scholarly journals Multi-Level Metric Learning Network for Fine-Grained Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166390-166397 ◽  
Author(s):  
Jiabao Wang ◽  
Yang Li ◽  
Zhuang Miao ◽  
Xun Zhao ◽  
Zhang Rui
Keyword(s):  
Metric Learning ◽  
Learning Network ◽  
Fine Grained ◽  
Multi Level

scholarly journals From Barriers to Enablers: The Role of Organizational Learning in Transitioning SMEs into the Circular Economy

2021 ◽  
Vol 13 (3) ◽  
pp. 1021
Author(s):  
Sara Scipioni ◽  
Meir Russ ◽  
Federico Niccolini
Keyword(s):  
Organizational Learning ◽  
Circular Economy ◽  
Business Models ◽  
Small And Medium Enterprises ◽  
Sustainable Business ◽  
Fine Grained ◽  
Contextual Elements ◽  
Transfer And Retention ◽  
Multi Level ◽  
Medium Enterprises

To contribute to small and medium enterprises’ (SMEs) sustainable transition into the circular economy, the study proposes the activation of organizational learning (OL) processes—denoted here as multi-level knowledge creation, transfer, and retention processes—as a key phase in introducing circular business models (CBMs) at SME and supply chain (SC) level. The research employs a mixed-method approach, using the focus group methodology to identify contextual elements impacting on CBM-related OL processes, and a survey-based evaluation to single out the most frequently used OL processes inside Italian construction SMEs. As a main result, a CBM-oriented OL multi-level model offers a fine-grained understanding of contextual elements acting mutually as barriers and drivers for OL processes, as possible OL dynamics among them. The multi-level culture construct—composed of external stakeholders’, SC stakeholders’, and organizational culture—identify the key element to activate CBM-oriented OL processes. Main implications are related to the identification of cultural, structural, regulatory, and process contextual elements across the external, SC, and organizational levels, and their interrelation with applicable intraorganizational and interorganizational learning processes. The proposed model would contribute to an improved implementation of transitioning into the circular economy utilizing sustainable business models in the construction SMEs.

scholarly journals A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2158
Author(s):  
Xin Zhang ◽  
Jiwei Qin ◽  
Jiong Zheng
Keyword(s):  
Matrix Factorization ◽  
Triangle Inequality ◽  
Metric Learning ◽  
Social Recommendation ◽  
Rating Data ◽  
Fine Grained ◽  
Information Metric ◽  
Occurrence Pattern ◽  
Public Datasets ◽  
Occurrence Patterns

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.

scholarly journals Enhancing remote sensing image retrieval using a triplet deep metric learning network

2019 ◽  
Vol 41 (2) ◽  
pp. 740-751 ◽  
Author(s):  
Rui Cao ◽  
Qian Zhang ◽  
Jiasong Zhu ◽  
Qing Li ◽  
Qingquan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Learning Network ◽  
Deep Metric Learning

scholarly journals Deep Metric Learning by Online Soft Mining and Class-Aware Attention

2019 ◽  
Vol 33 ◽  
pp. 5361-5368 ◽  
Author(s):  
Xinshao Wang ◽  
Yang Hua ◽  
Elyor Kodirov ◽  
Guosheng Hu ◽  
Neil M. Robertson
Keyword(s):  
Large Fraction ◽  
Metric Learning ◽  
Improve Performance ◽  
Fine Grained ◽  
Training Samples ◽  
Deep Embedding ◽  
Data Points ◽  
Accelerate Convergence ◽  
Deep Metric Learning ◽  
Mining Methods

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.

scholarly journals Sexual networks: measuring sexual selection in structured, polyandrous populations

2013 ◽  
Vol 368 (1613) ◽  
pp. 20120356 ◽  
Author(s):  
Grant C. McDonald ◽  
Richard James ◽  
Jens Krause ◽  
Tommaso Pizzari
Keyword(s):  
Sexual Selection ◽  
Temporal Structure ◽  
Natural Populations ◽  
Population Level ◽  
Global Scale ◽  
Local Scale ◽  
Emergent Property ◽  
Sexual Competition ◽  
Fine Grained ◽  
Multi Level

Sexual selection is traditionally measured at the population level, assuming that populations lack structure. However, increasing evidence undermines this approach, indicating that intrasexual competition in natural populations often displays complex patterns of spatial and temporal structure. This complexity is due in part to the degree and mechanisms of polyandry within a population, which can influence the intensity and scale of both pre- and post-copulatory sexual competition. Attempts to measure selection at the local and global scale have been made through multi-level selection approaches. However, definitions of local scale are often based on physical proximity, providing a rather coarse measure of local competition, particularly in polyandrous populations where the local scale of pre- and post-copulatory competition may differ drastically from each other. These limitations can be solved by social network analysis, which allows us to define a unique sexual environment for each member of a population: ‘local scale’ competition, therefore, becomes an emergent property of a sexual network. Here, we first propose a novel quantitative approach to measure pre- and post-copulatory sexual selection, which integrates multi-level selection with information on local scale competition derived as an emergent property of networks of sexual interactions. We then use simple simulations to illustrate the ways in which polyandry can impact estimates of sexual selection. We show that for intermediate levels of polyandry, the proposed network-based approach provides substantially more accurate measures of sexual selection than the more traditional population-level approach. We argue that the increasing availability of fine-grained behavioural datasets provides exciting new opportunities to develop network approaches to study sexual selection in complex societies.

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