Sensor data management with probabilistic models

The anticipated ‘sensing environments’ of the near future pose new requirements to the data management systems that mediate between sensor data supply and demand sides. We identify and investigate one of them: the need to deal with the inherent uncertainty in sensor data due to measurement noise, missing data, the semantic gap between the measured data and relevant information, and the integration of data from different sensors.\ud \ud Probabilistic models of sensor data can be used to deal with these uncertainties in the well-understood and fruitful framework of probability theory. In particular, the Bayesian network formalism proves useful for modeling sensor data in a flexible environment, because its comprehensiveness and modularity. We provide extensive technical argumentation for this claim. As a demonstration case, we define a discrete Bayesian network for location tracking using Bluetooth transceivers.\ud \ud In order to scale up sensor models, efficient probabilistic inference on the Bayesian network is crucial. However, we observe that the conventional inference methods do not scale well for our demonstration case. We propose several optimizations, making it possible to jointly scale up the number of locations and sensors in sublinear time, and to scale up the time resolution in linear time. Moreover, we define a theoretical framework in which these optimizations are derived by translating an inference query into relational algebra. This allows the query to be analyzed and optimized using insights and techniques from the database community; for example, using cost metrics based on cardinality rather than dimensionality.\ud \ud An orthogonal research question investigates the possibility of collecting transition statistics in a local, clustered fashion, in which transitions between states of different clusters cannot be directly observed. We show that this problem can be written as a constrained system of linear equations, for which we describe a specialized solution method

Pseudorapidity densities of charged particles with transverse momentum thresholds in pp collisions at √ s = 5.02 and 13 TeV

The pseudorapidity density of charged particles with minimum transverse momentum (pT) thresholds of 0.15, 0.5, 1, and 2 GeV/c is measured in pp collisions at the center of mass energies of √s=5.02 and 13 TeV with the ALICE detector. The study is carried out for inelastic collisions with at least one primary charged particle having a pseudorapidity (η) within 0.8pT larger than the corresponding threshold. In addition, measurements without pT-thresholds are performed for inelastic and nonsingle-diffractive events as well as for inelastic events with at least one charged particle having |η|2GeV/c), highlighting the importance of such measurements for tuning event generators. The new measurements agree within uncertainties with results from the ATLAS and CMS experiments obtained at √s=13TeV.

Additional file 28 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 28: Table S18. Sex-participation association of the variants with significant sex-specific lipid results

Additional file 17 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 17: Table S9. PheWAS UKB-MVP meta-analysis results for each index lipid variant at Bonferroni threshold for multiple testing