13,974 research outputs found
Beyond similarity: A network approach for identifying and delimiting biogeographical regions
Biogeographical regions (geographically distinct assemblages of species and
communities) constitute a cornerstone for ecology, biogeography, evolution and
conservation biology. Species turnover measures are often used to quantify
biodiversity patterns, but algorithms based on similarity and clustering are
highly sensitive to common biases and intricacies of species distribution data.
Here we apply a community detection approach from network theory that
incorporates complex, higher order presence-absence patterns. We demonstrate
the performance of the method by applying it to all amphibian species in the
world (c. 6,100 species), all vascular plant species of the USA (c. 17,600),
and a hypothetical dataset containing a zone of biotic transition. In
comparison with current methods, our approach tackles the challenges posed by
transition zones and succeeds in identifying a larger number of commonly
recognised biogeographical regions. This method constitutes an important
advance towards objective, data derived identification and delimitation of the
world's biogeographical regions.Comment: 5 figures and 1 supporting figur
Multiple multimodal mobile devices: Lessons learned from engineering lifelog solutions
For lifelogging, or the recording of oneâs life history through digital means, to be successful, a range of separate multimodal mobile devices must be employed. These include smartphones such as the N95, the Microsoft SenseCam â a wearable passive photo capture device, or
wearable biometric devices. Each collects a facet of the bigger picture, through, for example, personal digital photos, mobile messages and documents access history, but unfortunately, they operate independently and unaware of each other. This creates significant challenges for the practical application of these devices, the use and integration of their data and their operation by a user. In this chapter we discuss the software engineering challenges and their implications for individuals working on integration of data from multiple ubiquitous mobile devices drawing on our experiences working with such technology over the past several years for the development of integrated personal lifelogs. The chapter serves as an engineering guide to those considering working in the domain of lifelogging and more generally to those working with multiple multimodal devices and integration of their data
Circadian patterns of Wikipedia editorial activity: A demographic analysis
Wikipedia (WP) as a collaborative, dynamical system of humans is an
appropriate subject of social studies. Each single action of the members of
this society, i.e. editors, is well recorded and accessible. Using the
cumulative data of 34 Wikipedias in different languages, we try to characterize
and find the universalities and differences in temporal activity patterns of
editors. Based on this data, we estimate the geographical distribution of
editors for each WP in the globe. Furthermore we also clarify the differences
among different groups of WPs, which originate in the variance of cultural and
social features of the communities of editors
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Proceedings Ocean Biodiversity Informatics: International Conference on Marine Biodiversity Data Management, Hamburg, Germany 29 November to 1 December, 2004
The International conference on Marine Biodiversity Data management âOcean Biodiversity Informaticsâ was held in Hamburg, Germany, from 29 November to 1 December 2004. Its objective was to offer a forum to marine biological data managers to discuss the state of the field, and to exchange ideas on how to further develop marine biological data systems. Many marine biologists are actively gathering knowledge, as they have been doing for a long time. What is new is that many of these scientists are willing to share their knowledge, including basic data, with others over the Internet. Our challenge now is to try and manage this trend, avoid confusing users with a multitude of contradicting sources of information, and make sure different data systems can be and are effectively integrated
Precise Single-stage Detector
There are still two problems in SDD causing some inaccurate results: (1) In
the process of feature extraction, with the layer-by-layer acquisition of
semantic information, local information is gradually lost, resulting into less
representative feature maps; (2) During the Non-Maximum Suppression (NMS)
algorithm due to inconsistency in classification and regression tasks, the
classification confidence and predicted detection position cannot accurately
indicate the position of the prediction boxes. Methods: In order to address
these aforementioned issues, we propose a new architecture, a modified version
of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector
(PSSD). Firstly, we improve the features by adding extra layers to SSD.
Secondly, we construct a simple and effective feature enhancement module to
expand the receptive field step by step for each layer and enhance its local
and semantic information. Finally, we design a more efficient loss function to
predict the IOU between the prediction boxes and ground truth boxes, and the
threshold IOU guides classification training and attenuates the scores, which
are used by the NMS algorithm. Main Results: Benefiting from the above
optimization, the proposed model PSSD achieves exciting performance in
real-time. Specifically, with the hardware of Titan Xp and the input size of
320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28
mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object
detection models. Besides, the proposed model performs significantly well with
larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS
COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove
that the proposed model has a better trade-off between speed and accuracy.Comment: We will submit it soon to the IEEE transaction. Due to characters
limitation, we can not upload the full abstract. Please read the pdf file for
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A formal ontology for industrial maintenance
International audienceThe rapid advancement of information and communication technologies has resulted in a variety of maintenance support systems and tools covering all sub-domains of maintenance. Most of these systems are based on different models that are sometimes redundant or incoherent and always heterogeneous. This problem has lead to the development of maintenance platforms integrating all of these support systems. The main problem confronted by these integration platforms is to provide semantic interoperability between different applications within the same environment. In this aim, we have developed an ontology for the field of industrial maintenance, adopting the METHONTOLOGY approach to manage the life cycle development of this ontology, that we have called IMAMO (Industrial MAintenance Management Ontology). This ontology can be used not only to ensure semantic interoperability but also to generate new knowledge that supports decision making in the maintenance process. This paper provides and discusses some tests so as to evaluate the ontology and to show how it can ensure semantic interoperability and generate new knowledge within the platform
Patterns, Information, and Causation
This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennettâs account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques
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