Purpose
As one of the largest industries in the global economy, the fashion industry has emphasized the symbolic and aspirational features of its products while maximizing the efficiency of its ...manufacturing processes. However, the labor-intensive and competitive nature of the industry has meant that brand moral transgressions often occur. This study aims to understand the role of moral emotions and concerns (i.e. perceived spillover) caused by different moral transgressions and explain consumer anti-brand behaviors (i.e. negative word of mouth WOM and patronage cessation).
Design/methodology/approach
Structural equation modeling was conducted to examine group differences (ethical vs social transgressions) in Study 1 (n = 584). Also, the moderation effect of moral disengagement was examined in Study 2 (n = 324).
Findings
The results indicate that, for ethical transgressions, both moral emotions and perceived spillovers explain negative behaviors while moral emotions alone explain negative WOM on social media for social transgressions. Additionally, for social transgressions, the results of Study 2 indicate a negative interaction effect of moral emotions and moral disengagement on anti-brand behavior of patronage cessation.
Originality/value
Based on the literature’s theoretical approach to moral crises, this paper examines the emotional and cognitive reactions of consumers to the fashion industry’s moral transgressions.
Multidimensional MR experiments of relaxation and diffusion have been successful for material characterization and have attracted attention recently for biomedical applications. However, such ...experiments typically require many scans of data acquisition and are time‐consuming. This work discusses a method for systematic optimization of the pulse‐sequence parameters to obtain optimal resolution within the experimental conditions, such as the number of acquisitions. Other optimization goals can also be incorporated in this framework.
This work discusses a method for systematic optimization of multidimensional MR experiments for the improvement of resolution and execution speed. Other optimization goals and constraints can also be incorporated in this framework.
Characterizing the microbial communities inhabiting specimens is one of the primary objectives of microbiome studies. A short-read sequencing platform for reading partial regions of the 16S rRNA gene ...is most commonly used by reducing the cost burden of next-generation sequencing (NGS), but misclassification at the species level due to its length being too short to consider sequence similarity remains a challenge. Loop Genomics recently proposed a new 16S full-length-based synthetic long-read sequencing technology (sFL16S). We compared a 16S full-length-based synthetic long-read (sFL16S) and V3-V4 short-read (V3V4) methods using 24 human GUT microbiota samples. Our comparison analyses of sFL16S and V3V4 sequencing data showed that they were highly similar at all classification resolutions except the species level. At the species level, we confirmed that sFL16S showed better resolutions than V3V4 in analyses of alpha-diversity, relative abundance frequency and identification accuracy. Furthermore, we demonstrated that sFL16S could overcome the microbial misidentification caused by different sequence similarity in each 16S variable region through comparison the identification accuracy of Bifidobacterium, Bacteroides, and Alistipes strains classified from both methods. Therefore, this study suggests that the new sFL16S method is a suitable tool to overcome the weakness of the V3V4 method.
Extracellular vesicles (EVs), a class of heterogeneous membrane vesicles, are generally divided into exosomes and microvesicles on basis of their origination from the endosomal membrane or the plasma ...membrane, respectively. EV-mediated bidirectional communication among various cell types supports cancer cell growth and metastasis. EVs derived from different cell types and status have been shown to have distinct RNA profiles, comprising messenger RNAs and non-coding RNAs (ncRNAs). Recently, ncRNAs have attracted great interests in the field of EV-RNA research, and growing numbers of ncRNAs ranging from microRNAs to long ncRNAs have been investigated to reveal their specific functions and underlying mechanisms in the tumor microenvironment and premetastatic niches. Emerging evidence has indicated that EV-RNAs are essential functional cargoes in modulating hallmarks of cancers and in reciprocal crosstalk within tumor cells and between tumor and stromal cells over short and long distance, thereby regulating the initiation, development and progression of cancers. In this review, we discuss current findings regarding EV biogenesis, release and interaction with target cells as well as EV-RNA sorting, and highlight biological roles and molecular mechanisms of EV-ncRNAs in cancer biology.
Episodic Training for Domain Generalization Li, Da; Zhang, Jianshu; Yang, Yongxin ...
2019 IEEE/CVF International Conference on Computer Vision (ICCV),
2019-Oct.
Conference Proceeding
Odprti dostop
Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The ...simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. Furthermore, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training a feature for robustness to novel problems. This shows that DG training can benefit standard practice in computer vision.
