forecasting performance,and valuable data sources and proper processing may be more essential than providing a large quantity of data source.
基金Sponsored by National Natural Science Foundation of China (51808413)General Project of Hubei Social Science Fund (2018193)Innovation and Entrepreneurship Training Program for College Students in Hubei Province (S201910490027)。
文摘The housing price has been paid close attention by people in all walks of life,and the development of big data provides a new data environment for the study of urban housing price.Housing price data of four national central cities (Beijing,Shanghai,Guangzhou and Wuhan) are taken as research samples.With the help of software GIS,exploratory spatial data analysis method is used to depict the spatial distribution pattern of urban housing price,and commonness and difference of spatial distribution of housing price are explored.The conclusions are as below:①regional imbalance of housing price in national central cities is significant.②Spatial distribution of urban housing price in Beijing,Shanghai,Guangzhou and Wuhan presents a polycentric pattern,and there is obvious spatial agglomeration.③The internal change of housing price in different cities has significant spatial difference.Beijing,Shanghai,Guangzhou and Wuhan are taken as typical city samples for research,with reference and practical significance,which could help to effectively predict spatial development trend of housing prices in other first and second tier cities.The research aims to provide certain reference for the government implementing real estate control policies according to local conditions,project location and reasonable pricing of real estate developers.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
文摘Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.
基金The Science Foundation(JA12301)of Fujian Educational Committeethe Teaching Quality Project(ZL0902/TZ(SJ))of Higher Education in Fujian Provincial Education Department
文摘Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.
基金supported by the National Natural Science Foundation of China under Grant No.U19B2021the Key Research and Development Program of Shaanxi under Grant No.2020ZDLGY08-04+1 种基金the Key Technologies R&D Program of He’nan Province under Grant No.212102210084the Innovation Scientists and Technicians Troop Construction Projects of Henan Province.
文摘As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.
基金supported by Spaceage Geoconsulting,a research oriented consulting firm.
文摘Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.
基金supported by Construction of Basic Conditions Platform of Sichuan Science and Technology Department (2019JDPT0020)China Biodiversity Observation Networks (Sino BON–Amphibian and Reptile)。
文摘A new species of the Asian leaf litter toad genus Leptobrachella is described from Sichuan Province, China. Molecular phylogenetic analyses based on mitochondrial and nuclear gene sequences clustered the new species as an independent clade nested into L. oshanensis species group. The new species could be distinguished from its congeners by a combination of following characters: body size moderate(25.8–32.6 mm in male, 33.7–34.1 mm in female);distinct black spots present on flanks;toes rudimentary webbed, with narrow lateral fringes, dermal ridges under toes interrupted at articulations;ventral belly cream white with variable brown specking;skin on dorsum relatively smooth with fine tiny granules or short ridges;iris copper above, silver bellow;greyish black patches on posterior thigh absent or small;spines on surface of chest absent in male during breeding season;nasals entirely or partially separated from sphenethmoid in male;dorsal surface of tadpoles semitransparent light brown, spots on tail absent, keratodont row formula I: 3+3(2+2)/2+2: I;calls simple, call series basically consist of repeated long calls, at dominant frequency(4831.9 ± 155.8) Hz and call duration(544.5 ± 146.8) ms. In addition, we made supplementary description on L. oshanensis including holotype, variations, tadpoles, skull and bioacoustics. Besides, this paper reports cases of femoral adipose glands in the genus Leptobrachella as first known sexual dimorphism skin glands for males of Megophryidae.
