Research on the Market Access System of Renewable Resource Management in China

As an independent module of the countries’ economic development, the renewable resource industry is a part of circular economic development. It is the key element for national economic development and environmental protection. Anyone who wants to regulate the whole renewable resources industry development orderly cannot be separated from the strong supervision and management measures. However, the planning of the management measures is based on a series of standardized system design. This thesis focuses on the market access system of renewable resources management as a starting point, first of all, understand and learn the world most advanced countries’ (Japan, Germany, Singapore) renewable resources market access system design, then according to the objective conditions of China, we draw a lesson from the experience of developed countries and construct a new series of market access system for China’ renewable resource industry, which include renewable resources list announcement system, enterprise technical standard system, extended producer responsibility system and so on, all of these are designed to provide reference management standards for the regeneration resources industry development in China

Achieving Long-Term Fairness in Sequential Decision Making

In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets

Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning

While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static population. In this paper, we study fairness in dynamic systems where sequential decisions are made. Each decision may shift the underlying distribution of features or user behavior. We model the dynamic system through a Markov Decision Process (MDP). By acknowledging that traditional fairness notions and long-term fairness are distinct requirements that may not necessarily align with one another, we propose an algorithmic framework to integrate various fairness considerations with reinforcement learning using both pre-processing and in-processing approaches. Three case studies show that our method can strike a balance between traditional fairness notions, long-term fairness, and utility

Capture and sorting of multiple cells by polarization-controlled three-beam interference

For the capture and sorting of multiple cells, a sensitive and highly efficient polarization-controlled three-beam interference set-up has been developed. With the theory of superposition of three beams, simulations on the influence of polarization angle upon the intensity distribution and the laser gradient force change with different polarization angles have been carried out. By controlling the polarization angle of the beams, various intensity distributions and different sizes of dots are obtained. We have experimentally observed multiple optical tweezers and the sorting of cells with different polarization angles, which are in accordance with the theoretical analysis. The experimental results have shown that the polarization angle affects the shapes and feature sizes of the interference patterns and the trapping force

Million-scale Object Detection with Large Vision Model

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such a system would have the potential to solve a wide range of vision tasks simultaneously, without being restricted to a specific problem or data domain. This is crucial for practical, real-world computer vision applications. In this study, we focus on the million-scale multi-domain universal object detection problem, which presents several challenges, including cross-dataset category label duplication, label conflicts, and the need to handle hierarchical taxonomies. Furthermore, there is an ongoing challenge in the field to find a resource-efficient way to leverage large pre-trained vision models for million-scale cross-dataset object detection. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training using a pre-trained large model. Our method was ranked second in the object detection track of the Robust Vision Challenge 2022 (RVC 2022). We hope that our detailed study will serve as a useful reference and alternative approach for similar problems in the computer vision community. The code is available at https://github.com/linfeng93/Large-UniDet.Comment: This paper is revised by ChatGP

Microscopie électronique ultrarapide des états transitoires dans les nanomatériaux

Le projet de recherche de cette thèse est focalisé sur l‘application de la microscopie électronique à transmission ultrarapide (UTEM) à l'étude des transitions de phase dans différents nanomatériaux. Des impulsions laser ultracourtes sont utilisées pour déclencher des transitions électroniques et/ou structurelles, dont l'évolution temporelle est étudiée grâce à des impulsions électroniques ultracourtes. Grâce à cette méthode « pompe-sonde », le comportement de nanoparticules individuelles peut ainsi être étudié avec des résolutions spatiale et temporelle élevées pour évaluer le potentiel de ces matériaux en tant que photo-commutateurs rapides à l'échelle nanométrique. Dans la première partie de la thèse, la dynamique d’élongation associée au changement de spin de nanoparticules à commutation de spin (dits « spin cross-over », SCO), déclenchée par impulsion laser, est étudiée avec une résolution nanoseconde. Il est démontré que la présence dans ces SCO de nanobâtonnets d'or plasmoniquement actifs permet d’augmenter l'efficacité et la vitesse de commutation de ces nanodispositifs. Dans la deuxième partie, il est question de transformations de phase induites par des impulsions laser dans des nanocristaux de Ti3O5. L’existence d’une bistabilité entre deux phases à température ambiante a permis une commutation réversible par impulsions laser.The research projects of this thesis are based on the applications of ultrafast transmission electron microscopy (UTEM) to the study of phase transitions in different nanomaterials. Ultrashort laser pulses are used for inducing electronic and structural transitions whose temporal evolution is studied with ultrashort electron pulses. The behaviour of individual nanoparticles is studied with high spatial and high temporal resolution to evaluate the potential of these materials as fast photoswitches at the nanoscale. In the first part of the thesis, reversible length changes of spin-crossover (SCO) nanoparticles under laser pulses are studied with nanosecond resolution. Plasmonically active gold nanorods that are embedded into the SCO particles are found to increase the efficiency and to speed up the expansion process of SCO under laser pulses. In the second part, phase transformations in Ti3O5 nanocrystals were induced by laser pulses. A bistability between two phases at room temperature allowed reversible switching between the phases with laser pulses

Towards Long-term Fairness in Sequential Decision Making

With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has garnered considerable attention, emerging as a crucial research area. To tackle this challenge, association-based fairness notions are proposed based on two legal doctrines of disparate treatment and disparate impact. Subsequently, several causality-based fairness notions are introduced to provide a more comprehensive understanding of how sensitive attributes influence decisions. Moreover, researchers have devised a range of pre-process, in-process, and post-process fairness algorithms to adhere to the above fairness metrics. However, much of the literature on fair machine learning focuses on static or one-shot scenarios, whereas real-world automated decision systems often make sequential decisions within dynamic environments. Consequently, current fairness algorithms cannot be directly applied to dynamic settings to achieve long-term fairness. In this dissertation, we investigate how to achieve long-term fairness in sequential decision making by addressing the issue of distribution shift, defining appropriate long-term fairness notion, and designing different fairness algorithms. Leveraging Pearl’s structural causal model, we view the deployment of each model as a soft intervention, enabling us to infer the post-intervention distribution and approximate the actual data distribution, thereby mitigating the problem of distribution shift. Additionally, we propose to measure indirect causal effects in time-lagged causal graphs as the causality-based long-term fairness. By integrating the aforementioned techniques, we introduce an algorithm that can concurrently learn multiple fairness models from a static dataset containing multi-step data. Furthermore, we convert traditional optimization into performative risk optimization, facilitating the training of a single model to achieve long-term fairness. Then, we design a three-phase deep generative framework where a single decision model is trained using high-fidelity generated time series data, significantly enhancing the performance of the decision model. Finally, we extend our focus to Markov decision processes, formulating a novel reinforcement learning algorithm that can effectively achieve both long-term and short-term fairness simultaneously