Omni-Dimensional Dynamic Convolution

Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of $n$ convolutional kernels weighted with their input-dependent attentions can significantly improve the accuracy of light-weight CNNs, while maintaining efficient inference. However, we observe that existing works endow convolutional kernels with the dynamic property through one dimension (regarding the convolutional kernel number) of the kernel space, but the other three dimensions (regarding the spatial size, the input channel number and the output channel number for each convolutional kernel) are overlooked. Inspired by this, we present Omni-dimensional Dynamic Convolution (ODConv), a more generalized yet elegant dynamic convolution design, to advance this line of research. ODConv leverages a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary attentions for convolutional kernels along all four dimensions of the kernel space at any convolutional layer. As a drop-in replacement of regular convolutions, ODConv can be plugged into many CNN architectures. Extensive experiments on the ImageNet and MS-COCO datasets show that ODConv brings solid accuracy boosts for various prevailing CNN backbones including both light-weight and large ones, e.g., 3.77%~5.71%|1.86%~3.72% absolute top-1 improvements to MobivleNetV2|ResNet family on the ImageNet dataset. Intriguingly, thanks to its improved feature learning ability, ODConv with even one single kernel can compete with or outperform existing dynamic convolution counterparts with multiple kernels, substantially reducing extra parameters. Furthermore, ODConv is also superior to other attention modules for modulating the output features or the convolutional weights.Comment: Spotlight paper at ICLR 2022. Code and models are available at https://github.com/OSVAI/ODCon

Elemental and mineralogical mechanisms controlling the storage and flow performances of tight conglomerates

This study aims to shed light on the elemental and mineralogical mechanisms controlling the storage and flow performances of tight conglomerates. This is achieved through the application of Field Emission Scanning Electron Microscopy (FE-SEM), Energy Dispersive Spectrometer (EDS), TESCAN Integrated Mineral Analyzer (TIMA), and geophysical logging data. The findings suggest that augmenting the Mg and CaO content in silicates can markedly enhance the storage capacity of tight conglomerates within the Upper Wuerhe Formation. Chlorite exerts a positive influence on the evolution of reservoir porosity, whereas an escalation in Na content does not foster the development of reservoirs with superior physical attributes. A reduction in the chemical index of alteration (CIA) facilitates the creation of a more expansive pore space, while an elevation in the index of component variation (ICV) augments porosity and diminishes mud content, albeit it may also curtail oil content. It is pertinent to note that an increase in ICV is not invariably advantageous. The emergence of orthoclase results in a decrease in porosity and an enhancement in permeability, whereas the progression of kaolinite adversely affects reservoir porosity. The conclusion may yield significant insights for hydrocarbon exploration in these regions, as well as in reservoirs exhibiting analogous lithologies

Asymmetric correlation between experienced parental attachment and event-related potentials evoked in response to parental faces.

This study aims to explore the modulation effects of attachment relationships with parents on the neural correlates that are associated with parental faces. The event-related potentials elicited in 31 college students while viewing facial stimuli of their parents in two single oddball paradigms (father vs. unfamiliar male and mother vs. unfamiliar female) were measured. We found that enhanced P3a and P3b and attenuated N2b were elicited by parental faces; however, the N170 component failed to discriminate parental faces from unfamiliar faces. An experienced attachment relationship with the father was positively correlated to the P3a response associated with the father's face, whereas no correlation was found in the case of mothers. Further exploration in dipole source localization showed that, within the time window of the P300, distinctive brain regions were involved in the processing of parental faces; the father's face was located in the medial frontal gyrus, which might be involved in self effect, and the anterior cingulate gyrus was activated in response to the mother's face. This research is the first to demonstrate that neural mechanisms involved with parents can be modulated differentially by the qualities of the attachments to the parents. In addition, parental faces share a highly similar temporal pattern, but the origins of these neural responses are distinct, which could merit further investigation

Three‐dimensional simulation of the acidizing process under different influencing factors in fractured carbonate reservoirs

Abstract Matrix acidizing is widely used to enhance oil/gas production in the exploitation of carbonate reservoirs. In this work, a three‐dimensional (3D) hydro‐chemical‐thermal (H‐C‐T)‐coupled model was presented to improve the understanding of the acidizing process. The influence of different influencing factors was analyzed, especially the coupling effect of natural fractures and in situ stress. With the increase in acid injection concentration, the minimum pore volume of acid required for breakthrough (PVBT) decreases. The optimal injection rate and the minimum PVBT increase with increasing initial reservoir temperature. With the increasing initial reservoir permeability, the minimum PVBT increases. With the increasing initial reservoir pore diameter and specific surface area, the minimum PVBT and the optimal acid injection rate increase. When the fracture direction is perpendicular to the direction of the maximum principal stress, the fracture apertures decrease with the increase of the maximum principal stress, which leads to an increase in PVBT and wider paths of wormholes. Lastly, the present H‐C‐T‐coupled model was applied in the context of Tahe reservoir exploitation, which shows that optimizing the acid injection rate is able to enhance the connection between wellbores and natural caves