Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples

On the mixing rules for interstellar inhomogeneous grains

We present the computation of effective refractive coefficients for inhomogeneous two-component grains with 3 kinds of inclusions with ${\rm m_{incl}=3.0+4.0i, 2.0+1.0i, 2.5+0.0001i}$ and a matrix with ${\rm m_m=1.33+0.01i}$ for 11 volume fractions of inclusions from 0% to 50% and wavelengths ${\rm\lambda}$=0.5, 1.0, 2.0 and 5.0 ${\rm \mu m}$. The coefficients of extinction for these grains have been computed using a discrete dipole approximation (DDA). Computation of the extinction by the same method for grains composed of a matrix material with randomly embedded inclusions has been carried out for different volume fractions of inclusions. A comparison of extinction coefficients obtained for both models of grain materials allows to choose the best mixing rule for a mixture. In cases of inclusions with ${\rm m_{incl}}$=2.0+1.0i and 2.5+0.0001i the best fit for the whole wavelengths range and volume fractions of inclusions from 0 to 50% has been obtained for Lichtenecker mixing rule. In case of ${\rm m_{incl}=3.0+4.0i}$ the fit for the whole wavelength range and volume fractions of inclusions from 0 to 50% is not very significant but the best has been obtained for Hanai rule. For volume fractions of inclusion from 0 to 15% a very good fit has been obtained for the whole wavelength range for Rayleigh and Maxwell-Garnett mixing rules.Comment: 11 pages, 13 figures, accepted for publication in MNRA

Pulse to pulse flux density modulation from pulsars at 8.35 GHz

Aims. To investigate the flux density modulation from pulsars and the existence of specific behaviour of modulation index versus frequency. Methods. Several pulsars have been observed with the Effelsberg radio telescope at 8.35 GHz. Their flux density time series have been corrected for interstellar scintillation effects. Results. We present the measurement of modulation indices for 8 pulsars. We confirm the presence of a critical frequency at ~1 GHz for these pulsars (including 3 new ones from this study). We derived intrinsic modulation indices for the resulting flux density time series. Our data analysis revealed strong single pulses detected from 5 pulsars.Comment: accepted for publication in A&

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: +e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. +us, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. +e NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. +e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Observations of pulsars at 9 millimetres

The behaviour of the pulsar spectrum at high radio frequencies can provide decisive information about the nature of the radio emission mechanism. We report recent observations of a selected sample of pulsars at lambda=9mm (32 GHz) with the 100-m Effelsberg radio telescope.Three pulsars, PSR B0144+59, PSR B0823+26, and PSR B2022+50, were detected for the first time at this frequency. We confirm the earlier flux density measurements for a sample of six pulsars, and we are able to place upper flux density limits for another 12 pulsars. We find that all pulsar spectra have a simple form that can be described using only three parameters, one of which is the lifetime of short nano-pulses in the emission region.The study of the transition region from coherent to incoherent emission needs further and more sensitive observations at even higher radio frequencies.Comment: to appear in A&A (in press), 7 pages, 3 figure

Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc