Epidemiological studies have recently revealed that the global prevalence of Helicobacter pylori infection varies between 30% and 50% in developed countries and between 85% and 95% in developing countries. 1,2 H. pylori is a Gram-negative, microaerophilic bacteria that infects humans through oral-oral or fecal-oral transmission. Once inside the body, it adapts well to the acidic environment of the gastric epithelium, and most often, it induces an inflammatory response in the mucosa. 3 The presence of H. pylori has been associated with peptic ulcers and gastritis; if left untreated, H. pylori-induced gastritis can become chronic. The majority of H. pylori-positive subjects
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we present a novel CAscade Deep REinforcement learning framework, CADRE, to achieve model-free vision-based autonomous urban driving. In CADRE, to derive representative latent features from raw observations, we first offline train a Co-attention Perception Module (CoPM) that leverages the co-attention mechanism to learn the inter-relationships between the visual and control information from a pre-collected driving dataset. Cascaded by the frozen CoPM, we then present an efficient distributed proximal policy optimization framework to online learn the driving policy under the guidance of particularly designed reward functions. We perform a comprehensive empirical study with the CARLA NoCrash benchmark as well as specific obstacle avoidance scenarios in autonomous urban driving tasks. The experimental results well justify the effectiveness of CADRE and its superiority over the state-of-the-art by a wide margin.
The International Accounting Standards Board (IASB) is the setter and issuers of international accounting standards, with a goal to establish high-quality, easy-to-understand and feasible international accounting standard around the world. However, the truth is that IASB has not yet reach its goal. This article aims to discuss the factors influencing the process to reach a fair international accounting standard. By defining and analyzing political and technical influence respectively, we mainly illustrated the relationship and effects between influential institution in the standard-setting process with the help of Key-actors Model. We concluded that the IASB standard setting process is more influenced by political factors than technical, and joint efforts between each related institutions are needed to reach a balance between these two factors and formulate a fair international accounting standard.
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