EL ESTUDIO DE LA RELACIÓN ENTRE EL GRAN PREMIO DE LA F1, LA ECONOMÍA TURÍSTICA DE SHANGHAI Y EL DESARROLLO SOSTENIBLE DEL TURISMO

Este trabajo es sobre la relación entre la F1, la economía turística de Shanghai y el desarrollo sostenible del turismo. En el trabajo se utilizan los datos de turistas internacionales pernoctados según procedencia, las cifras de la industria hotelera en Shanghai. Se analizan aspectos positivos y negativos en el desarrollo sostenible en Shanghai

Bi-level Actor-Critic for Multi-agent Coordination

Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the Markov game to an arbitrary Nash equilibrium (NE) when multiple equilibra exist, thus lacking a solution for NE selection. In this paper, we treat agents \emph{unequally} and consider Stackelberg equilibrium as a potentially better convergence point than Nash equilibrium in terms of Pareto superiority, especially in cooperative environments. Under Markov games, we formally define the bi-level reinforcement learning problem in finding Stackelberg equilibrium. We propose a novel bi-level actor-critic learning method that allows agents to have different knowledge base (thus intelligent), while their actions still can be executed simultaneously and distributedly. The convergence proof is given, while the resulting learning algorithm is tested against the state of the arts. We found that the proposed bi-level actor-critic algorithm successfully converged to the Stackelberg equilibria in matrix games and find an asymmetric solution in a highway merge environment

Siamese DETR

Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks, i.e., localization and discrimination, in a novel multi-view learning framework. Two self-supervised pretext tasks are designed: (i) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (ii) Multi-View Semantic Discrimination attempts to improve object-level discrimination for each region. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection using different DETR variants in all setups. Code is available at https://github.com/Zx55/SiameseDETR.Comment: 10 pages, 11 figures. Accepted in CVPR 202

Learning to Select Cuts for Efficient Mixed-Integer Programming

Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in the early stage, thus largely reducing the computational burden without hurting the solution accuracy. However, the major cut selection approaches heavily rely on heuristics, which strongly depend on the specific problem at hand and thus limit their generalization capability. In this paper, we propose a data-driven and generalizable cut selection approach, named Cut Ranking, in the settings of multiple instance learning. To measure the quality of the candidate cuts, a scoring function, which takes the instance-specific cut features as inputs, is trained and applied in cut ranking and selection. In order to evaluate our method, we conduct extensive experiments on both synthetic datasets and real-world datasets. Compared with commonly used heuristics for cut selection, the learning-based policy has shown to be more effective, and is capable of generalizing over multiple problems with different properties. Cut Ranking has been deployed in an industrial solver for large-scale MIPs. In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12.42% over the production solver without any accuracy loss of solution.Comment: Paper accepted at Pattern Recognition journa

Controlling mass and energy diffusion with metamaterials

Diffusion driven by temperature or concentration gradients is a fundamental mechanism of energy and mass transport, which inherently differs from wave propagation in both physical foundations and application prospects. Compared with conventional schemes, metamaterials provide an unprecedented potential for governing diffusion processes, based on emerging theories like the transformation and the scattering cancellation theory, which enormously expanded the original concepts and suggest innovative metamaterial-based devices. We hereby use the term ``diffusionics'' to generalize these remarkable achievements in various energy (e.g., heat) and mass (e.g., particles and plasmas) diffusion systems. For clarity, we categorize the numerous studies appeared during the last decade by diffusion field (i.e., heat, particles, and plasmas) and discuss them from three different perspectives: the theoretical perspective, to detail how the transformation principle is applied to each diffusion field; the application perspective, to introduce various intriguing metamaterial-based devices, such as cloaks and radiative coolers; and the physics perspective, to connect with concepts of recent concern, such as non-Hermitian topology, nonreciprocal transport, and spatiotemporal modulation. We also discuss the possibility of controlling diffusion processes beyond metamaterials. Finally, we point out several future directions for diffusion metamaterial research, including the integration with artificial intelligence and topology concepts.Comment: This review article has been accepted for publication in Rev. Mod. Phy

Possible Lithosphere-Atmosphere-Ionosphere Coupling effects prior to the 2018 Mw = 7.5 Indonesia earthquake from seismic, atmospheric and ionospheric data

In this study, we analyse Lithosphere Atmosphere Ionosphere Coupling (LAIC) effects to identify some phenomena that could, possibly, be linked to the preparation phase of the MW=7.5 earthquake occurred in Indonesia on September 28th, 2018, by investigating the eight months preceding the seismic event. First, we find a seismic acceleration that started two months before the mainshock. Then, studying some physical properties of the atmosphere (skin temperature, total column water vapor and aerosol optical thickness), we find two increases of atmospheric anomalies about 6 and 3.7 months before the mainshock, and the latter one is very promising as a candidate for seismic-related phenomena. Furthermore, we investigate ionospheric disturbances, by analysing the Swarm and, for the first time, China Seismo-Electromagnetic Satellite (CSES), magnetic and electron density data during quiet geomagnetic time. From different techniques, we find interesting anomalies concentrated around 2.7 months before the mainshock. On August 19th, 2018, Swarm and CSES showed an enhancement of the electron density during night time. We critically discuss the possibility that such phenomenon can be a possible pre-seismic-induced ionospheric effect. Finally, we performed a cumulative analysis using all detected anomalies, as a test case for a possible chain of physical phenomena that could happen before the earthquake occurrence. With this study, we support the usefulness to collect and store large Earth ground and satellite observational dataset that in the future could be useful to monitor in real time the seismic zones to anticipate earthquakes, although nowadays, there is no evidence about useful prediction capabilities.Published1040972A. Fisica dell'alta atmosferaJCR Journa

New Insight into the Finance-Energy Nexus: Disaggregated Evidence from Turkish Sectors

Seeing that reshaped energy economics literature has adopted some new variables in energy demand function, the number of papers looking into the relationship between financial development and energy consumption at the aggregate level has been increasing over the last few years. This paper, however, proposes a new framework using disaggregated data and investigates the nexus between financial development and sectoral energy consumption in Turkey. To this end, panel time series regression and causality techniques are adopted over the period 1989–2011. Empirical results confirm that financial development does have a significant impact on energy consumption, even with disaggregated data. It is also proved that the magnitude of financial development is larger in energy-intensive industries than in less energy-intensive ones