Gendering a Sacred Nature: Between Essentialism and Queer; Insights from a Study on Eco-Spiritual Activism in Switzerland

Among eco-spiritual activists in the French-speaking part of Switzerland, gendered notions such as “Mother Earth” or gendered “nature spirits” are ubiquitous. Drawing on an in-depth ethnographic study of this milieu (2015–2020), this article presents some of the ways in which these activists articulate gender issues with reference to nature. The authors discuss the centrality of the notion of the self and ask what outputs emerge from linking environmental with spiritual action. We demonstrate that activists in three milieus—the New Age and holistic milieu, the transition network, and neo-shamanism—handle this link differently and thereby give birth to a variety of emic perspectives upon the nature/culture divide, as well as upon gender—ranging from essentialist and organicist views to queer approaches. The authors also present more recent observations on the increasing visibility of women and feminists as key public speakers. They conclude with the importance of contextualizing imaginaries that circulate as universalistic and planetary and of relating them to individuals’ gendered selves and their social, political, and economic capital.

Short-term Inbetweening of 3D Human Motions

Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between frames. A modified version of LSTM, called Recurrent Transition Network (RTN), solves the inbetweening task for walking motion based on ten initial frames and two final frames. In this work, we are interested on the short-term inbetweening task, where we need to use the least amount of frames to generate the missing frames for short-term transitions. We are also interested on different kinds of movements, such as martial arts and Indian dance. Thus, we adapt the Recurrent Transition Network (RTN) to require only the two firts frames and the last one, called ARTN, and propose a simple post processing method combining ARTN with linear interpolation, called ARTN+. The results show that the average error of ARTN+ is less than the average error of each method (RTN and interpolation) separately in the martial arts and Indian dance dataset.

A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network.

Narratives of Socioecological Transition

This article explores narratives and characteristics of sociological transitions displayed by members of the Transition Network (TN) in Portugal. It is informed by scholarly work on grassroots innovations, sociological transition narratives, and environmental engagement in Portugal. It furthers this research in three ways: (1) it analyzes an original case study—the Portuguese TN; (2) it identifies and defines the various socioecological narratives conveyed by its participants; and (3) it interprets the TN’s sociopolitical appeal as a grassroots innovation in the context of environmental mobilization in Portugal. Drawing on 20 semistructured interviews with current and former members of the Portuguese TN, three narratives of sociological transition were identified—utopianism, inevitability, and pessimism—as well as seven characteristics that motivated interviewees’ engagement with the TN.

Leveraging the Self-Transition Probability of Ordinal Patterns Transition Network for Transportation Mode Identification Based on GPS Data

Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Mining this type of data, however, faces several complexities due to its unique properties. In this work, we propose the use of Information Theory quantifiers retained from the Ordinal Patterns (OP) transformation, for transportation mode identification. As an initial exploration, our results show that OP satisfactorily characterizes the trajectories. Moreover, in this scenario, the characteristics of OP transformation can be advantageous, such as its simplicity, robustness, and speed.

Ordinal partition transition network based complexity measures for climate data analysis

<p>Complex network approaches have been recently emerging as novel and complementary concepts of nonlinear time series analysis which are able to unveil many features that are hidden to more traditional analysis methods. In this talk, we focus on one particular approach of ordinal pattern transition networks (OPTNs) for characterizing time series data. In particular, we introduce a suite of OPTN based complexity measures to infer the coupling direction between two dynamical systems from pairs of time series. For several examples of both coupled stochastic processes and chaotic Henon maps, we demonstrate that our approach is able to successfully identify interaction delays of both unidirectional and bidirectional coupling configurations.</p><p>Furthermore, we focus on applying these methods to characterize the recent extreme drought events in the semiarid region of Northeast Brazil (NEB) where has been experiencing a continuous dry condition since 2012. Therefore, we propose a three-step strategy to establish the episodic coupling directions on intraseasonal time scales from the surrounding ocean to the precipitation patterns in the NEB, focusing on the distinctive roles of the oceans during the recent extreme drought events of 2012-2013 and 2015-2016. Our algorithm involves: (i) computing drought period length from daily precipitation anomalies to capture extreme drought events, (ii) characterizing the episodic coupling delays from the surrounding oceans to the precipitation by applying Kullback-Leibler divergence (KLD) of complexity measure which is based on OPTN representation of time series, and (iii) calculating the ratio of high temperature in the ocean during the extreme drought events with proper time lags that are identified by KLD measures. From the viewpoint of climatology, our analysis provides data-based evidence of showing significant influence from the North Atlantic in 2012-2013 to the NEB, but in 2015-2016 the Pacific played a dominant role than that of the Atlantic. The episodic intra-seasonal time scale properties are potential for monitoring and forecasting droughts in the NEB, in order to propose strategies for drought impacts reduction.</p><p>In conclusion, our results suggest that ordinal partition transition networks can be used as complementary tools for causal inference tasks and provide insights into the potentials and theoretical foundations of time series networks.</p><p>References:</p><p>[1] H. Y. Wu, Y. Zou, L. M. Alves, E. E. N. Macau, G. Sampaio, and J. A. Marengo. Uncovering episodic influence of oceans on extreme drought events in Northeast Brazil by ordinal partition network approaches. Chaos, 30, 053104, 2020.</p><p>[2] Y. J. Ruan, R. V. Donner, S. G. Guan, and Y. Zou. Ordinal partition transition network based complexity measures for inferring coupling direction and delay from time series. Chaos, 29, 043111, 2019.</p><p>[3] Y. Zou, R. V. Donner, N. Marwan, J. F. Donges, and J. Kurths. Complex network approaches to nonlinear time series analysis. Physics Reports, 787, 1 – 97, 2019.</p>