The efficiency of organic semiconductor photocatalysts is typically limited by their capability of photogenerated electron transport.Herein,a photocatalyst is proposed initially through the specific axial coordination...The efficiency of organic semiconductor photocatalysts is typically limited by their capability of photogenerated electron transport.Herein,a photocatalyst is proposed initially through the specific axial coordination interaction between imidazole-C_(60)(ImC_(60))and zinc tetraphenyl porphyrin(ZnTPP)named ImC_(60)-ZnTPP.Subsequently,detailed structural characterizations along with theoretical calculation reveal that the unique ImC_(60)-ZnTPP possesses head-to-tail stacking supra-structures,leading to the formation of a continuous array of C_(60)–C_(60) with ultrashort spacing and ensuring strongπ–πinteractions and homogeneous electronic coupling,which could tremendously promote electron transport along the(−111)crystal facet of ImC_(60)-ZnTPP.Consequently,compared to other fullerene-based photocatalysts,ImC_(60)-ZnTPP shows exceptional photocatalytic hydrogen production activity,with an efficiency of up to 80.95 mmol g^(-1) h^(-1).This study provides a novel strategy to design highly efficient fullerene-based photocatalytic systems for solar-driven energy conversion and extend their artificial photosynthetic use.展开更多
Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or select...Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.展开更多
近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接...近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接口规范(nonvolatile memory express, NVMe)协议与串行SCSI(serial attached small computer system interface, SAS)协议,即SAS. NVMe是专为SSD设计的高性能存储协议,能够很大限度地发挥SSD的性能;而SAS协议则充分考虑数据中心的需求,在提供高可靠性与高可扩展性的同时,兼顾了系统性能与成本的平衡.相对于日益增速的存储介质,针对慢速存储设备所设计的软件栈在一次I/O过程中所耗费的时间开销愈发显著.针对该问题学界及工业界都相继提出了众多解决方案,例如Intel提出的高性能存储开发包(storage performance development kit, SPDK)通过将设备驱动实现在用户空间,并采用轮询感知I/O完成等方式大幅度缩短了NVMe SSD对应用程序的响应时间,极大地提升了整个系统的整体性能.然而之前的研究工作针对SAS SSD存储软件栈的优化非常有限,为此在用户空间实现了针对SAS SSD的软件栈优化.实验结果表明,该优化能够有效缩短存储设备对应用程序的响应时间,提高应用对存储设备的访存效率.此外,为了准确评估I/O栈中存储设备的时间开销,硬件性能测试工具HwPerfIO被提出,能够消除大部分软件开销的影响以测得更加准确的存储设备性能.展开更多
针对压电陶瓷的迟滞非线性,建立了基于一种加权最小二乘支持向量机(Weighted Least Squares Support Vector Machine,WLS-SVM)的迟滞动态模型。为了能够方便使用支持向量机,应用了一种动态算子,将迟滞的多值映射变成一一映射。并在传统...针对压电陶瓷的迟滞非线性,建立了基于一种加权最小二乘支持向量机(Weighted Least Squares Support Vector Machine,WLS-SVM)的迟滞动态模型。为了能够方便使用支持向量机,应用了一种动态算子,将迟滞的多值映射变成一一映射。并在传统的最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的基础上加入了支持向量度,以区分数据的重要程度。为了减轻计算量,将数据按照支持向量度从大到小排序,其中支持向量度接近于零的数据被视作非支持向量。使用剪枝法按一定比例减去支持向量度比较小的数据,利用粒子群算法(Particle Swarm Optimization, PSO)辨识出模型的未知参数。最后,实验的结果验证了模型的可行性。展开更多
We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characte...We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characterizing conformal classes in the space-time that determine a space moduli [1] on coherent sheaves for the securing solutions in field theory [2]. In a major context, elements of derived categories like D-branes and heterotic strings are considered, and using the geometric Langlands program, a moduli space is obtained of equivalence between certain geometrical pictures (non-conformal world sheets [3]) and physical stacks (derived sheaves), that establishes equivalence between certain theories of super symmetries of field of a Penrose transform that generalizes the implications given by the Langlands program. With it we obtain extensions of a cohomology of integrals for a major class of field equations to corresponding Hecke category.展开更多
基金supported by the National Natural Science Foundation of China(52322204,52072374,52272052)the National Key R&D Program of China(Grant No.2022YFA1205900)the Youth Innovation Promotion Association of CAS(Y2022015).
