Different ways of using space: traces of domestic and ritual activities at a Late Neolithic settlement at Sormás-Török-földek

The present paper studies questions of the use of space in various ways on the basis of data obtained from a site at Sormás-Török-földek. The significance of this site lies in the fact that two enclosures were excavated here which differ in character, but which are in a close relationship physically and chronologically. They demonstrate precisely the radical change, which took place in the mode of space-use, representing two important stages of the progression as a result of which the separation of territories used for domestic and ritual activities were physically manifested.V članku predstavljamo različna vprašanja o rabi prostora na različne načine, in sicer na podlagi podatkov iz najdišča Sormás-Török-földek. Najdišče ima poseben pomen predvsem zato, ker sta bili tukaj odkriti dve ogradi, ki imata različne značilnosti, vendar sta v tesni povezavi tako fizično kot kronološko. Predstavljata natanko tisto radikalno spremembo, ki se je zgodila v načinu rabe prostora, in dve pomembni fazi napredovanja, zaradi katerega se kažejo dejanske ločitve prostorov za gospodinjske in ritualne dejavnosti

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning

Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning addresses the resulting scalability challenge by optimizing the policy of a representative agent. In this paper, we address an important generalization where there exist global constraints on the distribution of agents (e.g., requiring capacity constraints or minimum coverage requirements to be met). We propose Safe-$\text{M}^3$-UCRL, the first model-based algorithm that attains safe policies even in the case of unknown transition dynamics. As a key ingredient, it uses epistemic uncertainty in the transition model within a log-barrier approach to ensure pessimistic constraints satisfaction with high probability. We showcase Safe-$\text{M}^3$-UCRL on the vehicle repositioning problem faced by many shared mobility operators and evaluate its performance through simulations built on Shenzhen taxi trajectory data. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.Comment: 25 pages, 14 figures, 3 table

Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning

Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents’ interactions and the combinatorial nature of their state and action spaces. In particular, we consider the Mean-Field Control (MFC) problem which assumes an asymptotically infinite population of identical agents that aim to collaboratively maximize the collective reward. In many cases, solutions of an MFC problem are good approximations for large systems, hence, efficient learning for MFC is valuable for the analogous discrete agent setting with many agents. Specifically, we focus on the case of unknown system dynamics where the goal is to simultaneously optimize for the rewards and learn from experience. We propose an efficient model-based reinforcement learning algorithm, M3–UCRL, that runs in episodes, balances between exploration and exploitation during policy learning, and provably solves this problem. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC, obtained via a novel mean-field type analysis. To learn the system’s dynamics, M3–UCRL can be instantiated with various statistical models, e.g., neural networks or Gaussian Processes. Moreover, we provide a practical parametrization of the core optimization problem that facilitates gradient-based optimization techniques when combined with differentiable dynamics approximation methods such as neural networks

On the impact of publicly available news and information transfer to financial markets

We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a non-profit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles associated with the constituent companies in the S&P 500 index, an equity market index that measures the stock performance of US companies. Using machine learning techniques, we extract sentiment scores from the Common Crawl News data and employ tools from information theory to quantify the information transfer from public news articles to the US stock market. Furthermore, we analyse and quantify the economic significance of the news-based information with a simple sentiment-based portfolio trading strategy. Our findings provide support for that information in publicly available news on the World Wide Web has a statistically and economically significant impact on events in financial markets.ISSN:2054-570

Safe model-based multi-agent mean-field reinforcement learning

Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning addresses the resulting scalability challenge by optimizing the policy of a representative agent. In this paper, we address an important generalization where there exist global constraints on the distribution of agents (e.g., requiring capacity constraints or minimum coverage requirements to be met). We propose Safe-$\text{M}^3$-UCRL, the first model-based algorithm that attains safe policies even in the case of unknown transition dynamics. As a key ingredient, it uses epistemic uncertainty in the transition model within a log-barrier approach to ensure pessimistic constraints satisfaction with high probability. We showcase Safe-$\text{M}^3$-UCRL on the vehicle repositioning problem faced by many shared mobility operators and evaluate its performance through simulations built on Shenzhen taxi trajectory data. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand

Bezeréd-Teleki-dűlő II.: Egy késő neolitikus körárok a Kr. e. 5. évezredből = Bezeréd-Teleki-dűlő II. – A Late Neolithic Circular Enclosure from the 5th Millennium BC

Bezeréd-Teleki-dűlő II. lelőhelyen terepbejárás és geofizikai felmérés eredményeként a késő neolitikus lengyeli kultúra körárka vált ismertté. A feltűnően szimmetrikus alaprajzú, kettős körárok mindkét árkán 8–8 bejárat figyelhető meg, melyek némelyike előtt félköríves toldalékok jelentkeztek. A belső árok mindkét oldalán paliszádárkok mutatkoztak. A kapuk tájolása megfelel a dunántúli lengyeli körárkokra jellemző mintázatnak. A körárok belsejében kirajzolódó épületek, a körárok körüli lengyeli kultúrás lelőhelyek, továbbá a körárok időrendi viszonya egyelőre nem tisztázott