420 research outputs found
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
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