NiFe-based (oxy)hydroxides are the benchmark catalysts for the oxygen evolution reaction (OER) in alkaline medium, however, it is still challenging to control their structures and compositions. ...Herein, molybdates (NiFe(MoO
)
) are applied as unique precursors to synthesize ultrafine Mo modified NiFeO
H
(oxy)hydroxide nanosheet arrays. The electrochemical activation process enables the molybdate ions (MoO
) in the precursors gradually dissolve, and at the same time, hydroxide ions (OH
) in the electrolyte diffuse into the precursor and react with Ni
and Fe
ions in confined space to produce ultrafine NiFeO
H
(oxy)hydroxides nanosheets (<10 nm), which are densely arranged into microporous arrays and maintain the rod-like morphology of the precursor. Such dense ultrafine nanosheet arrays produce rich edge planes on the surface of NiFeO
H
(oxy)hydroxides to expose more active sites. More importantly, the capillary phenomenon of microporous structures and hydrophilic hydroxyl groups induce the superhydrophilicity and the rough surface produces the superaerophobic characteristic for bubbles. With these advantages, the optimized catalyst exhibits excellent performance for OER, with a small overpotential of 182 mV at 10 mA cm
and long-term stability (200 h) at 200 mA cm
. Theoretical calculations show that the modification of Mo enhances the electron delocalization and optimizes the adsorption of intermediates.
Current methods for skeleton-based human action recognition usually work with completely observed skeletons. However, in real scenarios, it is prone to capture incomplete and noisy skeletons, which ...will deteriorate the performance of traditional models. To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton joints. Here, each stream of the network is only responsible for learning features from currently unactivated joints, which are distinguished by the class activation maps (CAM) obtained by preceding streams, so that the activated joints of the proposed method are obviously more than traditional methods. Thus, the proposed method is termed richly activated GCN (RA-GCN), where the richly discovered features will improve the robustness of the model. Compared to the state-of-the-art methods, the RA-GCN achieves comparable performance on the NTU RGB+D dataset. Moreover, on a synthetic occlusion dataset, the performance deterioration can be alleviated by the RA-GCN significantly.
Impaired remyelination of demyelinated axons is a major cause of neurological disability. In inflammatory demyelinating disease of the central nervous system (CNS), although remyelination does ...happen, it is often incomplete, resulting in poor clinical recovery. Poly-IC a known TLR3 agonist and IL-33, a cytokine which is induced by poly-IC are known to influence recovery and promote repair in experimental models of CNS demyelination.
We examined the effect of addition of poly-IC and IL-33 on the differentiation and maturation of oligodendrocyte precursor cells (OPC) cultured in vitro. Both Poly-IC and IL-33 induced transcription of myelin genes and the differentiation of OPC to mature myelin forming cells. Poly-IC induced IL-33 in OPC and addition of IL-33 to in vitro cultures, amplified further, IL-33 expression suggesting an autocrine regulation of IL-33. Poly-IC and IL-33 also induced phosphorylation of p38MAPK, a signaling molecule involved in myelination. Following the induction of gliotoxic injury with lysolecithin to the corpus callosum (CC), treatment of animals with poly-IC resulted in greater recruitment of OPC and increased staining for myelin in areas of demyelination. Also, poly-IC treated animals showed greater expression of IL-33 and higher expression of M2 phenotype macrophages in the CC.
Our studies suggest that poly-IC and IL-33 play a role in myelin repair by enhancing expression of myelin genes and are therefore attractive therapeutic agents for use as remyelinating agents in human demyelinating disease.
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) ...importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments with candidate photos which in turn make subtle visual detail matching difficult. Existing FG-SBIR approaches focus only on coarse holistic matching via deep cross-domain representation learning, yet ignore explicitly accounting for fine-grained details and their spatial context. In this paper, a novel deep FG-SBIR model is proposed which differs significantly from the existing models in that: (1) It is spatially aware, achieved by introducing an attention module that is sensitive to the spatial position of visual details: (2) It combines coarse and fine semantic information via a shortcut connection fusion block: and (3) It models feature correlation and is robust to misalignments between the extracted features across the two domains by introducing a novel higher-order learnable energy function (HOLEF) based loss. Extensive experiments show that the proposed deep spatial-semantic attention model significantly outperforms the state-of-the-art.
Sketch Me That Shoe Qian Yu; Feng Liu; Yi-Zhe Song ...
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2016-June
Conference Proceeding
Odprti dostop
We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely ...challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for finegrained cross-domain ranking tasks.
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