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Outward foreign direct investment (OFDI) is an important way to promote China’s economic transformation and achieve high-quality economic development, and it plays an important role in building a new development pattern that focuses on the domestic systemic circulation as the main body, with domestic and international double circulations promoting each other. In the context of the uncertainty of the current global economic environment, investment protectionism is arising in various countries, and there is an obvious trend of anti-globalization. The sudden outbreak of COVID-19, the Russian Ukrainian war, and other conflicts have severely damaged the global economy and investment, significantly increased the risk of overseas investment, and reduced the confidence in outward foreign direct investment. Chinese enterprises face serious challenges and problems in OFDI. This article starts from explaining the current situation of Chinese enterprises’ OFDI and continues with an analysis on the existing problems and puts forward suggestions to increase foreign investment in China.

Cadmium (Cd), a ubiquitous toxic heavy metal, with the intractable trait of low degradation, can induce multiple organ damage. Whereas, far less is known about its neurotoxicity and the specific mechanism in the chronic low Cd exposure. To investigate the chronic neurotoxicity of Cd

Deep-learning-based methods have been widely used in hyperspectral image classification. In order to solve the problems of the excessive parameters and computational cost of 3D convolution, and loss of detailed information due to the excessive increase in the receptive field in pursuit of multi-scale features, this paper proposes a lightweight hybrid convolutional network called the 3D lightweight receptive control network (LRCNet). The proposed network consists of a 3D depthwise separable convolutional network and a receptive field control network. The 3D depthwise separable convolutional network uses the depthwise separable technique to capture the joint features of spatial and spectral dimensions while reducing the number of computational parameters. The receptive field control network ensures the extraction of hyperspectral image (HSI) details by controlling the convolution kernel. In order to verify the validity of the proposed method, we test the classification accuracy of the LRCNet based on three public datasets, which exceeds 99.50% The results show that compare with state-of-the-art methods, the proposed network has competitive classification performance.

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing., To appear in Interspeech 2021

In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entity Resolution (ER) features in NLU reranking and introduce a novel loss term based on ER signals to better learn model weights in the reranking framework. In addition, for a multi-domain dialog scenario, we propose a score distribution matching method to ensure scores generated by the NLU reranking models for different domains are properly calibrated. In offline experiments, we demonstrate our proposed approach significantly outperforms the baseline model on both single-domain and cross-domain evaluations.

In California, 1 in 3 hospital beds are occupied by adults with diabetes. The aim of this study was to examine whether diabetes impacts length of stay (LOS) following common elective orthopedic procedures compared to nondiabetic individuals, and also the performance of hospitals across California for these procedures. Using the Public Use California Patient Discharge Data Files for 2010-2012, the authors examined LOS for elective discharges for hip, spine, or knee surgery (n = 318,861) from the total population of all discharges (n = 11,476,073) for 309 hospitals across California. In all, 16% of discharges had a codiagnosis of diabetes. Unadjusted average LOS was 3.11 days without and 3.40 days with diabetes (mean difference 0.29 [95% confidence interval (0.27, 0.31) days, P