A Multi-Level Power-on Reset for Fine-Grained Power Management

2018 ◽  
Author(s):  
Andres Amaya ◽  
Luis E. Rueda G ◽  
Elkim Roa
Keyword(s):  
Power Management ◽  
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 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 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.

scholarly journals Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings

2020 ◽  
Vol 34 (01) ◽  
pp. 262-269
Author(s):  
Qianqian Xu ◽  
Jiechao Xiong ◽  
Zhiyong Yang ◽  
Xiaochun Cao ◽  
Qingming Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Preference ◽  
Learning To Rank ◽  
User Preferences ◽  
Coarse Grained ◽  
Fast Analysis ◽  
Fine Grained ◽  
Level Model ◽  
The Common ◽  
Multi Level

In recent years, learning user preferences has received significant attention. A shortcoming of existing learning to rank work lies in that they do not take into account the multi-level hierarchies from social choice to individuals. In this paper, we propose a multi-level model which learns both the common preference or utility function over the population based on features of alternatives to-be-compared, and preferential diversity functions conditioning on user categories. Such a multi-level model, enables us to simultaneously learn a coarse-grained social preference function together with a fine-grained personalized diversity. It provides us prediction power for the choices of new users on new alternatives. The key algorithm in this paper is based on Split Linearized Bregman Iteration (SplitLBI) algorithm which generates a dynamic path from the common utility to personalized preferential diversity, at different levels of sparsity on personalization. A synchronized parallel version of SplitLBI is proposed to meet the needs of fast analysis of large-scale data. The validity of the methodology are supported by experiments with both simulated and real-world datasets such as movie and dining restaurant ratings which provides us a coarse-to-fine grained preference learning.

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