Green building materials constitute a core pillar for decarbonization in the global construction industry. Nevertheless, rampant greenwashing by building materials enterprises (BMEs) and illegal rent-seeking by third-party certification agencies (TCAs) throughout the certification process severely hinder the healthy development of the green building sector. This research develops new targeted regulatory frameworks to curb greenwashing and rent-seeking in green building material certification (GBMC) while elucidating the strategic interactions and evolutionary dynamics among stakeholders. Specifically, this research overcomes the limitations of traditional models, constructing a tripartite evolutionary game model involving BMEs, TCAs, and the government, which complemented by numerical simulations in MATLAB 2023b to evaluate the influence of critical game parameters on evolutionary trajectories. The findings identify three evolutionarily stable strategies (ESS), each corresponding to a distinct developmental stage of GBMC. The most desirable self-discipline-oriented “Benign cycle stage” ESS is achieved when BMEs adopt compliant green production, TCAs conduct impartial certifications, and the government maintains lenient regulation. Furthermore, the initial strategic inclinations of the tripartite players substantially dictate their respective evolutionary paths. Sensitivity analysis reveals that BME financial parameters, alongside government incentives and punitive measures, exhibit high-frequency sensitivity. Enhancing the spillover benefits of green compliance and lowering the costs of technological innovation, while increasing government intervention to raise the costs of greenwashing and rent-seeking, can significantly speed up the system’s progress toward the ideal stable state. This research broadens the application of evolutionary game theory to GBMC regulation, offering both theoretical foundations and practical policy insights for regulatory environment.

Approximate nearest neighbor (ANN) search in the high-dimensional Euclidean space is a pivotal problem for various data science and AI applications. The performance of traditional in-memory ANN methods decreases dramatically when handling large-scale data due to their substantial expansion of main memory. Recently, disk-resident ANN methods have drawn considerable attention to enable large-scale ANN search. However, despite their significant improvements, they still suffer from high query latency due to heavy I/O cost. In this paper, we propose ANN-CACHE, an I/O-efficient cache-conscious ANN framework that leverages the skew in query workloads to reduce I/O cost. To the best of our knowledge, we are the first to formally study exploring caching to accelerate disk-based ANN search. ANN-CACHE consists of two components: (i) a discriminator that checks whether a query's ANN points are cached. If so, an in-memory lookup in the cache is triggered; otherwise, a lookup in the disk-resident index is conducted; and (ii) an LSH-based cache that stores historical queries and their results to support efficient ANN search. Considering a query q, if the cache contains historical queries that are similar to q and their ANNs, we can obtain q's result from these queries' results without accessing the disk-resident index. Experimental results demonstrate that our framework achieves 3×-9× speedup while maintaining consistently higher accuracy compared to the baselines, by introducing less than 50 MB of additional memory overhead on datasets of 10M size.

The problem of similarity search in high-dimensional space is a fundamental problem with numerous applications in computer science, yet it remains challenging due to the curse of dimensionality. This paper introduces DForest, a novel indexing approach designed to address this challenge for both range and kNN queries on high-dimensional data. Unlike previous similarity search approaches that apply a fixed dimensionality reduction to all objects uniformly, our approach determines the minimal dimensionality required for each object within a specified loss threshold and then reduces the dimensionality for each object individually. Furthermore, the query performance is also optimized by deriving the upper and lower bounds of retrieved blocks and computing distances in a low-embedding space preferentially. Theoretical analysis is provided to support our search strategy. Extensive experiments on a variety of datasets verify the superiority of DForest over the state-of-the-art methods.

