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Polyvinyl alcohol (PVA), polyvinyl butyral (PVB) and poly-N-vinylpyrrolidone (PNVP) were used to trap the alcohols and acids during a kiwifruit ripening period. This research proved that discrimination of differences is feasible from an unripe stage to a ripe stage and from a ripe stage to an over-ripe stage.Facial expression recognition (FER) in the wild received broad concerns in which occlusion and pose variation are two key issues. This paper proposed a global multi-scale and local attention network (MA-Net) for FER in the wild. Specifically, the proposed network consists of three main components a feature pre-extractor, a multi-scale module, and a local attention module. The feature pre-extractor is utilized to pre-extract middle-level features, the multi-scale module to fuse features with different receptive fields, which reduces the susceptibility of deeper convolution towards occlusion and variant pose, while the local attention module can guide the network to focus on local salient features, which releases the interference of occlusion and non-frontal pose problems on FER in the wild. Extensive experiments demonstrate that the proposed MA-Net achieves the state-of-the-art results on several in-the-wild FER benchmarks CAER-S, AffectNet-7, AffectNet-8, RAFDB, and SFEW with accuracies of 88.42%, 64.53%, 60.29%, 88.40%, and 59.40% respectively. The codes and training logs are publicly available at https//github.com/zengqunzhao/MA-Net.Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and target domain data to perform domain alignment, which greatly limits their applications when source domain data are restricted due to privacy concerns. In this paper we address the challenging hypothesis transfer setting for UODA, where data from source domain are no longer available during adaptation on target domain. Specifically, we propose to use pseudo-labels and a novel consistency regularization on target data, where using conventional formulations fails in this open-set setting. Firstly, our method discovers confident predictions on target domain and performs classification with pseudo-labels. Then we enforce the model to output consistent and definite predictions on semantically similar transformed inputs, discovering all latent class semantics. As a result, unlabeled data can be classified into discriminative classes coincided with either source classes or unknown classes. We theoretically prove that under perfect semantic transformation, the proposed objective that enforces consistency can recover the information of true labels in prediction. Experimental results show that our model outperforms state-of-the-art methods on UODA benchmarks.Traditional operations, e.g. graph edit distance (GED), are no longer suitable for processing the massive quantities of graph-structured data now available, due to their irregular structures and high computational complexities. With the advent of graph neural networks (GNNs), the problems of graph representation and graph similarity search have drawn particular attention in the field of computer vision. However, GNNs have been less studied for efficient and fast retrieval after graph representation. To represent graph-based data, and maintain fast retrieval while doing so, we introduce an efficient hash model with graph neural networks (HGNN) for a newly designed task (i.e. fast graph-based data retrieval). Due to its flexibility, HGNN can be implemented in both an unsupervised and supervised manner. Specifically, by adopting a graph neural network and hash learning algorithms, HGNN can effectively learn a similarity-preserving graph representation and compute pair-wise similarity or provide classification via low-dimensional compact hash codes. To the best of our knowledge, our model is the first to address graph hashing representation in the Hamming space. Our experimental results reach comparable prediction accuracy to full-precision methods and can even outperform traditional models in some cases. In real-world applications, using hash codes can greatly benefit systems with smaller memory capacities and accelerate the retrieval speed of graph-structured data. Hence, we believe the proposed HGNN has great potential in further research.The task of human interaction understanding involves both recognizing the action of each individual in the scene and decoding the interaction relationship among people, which is useful to a series of vision applications such as camera surveillance, video-based sports analysis and event retrieval. This paper divides the task into two problems including grouping people into clusters and assigning labels to each of them, and presents an approach to solving these problems in a joint manner. Our method does not assume the number of groups is known beforehand as this will substantially restrict its application. With the observation that the two challenges are highly correlated, the key idea is to model the pairwise interacting relations among people via a complete graph and its associated energy function such that the labeling and grouping problems are translated into the minimization of the energy function. We implement this joint framework by fusing both deep features and rich contextual cues, and learn the fusion parameters from data. An alternating search algorithm is developed in order to efficiently solve the associated inference problem. By combining the grouping and labeling results obtained with our method, we are able to achieve the semantic-level understanding of human interactions. Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our approach, which outperforms state-of-the-art methods on several important benchmarks. An ablation study is also performed to verify the effectiveness of different modules within our approach.Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This paper introduces the largest known international database of ultrasound channel data and describes associated evaluation methods that were initially developed for the Challenge on Ultrasound Beamforming with Deep Learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared to a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural network-based global sound speed estimator implementation that was necessary to fairly evaluate results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https//cubdl.jhu.edu for details).The responsivity of an ultrasonic transducer is an important parameter in evaluating its effective frequency band, the electroacoustic conversion efficiency, and the measurement capability of the system. The determination of the responsivity of a traditional immersion or contact piezoelectric transducer has been well investigated. https://www.selleckchem.com/products/liproxstatin-1.html However, due to the high attenuation of waves propagating in air and the large acoustic impedance mismatch between the active piezoceramic material and the load medium, there are few reports of the calibration of an air-coupled piezoelectric transducer. In this work, we present a comparative method of measuring the responsivity of an air-coupled transducer the air-coupled transducer is used to receive a broadband pulse signal to evaluate its frequency spectrum, and a toneburst signal with known vibration displacement is measured by the air-coupled transducer in order to calibrate the amplitude of the responsivity. link2 The effects of transmitter responsivity, input pulse characteristics, attenuation and diffraction are taken into account to improve the accuracy of the responsivity determination. In addition, measurement of the amplitude of the responsivity by comparing the measured displacements avoids the complicated task of characterizing the effects of electrical equipment. The determined responsivity is checked by comparing the measured displacements using different methods at different frequencies in order to evaluate its frequency spectrum and by measuring the nonlinearity parameters of the material to evaluate its amplitude. link3 The agreement between results obtained using different methods demonstrates that the calibrated responsivity of the air-coupled transducer is valid, and that the proposed method is effective.Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n=317) and EMCAP (n=22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, which adversely degrades the scan's interpretability. In recent times, the deep learning-based technique has emerged as a promising method for low dose CT(LDCT) denoising. However, some common bottleneck still exists, which hinders deep learning-based techniques from furnishing the best performance. In this study, we attempted to mitigate these problems with three novel accretions. First, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Our proposed module assisted in boosting the denoising by a significant margin. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising. The loss mentioned above also assisted to alleviate the laborious effort required while training CT denoising network using image patches.