Multimedia, including audio, image, and video, etc., is a ubiquitous part of modern life. Quality evaluation, both objective and subjective, is of fundamental importance for various multimedia ...applications. In this letter, a novel quality-aware feature is proposed for blind/no-reference (NR) image quality assessment (IQA). The new quality-aware feature is generated from the proposed joint generalized local binary pattern (GLBP) statistics. In this method, using the Laplacian of Gaussian (LOG) filters, the images are first decomposed into multi-scale subband images. Then, the subband images are encoded with the proposed GLBP operator and the quality-aware features are formed from the joint GLBP histograms from the encoding maps of each subband image. Finally, using support vector regression (SVR), the quality-aware features are mapped to the image's subjective quality score for NR-IQA. The experimental results for two representative databases show that the proposed method is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art NR-IQA methods.
Abstract
Currently, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported in almost all countries globally. No effective ...therapy has been documented for COVID-19, and the role of convalescent plasma therapy is unknown. In the current study, 6 patients with COVID-19 and respiratory failure received convalescent plasma a median of 21.5 days after viral shedding was first detected, all tested negative for SARS-CoV-2 RNA within 3 days after infusion, and 5 eventually died. In conclusion, convalescent plasma treatment can end SARS-CoV-2 shedding but cannot reduce the mortality rate in critically ill patients with end-stage COVID-19, and treatment should be initiated earlier.
Six patients with coronavirus 2019 disease and respiratory failure received convalescent plasma a median of 21.5 days after first detected viral shedding, all tested negative for severe acute respiratory syndrome coronavirus 2 within 3 days after infusion, and 5 eventually died.
Lift: Multi-Label Learning with Label-Specific Features Zhang, Min-Ling; Wu, Lei
IEEE transactions on pattern analysis and machine intelligence,
2015-Jan.-1, 2015-Jan, 2015-1-1, 20150101, Volume:
37, Issue:
1
Journal Article
Peer reviewed
Open access
Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from ...multi-label data by manipulating with identical feature set, i.e. the very instance representation of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. In this paper, another strategy to learn from multi-label data is studied, where label-specific features are exploited to benefit the discrimination of different class labels. Accordingly, an intuitive yet effective algorithm named LIFT, i.e. multi-label learning with Label specific Features, is proposed. LIFT firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Comprehensive experiments on a total of 17 benchmark data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms as well as the effectiveness of label-specific features.
A Review on Multi-Label Learning Algorithms Zhang, Min-Ling; Zhou, Zhi-Hua
IEEE transactions on knowledge and data engineering,
08/2014, Volume:
26, Issue:
8
Journal Article
Peer reviewed
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of ...progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.
Automatic chatbots (also known as chat-agents) have attracted much attention from both researching and industrial fields. Generally, the semantic relevance between users' queries and the ...corresponding responses is considered as the essential element for conversation modeling in both generation and ranking based chat systems. By contrast, it is a nontrivial task to adopt the users' information, such as preference, social role, etc., into conversational models reasonably, while users' profiles play a significant role in the procedure of conversations by providing the implicit contexts. This paper aims to address the personalized response ranking task by incorporating user profiles into the conversation model. In our approach, users' personalized representations are latently learned from the contents posted by them via a two-branch neural network. After that, a deep neural network architecture is further presented to learn the fusion representation of posts, responses, and personal information. In this way, the proposed model could understand conversations from the users' perspective; hence, the more appropriate responses are selected for a specified person. The experimental results on two datasets from social network services demonstrate that our approach is hopeful to represent users' personal information implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each user.
Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of ...these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed.
Achieving efficient dissolution of carbon monoxide (CO) in the solvent is very helpful for the implementation of carbonylation reaction at ambient pressure. However, almost all of common solvents ...show very low solubilities of CO at high temperature. Herein, a series of cuprous‐based ternary deep eutectic solvent (DES) was prepared by mixing imidazolium hydrochloride with CuCl and ZnCl2. The ternary DES BimHCl‐CuCl‐1.0ZnCl2 exhibited very large CO absorption capacity (0.075 mol mol−1, 1 bar) even at a high temperature (353.2 K), which is superior to all of the reported absorbents. Moreover, the ternary DES BimHCl‐CuCl‐1.0ZnCl2 could further promote the reactive absorption of CO to conduct the aminocarbonylation reaction effortlessly at ambient pressure, and thus the targeted products benzamides were obtained in 70–97 yields. We believe that this finding opens a new way to design advanced solvents for efficient capture of CO at high temperature.
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