Communication bandwidth and network topology are two important factors that affect performance of distributed consensus in multi-agent systems.The available works about quantized average consensus assume that the adja...Communication bandwidth and network topology are two important factors that affect performance of distributed consensus in multi-agent systems.The available works about quantized average consensus assume that the adjacency matrices associated with the digraphs are doubly stochastic,which amounts to that the digital networks are balanced.However,this assumption may be unrealistic in practice.In this paper,without assuming double stochasticity,the authors revisit an existing quantized average consensus protocol with the logarithmic quantization scheme,and investigate the quantized consensus problem in general directed digital networks that are strongly connected but not necessarily balanced.The authors first derive an achievable upper bound of the quantization precision parameter to design suitable logarithmic quantizer,and this bound explicitly depends on network topology.Subsequently,by means of the matrix transformation and the Lyapunov techniques,the authors provide a testable condition under which the weighted average consensus can be achieved with the proposed quantized protocol.展开更多
Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i...Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i=1Xi/Vm,p ,where Vn,p （∑^n_i=1｜Xi｜p）^1/p （P 〉 1）.Applications to the self-normalized law of the iteratedlogarithm, Studentized increments of partial sums, t-statistic, and weighted sum of independent and identically distributed （i.i.d.） r.v.s are considered.展开更多
Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aid...Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aided diagnosis(CAD),which is capable of automatically extracting and evaluating image features,can integrate multiple parameters and improve the detection of PCa.In this study,13 quantitative image features were extracted from DWI by CAD,and diagnostic efficacy was analyzed in both the PZ and transition zone(TZ).The results demonstrated that there was a significant difference(P<0.05) between PCa and non-PCa for nine of the 13 features in the PZ and five of the 13 features in the TZ.Besides,the prediction outcome of CAD had a strong correlation with the DWI scores that were graded by experienced radiologists according to the Prostate Imaging-Reporting and Data System Version 2(PI-RADS v2).展开更多
基金supported by the Major State Basic Research Development Program of China(973 Program)under Grant No.2010CB731400the Natural Science Foundation of China under Grant Nos.61074125,61073102,61170059,61170172,61272153Anhui Provincial Natural Science Foundation under Grant No.090412251
文摘Communication bandwidth and network topology are two important factors that affect performance of distributed consensus in multi-agent systems.The available works about quantized average consensus assume that the adjacency matrices associated with the digraphs are doubly stochastic,which amounts to that the digital networks are balanced.However,this assumption may be unrealistic in practice.In this paper,without assuming double stochasticity,the authors revisit an existing quantized average consensus protocol with the logarithmic quantization scheme,and investigate the quantized consensus problem in general directed digital networks that are strongly connected but not necessarily balanced.The authors first derive an achievable upper bound of the quantization precision parameter to design suitable logarithmic quantizer,and this bound explicitly depends on network topology.Subsequently,by means of the matrix transformation and the Lyapunov techniques,the authors provide a testable condition under which the weighted average consensus can be achieved with the proposed quantized protocol.
基金supported by Hong Kong Research Grant Committee (Grant Nos.HKUST6019/10P and HKUST6019/12P)National Natural Science Foundation of China (Grant Nos. 10871146 and 11271286)the National University of Singapore (Grant No. R-155-000-106-112)
文摘Let X1,X2,... be a sequence of independent random variables （r.v.s） belonging to the domain of attraction of a normal or stable law. In this paper, we study moderate deviations for the self-normalized sum n X ∑^n_i=1Xi/Vm,p ,where Vn,p （∑^n_i=1｜Xi｜p）^1/p （P 〉 1）.Applications to the self-normalized law of the iteratedlogarithm, Studentized increments of partial sums, t-statistic, and weighted sum of independent and identically distributed （i.i.d.） r.v.s are considered.
文摘Diffusion-weighted imaging(DWI) is considered to be one of the dominant modalities used in prostate cancer(PCa) detection and the assessment of lesion aggressiveness,especially for peripheral zone(PZ) PCa.Computer-aided diagnosis(CAD),which is capable of automatically extracting and evaluating image features,can integrate multiple parameters and improve the detection of PCa.In this study,13 quantitative image features were extracted from DWI by CAD,and diagnostic efficacy was analyzed in both the PZ and transition zone(TZ).The results demonstrated that there was a significant difference(P<0.05) between PCa and non-PCa for nine of the 13 features in the PZ and five of the 13 features in the TZ.Besides,the prediction outcome of CAD had a strong correlation with the DWI scores that were graded by experienced radiologists according to the Prostate Imaging-Reporting and Data System Version 2(PI-RADS v2).
