Entry mode selection for multinational corporations: the case of entry in China of Johnson&Johnson: mainly focus on Xian-Janssen Pharmaceutical

In the process of economic globalization, Multinational Corporations (MNCs) are an important force to promote globalization. China, since its reform and opening-up in 1978 and joining in the World Trade Organization in 2001, has been attracting foreign investment. During the last decades, a great number of foreign-funded enterprises had the opportunity to enter the Chinese market. Therefore, particularly important is which mode of entry strategy to choose. The purpose of this dissertation is to identify the characteristics of entry mode strategy by literature review and correlate them with a case study of the company Xian Janssen in order to clarify which entry mode strategy this company used to establish and obtain prominence in the Chinese markets. For this purpose, it is used a qualitative analysis method with primary and secondary data. After the case study, it was concluded that Janssen pharmaceutical entered the Chinese market by means of establishing a joint venture. After that, the advantages and disadvantages of the joint venture were discussed. And the obstacles that Janssen pharmaceutical faced after entering the Chinese market, the importance of market research, and the reasons for Janssen's success in the Chinese market were also introduced. The author's main intention in writing this dissertation is to investigate and analyze the entry mode strategic of foreign-funded enterprises to enter the Chinese market. And hopes to provide some references for foreign-funded enterprises that are preparing to enter the Chinese market, and also provide the reference for Chinese enterprises "go out".No processo de globalização econômica, as Corporações Multinacionais (EMNs) são uma força importante para promover a globalização. A China, desde a sua reforma e abertura em 1978 e a sua adesão à Organização Mundial do Comércio em 2001, tem atraído investimentos estrangeiros. Durante as últimas décadas, um grande número de empresas financiadas por estrangeiros teve a oportunidade de entrar no mercado chinês. Portanto, é particularmente importante escolher o modo de estratégia de entrada. O objetivo desta dissertação é identificar as características da estratégia de modo de entrada pela revisão de literatura e correlacioná-las com um estudo do caso da empresa Xian Janssen, a fim de esclarecer qual estratégia de entrada esta empresa usou e obteve destaque nos mercados chineses. Portanto, utiliza-se um método de análise qualitativa com dados primários e secundários. Após o estudo de caso, concluiu-se que a farmacêutica Janssen entrou no mercado chinês por meio da criação de uma joint venture. Depois disso, as vantagens e desvantagens da joint venture foram discutidas. E os obstáculos que a farmacêutica Janssen enfrentou após entrar no mercado chinês, a importância da pesquisa de mercado e as razões para o sucesso da Janssen no mercado chinês também foram apresentados. A principal intenção do autor ao escrever esta dissertação é investigar e analisar o modo de entrada estratégico de empresas de capital estrangeiro ao entrarem no mercado chinês. E espera-se fornecer alguma referência e ajuda para as empresas de capital estrangeiro que estão a preparar-se para entrar no mercado chinês, e também se espera fornecer a referência para as empresas chinesas "sair"

Evolution and control of the phase competition morphology in a manganite film

The competition among different phases in perovskite manganites is pronounced since their energies are very close under the interplay of charge, spin, orbital and lattice degrees of freedom. To reveal the roles of underlying interactions, many efforts have been devoted towards directly imaging phase transitions at microscopic scales. Here we show images of the charge-ordered insulator (COI) phase transition from a pure ferromagnetic metal with reducing field or increasing temperature in a strained phase-separated manganite film, using a home-built magnetic force microscope. Compared with the COI melting transition, this reverse transition is sharp, cooperative and martensitic-like with astonishingly unique yet diverse morphologies. The COI domains show variable-dimensional growth at different temperatures and their distribution can illustrate the delicate balance of the underlying interactions in manganites. Our findings also display how phase domain engineering is possible and how the phase competition can be tuned in a controllable manner.Comment: Published versio

Understanding the diffusion models by conditional expectations

This paper provide several mathematical analyses of the diffusion model in machine learning. The drift term of the backwards sampling process is represented as a conditional expectation involving the data distribution and the forward diffusion. The training process aims to find such a drift function by minimizing the mean-squared residue related to the conditional expectation. Using small-time approximations of the Green's function of the forward diffusion, we show that the analytical mean drift function in DDPM and the score function in SGM asymptotically blow up in the final stages of the sampling process for singular data distributions such as those concentrated on lower-dimensional manifolds, and is therefore difficult to approximate by a network. To overcome this difficulty, we derive a new target function and associated loss, which remains bounded even for singular data distributions. We illustrate the theoretical findings with several numerical examples

Finite Difference Approximation with ADI Scheme for Two-dimensional Keller-Segel Equations

Keller-Segel systems are a set of nonlinear partial differential equations used to model chemotaxis in biology. In this paper, we propose two alternating direction implicit (ADI) schemes to solve the 2D Keller-Segel systems directly with minimal computational cost, while preserving positivity, energy dissipation law and mass conservation. One scheme unconditionally preserves positivity, while the other does so conditionally. Both schemes achieve second-order accuracy in space, with the former being first-order accuracy in time and the latter second-order accuracy in time. Besides, the former scheme preserves the energy dissipation law asymptotically. We validate these results through numerical experiments, and also compare the efficiency of our schemes with the standard five-point scheme, demonstrating that our approaches effectively reduce computational costs.Comment: 29 page

LDE-Net: L\'evy Induced Stochastic Differential Equation Equipped with Neural Network for Time Series Forecasting

With the fast development of modern deep learning techniques, the study of dynamic systems and neural networks is increasingly benefiting each other in a lot of different ways. Since uncertainties often arise in real world observations, SDEs (stochastic differential equations) come to play an important role in scientific modeling. To this end, we employ a collection of SDEs with drift and diffusion terms approximated by neural networks to predict the trend of chaotic time series which has big jump properties. Our contributions are, first, we propose LDE-Net, which explores compounded SDEs with $\alpha$-stable L\'evy motion to model complex time series data and solve the problem through neural network approximation. Second, we theoretically prove the convergence of our algorithm with respect to hyper-parameters of the neural network, and obtain the error bound without curse of dimensionality. Finally, we illustrate our method by applying it to real time series data and find the accuracy increases through the use of non-Gaussian L\'evy processes. We also present detailed comparisons in terms of data patterns, various models, different shapes of L\'evy motion and the prediction lengths.Comment: 18 pages, 38 figure

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202