Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular auto...Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular automaton(CA)models,recognized as powerful tools for simulating dynamic LUCC processes,are traditionally applied in LUCC,focusing on time-slice driving factor data,often neglecting the temporal dimension.However,the transformer architecture,a highly acclaimed model in machine learning,has been rarely integrated into CA models for the simulation of dynamic LUCC processes.To fill this gap,we proposed a novel spatiotemporal urban LUCC simulation model,namely,transformer-convolutional neural network(TC)-CA.Based on CA models that involve the utilization of a convolutional neural network(CNN)for extracting latent spatial features,TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features.The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA,conventional CNN-CA,artificial neural network-CA,and transformer-CA.Compared with the three non-transformer-based CAs,the TC-CA improved the figure of merit by up to 2.85%-8.14%.This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling.展开更多
目的 探讨维得利珠单抗与美沙拉嗪治疗活动期溃疡性结肠炎(UC)的临床疗效及安全性。方法 选择2019年1月至2023年12月海安市人民医院活动期UC患者105例为研究对象,随机分为维得利珠单抗组(n=53)和美沙拉嗪组(n=52)。2组患者均给予常规治...目的 探讨维得利珠单抗与美沙拉嗪治疗活动期溃疡性结肠炎(UC)的临床疗效及安全性。方法 选择2019年1月至2023年12月海安市人民医院活动期UC患者105例为研究对象,随机分为维得利珠单抗组(n=53)和美沙拉嗪组(n=52)。2组患者均给予常规治疗,在此基础上维得利珠单抗组予维得利珠单抗治疗,美沙拉嗪组给予美沙拉嗪治疗。观察并对比2组患者临床疗效,治疗前后肠黏膜屏障功能相关指标、炎症因子水平及肠道菌群含量,并记录不良反应发生率。结果 维得利珠单抗组总有效率为94.34%,高于美沙拉嗪组的80.77%(χ^(2)=4.456,P <0.05)。治疗后,维得利珠单抗组D-乳酸、内毒素水平及炎症因子白细胞介素17(IL-17)、IL-23水平[分别为(4.98±1.23)μg/L、(2.26±0.92) U/mL、(206.74±15.34) pg/L、(252.93±19.43) pg/L]均低于美沙拉嗪组[分别为(6.25±1.87)μg/L、(3.41±1.13) U/mL、(224.89±16.56) pg/L、(280.32±20.89) pg/L](t=4.119、5.724、5.828、6.958,均P <0.05),乳酸杆菌、双歧杆菌含量[分别为(19.65±2.34) lg CFU/g、(17.34±1.98) lg CFU/g]高于美沙拉嗪组[分别为(14.89±1.76) lg CFU/g、(14.01±1.45) lg CFU/g],大肠杆菌、肠球菌含量[分别为(3.92±0.78) lg CFU/g、(3.71±0.56) lg CFU/g]低于美沙拉嗪组[分别为(6.25±1.23) lg CFU/g、(6.05±0.89) lg CFU/g](t=11.763、9.817、11.615、16.157,均P <0.05)。治疗期间,2组患者的不良反应发生率差异无统计学意义(P> 0.05)。结论 维得利珠单抗治疗活动期UC较美沙拉嗪能更有效地缓解临床症状,改善肠道黏膜屏障功能及菌群水平,降低炎症因子水平,且不良反应发生率低。展开更多
As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dim...As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dimensional stochastic gradients to edge server in training,which cause severe communication bottleneck.To address this problem,we compress the communication by sparsifying and quantizing the stochastic gradients of edge devices.We first derive a closed form of the communication compression in terms of sparsification and quantization factors.Then,the convergence rate of this communicationcompressed system is analyzed and several insights are obtained.Finally,we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound,under the constraint of multiple-access channel capacity.Simulations show that the proposed scheme outperforms the benchmarks.展开更多
由于背景环境复杂,检测物体易受部分遮挡、天气以及光线变化等因素的影响,传统目标检测方法存在提取特征难、检测准确率低、检测耗时长等缺陷.为了改善传统目标检测方法存在的缺陷,实现快速准确的目标检测,提出了一种基于快速区域卷积...由于背景环境复杂,检测物体易受部分遮挡、天气以及光线变化等因素的影响,传统目标检测方法存在提取特征难、检测准确率低、检测耗时长等缺陷.为了改善传统目标检测方法存在的缺陷,实现快速准确的目标检测,提出了一种基于快速区域卷积神经网络(faster regions with convolutional neural network,Faster-RCNN)算法的轻量化改进方法,即针对算法Inception-V2特征提取网络进行轻量化改进,并以带泄露线性整流(leaky rectified linear unit,Leaky ReLU)作为激活函数,解决使用线性整流(rectified linear unit,ReLU)激活函数存在的神经元输入为负数时输出为0的问题.基于上述改进方法,选择沙滩废弃物的检测为案例以验证方法的有效性,并且结合不同特征提取网络在检测沙滩废弃物时的表现,对比了SSD(single shot multibox detector)与Faster-RCNN算法.实验结果表明:所提改进算法在实际检测中有较好的综合性能,且相比原算法Faster-RCNN_Inception-V2,轻量化改进后的Inception-V2特征提取网络卷积计算量减少51.8%,模型训练耗时缩短了9.1%,检测耗时减少了10.9%,各类别AP的平均值(mean average precision,mAP)增加了1.02%,可见所提的改进方法能够有效提高目标检测的准确率,减少检测耗时,并在沙滩废弃物检测上得到成功应用,为海滨城市的沙滩清理维护提供了技术支持与保障.展开更多
基金National Natural Science Foundation of China,No.42271418,No.42171088State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04,No.2023-WT-02。
文摘Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular automaton(CA)models,recognized as powerful tools for simulating dynamic LUCC processes,are traditionally applied in LUCC,focusing on time-slice driving factor data,often neglecting the temporal dimension.However,the transformer architecture,a highly acclaimed model in machine learning,has been rarely integrated into CA models for the simulation of dynamic LUCC processes.To fill this gap,we proposed a novel spatiotemporal urban LUCC simulation model,namely,transformer-convolutional neural network(TC)-CA.Based on CA models that involve the utilization of a convolutional neural network(CNN)for extracting latent spatial features,TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features.The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA,conventional CNN-CA,artificial neural network-CA,and transformer-CA.Compared with the three non-transformer-based CAs,the TC-CA improved the figure of merit by up to 2.85%-8.14%.This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling.
