Supplementary measurements in the Sofia Geodynamic Network. Significance for contemporary local and regional geodynamics

The supplementary results are in agreement with previously obtained data that show southern motion of the stations with increasing velocities from north to south. They supplement the network in areas with missing data, but provide also new more accurate data at a local level. The velocities are from about 1.5 mm/y of the northernmost station to slightly over 3 mm in the southernmost station. They are significant for the interpretation of the present-day geodynamics and may be used as well as for the evaluation of the geological hazards.

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Causal Feature Selection with Missing Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3488055 ◽

2022 ◽

Vol 16 (4) ◽

pp. 1-24

Author(s):

Kui Yu ◽

Yajing Yang ◽

Wei Ding

Keyword(s):

Feature Selection ◽

Missing Data ◽

Real World ◽

Missing Values ◽

Prediction Models ◽

Causal Structure ◽

Data Imputation ◽

Accurate Data ◽

Unified Framework ◽

Class Variable

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically independent of the class variable conditioning on its MB, this enables causal feature selection to identify potential causal features for feature selection for building robust and physically meaningful prediction models. Missing data, ubiquitous in many real-world applications, remain an open research problem in causal feature selection due to its technical complexity. In this article, we discuss a novel multiple imputation MB (MimMB) framework for causal feature selection with missing data. MimMB integrates Data Imputation with MB Learning in a unified framework to enable the two key components to engage with each other. MB Learning enables Data Imputation in a potentially causal feature space for achieving accurate data imputation, while accurate Data Imputation helps MB Learning identify a reliable MB of the class variable in turn. Then, we further design an enhanced kNN estimator for imputing missing values and instantiate the MimMB. In our comprehensively experimental evaluation, our new approach can effectively learn the MB of a given variable in a Bayesian network and outperforms other rival algorithms using synthetic and real-world datasets.

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Multiconfiguration Dirac–Hartree–Fock calculations of energy levels, wavelengths, weighted oscillator strengths, transitions probabilities and lifetimes of Cd XLVII

Zeitschrift für Naturforschung A ◽

10.1515/zna-2021-0245 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Dhia Elhak Salhi ◽

Soumaya Manai ◽

Sirine Ben Nasr ◽

Haikel Jelassi

Keyword(s):

Missing Data ◽

Quantum Electrodynamics ◽

Transition Probabilities ◽

Energy Levels ◽

Oscillator Strengths ◽

Accurate Data ◽

Cadmium Ion ◽

Hartree Fock ◽

First Time ◽

Good Agreement

Abstract Energy levels, wavelengths, weighted oscillator strengths, transition probabilities and lifetimes are calculated for all levels of 1s 2 and 1snl (n = 2–6) configurations of He-like cadmium ion (Cd XLVII). The calculations were carried out using three codes GRASP2018, FAC and AMBiT in order to provide theoretically the most accurate data. Transition probabilities are reported for all the E1, E2, M1 and M2 transitions. Breit interactions and quantum electrodynamics effects are included in the RCI calculations. Comparisons were made with other calculations and a good agreement was found which confirms the reliability of our results. We present some missing data for the He-like cadmium in this paper for the first time.

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Geological Hazards

Earth Science for Civil and Environmental Engineers ◽

10.1017/9781139046336.018 ◽

2019 ◽

pp. 301-302

Keyword(s):

Geological Hazards

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Preventing and Treating Missing Data in Longitudinal Clinical Trials

10.1017/cbo9781139381666 ◽

2013 ◽

Cited By ~ 46

Author(s):

Craig Mallinckrodt

Keyword(s):

Clinical Trials ◽

Missing Data

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Do You Want to See the Tree? Ignore the Forest

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000240 ◽

2014 ◽

Vol 61 (3) ◽

pp. 205-214 ◽

Cited By ~ 12

Author(s):

Nicolas Poirel ◽

Claire Sara Krakowski ◽

Sabrina Sayah ◽

Arlette Pineau ◽

Olivier Houdé ◽

...

Keyword(s):

Visual Recognition ◽

Local Level ◽

Local Information ◽

Visual Environment ◽

Global Information ◽

Focus Attention ◽

Global Level ◽

Hierarchical Stimuli ◽

Priming Paradigm ◽

The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

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