A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

No-reference stereoscopic image-quality metric accounting for left and right similarity map and spatial structure degradation

Blind quality assessment of 3D images is used to confront more real challenges than 2D images. In this Letter, we develop a no-reference stereoscopic image quality assessment (SIQA) model based on the proposed left and right (LR)-similarity map and structural degradation. In the proposed method, local binary pattern features are extracted from the cyclopean image that are effective for describing the distortion of 3D images. More importantly, we first propose the LR-similarity map that can indicate the stereopair quality and demonstrate that the use of LR-similarity information results in a consistent improvement in the performance. The massive experimental results on the LIVE 3D and IRCCyN IQA databases demonstrate that the designed model is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art SIQA algorithms

Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints

Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve these goals, we not only need the context information of road geometry and other traffic participants but also need fine-grained information of the human pose, motion and activity, which can be inferred from human keypoints. In this paper, we propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction, which utilizes 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. Moreover, we propose to apply two auxiliary tasks and contrastive learning to enable auxiliary supervisions to improve the learned keypoints representation, which further enhances the performance of major tasks. We validate our approach on a large-scale in-house dataset, as well as a public benchmark dataset, and show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics. The effectiveness of each model component is validated in a detailed ablation study.Comment: ICRA 202

A potential relationship between MMP-9 rs2250889 and ischemic stroke susceptibility

PurposeIschemic stroke (IS), a serious cerebrovascular disease, greatly affects people's health and life. Genetic factors are indispensable for the occurrence of IS. As a biomarker for IS, the MMP-9 gene is widely involved in the pathophysiological process of IS. This study attempts to find out the relationship between MMP-9 polymorphisms and IS susceptibility.MethodsA total of 700 IS patients and 700 healthy controls were recruited. The single nucleotide polymorphism (SNP) markers of the MMP-9 gene were genotyped by the MassARRAY analyzer. Multifactor dimensionality reduction (MDR) was applied to generate SNP–SNP interaction. Furthermore, the relationship between genetic variations (allele and genotype) of the MMP-9 gene and IS susceptibility was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CIs).ResultsOur results demonstrated that rs2250889 could significantly increase the susceptibility to IS in the codominant, dominant, overdominant, and log-additive models (p < 0.05). Further stratification analysis showed that compared with the control group, rs2250889 was associated with IS risk in different case groups (age, female, smoking, and non-drinking) (p < 0.05). Based on MDR analysis, rs2250889 was the best model for predicting IS risk (cross-validation consistency: 10/10, OR = 1.56 (1.26–1.94), p < 0.001).ConclusionOur study preliminarily confirmed that SNP rs2250889 was significantly associated with susceptibility to IS

A Fast Image Retrieval Method Designed for Network Big Data

Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksIn the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

Modular Synthesis of Functionalized Butenolides by Oxidative Furan Fragmentation

The development of new chemical transformations to simplify the synthesis of valuable building blocks is a challenging task in organic chemistry and has been the focus of considerable research effort. From a synthetic perspective, it would be ideal if the natural reactivities of feedstock chemicals could be diverted to the production of high value-added compounds which are otherwise tedious to prepare. Here we report a chemical transformation that enables facile and modular synthesis of synthetically challenging yet biologically important functionalized butenolides from easily accessible furans. Specifically, Diels–Alder reactions between furans and singlet oxygen generate versatile hydroperoxide intermediates, which undergo iron(II)-mediated radical fragmentation in the presence of Cu(OAc)2 or various radical trapping reagents to afford butenolides bearing a wide variety of appended remote functional groups, including olefins, halides, azides and aldehydes. The practical utility of this transformation is demonstrated by easy diversification of the products by means of cross-coupling reactions and, most importantly, by its ability to simplify the syntheses of known building blocks of eight biologically active natural products

Reducing Sentiment Bias in Pre-trained Sentiment Classification via Adaptive Gumbel Attack

Pre-trained language models (PLMs) have recently enabled rapid progress on sentiment classification under the pre-train and fine-tune paradigm, where the fine-tuning phase aims to transfer the factual knowledge learned by PLMs to sentiment classification. However, current fine-tuning methods ignore the risk that PLMs cause the problem of sentiment bias, that is, PLMs tend to inject positive or negative sentiment from the contextual information of certain entities (or aspects) into their word embeddings, leading them to establish spurious correlations with labels. In this paper, we propose an adaptive Gumbel-attacked classifier that immunes sentiment bias from an adversarial-attack perspective. Due to the complexity and diversity of sentiment bias, we construct multiple Gumbel-attack expert networks to generate various noises from mixed Gumbel distribution constrained by mutual information minimization, and design an adaptive training framework to synthesize complex noise by confidence-guided controlling the number of expert networks. Finally, we capture these noises that effectively simulate sentiment bias based on the feedback of the classifier, and then propose a multi-channel parameter updating algorithm to strengthen the classifier to recognize these noises by fusing the parameters between the classifier and each expert network. Experimental results illustrate that our method significantly reduced sentiment bias and improved the performance of sentiment classification