S&T activities and firm performance - microeconomic evidence from manufacturing in Shanghai

This paper examines the impact of R&D expenditure and technology import on the level and the growth of productivity, as well as on the general economic performance in manufacturing firms with various ownership structures in Shanghai, China. The empirical analyses are based on the firm-level information of a sample of manufacturing firms for the period 1998–2003. We find clear-cut evidence indicating that firms with foreign participation have a productivity advantage over their domestic counterparts. The expenditures on technology import not only have a direct and positive effect on productivity, but also indirectly enhance the absorptive capacity of firms to facilitate in-house R&D activities. This is particularly true for firms with foreign participation, or for firms in sectors with relatively high technical standards. Furthermore, R&D expenditure and technology import may also have positive effects on profitability and export performance, depending on the ownership structure of the firm and the technical standard in the sector.Science and Technology policy; Science and Technology investment; R&D

HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection

With the popularity of smart devices and the development of computer vision technology, concerns about face privacy protection are growing. The face de-identification technique is a practical way to solve the identity protection problem. The existing facial de-identification methods have revealed several problems, including the impact on the realism of anonymized results when faced with occlusions and the inability to maintain identity-irrelevant details in anonymized results. We present a High-Fidelity and Occlusion-Robust De-identification (HFORD) method to deal with these issues. This approach can disentangle identities and attributes while preserving image-specific details such as background, facial features (e.g., wrinkles), and lighting, even in occluded scenes. To disentangle the latent codes in the GAN inversion space, we introduce an Identity Disentanglement Module (IDM). This module selects the latent codes that are closely related to the identity. It further separates the latent codes into identity-related codes and attribute-related codes, enabling the network to preserve attributes while only modifying the identity. To ensure the preservation of image details and enhance the network's robustness to occlusions, we propose an Attribute Retention Module (ARM). This module adaptively preserves identity-irrelevant details and facial occlusions and blends them into the generated results in a modulated manner. Extensive experiments show that our method has higher quality, better detail fidelity, and stronger occlusion robustness than other face de-identification methods

Co-up-regulation of three P450 genes in response to permethrin exposure in permethrin resistant house flies, Musca domestica

BACKGROUND: Insects may use various biochemical pathways to enable them to tolerate the lethal action of insecticides. For example, increased cytochrome P450 detoxification is known to play an important role in many insect species. Both constitutively increased expression (overexpression) and induction of P450s are thought to be responsible for increased levels of detoxification of insecticides. However, unlike constitutively overexpressed P450 genes, whose expression association with insecticide resistance has been extensively studied, the induction of P450s is less well characterized in insecticide resistance. The current study focuses on the characterization of individual P450 genes that are induced in response to permethrin treatment in permethrin resistant house flies. RESULTS: The expression of 3 P450 genes, CYP4D4v2, CYP4G2, and CYP6A38, was co-up-regulated by permethrin treatment in permethrin resistant ALHF house flies in a time and dose-dependent manner. Comparison of the deduced protein sequences of these three P450s from resistant ALHF and susceptible aabys and CS house flies revealed identical protein sequences. Genetic linkage analysis located CYP4D4v2 and CYP6A38 on autosome 5, corresponding to the linkage of P450-mediated resistance in ALHF, whereas CYP4G2 was located on autosome 3, where the major insecticide resistance factor(s) for ALHF had been mapped but no P450 genes reported prior to this study. CONCLUSION: Our study provides the first direct evidence that multiple P450 genes are co-up-regulated in permethrin resistant house flies through the induction mechanism, which increases overall expression levels of P450 genes in resistant house flies. Taken together with the significant induction of CYP4D4v2, CYP4G2, and CYP6A38 expression by permethrin only in permethrin resistant house flies and the correlation of the linkage of the genes with resistance and/or P450-mediated resistance in resistant ALHF house flies, this study sheds new light on the functional importance of P450 genes in response to insecticide treatment, detoxification of insecticides, the adaptation of insects to their environment, and the evolution of insecticide resistance

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NP-completeness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomial-time approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution

Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation

Facial sketch synthesis (FSS) aims to generate a vivid sketch portrait from a given facial photo. Existing FSS methods merely rely on 2D representations of facial semantic or appearance. However, professional human artists usually use outlines or shadings to covey 3D geometry. Thus facial 3D geometry (e.g. depth map) is extremely important for FSS. Besides, different artists may use diverse drawing techniques and create multiple styles of sketches; but the style is globally consistent in a sketch. Inspired by such observations, in this paper, we propose a novel Human-Inspired Dynamic Adaptation (HIDA) method. Specially, we propose to dynamically modulate neuron activations based on a joint consideration of both facial 3D geometry and 2D appearance, as well as globally consistent style control. Besides, we use deformable convolutions at coarse-scales to align deep features, for generating abstract and distinct outlines. Experiments show that HIDA can generate high-quality sketches in multiple styles, and significantly outperforms previous methods, over a large range of challenging faces. Besides, HIDA allows precise style control of the synthesized sketch, and generalizes well to natural scenes and other artistic styles. Our code and results have been released online at: https://github.com/AiArt-HDU/HIDA.Comment: To appear on ICCV'2

All-to-key Attention for Arbitrary Style Transfer

Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism -- each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity, limiting the effectiveness and efficiency of arbitrary style transfer. In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to key style representations that depict the style distribution of local regions; Progressive attention pays attention from coarse-grained regions to fine-grained key positions. The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate the superior performance of our approach

Diff-Privacy: Diffusion-based Face Privacy Protection

Privacy protection has become a top priority as the proliferation of AI techniques has led to widespread collection and misuse of personal data. Anonymization and visual identity information hiding are two important facial privacy protection tasks that aim to remove identification characteristics from facial images at the human perception level. However, they have a significant difference in that the former aims to prevent the machine from recognizing correctly, while the latter needs to ensure the accuracy of machine recognition. Therefore, it is difficult to train a model to complete these two tasks simultaneously. In this paper, we unify the task of anonymization and visual identity information hiding and propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding. Extensive experiments have been conducted to validate the effectiveness of our proposed framework in protecting facial privacy.Comment: 17page

CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization

We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images that embody the personalized concept, thereby achieving text-to-image personalization. In contrast to existing approaches that emphasize word embedding learning or parameter fine-tuning for the diffusion model, which potentially causes concept dilution or overfitting, our method concatenates embeddings on the feature-dense space of the text encoder in the diffusion model to learn the gap between the personalized concept and its base class, aiming to maximize the preservation of prior knowledge in diffusion models while restoring the personalized concepts. To this end, we first dissect the text encoder's integration in the image generation process to identify the feature-dense space of the encoder. Afterward, we concatenate embeddings on the Keys and Values in this space to learn the gap between the personalized concept and its base class. In this way, the concatenated embeddings ultimately manifest as a residual on the original attention output. To more accurately and unbiasedly quantify the results of personalized image generation, we improve the CLIP image alignment score based on masks. Qualitatively and quantitatively, CatVersion helps to restore personalization concepts more faithfully and enables more robust editing.Comment: For the project page, please visit https://royzhao926.github.io/CatVersion-page

In situ investigations of the phase change behaviour of tungsten oxide nanostructures

This study appraises the use of in-situ diffraction and spectroscopy techniques, complemented with ex-situ electron microscopy analyses, to investigate the geometry and phase change behaviour of bundled ultrathin W18O49 nanowires and WO3 nanoparticles. Our in-situ X-ray diffraction (XRD) results have shown that the phase transition of WO3 nanoparticles occurs in sequence as the temperature increases, from monoclinic (room temperature) → orthorhombic (350 ºC) → tetragonal (800 °C), akin to bulk WO3; however, W18O49 nanowires remain stable as the monoclinic phase up to 500 °C, after which complete oxidation to WO3 and transformation to the orthorhombic β-phase at 550 °C is observed. The in-situ Raman spectroscopy investigations have shown that as the temperature increases, the Raman peaks downshift toward lower wavenumbers in both structures, which can be attributed to the increased bond lengths in the lattice. We have also demonstrated that the Raman shift at 187.6 cm-1 can be used as a fingerprint band for the phase transition from the γ- to the β-phase of the WO3 nanoparticle. Furthermore, WO3 nanoparticles exhibit the γ- to β-phase conversion at 275 °C, which is about 75 °C lower than the relaxation temperature of 350 °C for the monoclinic γ-W18O49 nanowires. We propose that this fundamental phase transition understanding can offer important guidance for the design and development of WOx-based nanodevices by defining their allowed operating conditions