Month: February 2017

Postdoctoral position in Big Data and Data Science

We are looking for a highly motivated postdoctoral fellow in the area of Big Data and Data Science with a particular focus on Social Mining within a EU funded project. The project aims to establish a Social Mining and Big Data Ecosystem for ethically sensitive scientific discoveries and advanced applications of social data mining to the various dimensions of social life.
The ideal candidate shall pursue exciting research in the areas of Big Data, social data analytics, machine learning, large-scale networks, deep learning, participatory smart cities platforms, and/or in connection with the Nervousnet.info platform.

Source: apply.refline.ch

University of Sydney – Postdoctoral Research Associate – Student Interactions

The Postdoctoral Research Associate will conduct a longitudinal and a cross-sectional analysis of large-scale data of student interactions. The primary purpose of the network analysis is to cast light on the social and cultural landscape of the Universitys student body. The results will inform the targeting of network interventions.

The successful person will work closely with Dr. Petr Matous, Prof Philippa Pattison (Deputy Vice-Chancellor for Education), and Prof. Shane Houston (Deputy Vice-Chancellor for Indigenous Strategy and Services).

Source: sydney.nga.net.au

2017 IEEE Symposium on Artificial Life (IEEE ALIFE 2017)

IEEE ALIFE 2017
2017 IEEE Symposium on Artificial Life
http://www.ele.uri.edu/ieee-ssci2017/ALIFE.htm

as part of
2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017)
Hilton Hawaiian Village Resort, Honolulu, Hawaii
Nov. 27-Dec. 1, 2017
http://www.ieee-ssci.org

 

IEEE ALIFE 2017 brings together researchers working on the emerging areas of Artificial Life and Complex Adaptive Systems, aiming to understand and synthesize life-like systems and applying bio-inspired synthetic methods to other science/engineering disciplines, including Biology, Robotics, Social Sciences, among others.

IEEE ALIFE 2017 will be a part of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017). IEEE SSCI 2017 plans to have 24 separate symposia, plus plenary sessions, tutorials, and more, all for one registration price. Accepted papers after peer-review will be published in the IEEE SSCI conference proceedings.

 

Important dates:

Tutorial Proposals:            Mar. 10, 2017
Paper Submissions:          July 2, 2017 
Notification to Authors:    Aug. 27, 2017
Final Submission:              Sept. 24, 2017
Early Registration:            Sept. 24, 2017

Source: www.ele.uri.edu

Fundamental limitations of network reconstruction from temporal data

Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g. adjacency pattern, sign pattern or degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here, we rigorously derive the necessary conditions to reconstruct any property of the interaction matrix. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations sheds light on the design of better network reconstruction algorithms that offer practical improvements over existing methods.

 

Fundamental limitations of network reconstruction from temporal data
Marco Tulio Angulo, Jaime A. Moreno, Gabor Lippner, Albert-László Barabási, Yang-Yu Liu

JRS Interface

February 2017
Volume 14, issue 127

Source: rsif.royalsocietypublishing.org

Ecosystem restoration strengthens pollination network resilience and functions.

Land degradation results in declining biodiversity and the disruption of ecosystem functioning worldwide, particularly in the tropics1. Vegetation restoration is a common tool used to mitigate these impacts and increasingly aims to restore ecosystem functions rather than species diversity2. However, evidence from community experiments on the effect of restoration practices on ecosystem functions is scarce3. Pollination is an important ecosystem function and the global decline in pollinators attenuates the resistance of natural areas and agro-environments to disturbances4. Thus, the ability of pollination functions to resist or recover from disturbance (that is, the functional resilience)5, 6 may be critical for ensuring a successful restoration process7. Here we report the use of a community field experiment to investigate the effects of vegetation restoration, specifically the removal of exotic shrubs, on pollination. We analyse 64 plant–pollinator networks and the reproductive performance of the ten most abundant plant species across four restored and four unrestored, disturbed mountaintop communities. Ecosystem restoration resulted in a marked increase in pollinator species, visits to flowers and interaction diversity. Interactions in restored networks were more generalized than in unrestored networks, indicating a higher functional redundancy in restored communities. Shifts in interaction patterns had direct and positive effects on pollination, especially on the relative and total fruit production of native plants. Pollinator limitation was prevalent at unrestored sites only, where the proportion of flowers producing fruit increased with pollinator visitation, approaching the higher levels seen in restored plant communities. Our results show that vegetation restoration can improve pollination, suggesting that the degradation of ecosystem functions is at least partially reversible. The degree of recovery may depend on the state of degradation before restoration intervention and the proximity to pollinator source populations in the surrounding landscape5, 8. We demonstrate that network structure is a suitable indicator for pollination quality, highlighting the usefulness of interaction networks in environmental management6, 9.

Source: www.nature.com