We study the effects of running coupling and gluon number fluctuations in the latest diffractive deep inelastic scattering data. It is found that the description of the data is improved once the running coupling and g...We study the effects of running coupling and gluon number fluctuations in the latest diffractive deep inelastic scattering data. It is found that the description of the data is improved once the running coupling and gluon number fluctuations are included with x2/d.o.f. = 0.867, x2/d.o.f. = 0.923 and x2/d.o.f. = 0.878 for three different groups of experimental data. The values of diffusive coefficient subtracted from the fit are smaller than the ones obtained by considering only the gluon number fluctuations in our previous studies. The smaller values of the diffusive coefficient are in agreement with the theoretical predictions, where the gluon number fluctuations are suppressed by the running coupling which leads to smaller values of the diffusive coefficient.展开更多
To address the problem in boundary tracing where there is no direct association between entities and boundary pixels-that is,determining which entity a boundary belongs to-a novel run data-based boundary tracing algor...To address the problem in boundary tracing where there is no direct association between entities and boundary pixels-that is,determining which entity a boundary belongs to-a novel run data-based boundary tracing algorithm is proposed.Unlike traditional t racing algorithms,this approach first extracts boundary pixels and then classifies them to ensure 100%extraction accuracy.A region labeling algorithm is introduced to establish a direct link between boundaries and objects.The concept of boundary run da ta is proposed to avoid errors in previous run data algorithms,particularly at corners.Furthermore,the proposed algorithm is parallelized using MPI to further improve its speed.Experiments conducted on the MPEG-7 CE standard dataset demonstrate that th e proposed algorithm achieves 100%accuracy,offers significant speed improvements over traditional algorithms,and exhibits further performance gains after parallelization.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11305040,11505036 and 11447203the Education Department of Guizhou Province Talent Fund under Grant No[2015]5508the Science and Technology Department of Guizhou Province Fund under Grant Nos[2015]2114 and [2014]7053
文摘We study the effects of running coupling and gluon number fluctuations in the latest diffractive deep inelastic scattering data. It is found that the description of the data is improved once the running coupling and gluon number fluctuations are included with x2/d.o.f. = 0.867, x2/d.o.f. = 0.923 and x2/d.o.f. = 0.878 for three different groups of experimental data. The values of diffusive coefficient subtracted from the fit are smaller than the ones obtained by considering only the gluon number fluctuations in our previous studies. The smaller values of the diffusive coefficient are in agreement with the theoretical predictions, where the gluon number fluctuations are suppressed by the running coupling which leads to smaller values of the diffusive coefficient.
基金National Natural Science Foundation of China(42374152)Shandong Provincial Natural Science Foundation(ZR2020MD050)。
文摘To address the problem in boundary tracing where there is no direct association between entities and boundary pixels-that is,determining which entity a boundary belongs to-a novel run data-based boundary tracing algorithm is proposed.Unlike traditional t racing algorithms,this approach first extracts boundary pixels and then classifies them to ensure 100%extraction accuracy.A region labeling algorithm is introduced to establish a direct link between boundaries and objects.The concept of boundary run da ta is proposed to avoid errors in previous run data algorithms,particularly at corners.Furthermore,the proposed algorithm is parallelized using MPI to further improve its speed.Experiments conducted on the MPEG-7 CE standard dataset demonstrate that th e proposed algorithm achieves 100%accuracy,offers significant speed improvements over traditional algorithms,and exhibits further performance gains after parallelization.
