Two kinds of average approximation accuracy

Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is the absolute average approximation accuracy, which is based on undefinable sets and has yielded significant conclusions. We also explore the relationship between these two types of average approximation accuracy. Finally, the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables

Playing Lottery Tickets in Style Transfer Models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models

Design and integration of a high-precision material handling system with a six-axis hybrid micro-machine

Hybrid micro-machines are increasingly in demand for the manufacturing of miniature 3D products made of hard-to-machine materials. A high-precision material handling system for such miniature products can increase the overall efficiency significantly. This paper proposes the design of a machine vision based handling system, which is capable of handling various miniature 3D products. A cloud-based innovative integration method is also developed, a cloud server is deployed to collect and process data from the machine and the material handling system, their actions are coordinated based on the predefined protocol. This method can enhance the reconfigurability of the whole system

Transmission infrared micro-spectroscopic study of individual human hair

Understanding the optical transmission property of human hair, especially in the infrared regime, is vital in physical, clinical, and biomedical research. However, the majority of infrared spectroscopy on human hair is performed in the reflection mode, which only probes the absorptance of the surface layer. The direct transmission spectrum of individual hair without horizontal cut offers a rapid and non-destructive test of the hair cortex but is less investigated experimentally due to the small size and strong absorption of the hair. In this work, we conduct transmission infrared micro-spectroscopic study on individual human hair. By utilizing direct measurements of the transmission spectrum using a Fourier-transform infrared microscope, the human hair is found to display prominent band filtering behavior. The high spatial resolution of infrared micro-spectroscopy further allows the comparison among different regions of hair. In a case study of adult-onset Still's disease, the corresponding infrared transmission exhibits systematic variations of spectral weight as the disease evolves. The geometry effect of the internal hair structure is further quantified using the finite-element simulation. The results imply that the variation of spectral weight may relate to the disordered microscopic structure variation of the hair cortex during the inflammatory attack. Our work reveals the potential of hair infrared transmission spectrum in tracing the variation of hair cortex retrospectively

Highly Efficient and Selective Photocatalytic Nonoxidative Coupling of Methane to Ethylene over Pd-Zn Synergistic Catalytic Sites

Photocatalytic nonoxidative coupling of CH4 to multicarbon (C2+) hydrocarbons (e.g., C2H4) and H2 under ambient conditions provides a promising energy-conserving approach for utilization of carbon resource. However, as the methyl intermediates prefer to undergo self-coupling to produce ethane, it is a challenging task to control the selective conversion of CH4 to higher value-added C2H4. Herein, we adopt a synergistic catalysis strategy by integrating Pd-Zn active sites on visible light-responsive defective WO3 nanosheets for synergizing the adsorption, activation, and dehydrogenation processes in CH4 to C2H4 conversion. Benefiting from the synergy, our model catalyst achieves a remarkable C2+ compounds yield of 31.85 mu mol center dot g-1 center dot h-1 with an exceptionally high C2H4 selectivity of 75.3% and a stoichiometric H2 evolution. In situ spectroscopic studies reveal that the Zn sites promote the adsorption and activation of CH4 molecules to generate methyl and methoxy intermediates with the assistance of lattice oxygen, while the Pd sites facilitate the dehydrogenation of methoxy to methylene radicals for producing C2H4 and suppress overoxidation. This work demonstrates a strategy for designing efficient photocatalysts toward selective coupling of CH4 to higher value-added chemicals and highlights the importance of synergistic active sites to the synergy of key steps in catalytic reactions.Peer reviewe

Playing lottery tickets in style transfer models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that Style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models