基金supported by the National Natural Science Foundation of China under Grant No. 61176025 and No. 61006027the Fundamental Research Funds for the Central Universities under Grant No.ZYGX2012J003+1 种基金National Laboratory of Analogue Integrated Circuit Grants under Grants No. 9140C0901101002 and No. 9140C0901101003New Century Excellent Talents Program under Grant No.NCET-10-0297
文摘Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable technique is taken. In this paper, a dynamic power consumption model by using a lookup table as a unit is proposed. Then, we establish a system-level task scheduling model according to the task type. Based on single instruction multiple data （SIMD） architecture which contains a processing system and a control system with a Nios II processor, a practical dynamic reconfigurable system is built. The approach is evaluated on a hardware platform. The test results show that the system can automatically adjust the power consumption in case of external energy input changing. The utilization of the system dynamic power of their portion is from 80.05% to 91.75% during the first task assignment. During the entire processing cycle, the total energy efficiency is 97.67%.
基金supported by the MSIT(Ministry of Science,ICT),Korea,under the High-Potential Individuals Global Training Program)(2021-0-01547-001)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)the National Research Foundation of Korea(NRF)grant funded by the Ministry of Science and ICT(NRF-2022R1A2C2007255).
文摘Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.
基金Supported by the National Natural Science Foundation of China (No.60476013).
文摘This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three coprocessors that are used for synchronization, FFT and channel decoder. In this MIMO OFDM system, the Zero Correlation Zone （ZCZ） code is used as the synchronization word preamble of packet in the physical layer in order to avoid the interference from other transmitting antennas. Furthermore, a simple channel estimation algorithm is proposed which is appropriate tbr the SIMD DSP computation.
文摘We present novel vector permutation and branch reduction methods to minimize the number of execution cycles for bit reversal algorithms.The new methods are applied to single instruction multiple data(SIMD) parallel implementation of complex data floating-point fast Fourier transform(FFT).The number of operational clock cycles can be reduced by an average factor of 3.5 by using our vector permutation methods and by 1.1 by using our branch reduction methods,compared with conventional im-plementations.Experiments on MPC7448(a well-known SIMD reduced instruction set computing processor) demonstrate that our optimal bit-reversal algorithm consistently takes fewer than two cycles per element in complex array operations.
基金Project (No. 2005AA1Z1271) supported by the Hi-Tech Research and Development Program (863) of China
文摘To efficiently exploit the performance of single instruction multiple data （SIMD） architectures for video coding, a parallel memory architecture with power-of-two memory modules is proposed. It employs two novel skewing schemes to provide conflict-free access to adjacent elements （8-bit and 16-bit data types） or with power-of-two intervals in both horizontal and vertical directions, which were not possible in previous parallel memory architectures. Area consumptions and delay estimations are given respectively with 4, 8 and 16 memory modules. Under a 0.18-pm CMOS technology, the synthesis results show that the proposed system can achieve 230 MHz clock frequency with 16 memory modules at the cost of 19k gates when read and write latencies are 3 and 2 clock cycles, respectively. We implement the proposed parallel memory architecture on a video signal processor （VSP）. The results show that VSP enhanced with the proposed architecture achieves 1.28× speedups for H.264 real-time decoding.
文摘With the increasing demand for flexible and efficient implementation of image and video processing algorithms, there should be a good tradeoff between hardware and software design method. This paper utilized the HW-SW codesign method to implement the H.264 decoder in an SoC with an ARM core, a multimedia processor and a deblocking filter coprocessor. For the parallel processing features of the multimedia processor, clock cycles of decoding process can be dramatically reduced. And the hardware dedicated deblocking filter coprocessor can improve the efficiency a lot. With maximum clock frequency of 150 MHz, the whole system can achieve real time processing speed and flexibility.
文摘Single instruction multiple data （SIMD） instructions are often implemented in modem media processors. Although SIMD instructions are useful in multimedia applications, most compilers do not have good support for SIMD instructions. This paper focuses on SIMD instructions generation for media processors. We present an efficient code optimization approach that is integrated into a retargetable C compiler. SIMD instructions are generated by finding and combining the same operations in programs. Experimental results for the UltraSPARC VIS instruction set show that a speedup factor up to 2.639 is obtained.
基金Supported by the Industrial Internet Innovation and Development Project of Ministry of Industry and Information Technology (No.GHBJ2004)。
文摘A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.
