Evidential Communities for Complex Networks

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure

Modeling the effects of irrigation management on soil salinity and crop transpiration at the field level

Water management / Models / Environment / Soil salinity / Irrigation management / Calibrations / Flow / Groundwater / Sensitivity analysis / Pakistan / Fordwah Eastern Sadiqia / Punjab

Rational Design of Sustainable Liquid Microcapsules for Spontaneous Fragrance Encapsulation

The high volatility, water-immiscibility, and light/oxygen-sensitivity of most aroma compounds represent a challenge to their incorporation in liquid consumer products. Current encapsulation methods entail the use of petroleum-based materials, initiators, and crosslinkers as well as mixing, heating, and purification steps. Hence, more efficient and eco-friendly approaches to encapsulation must be sought. Herein, we propose a simple method by making use of a pre-formed amphiphilic polymer and employing the Hansen Solubility Parameters approach to determine which fragrances could be encapsulated by spontaneous coacervation in water. The coacervates do not precipitate as solids but they remain suspended as colloidally stable liquid microcapsules, as demonstrated by fluorescence correlation spectroscopy. The effective encapsulation of fragrance is proven through confocal Raman spectroscopy, while the structure of the capsules is investigated by means of cryo FIB/SEM, confocal laser scanning microscopy, and small-angle X-ray scattering

A reliability-based approach for influence maximization using the evidence theory

The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc

DNMT1 mutations found in HSANIE patients affect interaction with UHRF1 and neuronal differentiation

DNMT1 is recruited to substrate sites by PCNA and UHRF1 to maintain DNA methylation after replication. The cell cycle dependent recruitment of DNMT1 is mediated by the PCNA-binding domain (PBD) and the targeting sequence (TS) within the N-terminal regulatory domain. The TS domain was found to be mutated in patients suffering from hereditary sensory and autonomic neuropathies with dementia and hearing loss (HSANIE) and autosomal dominant cerebellar ataxia deafness and narcolepsy (ADCA-DN) and is associated with global hypomethylation and site specific hypermethylation. With functional complementation assays in mouse embryonic stem cells, we showed that DNMT1 mutations P496Y and Y500C identified in HSANIE patients not only impair DNMT1 heterochromatin association, but also UHRF1 interaction resulting in hypomethylation. Similar DNA methylation defects were observed when DNMT1 interacting domains in UHRF1, the UBL and the SRA domain, were deleted. With cell-based assays, we could show that HSANIE associated mutations perturb DNMT1 heterochromatin association and catalytic complex formation at methylation sites and decrease protein stability in late S and G2 phase. To investigate the neuronal phenotype of HSANIE mutations, we performed DNMT1 rescue assays and could show that cells expressing mutated DNMT1 were prone to apoptosis and failed to differentiate into neuronal lineage. Our results provide insights into the molecular basis of DNMT1 dysfunction in HSANIE patients and emphasize the importance of the TS domain in the regulation of DNA methylation in pluripotent and differentiating cells

Toward a General Framework for Information Fusion

National audienceDepending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory, the latter encompassing the two other ones as special cases. This unified discussion of combination rules across different settings is expected to provide a fresh look on some old but basic issues in information fusion

Meteorological source variability in atmospheric gravity wave parameters derived from a tropical infrasound station

Gravity waves are an important part of the momentum budget of the atmosphere. Despite this, parameterizations of gravity wave spectra in atmospheric models are poorly constrained. Gravity waves are formed by jet streams, flow over topography and convection, all of which produce pressure perturbations as they propagate over the Earth’s surface, detectable by microbarometer arrays used for sensing infrasound. In this study, observations of gravity waves between 2007 and 2011 at an infrasound station in the Ivory Coast, West Africa are combined with meteorological data to calculate parameters such as intrinsic phase speed and wavenumber. Through spectral analysis, the seasonal and daily variations in all gravity wave parameters are examined. The gravity wave back azimuth varies with the migration of the Inter-Tropical Convergence Zone, a region of intense convection, supporting previous studies. Daily variations in gravity wave arrivals at the station can be linked to two distinct convective cycles over the land and ocean. This was achieved by combining the gravity wave parameters with lightning strikes detected by the Met Office’s Arrival Time Difference lightning detection system. Noise generated by turbulence in the middle of the day was found to attenuate smaller pressure amplitude gravity waves, artificially amplifying the daily variations in some gravity wave parameters. Detection of daily and seasonal variations in gravity wave parameters has the potential be used to improve the representation of gravity wave spectra in atmospheric models

Fuzzy Implications: Some Recently Solved Problems

In this chapter we discuss some open problems related to fuzzy implications, which have either been completely solved or those for which partial answers are known. In fact, this chapter also contains the answer for one of the open problems, which is hitherto unpublished. The recently solved problems are so chosen to reflect the importance of the problem or the significance of the solution. Finally, some other problems that still remain unsolved are stated for quick reference

Performative Work: Bridging Performativity and Institutional Theory in the Responsible Investment Field

Callon’s performativity thesis has illuminated how economic theories and calculative devices shape markets, but has been challenged for its neglect of the organizational, institutional and political context. Our seven-year qualitative study of a large financial data company found that the company’s initial attempt to change the responsible investment field through a performative approach failed because of the constraints posed by field practices and organizational norms on the design of the calculative device. However, the company was subsequently able to put in place another form of performativity by attending to the normative and regulative associations of the device. We theorize this route to performativity by proposing the concept of performative work, which designates the necessary institutional work to enable translation and the subsequent adoption of the device. We conclude by considering the implications of performative work for the performativity and the institutional work literatures

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work