We present a theoretical study of radiative heat transfer between dielectric nanogratings in the scattering approach. As comparision with these exact results, we also evaluate domain validity Derjaguin's Proximity Approximation (PA). consider system two corrugated silica plates various grating geometries, separation distances, and lateral displacement respect to one another. Numerical computations show that while PA is good approximation for aligned gratings, it cannot be used when gratings...

We compute the radiative heat transfer between nanostructured gold plates in framework of scattering theory. predict an enhancement as we increase depth corrugations while keeping distance closest approach fixed. interpret this effect terms evolution plasmonic and guided modes a function grating's geometry.

We measure the Casimir force between a gold sphere and silicon plate with nanoscale, rectangular corrugations depth comparable to separation surfaces. In proximity approximation (PFA), both top bottom surfaces of contribute force, leading distance dependence that is distinct from flat surface. The measured found deviate PFA by up 15%, in good agreement calculations based on scattering theory includes geometry effects optical properties material.

We present detailed calculations for the Casimir force between a plane and nanostructured surface at finite temperature in framework of scattering theory. then study numerically effect as function grating parameters separation distance. also infer nontrivial geometrical effects on interaction via comparison with proximity approximation. Finally, we compare our data from experiments performed surfaces.

Recent advances in the field of machine learning have yielded novel research perspectives behavioural economics and financial markets microstructure studies. In this paper we study impact individual trader leaning characteristics on using a stock market simulator designed with multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, centralized double-auction limit order book. This approach allows us to traits whole at mesoscale...

We study the lateral dependence of Casimir energy for different corrugated gratings arbitrary periodic profile. To this end we model profiles as stacks horizontal rectangular slices following profiles' shape and evaluate numerically between them relative displacements two plates. compare our results with predictions obtained within proximity force approximation (PFA). At comparable separation plates geometric parameters, find a strong on corrugation profiles.

We compute the radiative heat transfer between nanostructured gold plates in framework of scattering theory. predict an enhancement as we increase depth corrugations while keeping distance closest approach fixed. interpret this effect terms evolution plasmonic and guided modes a function grating's geometry.

Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives practical inference behavioural economy financial markets microstructure study. We here present latest results from a recently published stock market simulator built around multi-agent system architecture, which each agent is an autonomous investor trading stocks by reinforcement (RL) via centralised double-auction limit order book. The RL framework allows for implementation...

Homeostasis is a biological process by which living beings maintain their internal balance. Previous research suggests that homeostasis learned behaviour. Recently introduced Homeostatic Regulated Reinforcement Learning (HRRL) framework attempts to explain this homeostatic behavior linking Drive Reduction Theory and Learning. This linkage has been proven in the discrete time-space, but not continuous time-space. In work, we advance HRRL time-space environment validate CTCS-HRRL (Continuous...

Building on a previous foundation work (Lussange et al. 2020), this study introduces multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike agent-based models (ABM) or systems (MAS) relied zero-intelligence agents single autonomous agent methodologies, our approach relies endowing with (RL) techniques in order markets. This...

Reconstructing urban areas in 3D out of satellite raster images has been a long-standing and challenging goal both academical industrial research. The rare methods today achieving this objective at Level Of Details $2$ rely on procedural approaches based geometry, need stereo and/or LIDAR data as input. We here propose method for reconstruction named KIBS(\textit{Keypoints Inference By Segmentation}), which comprises two novel features: i) full deep learning approach the detection roof...

In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often necessity to implement so-called noise traders sub-optimally emulate price formation processes. However recent advances in fields neuroscience machine learning overall brought possibility for new tools bottom-up statistical inference complex systems. Most importantly, such allows studying fields, as agent learning, which finance is...

The history of research in finance and economics has been widely impacted by the field Agent- based Computational Economics (ACE). While at same time being popular among natural science researchers for its proximity to successful methods physics chemistry example, ACE also received critics a part social community lack empiricism. Yet recent trends have shifted weights these general arguments potentially given whole new range realism. At base are found two present-day major scientific...

Homeostasis is a prevalent process by which living beings maintain their internal milieu around optimal levels. Multiple lines of evidence suggest that learn to act predicatively ensure homeostasis (allostasis). A classical theory for such regulation drive reduction, where function the difference between current and state. The recently introduced homeostatic regulated reinforcement learning (HRRL), defining within framework reward based on state agent, makes link theories reduction learning....

Quantitative finance has had a long tradition of bottom-up approach to complex systems inference via multi-agent (MAS). These statistical tools are based on modelling agents trading centralised order book, in emulate and diverse market phenomena. past financial models have all relied so-called zero-intelligence agents, so that the crucial issues agent information learning, central price formation hence activity, could not be properly assessed. In address this, we designed next-generation MAS...

Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess benefits of these developments. In parallel, ongoing machine learning revolution and its multiple applications trading can now be used design a next generation financial models, thereby explore systemic complexity in new ways. We here follow on previous groundwork, where we designed calibrated novel agent-based model market simulator, each agent autonomously...

The history of research in finance and economics has been widely impacted by the field Agent-based Computational Economics (ACE). While at same time being popular among natural science researchers for its proximity to successful methods physics chemistry example, ACE also received critics a part social community lack empiricism. Yet recent trends have shifted weights these general arguments potentially given whole new range realism. At base are found two present-day major scientific...