A Hesitant Fuzzy Linguistic TODIM Method Based on a Score Function

The authors are very grateful to the editor and anonymous referees for their insightful and valuable suggestions that have led to an improved version of this paper. The work was partly supported by the National Natural Science Foundation of China (71371107, 71171187), the National Science Foundation of Shandong Province (ZR2013GM011), the Spanish National research project TIN2012-31263, Spanish Ministry of Economy and Finance Postdoctoral Training (FPDI-2013-18193) and ERDF.Hesitant fuzzy linguistic term sets (HFLTSs) are very useful for dealing with the situations in which the decision makers hesitate among several linguistic terms to assess an alternative. Some multi-criteria decision-making (MCDM) methods have been developed to deal with HFLTSs. These methods are derived under the assumption that the decision maker is completely rational and do not consider the decision maker's psychological behavior. But some studies about behavioral experiments have shown that the decision maker is bounded rational in decision processes and the behavior of the decision maker plays an important role in decision analysis. In this paper, we extend the classical TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method to solve MCDM problems dealing with HFLTSs and considering the decision maker's psychological behavior. A novel score function to compare HFLTSs more effectively is defined. This function is also used in the proposed TODIM method. Finally, a decision-making problem that concerns the evaluation and ranking of several telecommunications service providers is used to illustrate the validity and applicability of the proposed method.National Natural Science Foundation of China 71371107 71171187Natural Science Foundation of Shandong Province ZR2013GM011Spanish National research project TIN2012-31263Spanish Ministry of Economy and Finance Postdoctoral Training FPDI-2013-18193European Union (EU

BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work, we propose to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level. Specifically, we propose vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes. We then pretrain vision Transformers by predicting the original visual tokens for the masked image patches. Furthermore, we introduce a patch aggregation strategy which associates discrete image patches to enhance global semantic representation. Experiments on image classification and semantic segmentation show that BEiT v2 outperforms all compared MIM methods. On ImageNet-1K (224 size), the base-size BEiT v2 achieves 85.5% top-1 accuracy for fine-tuning and 80.1% top-1 accuracy for linear probing. The large-size BEiT v2 obtains 87.3% top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and 56.7% mIoU on ADE20K for semantic segmentation. The code and pretrained models are available at https://aka.ms/beitv2

Bacterial Communities Associated With Healthy and Bleached Crustose Coralline Alga Porolithon onkodes

Crustose coralline algae (CCA) play vital roles in producing and stabilizing reef structures and inducing the settlement and metamorphosis of invertebrate larvae in coral reef ecosystems. However, little is known about the bacterial communities associated with healthy and bleached CCA and their interactions with coral larval settlement. We collected samples of healthy, middle semi-bleached, and bleached CCA Porolithon onkodes from Sanya Bay in the South China Sea and investigated their influences on the larval settlement and metamorphosis of the reef-building coral Pocillopora damicornis. The larval settlement/metamorphosis rates all exceeded 70% when exposed to healthy, middle semi-bleached, and bleached algae. Furthermore, the compositions of bacterial community using amplicon pyrosequencing of the V3–V4 region of 16S rRNA were investigated. There were no obvious changes in bacterial community structure among healthy, middle semi-bleached, and bleached algae. Alphaproteobacteria, Bacteroidetes, and Gammaproteobacteria were dominant in all samples, which may contribute to coral larval settlement. However, the relative abundances of several bacterial communities varied among groups. The relative abundances of Mesoflavibacter, Ruegeria, Nautella, and Alteromonas in bleached samples were more than double those in the healthy samples, whereas Fodinicurvata and unclassified Rhodobacteraceae were significantly lower in the bleached samples. Additionally, others at the genus level increased significantly from 8.5% in the healthy samples to 22.93% in the bleached samples, which may be related to algal bleaching. These results revealed that the microbial community structure associated with P. onkodes generally displayed a degree of stability. Furthermore, bleached alga was still able to induce larval settlement and metamorphosis

Enhanced Object Detection with Deep Convolutional Neural Networks for Advanced Driving Assistance

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper, we propose three enhancements for CNN-based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. The experimental results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set

EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization

Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate training to search optimized per-layer bit-width configurations in MQ. Recently, some training-free approaches have presented various MQ proxies and significantly improve search efficiency. However, the correlation between these proxies and quantization accuracy is poorly understood. To address the gap, we first build the MQ-Bench-101, which involves different bit configurations and quantization results. Then, we observe that the existing training-free proxies perform weak correlations on the MQ-Bench-101. To efficiently seek superior proxies, we develop an automatic search of proxies framework for MQ via evolving algorithms. In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy. We proposed a diversity-prompting selection strategy and compatibility screening protocol to avoid premature convergence and improve search efficiency. In this way, our Evolving proxies for Mixed-precision Quantization~(EMQ) framework allows the auto-generation of proxies without heavy tuning and expert knowledge. Extensive experiments on ImageNet with various ResNet and MobileNet families demonstrate that our EMQ obtains superior performance than state-of-the-art mixed-precision methods at a significantly reduced cost. The code will be released.Comment: Accepted by ICCV202