Measuring Regional Sustainability by a Coordinated Development Model of Economy, Society, and Environment: A Case Study of Hubei Province

AbstractRegional sustainability concerns a complex system that mainly consists of three subsystems, being the economy, society, and the environment. A complex system involves intensive interactions and correlations among its components. Therefore, the way how these components are organized to work together efficiently is of great significance to the development of a complex system. For that reason, measuring regional sustainability should not only focus on changes in each subsystem individually, but also consider the interactions and relationships among the subsystems. In this paper, we apply a modified method to assess coordinated development, which highlights the simultaneous promotions of economic grow, social well-being, and environment al conservation. By introducing the model of coordinated development, we evaluate the sustainable development of Hubei province which is a typical region in Central China. The result shows that Hubei performed poorly in coordinated development. Although the coordinated development index was consistently increasing, the speed was very slow. In a detailed analysis of the economic, societal, and environmental subsystems in Hubei, the shortage of an economic driving force was found the main cause of the poor development of Hubei Province

Land Use Spatial Optimization Using Accessibility Maps to Integrate Land Use and Transport in Urban Areas

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.</p

Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone

Knowledge of dispersion and attenuation is essential for better reservoir characterization and hydrocarbon identification. However, limited by reliable laboratory data at seismic frequency bands, the roles of rock and fluid properties in inducing dispersion and attenuation are still poorly understood. Here we perform a series of laboratory measurements on Bentheimer and Bandera sandstone under both vacuum-dry and partially water-saturated conditions at frequencies ranging from 2 to 600 Hz. At vacuum-dry conditions, the bulk dispersion and attenuation in Bandera sandstone with more clay contents are distinctly larger than those in Bentheimer sandstone, suggesting clay contents might contribute to the inelasticity of the rock frame. The partially water-saturated results show the combined effects of rock permeability and fluid saturation on bulk dispersion and attenuation. Even a few percent of gas can substantially dominate the pore-fluid relaxation by providing a quick and short communication path for pore pressure gradients. The consequent bulk dispersion and attenuation are negligible. Only as the samples are approaching fully water-saturated conditions, rock permeability begins to play an essential role in the pore-fluid relaxation. For Bandera sandstone with lower permeability, a partially relaxed status of pore fluids is achieved when the gas saturation is lower than 5%, accompanied by significant attenuation and dispersion.Cited as: Wei, Q., Wang, Y., Han, D., Sun, M., Huang, Q. Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone. Advances in Geo-Energy Research, 2021, 5(2): 181-190, doi: 10.46690/ager.2021.02.0

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist forgetting, while recent works find that storing models from history can substantially boost the performance. However, the stored models are not counted into the memory budget, which implicitly results in unfair comparisons. We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets. As a result, we need to holistically evaluate different CIL methods at different memory scales and simultaneously consider accuracy and memory size for measurement. On the other hand, we dive deeply into the construction of the memory buffer for memory efficiency. By analyzing the effect of different layers in the network, we find that shallow and deep layers have different characteristics in CIL. Motivated by this, we propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends specialized layers based on the shared generalized representations, efficiently extracting diverse representations with modest cost and maintaining representative exemplars. Extensive experiments on benchmark datasets validate MEMO's competitive performance. Code is available at: https://github.com/wangkiw/ICLR23-MEMOComment: Accepted to ICLR 2023 as a Spotlight Presentation. Code is available at: https://github.com/wangkiw/ICLR23-MEM

Unlocking the Transferability of Tokens in Deep Models for Tabular Data

Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks. However, such a paradigm becomes particularly challenging with tabular data when there are discrepancies between the feature sets of pre-trained models and the target tasks. In this paper, we propose TabToken, a method aims at enhancing the quality of feature tokens (i.e., embeddings of tabular features). TabToken allows for the utilization of pre-trained models when the upstream and downstream tasks share overlapping features, facilitating model fine-tuning even with limited training examples. Specifically, we introduce a contrastive objective that regularizes the tokens, capturing the semantics within and across features. During the pre-training stage, the tokens are learned jointly with top-layer deep models such as transformer. In the downstream task, tokens of the shared features are kept fixed while TabToken efficiently fine-tunes the remaining parts of the model. TabToken not only enables knowledge transfer from a pre-trained model to tasks with heterogeneous features, but also enhances the discriminative ability of deep tabular models in standard classification and regression tasks

ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse

The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning. Leveraging pre-trained models to extract knowledge and expedite learning in real-world tasks, known as "Model Reuse", has become crucial in various applications. Previous research focuses on reusing models within a certain aspect, including reusing model weights, structures, and hypothesis spaces. This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference. This empowers deep learning practitioners to explore downstream tasks and identify the complementary advantages among different methods. ZhiJian is readily accessible at https://github.com/zhangyikaii/lamda-zhijian facilitating seamless utilization of pre-trained models and streamlining the model reuse process for researchers and developers

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after a user click. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when a conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.Comment: This paper has been accepted by AAAI 202

Calibration of topological development in the procedure of parametric identification: application to the stochastic GreenLab model for Pinus Sylvestris var. Mongolica

International audienceArid climate, biophysical conditions and human activities all contribute to the occurrences of ecosystem and environment problems, i.e. water scarcity, desertification, salinization, in arid and semiarid zone of North China. Mongolian Scots pine tree (Pinus sylvestris var. mongolica) is one of the principal species of the windbreak and sand-fixing forest in this area. In this paper, we present the calibration process of stochastic GreenLab model based on experiment data. Specific plant topology and sink–source parameters were estimated for Mongolian Scots pine trees through optimizing procedure. The fitting results showed that the calibration was reasonable and acceptable. The model produces several three-dimensional visual representations of Mongolian Scots pine trees with different topological structures simulated by Monte Carlo methods. This model can be used to describe the plant development and growth in a stand level, taking into accounts the variations in plant topology and biomass

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems