Detection and aerosol treatment of small airway disease in pediatric cystic fibrosis and asthma

Respiratory disease is frequently present in childhood. It can be divided into upper airway disease and lower airway or lung disease. Two important lung diseases in childhood are cystic fibrosis (CF) and asthma. They are important because CF is the most common lethal genetic disease in the Caucasian population and asthma is one of the most frequent chronic diseases of childhood, with a prevalence of approximately 10% of children in the West

Open and free datasets for multimedia retrieval

Computer Systems, Imagery and Medi

Creating A Creative State of Mind: Promoting Creativity Through Proactive Vitality Management and Mindfulness

Most research on employee creativity has been focused on relatively distal antecedents, such as personality or job characteristics, which has resulted in top-down organizational approaches to promote employee creativity. However, such approaches overlook the self-regulating potential of employees and may not explain intraindividual fluctuations in creativity. In the present research, we build on proactive motivation theory to examine how employees may promote their own creativity on a daily basis through the use of proactive vitality management (PVM). To better understand the PVM-creativity link, we zoom in on this process by examining the role of mindfulness as an underlying mechanism. In two daily diary studies, employees from the United States (N = 133 persons, n = 521 data points) and the creative industry in Germany (N = 62 persons, n = 232 data points) reported on their use of PVM and states of mindfulness for five consecutive workdays. Additionally, participants completed a daily creativity test (brainstorming task) in Study 1, whereas supervisors rated participants' daily creative work performance in Study 2. In both studies, multilevel analyses showed that daily PVM was positively related to creative performance through daily mindfulness, supporting our hypotheses. These replicated findings suggest that individuals may bring themselves in a cognitive, creative state of mind on a daily basis, emphasizing the importance of proactive behavior in the creative process

Quality systems in Dutch health care institutions

The implementation of quality systems in Dutch health care was supervised by a national committee during 1990-1995. To monitor the progress of implementation a large survey was conducted in the beginning of 1995. The survey enclosed all subsectors in health care. A postal questionnaire-derived from the European Quality Award-was sent to 1594 health care institutions; the response was 74%. The results showed that in 13% of the institutions a coherent quality system had been implemented. These institutions reported, among other effects, an increase in staff effort and job satisfaction despite the increased workload; 59% of the institutions had implemented parts of a quality system. It appeared that management pay more attention to human resource management compared to documentation of the quality system. The medical staff pay relatively more attention to protocol development than to quality-assurance procedures. Patients were hardly involved in these quality activities. The research has shown that it is possible to monitor the progress of implementation of quality systems on a national level in all subsectors of health care. The results play an important role in the discussions and policy on quality assurance in health care. (aut.ref.

Deep hashing with self-supervised asymmetric semantic excavation and margin-scalable constraint

Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images asso-ciated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified similarity assignment (i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance are insufficient or even neglected in many of the existing methods. These problems significantly limit the dis-crimination of generated hash codes. In this paper, we propose a novel Deep Hashing with Self-Supervised Asymmetric Semantic Excavation and Margin-Scalable Constraint(SADH) approach to cope with these problems. SADH implements a self-supervised network to sufficiently preserve semantic information in a semantic feature dictionary and a semantic code dictionary for the semantics of the given dataset, which efficiently and precisely guides a feature learning network to preserve multi-label semantic information using an asymmetric learning strategy. By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. Extensive empirical research on four popular benchmarks validates the proposed method and shows it outperforms several state-of-the-art approaches. The source codes URL of our SADH is: http:// github.com/SWU-CS-MediaLab/SADH. (c) 2022 Elsevier B.V. All rights reserved.Computer Systems, Imagery and Medi

Multi-label modality enhanced attention based self-supervised deep cross-modal hashing

The recent deep cross-modal hashing (DCMH) has achieved superior performance in effective and efficient cross-modal retrieval and thus has drawn increasing attention. Nevertheless, there are still two limitations for most existing DCMH methods: (1) single labels are usually leveraged to measure the semantic similarity of cross-modal pairwise instances while neglecting that many cross-modal datasets contain abundant semantic information among multi-labels. (2) several DCMH methods utilized the multi-labels to supervise the learning of hash functions. Nevertheless, the feature space of multilabels suffers the weakness of sparse, resulting in sub-optimization for the hash functions learning. Thus, this paper proposed a multi-label modality enhanced attention-based self-supervised deep cross-modal hashing (MMACH) framework. Specifically, a multi-label modality enhanced attention module is designed to integrate the significant features from cross-modal data into multi-labels feature representations, aiming to improve its completion. Moreover, a multi-label cross-modal triplet loss is defined based on the criterion that the feature representations of cross-modal pairwise instances with more common categories should preserve higher semantic similarity than other instances. To the best of our knowledge, the multi-label cross-modal triplet loss is the first time designed for cross-modal retrieval. Extensive experiments on four multi-label cross-modal datasets demonstrate the effectiveness and efficiency of our proposed MMACH. Moreover, the MMACH also achieved superior performance and outperformed several state-of-the-art methods on the task of cross-modal retrieval. The source code of MMACH is available at https://github.com/SWU-CS-MediaLab/MMACH. (c) 2021 Elsevier B.V. All rights reserved.Computer Systems, Imagery and Medi

Integrating information theory and adversarial learning for cross-modal retrieval

Accurately matching visual and textual data in cross-modal retrieval has been widely studied in the multimedia community. To address these challenges posited by the heterogeneity gap and the semantic gap, we propose integrating Shannon information theory and adversarial learning. In terms of the heterogeneity gap, we integrate modality classification and information entropy maximization adversarially. For this purpose, a modality classifier (as a discriminator) is built to distinguish the text and image modalities according to their different statistical properties. This discriminator uses its output probabilities to compute Shannon information entropy, which measures the uncertainty of the modality classification it performs. Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy. Thus, maximizing information entropy gradually reduces the distribution discrepancy of cross-modal features, thereby achieving a domain confusion state where the discriminator cannot classify two modalities confidently. To reduce the semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss are used to associate the intra- and inter-modality similarity between features in the shared space. Furthermore, a regularization term based on KL-divergence with temperature scaling is used to calibrate the biased label classifier caused by the data imbalance issue. Extensive experiments with four deep models on four benchmarks are conducted to demonstrate the effectiveness of the proposed approach.Computer Systems, Imagery and Medi

Dual Gaussian-based variational subspace disentanglement for visible-infrared person re-identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person reidentification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modalityfeature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets. Codes will be available at https://github.com/TPCD/DG-VAE.Computer Systems, Imagery and Medi

Meta reconciliation normalization for lifelong person re-identification

Lifelong person re-identification (LReID) is a challenging and emerging task, which concerns the ReID capability on both seen and unseen domains after learning across different domains continually. Existing works on LReID are devoted to introducing commonlyused lifelong learning approaches, while neglecting a serious side effect caused by using normalization layers in the context of domainincremental learning. In this work, we aim to raise awareness of the importance of training proper batch normalization layers by proposing a new meta reconciliation normalization (MRN) method specifically designed for tackling LReID. Our MRN consists of grouped mixture standardization and additive rectified rescaling components, which are able to automatically maintain an optimal balance between domain-dependent and domain-independent statistics, and even adapt MRN for different testing instances. Furthermore, inspired by synaptic plasticity in human brain, we present a MRNbased meta-learning framework for mining the meta-knowledge shared across different domains, even without replaying any previous data, and further improve the model’s LReID ability with theoretical analyses. Our method achieves new state-of-the-art performances on both balanced and imbalanced LReID benchmarks.Computer Systems, Imagery and Medi

Purpose and enactment in job design: An empirical examination of the processes through which job characteristics have their effects

Job characteristics are linked with health, safety, well-being and other performance outcomes. Job characteristics are usually assessed by their presence or absence, which gives no indication of the specific purposes for which workers might use some job characteristics. We focused on job control and social support as two job characteristics embedded in the well-known Demand-Control-Support model (Karasek & Theorell, 1990). In Study 1, using an experience sampling methodology (N = 67) and a cross-sectional survey methodology (N = 299), we found that relationships between the execution of job control or the elicitation of social support and a range of other variables depended on the purposes for which job control was executed or social support elicited. In Study 2 (N = 28), we found that it may be feasible to improve aspects of well-being and performance through training workers on how to use job control or social support for specific purposes