Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Mg gas infiltration for the fabrication of MgB2 pellets using nanosized and microsized B powders

MgB₂ superconductor pellets were synthesized through Mg gas infiltration method using nanosized- and microsized B powders. There was a marked difference in the superconducting properties of the two samples, particularly in the pinning force and dominant pinning mechanism. The microstructures of the samples were observed using HR-TEM and STEM-HAADF, and the results showed that the primary reason for the difference in the superconducting properties is the distribution of the nanosized second-phase particle MgO. Additionally, a feasible reaction model for the Mg gas infiltration method was established. Compared to the Mg liquid infiltration method, the gas infiltration showed better penetrability ability with a small amount of residual Mg. This study presents a novel synthesis process to fabricate an MgB₂ pellet with superior density and superconducting properties. This method can be used in multiple applications such as superconducting bearings, compact superconductor magnets, and magnetic shielding.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

     

Method of sensitive data mining based on Pan-Bull algebra

Wireless Networks - In order to improve the transmission stability of sensor networks, a sensitive data mining method based on Pan Boolean algebra is proposed. According to the output correctness,...     

Cold sintered,temperature-stable CaSnSiO5-K2MoO4 composite microwave ceramics and its prototype microstrip patch antenna

Dense (1-x)wt%CaSnSiO₅-xwt%K₂MoO₄ (CSSO-KMO) composite ceramics were fabricated by the cold sintering process at 180 °C under 400 MPa for 60 min. X-ray diffraction, Energy dispersive X-ray and Raman spectroscopy confirmed that CSSO and KMO coexisted without intermediate phases. As KMO weight fraction increased, relative permittivity (ε_r) and temperature coefficient of resonant frequency (τ_f) decreased and the microwave quality factor (Q×f, where f is resonant frequency) increased. Near-zero τ_f (-0.5 ppm/°C) was obtained for 65 wt%CSSO-35 wt%KMO with ε_r ～ 9.2 and Q×f ～ 6240 GHz. No chemical reaction between ceramic composites and silver was observed, demonstrating potential for cofiring with Ag-paste. A prototype antenna was fabricated from 65 wt%CSSO-35 wt%KMO composite ceramic with a bandwidth of 144 MHz @ -10 dB, a gain of 5.7 dBi and a total efficiency of 88.4 % at 5.2 GHz, suitable for 5 G mobile communication systems.     

An approach to predict lower-order dynamic behaviors of a 5-DOF hybrid robot using a minimum set of generalized coordinates

This paper presents an effective semi-analytical approach for predicting lower-order dynamics of a five degrees-of-freedom (DOF) hybrid robot named TriMule, which is composed of a 3-DOF parallel mechanism plus a 2-DOF A/C wrist. In this method, the governing equations of motion of limbs within the parallel mechanism are first formulated by finite element analysis (FEA) and then reduced to super-element models. This is followed by exploiting a general stiffness model of multiple DOF joints connecting the super-elements. These two threads lead to the reduced dynamic model of the parallel mechanism while keeping the full set of lower-order modes retained. Finally, the dynamic model of entire system is established by merging the models of parallel mechanism and wrist. The computational results show that the lower-order natural frequencies, mode shapes of the entire system, and the frequency response functions (FRFs) of the robot tool center point (TCP) estimated by the proposed approach have very good agreement with those obtained by a full order FE model and experimental modal tests. The merits of this approach lie in that the established model allows the full set of lower-order dynamics of the entire system to be predicted effectively and accurately by only using fourteen generalized coordinates.     

Resolving Deep Sub-Wavelength Scattering of Nanoscale Sidewalls Using Parametric Microscopy

The quantitative optical measurement of deep sub-wavelength features with sub-nanometer sensitivity addresses the measurement challenge in the semiconductor fabrication process. Optical scatterings from the sidewalls of patterned devices reveal abundant structural and material information. We demonstrated a parametric indirect microscopic imaging (PIMI) technique that enables recovery of the profile of wavelength-scale objects with deep sub-wavelength resolution, based on measuring and filtering the variations of far-field scattering intensities when the illumination was modulated. The finite-difference time-domain (FDTD) numerical simulation was performed, and the experimental results were compared with atomic force microscopic (AFM) images to verify the resolution improvement achieved with PIMI. This work may provide a new approach to exploring the detailed structure and material properties of sidewalls and edges in semiconductor-patterned devices with enhanced contrast and resolution, compared with using the conventional optical microscopy, while retaining its advantage of a wide field of view and relatively low cost.