The problem of series of days without rainfall in a view of efficiency of agricultural output under climate change

Modelling future is key issue in studying CC impacts on agriculture across disciplines and scales. Improving models basing on empirical data coming from diverse micro regions let obtain synergic effects important in shaping food security. Especially, rainfall distribution is most important factor determining agricultural output.The amount of cereal yield depends on an occurrence of long series of days without rain during a growing season. Based on statistical analysis of daily totals it was found that in Central Poland the length of series of days without rainfall during growing season is 40 days. Statistical analysis was done for years 1971-2015. The data allowed finding empirical probability distribution of a length of the series. Average value of the length of series is 4.31 while SD is 4.41. Values of parameters of gamma distribution estimated by the likelihood method are: α=0.9542, β=4.5150. Value of the parameter α (shape parameter) suggests that distribution of the length of series is similar to exponential distribution.Goodness of fit test with gamma distribution was carried out using λ-Kolmogorov and χ2-Pearson tests. Both prove high conformity between empirical and gamma distribution. Based on assumption that gamma distribution can be accepted as distribution of the length of rainless series, further is determined distribution of the length of the longest series in n-element random sample. On the theory of distributions of asymptotic order statistics it is known that the random variable T(n) with appropriate normalization has asymptotic double exponential distribution. Based on that one can conclude that probability to occur 30-day rainless series or longer equals approx. to 0.48. This is useful in forecasting agricultural output depended on rainfall distribution

The Effectiveness of World Models for Continual Reinforcement Learning

World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.Comment: Accepted at CoLLAs 2023, 21 pages, 15 figure

A 41,500 year-old decorated ivory pendant from Stajnia Cave (Poland)

Evidence of mobiliary art and body augmentation are associated with the cultural innovations introduced by Homo sapiens at the beginning of the Upper Paleolithic. Here, we report the discovery of the oldest known human-modified punctate ornament, a decorated ivory pendant from the Paleolithic layers at Stajnia Cave in Poland. We describe the features of this unique piece, as well as the stratigraphic context and the details of its chronometric dating. The Stajnia Cave plate is a personal 'jewellery' object that was created 41,500 calendar years ago (directly radiocarbon dated). It is the oldest known of its kind in Eurasia and it establishes a new starting date for a tradition directly connected to the spread of modern Homo sapiens in Europe