Abstract The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest. The existing dual‐stream models can be used for high‐level feature fusion or unidirectionally transferring depth features to RGB features; however, they are unable to fully exploit the differences in modality. Furthermore, owing to the influence of image background information, the generated salient object is affected by background swallow. Herein, a three‐stream RGB‐D SOD method based on cross‐layer and cross‐modal dual‐attention (CMDA) fusion is proposed. In the encoding stage, the CMDA fusion module is used to fuse RGB and depth features layer by layer. Through this module, merged interactive features may be used to extract the richer features of salient objects, realize the commonality and complementarity of fusion features, and achieve effective cross‐modal fusion. In addition, for the decoding stage, a cross‐level feature fusion module that introduces global context features into the up‐sampling process, reduces the impact of salient objects being swallowed by the background, and helps to accurately detect salient areas is proposed. Three different branch features are used for simultaneous end‐to‐end training. The experimental results demonstrate that the proposed method outperforms other methods in terms of multiple evaluation metrics on four datasets. Furthermore, the authors visualize the precision–recall curve, F‐measure curve, and saliency map, which indicate that the detection effect of the proposed method is superior to those of other methods. During the testing stage, our model ran at 14 frames per second (FPS).

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts of a test utterance. We introduce a novel model, termed hierarchical memory networks (HMN), to address the issues of recognizing utterance level emotions. HMN divides the contexts into different aspects and employs different step lengths to represent the weights of these aspects. To model the self dependencies, HMN takes independent local memory networks to model these aspects. Further, to capture the interpersonal dependencies, HMN employs global memory networks to integrate the local outputs into global storages. Such storages can generate contextual summaries and help to find the emotional dependent utterance that is most relevant to the test utterance. With an attention-based multi-hops scheme, these storages are then merged with the test utterance using an addition operation in the iterations. Experiments on the IEMOCAP dataset show our model outperforms the compared methods with accuracy improvement.

Low-dose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to over-smoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semantically-richer features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the network's ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

The objective of entity resolution (ER) is to identify records referring to the same real-world entity. Traditional ER approaches identify records based on pairwise similarity comparisons, which assumes that records referring to the same entity are more similar to each other than otherwise. However, this assumption does not always hold in practice and similarity comparisons do not work well when such assumption breaks. We propose a new class of rules which could describe the complex matching conditions between records and entities. Based on this class of rules, we present the rule-based entity resolution problem and develop an on-line approach for ER. In this framework, by applying rules to each record, we identify which entity the record refers to. Additionally, we propose an effective and efficient rule discovery algorithm. We experimentally evaluated our rule-based ER algorithm on real data sets. The experimental results show that both our rule discovery algorithm and rule-based ER algorithm can achieve high performance.

In this paper, we propose a new method to prune the joint 2D digital predistortion (2D-DPD) model for concurrent dual-band transmitter where in-phase and quadrature (I/Q) imbalance and local oscillator (LO) leakage often exist. The phenomenon of over compensation of the DPD model is found if too many terms in the DPD model are adopted. Thus redundant polynomial terms are removed in order to solve this problem, and meanwhile, to reduce the model complexity. Then the proposed model is evaluated on a 10W inverse class-F power amplifier operating at 940 MHz excited by two concurrent signals with center separation of 61.44MHz, in the presence of I/Q modulator imperfections. The experiment compares linearization performance and model complexity of the proposed model with other state-of-the-art models. It is shown to have the advantage of reducing the model complexity and avoiding the over compensation problem effectively, and the proposed method produces good performance on linearization and spectral regrowth suppression.

In this paper, we consider the entity resolution(ER) problem, which is to identify objects referring to the same real-world entity. Prior work of ER involves expensive similarity comparison and clustering approaches. Additionally, the quality of entity resolution may be low due to insufficient information. To address these problems, by adopting context information of data objects, we present a novel framework of entity resolution, context-based entity description (CED), to make context information help entity resolution. In our framework, each entity is described by a set of CEDs. During entity resolution, objects are only compared with CEDs to determine its corresponding entity. Additionally, we propose efficient algorithms for CED discovery and CED-based entity resolution. We experimentally evaluated our CED-based ER algorithm on the real DBLP datasets, and the experimental results show that our algorithm can achieve both high precision and recall as well as outperform existing methods.