International expansion and home-country resource acquisition: a signaling perspective of emerging-market firms’ internationalization

Despite growing attention to the role of home countries in studies of emerging-market multinational enterprises (EMNEs), there is limited focus on how international expansion affects EMNEs’ home conditions. Drawing on signaling theory, we propose that EMNEs’ international expansions serve as a signaling mechanism that shapes perceptions of stakeholders in their home countries and thus facilitate their resource acquisition from these stakeholders. The signaling effect is strengthened when EMNEs enter more advanced host countries where higher entry barriers incur higher signaling costs that serve as isolating mechanisms; and when they are located in less developed home markets where information asymmetry is more serious due to weaker institutional arrangements. Furthermore, congruent signals, such as patents, strengthen the main effect by cross-confirming the signaled content, while incongruent signals, such as political connections, weaken it due to ambiguity in interpreting the original signal. Using instrumental variables and a difference-in-differences design to account for potential endogeneity of international expansion, our empirical analysis of Chinese-listed privately owned enterprises from 1999 to 2019 supports our propositions

ARE STABLECOINS SAFE HAVENS FOR TRADITIONAL CRYPTOCURRENCIES? AN EMPIRICAL STUDY DURING THE COVID-19 PANDEMIC

We investigate whether stablecoins are safe havens for traditional cryptocurrencies with fresh evidence from the recent crisis period of the COVID-19 pandemic. Our results support the safe-haven properties of Tether for both before and during the pandemic. For Digix, a gold-backed stablecoin with relatively small market capitalization, we find a change in characteristics before and during the pandemic, but do not find statistically significant evidence for its safe-haven properties. Furthermore, we document that, when considering the economic benefits and costs of adding safe-haven assets into cryptocurrency portfolios, the one with Tether outperforms both a naked portfolio and the portfolio with a traditional safe-haven asset such as gold

ANGIOTENSIN-(1-7)/MAS AXIS AND VASCULAR INFLAMMATION

Atherosclerosis, as a potentially serious condition, has become one of the most prevalent causes of mortality over the world. RAS (Reninangiotensin- system) is recognized to be a key role in the development of atherosclerosis, which considered as a chronic inflammatory disease. Ang II (angiotensin II) is proven to cause atherosclerosis, hypertension and aortic aneurysms. While activation of Mas receptors by Ang-(1-7) [angiotensin-(1- 7)] shows an important role in prevention of atherosclerosis. The activation of Ang-(1-7)/Mas receptor axis counteracts Ang II-induced hypertension, inflammation, fibrosis and apoptosis responses. We have concluded that, the relationship between Ang-(1-7)/Mas axis and vascular inflammation could be the paving-stone of the avoidance and novel treatment for atherosclerosis. The scope of this study is to review the relationship between Ang-(1-7)/Mas axis and vascular inflammation in the development of atherosclerosis

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.Comment: The 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), 8 page

Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation

Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning. Previous approaches either introduce additional procedures for learning to reward, thereby increasing the complexity of optimization, or assume that user-agent interactions provide perfect demonstrations, which is not feasible in practice. Ideally, we aim to employ a unified approach that optimizes both the reward and policy using compositional demonstrations. However, this requirement presents a challenge since rewards inherently quantify user feedback on-policy, while recommender agents approximate off-policy future cumulative valuation. To tackle this challenge, we propose a novel batch inverse reinforcement learning paradigm that achieves the desired properties. Our method utilizes discounted stationary distribution correction to combine LTR and recommender agent evaluation. To fulfill the compositional requirement, we incorporate the concept of pessimism through conservation. Specifically, we modify the vanilla correction using Bellman transformation and enforce KL regularization to constrain consecutive policy updates. We use two real-world datasets which represent two compositional coverage to conduct empirical studies, the results also show that the proposed method relatively improves both effectiveness (2.3\%) and efficiency (11.53\%

A multi-channel photometric detector for multi-component analysis in flow injection analysis

The detector, a multi-channel photometric detector, described in this paper was developed using multi-wavelength LEDs (light emitting diode) and phototransistors for absorbance measurement controlled by an Intel 8031 8-bit single chip microcomputer. Up to four flow cells can be attached to the detector. The LEDs and phototransistors are both inexpensive, and reliable. The results given by the detector for simultaneous determination of trace amounts of cobalt and cadmium in zinc sulphate electrolyte are reported. Because of the newly developed detector, this approach employs much less hardware apparatus than by employing conventional photometric detectors