文摘目的 探讨维得利珠单抗与美沙拉嗪治疗活动期溃疡性结肠炎(UC)的临床疗效及安全性。方法 选择2019年1月至2023年12月海安市人民医院活动期UC患者105例为研究对象,随机分为维得利珠单抗组(n=53)和美沙拉嗪组(n=52)。2组患者均给予常规治疗,在此基础上维得利珠单抗组予维得利珠单抗治疗,美沙拉嗪组给予美沙拉嗪治疗。观察并对比2组患者临床疗效,治疗前后肠黏膜屏障功能相关指标、炎症因子水平及肠道菌群含量,并记录不良反应发生率。结果 维得利珠单抗组总有效率为94.34%,高于美沙拉嗪组的80.77%(χ^(2)=4.456,P <0.05)。治疗后,维得利珠单抗组D-乳酸、内毒素水平及炎症因子白细胞介素17(IL-17)、IL-23水平[分别为(4.98±1.23)μg/L、(2.26±0.92) U/mL、(206.74±15.34) pg/L、(252.93±19.43) pg/L]均低于美沙拉嗪组[分别为(6.25±1.87)μg/L、(3.41±1.13) U/mL、(224.89±16.56) pg/L、(280.32±20.89) pg/L](t=4.119、5.724、5.828、6.958,均P <0.05),乳酸杆菌、双歧杆菌含量[分别为(19.65±2.34) lg CFU/g、(17.34±1.98) lg CFU/g]高于美沙拉嗪组[分别为(14.89±1.76) lg CFU/g、(14.01±1.45) lg CFU/g],大肠杆菌、肠球菌含量[分别为(3.92±0.78) lg CFU/g、(3.71±0.56) lg CFU/g]低于美沙拉嗪组[分别为(6.25±1.23) lg CFU/g、(6.05±0.89) lg CFU/g](t=11.763、9.817、11.615、16.157,均P <0.05)。治疗期间,2组患者的不良反应发生率差异无统计学意义(P> 0.05)。结论 维得利珠单抗治疗活动期UC较美沙拉嗪能更有效地缓解临床症状,改善肠道黏膜屏障功能及菌群水平,降低炎症因子水平,且不良反应发生率低。
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807700in part by the National Science Foundation of China under Grant U200120122
文摘As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dimensional stochastic gradients to edge server in training,which cause severe communication bottleneck.To address this problem,we compress the communication by sparsifying and quantizing the stochastic gradients of edge devices.We first derive a closed form of the communication compression in terms of sparsification and quantization factors.Then,the convergence rate of this communicationcompressed system is analyzed and several insights are obtained.Finally,we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound,under the constraint of multiple-access channel capacity.Simulations show that the proposed scheme outperforms the benchmarks.
文摘由于背景环境复杂,检测物体易受部分遮挡、天气以及光线变化等因素的影响,传统目标检测方法存在提取特征难、检测准确率低、检测耗时长等缺陷.为了改善传统目标检测方法存在的缺陷,实现快速准确的目标检测,提出了一种基于快速区域卷积神经网络(faster regions with convolutional neural network,Faster-RCNN)算法的轻量化改进方法,即针对算法Inception-V2特征提取网络进行轻量化改进,并以带泄露线性整流(leaky rectified linear unit,Leaky ReLU)作为激活函数,解决使用线性整流(rectified linear unit,ReLU)激活函数存在的神经元输入为负数时输出为0的问题.基于上述改进方法,选择沙滩废弃物的检测为案例以验证方法的有效性,并且结合不同特征提取网络在检测沙滩废弃物时的表现,对比了SSD(single shot multibox detector)与Faster-RCNN算法.实验结果表明:所提改进算法在实际检测中有较好的综合性能,且相比原算法Faster-RCNN_Inception-V2,轻量化改进后的Inception-V2特征提取网络卷积计算量减少51.8%,模型训练耗时缩短了9.1%,检测耗时减少了10.9%,各类别AP的平均值(mean average precision,mAP)增加了1.02%,可见所提的改进方法能够有效提高目标检测的准确率,减少检测耗时,并在沙滩废弃物检测上得到成功应用,为海滨城市的沙滩清理维护提供了技术支持与保障.
