Microstructure evolution of reconstituted clays subject to compression and shearing

Clay is a very important material widely used in geotechnical engineering. The mechanical behaviours and engineering properties of clay, such as compressibility and shear strength, are largely controlled by the microstructure of clay. In recent years, many studies have been conducted to investigate the microstructure characteristics and micro-behaviour of different clays by means of a variety of advanced techniques. However, the complexity of the whole series of phenomena occurring at the micro-scale, when the clay is subjected to macroscopic mechanics, makes the prediction of the clay macro-behaviour through the modelling of micro-behaviour a major challenge. Most of the existing studies only focus on the microstructure characteristics of clays, and do not link these micro-features with the macroscopic mechanical behaviour of clay. The present research work systematically studies the intrinsic microstructure characteristics of three different reconstituted clays and the main physical processes underlying the mechanical response when they are subjected to compression and shearing, through experimental laboratory testing in combination with detailed microstructure analysis of clay specimens undergoing mechanical testing. With the aim to assess the microstructure evolution and the corresponding micro-scale processes while the clay exhibits the given macro-mechanical behaviour, this thesis reveals the effect of clay microstructure on its macro-mechanical behaviour and establishes the constitutive relationship between clay microstructure and macro-mechanical properties. The correspondence between the microstructure characteristics of three different types of clays and their macro-mechanical behaviour presented in this thesis can prompt a wider use of the constitutive models in practice, since it would support the geotechnical engineer in the selection of the model and the parameter values most appropriate for the clay involved in the design of interest

Happiness Maximizing Sets under Group Fairness Constraints (Technical Report)

Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and admission, it is crucial to ensure that the HMS can fairly represent different groups of candidates without bias and discrimination. However, although the HMS problem was extensively studied in the database community, existing algorithms do not take group fairness into account and may provide solutions that under-represent some groups. In this paper, we propose and investigate a fair variant of HMS (FairHMS) that not only maximizes the minimum happiness ratio but also guarantees that the number of tuples chosen from each group falls within predefined lower and upper bounds. Similar to the vanilla HMS problem, we show that FairHMS is NP-hard in three and higher dimensions. Therefore, we first propose an exact interval cover-based algorithm called IntCov for FairHMS on two-dimensional databases. Then, we propose a bicriteria approximation algorithm called BiGreedy for FairHMS on multi-dimensional databases by transforming it into a submodular maximization problem under a matroid constraint. We also design an adaptive sampling strategy to improve the practical efficiency of BiGreedy. Extensive experiments on real-world and synthetic datasets confirm the efficacy and efficiency of our proposal.Comment: Technical report, a shorter version to appear in PVLDB 16(2

Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use additional global state information to guide training in a centralized way and make their own decisions only based on decentralized local policies. Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training. Therefore, we argue that existing CTDE methods cannot fully utilize global information for training, leading to an inefficient joint-policy exploration and even suboptimal results. In this paper, we introduce a novel Centralized Advising and Decentralized Pruning (CADP) framework for multi-agent reinforcement learning, that not only enables an efficacious message exchange among agents during training but also guarantees the independent policies for execution. Firstly, CADP endows agents the explicit communication channel to seek and take advices from different agents for more centralized training. To further ensure the decentralized execution, we propose a smooth model pruning mechanism to progressively constraint the agent communication into a closed one without degradation in agent cooperation capability. Empirical evaluations on StarCraft II micromanagement and Google Research Football benchmarks demonstrate that the proposed framework achieves superior performance compared with the state-of-the-art counterparts. Our code will be made publicly available

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Improved Algorithm for Regret Ratio Minimization in Multi-Objective Submodular Maximization

Submodular maximization has attracted extensive attention due to its numerous applications in machine learning and artificial intelligence. Many real-world problems require maximizing multiple submodular objective functions at the same time. In such cases, a common approach is to select a representative subset of Pareto optimal solutions with different trade-offs among multiple objectives. To this end, in this paper, we investigate the regret ratio minimization (RRM) problem in multi-objective submodular maximization, which aims to find at most k solutions to best approximate all Pareto optimal solutions w.r.t. any linear combination of objective functions. We propose a novel HS-RRM algorithm by transforming RRM into HittingSet problems based on the notions of ε-kernel and δ-net, where any α-approximation algorithm for single-objective submodular maximization is used as an oracle. We improve upon the previous best-known bound on the maximum regret ratio (MRR) of the output of HS-RRM and show that the new bound is nearly asymptotically optimal for any fixed number d of objective functions. Experiments on real-world and synthetic data confirm that HS-RRM achieves lower MRRs than existing algorithms

Risk Assessment of Fracturing Induced Earthquake in the Qiabuqia Geothermal Field, China

In order to reduce the harm of induced earthquakes in the process of geothermal energy development, it is necessary to analyze and evaluate the induced earthquake risk of a geothermal site in advance. Based on the tectonic evolution and seismogenic history around the Qiabuqia geothermal field, the focal mechanism of the earthquake was determined, and then the magnitude and direction of in-situ stress were inversed with the survey data. At the depth of more than 5 km, the maximum principal stress is distributed along NE 37°, and the maximum principal stress reaches 82 MPa at the depth of 3500 m. The induced earthquakes are evaluated by using artificial neural network (ANN) combined with in-situ stress, focal mechanism, and tectonic conditions. The predicted earthquake maximum magnitude is close to magnitude 3