Multi-attribute Group Decision Making of Internet Public Opinion Emergency with Interval Intuitionistic Fuzzy Number

In this paper, an emergency group decision method is presented to cope with internet public opinion emergency with interval intuitionistic fuzzy linguistic values. First, we adjust the initial weight of each emergency expert by the deviation degree between each expert\u27s decision matrix and group average decision matrix with interval intuitionistic fuzzy numbers. Then we can compute the weighted collective decision matrix of all the emergencies based on the optimal weight of emergency expert. By utilizing the interval intuitionistic fuzzy weighted arithmetic average operator one can obtain the comprehensive alarm value of each internet public opinion emergency. According to the ranking of score value and accuracy value of each emergency, the most critical internet public emergency can be easily determined to facilitate government taking related emergency operations. Finally, a numerical example is given to illustrate the effectiveness of the proposed emergency group decision method

MeshNet: Mesh Neural Network for 3D Shape Representation

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation

Simulation on thermal load gradient mitigation with auxiliary multi-seeds amplification in fiber amplifier

A technique that employs auxiliary multi-seeds for mitigating the inhomogeneous of thermal load in Yb-doped double cladding amplifier is presented and verified in simulation. The results shows this technique can reduce the thermal load gradient by a significant ratio, thus has potential application in high power amplifiers

Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is our implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings.Comment: 14 page

Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT

The classification of benign versus malignant lung nodules using chest CT plays a pivotal role in the early detection of lung cancer and this early detection has the best chance of cure. Although deep learning is now the most successful solution for image classification problems, it requires a myriad number of training data, which are not usually readily available for most routine medical imaging applications. In this paper, we propose the transferable multi-model ensemble (TMME) algorithm to separate malignant from benign lung nodules using limited chest CT data. This algorithm transfers the image representation abilities of three ResNet-50 models, which were pre-trained on the ImageNet database, to characterize the overall appearance, heterogeneity of voxel values and heterogeneity of shape of lung nodules, respectively, and jointly utilizes them to classify lung nodules with an adaptive weighting scheme learned during the error back propagation. Experimental results on the benchmark LIDC-IDRI dataset show that our proposed TMME algorithm achieves a lung nodule classification accuracy of 93.40%, which is markedly higher than the accuracy of seven state-of-the-art approaches

More than Classification: A Unified Framework for Event Temporal Relation Extraction

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation \textit{Includes} could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3\% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at \url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training

This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguistic modalities, we categorize data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text. Following this protocol, we collect, from public international news, a large-scale multimodal knowledge graph dataset that consists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817 triplets. The dataset is also annotated with rich event tags, including 96 coarse labels and 9,185 fine labels, expanding its potential usage. To further verify that UKnow can serve as a standard protocol, we set up an efficient pipeline to help reorganize existing datasets under UKnow format. Finally, we benchmark the performance of some widely-used baselines on the tasks of common-sense reasoning and vision-language pre-training. Results on both our new dataset and the reformatted public datasets demonstrate the effectiveness of UKnow in knowledge organization and method evaluation. Code, dataset, conversion tool, and baseline models will be made public