This paper presented a new shared cache technique-the grouping cache, which could solve many invalid queries in the broadcast probe and the control bottleneck of the centralized web cache by dividing all cooperative c...This paper presented a new shared cache technique-the grouping cache, which could solve many invalid queries in the broadcast probe and the control bottleneck of the centralized web cache by dividing all cooperative caches into several groups according to their positions in the network topology. The technique has the following characteristics: The overhead of multi-cache query can be reduced efficiently by the cache grouping scheme; the compact summary of the cache directory can rapidly determine if a request exists in a cache within the group; the distribution algorithm based on the web-access logs can effectively balance the load among all the groups. The simulation test demonstrated that the grouping cache was more effective than any other existing shared cache techniques.展开更多
This paper addresses the problem of survivable traffic assignment with failure probability requirement in flexible bandwidth optical networks. We describe a Survivable Traffic Cognition (STC) algorithm with joint fail...This paper addresses the problem of survivable traffic assignment with failure probability requirement in flexible bandwidth optical networks. We describe a Survivable Traffic Cognition (STC) algorithm with joint failure probability. Survivable Traffic Assignment (STA) algorithm and Conventional Traffic Assignment (CTA) algorithm are added to illustrate the effectiveness of our proposed STC. We investigate the effect of joint failure probability on blocking probability, spectral utilization ratio, average joint failure probability, and the average hops. Simulation results show that our proposed STC not only achieves better performance in terms of blocking probability and spectral utilization ratio than CTA and STA, but also does not cause higher average joint failure probability or larger average hops compared with STA. As a result, STC makes the best use of spectral resources and does not cause large average joint failure probability.展开更多
Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with ...Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.展开更多
文摘This paper presented a new shared cache technique-the grouping cache, which could solve many invalid queries in the broadcast probe and the control bottleneck of the centralized web cache by dividing all cooperative caches into several groups according to their positions in the network topology. The technique has the following characteristics: The overhead of multi-cache query can be reduced efficiently by the cache grouping scheme; the compact summary of the cache directory can rapidly determine if a request exists in a cache within the group; the distribution algorithm based on the web-access logs can effectively balance the load among all the groups. The simulation test demonstrated that the grouping cache was more effective than any other existing shared cache techniques.
基金supported in part by 973 Program under Grants No. 2010CB328204,No. 2012CB315604863 Program under Grant No. 2012AA011301+3 种基金National Natural Science Foundation of China under Grants No. 61271189,No. 61201154, No. 60932004RFDP Project under Grants No. 20090005110013,No. 20120005120019the Fundamental Research Funds for the Central Universitiesthe State Scholarship Fund
文摘This paper addresses the problem of survivable traffic assignment with failure probability requirement in flexible bandwidth optical networks. We describe a Survivable Traffic Cognition (STC) algorithm with joint failure probability. Survivable Traffic Assignment (STA) algorithm and Conventional Traffic Assignment (CTA) algorithm are added to illustrate the effectiveness of our proposed STC. We investigate the effect of joint failure probability on blocking probability, spectral utilization ratio, average joint failure probability, and the average hops. Simulation results show that our proposed STC not only achieves better performance in terms of blocking probability and spectral utilization ratio than CTA and STA, but also does not cause higher average joint failure probability or larger average hops compared with STA. As a result, STC makes the best use of spectral resources and does not cause large average joint failure probability.
基金supported by the National Basic Research(973)Program(2015CB351702)the National Natural Science Foundation of China(81571756,81270023,81278412,81171409,81000583,81471740,81220108014)+2 种基金Beijing Nova Program(XXJH2015B079 to Z.Y.)the Outstanding Young Investigator Award of Institute of Psychology,Chinese Academy of Sciences(to Z.Y.)the Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences(KSZD-EW-TZ-002 to X.N.Z)
文摘Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.
