Programme of the world revolution

https://stars.library.ucf.edu/prism/1203/thumbnail.jp

Intrapancreatic accessory spleen

A case of accessory spleen located in the tail of the pancreas in a stillbirth male foetus is reported. The congenital anomaly was revealed at autopsy. The intrapancreatic spleen was well demarcated and was composed of red and white pulp; however, same pancreatic ducts were intermingled with the splenic parenchyma. As well as the intrapancreatic lesion another minute accessory spleen was also found at the hilum of the proper organ. Since a lack of morphological features of trisomy 13 syndrome were found in the foetus, the ectopic spleens were regarded as incidental findings

Ukraine moves forward improving the FinTech regulatiry environment

The thesis is devoted to the study of financial technologies, which are rapidly developing both in Ukraine and in foreign countries, today are an integral part of public life and are actively being introduced into all spheres of state functioning. Despite the fact of the dynamic development of the FinTech industry in Ukraine, a comprehensive system of its legal regulation has not been created yet, which can be explained by the economic and informational specifics of the industry itself and its ramified areas.Стаття присвячена дослідженню питання фінансових технологій , що активно розвиваються як в світі, так і в Україні, які повністю інтегровані в суспільні відносини та є невід’ємною часткою багатьох сфер публічного функціонування. Проте, не зважаючи на стрімкий розвиток фінтех галузі в Україні, правове регулювання цієї сфери потребує подальшого формування, враховуючи її економічну й інформаційну специфіку та розгалужену мережу взаємозв’язків

Deep Reinforcement Learning from Hierarchical Weak Preference Feedback

Reward design is a fundamental, yet challenging aspect of practical reinforcement learning (RL). For simple tasks, researchers typically handcraft the reward function, e.g., using a linear combination of several reward factors. However, such reward engineering is subject to approximation bias, incurs large tuning cost, and often cannot provide the granularity required for complex tasks. To avoid these difficulties, researchers have turned to reinforcement learning from human feedback (RLHF), which learns a reward function from human preferences between pairs of trajectory sequences. By leveraging preference-based reward modeling, RLHF learns complex rewards that are well aligned with human preferences, allowing RL to tackle increasingly difficult problems. Unfortunately, the applicability of RLHF is limited due to the high cost and difficulty of obtaining human preference data. In light of this cost, we investigate learning reward functions for complex tasks with less human effort; simply by ranking the importance of the reward factors. More specifically, we propose a new RL framework -- HERON, which compares trajectories using a hierarchical decision tree induced by the given ranking. These comparisons are used to train a preference-based reward model, which is then used for policy learning. We find that our framework can not only train high performing agents on a variety of difficult tasks, but also provide additional benefits such as improved sample efficiency and robustness. Our code is available at https://github.com/abukharin3/HERON.Comment: 28 Pages, 15 figure