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campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.

Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Metabolism inhibitor Public health implications include the need for effective surveillance and flexible campus operations.

The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject.

This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa.

A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network).

Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic.

This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. link2 Such knowledge could be critical in informing relevant public health and media engagement policies.

This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies.In recent years, multiobjective evolutionary algorithms (MOEAs) have been demonstrated to show promising performance in feature selection (FS) tasks. However, designing an MOEA for high-dimensional FS is more challenging due to the curse of dimensionality. To address this problem, in this article, a steering-matrix-based multiobjective evolutionary algorithm, called SM-MOEA, is proposed. In SM-MOEA, a steering matrix is suggested and harnessed to guide the evolution of the population, which not only improves the search efficiency greatly but also obtains the feature subsets with high quality. Specifically, each element SM(i, j) in the steering matrix SM reflects the probability of the jth feature that is selected in the ith individual (feature subset), which is generated by considering the importance of both the feature j and the individual i. Based on the suggested steering matrix, two important operators referred to as dimensionality reduction and individual repairing operators are developed to effectively steer the population evolution in each generation. In addition, an effective initialization and update strategy for the steering matrix is also designed to further improve the performance of SM-MOEA. The experimental results on 12 high-dimensional datasets with the number of features ranging from 3000 to 13,000 demonstrate the superiority of the proposed algorithm over several state-of-the-art algorithms (including single-objective and MOEAs for high-dimensional FS) in terms of both the number and quality of the selected features.This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the H∞, L₂-L∞, and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T-S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of ``relation reconstruction. We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.Learning to adapt to a series of different goals in visual navigation is challenging. In this work, we present a model-embedded actor-critic architecture for the multigoal visual navigation task. To enhance the task cooperation in multigoal learning, we introduce two new designs to the reinforcement learning scheme inverse dynamics model (InvDM) and multigoal colearning (MgCl). Specifically, InvDM is proposed to capture the navigation-relevant association between state and goal and provide additional training signals to relieve the sparse reward issue. MgCl aims at improving the sample efficiency and supports the agent to learn from unintentional positive experiences. Besides, to further improve the scene generalization capability of the agent, we present an enhanced navigation model that consists of two self-supervised auxiliary task modules. The first module, which is named path closed-loop detection, helps to understand whether the state has been experienced. The second one, namely the state-target matching module, tries to figure out the difference between state and goal. link3 Extensive results on the interactive platform AI2-THOR demonstrate that the agent trained with the proposed method converges faster than state-of-the-art methods while owning good generalization capability. The video demonstration is available at https//vsislab.github.io/mgvn.Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requirements of many emerging applications. However, in real-world applications, the scarcity of labeled data can negatively affect the performance of the semisupervised method. In this article, we present a new framework for semisupervised learning called joint label inference and discriminant embedding for soft label inference and linear feature extraction. The proposed criterion and its associated optimization algorithm take advantage of both labeled and unlabeled data samples in order to estimate the discriminant transformation. This type of criterion should allow learning more discriminant semisupervised models. Nine public image data sets are used in the experiments and method comparisons. These experimental results show that the performance of the proposed method is superior to that of many advanced semisupervised graph-based algorithms.Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic QN (SQN) methods have been developed to achieve a better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic QN method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of O(n+n1/2ε⁻²) in the finite-sum setting to obtain an ε-first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of O(ε⁻³) is developed, which also matches the existing best result. Extensive experiments on benchmark data sets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization.In the field of information visualization, the concept of tasks is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this paper, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization papers and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations .