Сербська книжка ХІХ століття у Львівській науковій бібліотеці ім. В. Стефаника (за матеріалами фонду відділу рідкісної книги)

UBUlink(opens in a new window)|Entitled full text(opens in a new window)|View at Publisher(opens in a new window)| In recent years the number and frequency of high-impact floods have increased and climate change effects are expected to increase flood risks even more. The European Union (EU) has recently established the Floods Directive as a framework for the assessment and management of these risks. The aim of this article is to explore factors that have hampered or stimulated the implementation process of the Floods Directive in the Netherlands, from its establishment in 2007 until January 2013. During this period, the first requirements of the Floods Directive had to be implemented, while the second and third obligations were to be in an advanced stage. Following a literature review of policy implementation theories and a content analysis of the Floods Directive, we have studied the implementation processes in the Dutch part of the Meuse and Rhine-West catchments. Perceptions of interviewees and survey respondents were used to identify influential factors. Our research shows that although the implementation process in the Netherlands is on schedule, it is iterative and complex. Various constraining and stimulating factors, affecting the implementation process, are distinguished. The article concludes with some suggestions for improving the further implementation of the Floods Directive

Sampled Policy Gradient for Learning to Play the Game Agar.io

In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples. This sampling allows SPG to search the action-Q-value space more globally than deterministic policy gradient (DPG), enabling it to theoretically avoid more local optima. SPG is compared to Q-learning and the actor-critic algorithms CACLA and DPG in a pellet collection task and a self play environment in the game Agar.io. The online game Agar.io has become massively popular on the internet due to intuitive game design and the ability to instantly compete against players around the world. From the point of view of artificial intelligence this game is also very intriguing: The game has a continuous input and action space and allows to have diverse agents with complex strategies compete against each other. The experimental results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task, but all algorithms fail to outperform this bot in a fighting scenario. The SPG algorithm is analyzed to have great extendability through offline exploration and it matches DPG in performance even in its basic form without extensive sampling

Hierarchical reinforcement learning for real-time strategy games

Real-Time Strategy (RTS) games can be abstracted to resource allocation applicable in many fields and industries. We consider a simplified custom RTS game focused on mid-level combat using reinforcement learning (RL) algorithms. There are a number of contributions to game playing with RL in this paper. First, we combine hierarchical RL with a multi-layer perceptron (MLP) that receives higher-order inputs for increased learning speed and performance. Second, we compare Q-learning against Monte Carlo learning as reinforcement learning algorithms. Third, because the teams in the RTS game are multi-agent systems, we examine two different methods for assigning rewards to agents. Experiments are performed against two different fixed opponents. The results show that the combination of Q-learning and individual rewards yields the highest win-rate against the different opponents, and is able to defeat the opponent within 26 training games

Perioperative Pleural Drainage in Liver Transplantation: A Retrospective Analysis from a High-Volume Liver Transplant Center

BACKGROUND Pleural effusions represent a common complication after liver transplantation (LT) and chest drain (CD) placement is frequently necessary. MATERIAL AND METHODS In this retrospective cohort study, adult LT recipients between 2009 and 2016 were analyzed for pleural effusion formation and its treatment within the first 10 postoperative days. The aim of the study was to compare different settings of CD placement with regard to intervention-related complications. RESULTS Overall, 597 patients met the inclusion criteria, of which 361 patients (60.5%) received at least 1 CD within the study period. Patients with a MELD >25 were more frequently affected (75.7% versus 56.0%, P<0.001). Typically, CDs were placed in the intensive care unit (ICU) (66.8%) or in the operating room (14.1% during LT, 11.5% in the context of reoperations). In total, 97.0% of the patients received a right-sided CD, presumably caused by local irritations. Approximately one-third (35.4%) of ICU-patients required pre-interventional optimization of coagulation. Of the 361 patients receiving a CD, 15 patients (4.2%) suffered a post-interventional hemorrhage and 6 patients (1.4%) had a pneumothorax requiring further treatment. Less complications were observed when the CD was performed in the operating room compared to the ICU: 1 out 127 patients (0.8%) versus 20 out of 332 patients (6.0%); P=0.016. CONCLUSIONS CD placement occurring in the operating room was associated with fewer complications in contrast to placement occurring in the ICU. Planned CD placement in the course of surgery might be favorable in high-risk patients