文摘The efficiency of organic semiconductor photocatalysts is typically limited by their capability of photogenerated electron transport.Herein,a photocatalyst is proposed initially through the specific axial coordination interaction between imidazole-C_(60)(ImC_(60))and zinc tetraphenyl porphyrin(ZnTPP)named ImC_(60)-ZnTPP.Subsequently,detailed structural characterizations along with theoretical calculation reveal that the unique ImC_(60)-ZnTPP possesses head-to-tail stacking supra-structures,leading to the formation of a continuous array of C_(60)–C_(60) with ultrashort spacing and ensuring strongπ–πinteractions and homogeneous electronic coupling,which could tremendously promote electron transport along the(−111)crystal facet of ImC_(60)-ZnTPP.Consequently,compared to other fullerene-based photocatalysts,ImC_(60)-ZnTPP shows exceptional photocatalytic hydrogen production activity,with an efficiency of up to 80.95 mmol g^(-1) h^(-1).This study provides a novel strategy to design highly efficient fullerene-based photocatalytic systems for solar-driven energy conversion and extend their artificial photosynthetic use.
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.
文摘近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接口规范(nonvolatile memory express, NVMe)协议与串行SCSI(serial attached small computer system interface, SAS)协议,即SAS. NVMe是专为SSD设计的高性能存储协议,能够很大限度地发挥SSD的性能;而SAS协议则充分考虑数据中心的需求,在提供高可靠性与高可扩展性的同时,兼顾了系统性能与成本的平衡.相对于日益增速的存储介质,针对慢速存储设备所设计的软件栈在一次I/O过程中所耗费的时间开销愈发显著.针对该问题学界及工业界都相继提出了众多解决方案,例如Intel提出的高性能存储开发包(storage performance development kit, SPDK)通过将设备驱动实现在用户空间,并采用轮询感知I/O完成等方式大幅度缩短了NVMe SSD对应用程序的响应时间,极大地提升了整个系统的整体性能.然而之前的研究工作针对SAS SSD存储软件栈的优化非常有限,为此在用户空间实现了针对SAS SSD的软件栈优化.实验结果表明,该优化能够有效缩短存储设备对应用程序的响应时间,提高应用对存储设备的访存效率.此外,为了准确评估I/O栈中存储设备的时间开销,硬件性能测试工具HwPerfIO被提出,能够消除大部分软件开销的影响以测得更加准确的存储设备性能.
文摘针对压电陶瓷的迟滞非线性,建立了基于一种加权最小二乘支持向量机(Weighted Least Squares Support Vector Machine,WLS-SVM)的迟滞动态模型。为了能够方便使用支持向量机,应用了一种动态算子,将迟滞的多值映射变成一一映射。并在传统的最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的基础上加入了支持向量度,以区分数据的重要程度。为了减轻计算量,将数据按照支持向量度从大到小排序,其中支持向量度接近于零的数据被视作非支持向量。使用剪枝法按一定比例减去支持向量度比较小的数据,利用粒子群算法(Particle Swarm Optimization, PSO)辨识出模型的未知参数。最后,实验的结果验证了模型的可行性。
文摘We consider generalizations of the Radon-Schmid transform on coherent DG/H-Modules, with the intention of obtaining the equivalence between geometric objects (vector bundles) and algebraic objects (D-Modules) characterizing conformal classes in the space-time that determine a space moduli [1] on coherent sheaves for the securing solutions in field theory [2]. In a major context, elements of derived categories like D-branes and heterotic strings are considered, and using the geometric Langlands program, a moduli space is obtained of equivalence between certain geometrical pictures (non-conformal world sheets [3]) and physical stacks (derived sheaves), that establishes equivalence between certain theories of super symmetries of field of a Penrose transform that generalizes the implications given by the Langlands program. With it we obtain extensions of a cohomology of integrals for a major class of field equations to corresponding Hecke category.
