517 research outputs found

    Studies on Enlightenment of China: Haier Group’s Transnational Operations to Chinese Enterprise

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    After China joins World Trade Organization, enterprise carry on transnational operations is necessity; enterprise’s transnational operations are generally started from the export. Regarding the mature product, when after exporting develops certain stage, to follow the need of overseas market development, must carry on the comparison, by determined that which modes of business operation do serve the enterprise benefit. It must develop the transnational operations, the government should increase the support dynamics to the enterprise; The enterprise should raise own competitive advantage diligently; Speeds up the business management and the international trail connection step; Creates the new technology as circumstances permit; Pays special attention to the capital operation. Haier’s transnational operations have given Chinese Enterprise much enlightenment.Key words: Haier group; Transnational operations; Competitive; Internationa

    Phase transition of a one-dimensional Ising model with distance-dependent connections

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    The critical behavior of Ising model on a one-dimensional network, which has long-range connections at distances l>1l>1 with the probability Θ(l)∼l−m\Theta(l)\sim l^{-m}, is studied by using Monte Carlo simulations. Through studying the Ising model on networks with different mm values, this paper discusses the impact of the global correlation, which decays with the increase of mm, on the phase transition of the Ising model. Adding the analysis of the finite-size scaling of the order parameter [][], it is observed that in the whole range of 0<m<20<m<2, a finite-temperature transition exists, and the critical exponents show consistence with mean-field values, which indicates a mean-field nature of the phase transition.Comment: 5 pages,8 figure

    S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields

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    Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images. NeRF and relevant neural field methods (e.g., neural surface representation) typically optimize a point-wise loss and make point-wise predictions, where one data point corresponds to one pixel. Unfortunately, this line of research failed to use the collective supervision of distant pixels, although it is known that pixels in an image or scene can provide rich structural information. To the best of our knowledge, we are the first to design a nonlocal multiplex training paradigm for NeRF and relevant neural field methods via a novel Stochastic Structural SIMilarity (S3IM) loss that processes multiple data points as a whole set instead of process multiple inputs independently. Our extensive experiments demonstrate the unreasonable effectiveness of S3IM in improving NeRF and neural surface representation for nearly free. The improvements of quality metrics can be particularly significant for those relatively difficult tasks: e.g., the test MSE loss unexpectedly drops by more than 90% for TensoRF and DVGO over eight novel view synthesis tasks; a 198% F-score gain and a 64% Chamfer L1L_{1} distance reduction for NeuS over eight surface reconstruction tasks. Moreover, S3IM is consistently robust even with sparse inputs, corrupted images, and dynamic scenes.Comment: ICCV 2023 main conference. Code: https://github.com/Madaoer/S3IM. 14 pages, 5 figures, 17 table

    Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples

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    Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end,unlearnable examples (UEs) have been recently proposed as a compelling protection, by adding imperceptible perturbation to data so that models trained on them cannot classify them accurately on original clean distribution. Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again. Motivated by this observation, we formally define a new threat by introducinglearnable unauthorized examples (LEs) which are UEs with their protection removed. The core of this approach is a novel purification process that projects UEs onto the manifold of LEs. This is realized by a new joint-conditional diffusion model which denoises UEs conditioned on the pixel and perceptual similarity between UEs and LEs. Extensive experiments demonstrate that LE delivers state-of-the-art countering performance against both supervised UEs and unsupervised UEs in various scenarios, which is the first generalizable countermeasure to UEs across supervised learning and unsupervised learning. Our code is available at https://github.com/jiangw-0/LE_JCDP

    Zonal Soil Type Determines Soil Microbial Responses to Maize Cropping and Fertilization.

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    Soil types heavily influence ecological dynamics. It remains controversial to what extent soil types shape microbial responses to land management changes, largely due to lack of in-depth comparison across various soil types. Here, we collected samples from three major zonal soil types spanning from cold temperate to subtropical climate zones. We examined bacterial and fungal community structures, as well as microbial functional genes. Different soil types had distinct microbial biomass levels and community compositions. Five years of maize cropping (growing corn or maize) changed the bacterial community composition of the Ultisol soil type and the fungal composition of the Mollisol soil type but had little effect on the microbial composition of the Inceptisol soil type. Meanwhile, 5 years of fertilization resulted in soil acidification. Microbial compositions of the Mollisol and Ultisol, but not the Inceptisol, were changed and correlated (P &lt; 0.05) with soil pH. These results demonstrated the critical role of soil type in determining microbial responses to land management changes. We also found that soil nitrification potentials correlated with the total abundance of nitrifiers and that soil heterotrophic respiration correlated with the total abundance of carbon degradation genes, suggesting that changes in microbial community structure had altered ecosystem processes. IMPORTANCE Microbial communities are essential drivers of soil functional processes such as nitrification and heterotrophic respiration. Although there is initial evidence revealing the importance of soil type in shaping microbial communities, there has been no in-depth, comprehensive survey to robustly establish it as a major determinant of microbial community composition, functional gene structure, or ecosystem functioning. We examined bacterial and fungal community structures using Illumina sequencing, microbial functional genes using GeoChip, microbial biomass using phospholipid fatty acid analysis, as well as functional processes of soil nitrification potential and CO2 efflux. We demonstrated the critical role of soil type in determining microbial responses to land use changes at the continental level. Our findings underscore the inherent difficulty in generalizing ecosystem responses across landscapes and suggest that assessments of community feedback must take soil types into consideration. Author Video: An author video summary of this article is available
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