Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Development of a simulated lung fluid leaching method to assess the release of potentially toxic elements from volcanic ash

Freshly erupted volcanic ash contains a range of soluble elements, some of which can generate harmful effects in living cells and are considered potentially toxic elements (PTEs). This work investigates the leaching dynamics of ash-associated PTEs in order to optimize a method for volcanic ash respiratory hazard assessment. Using three pristine (unaffected by precipitation) ash samples, we quantify the release of PTEs (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) and major cations typical of ash leachates (Mg, Na, Ca, K) in multiple simulated lung fluid (SLF) preparations and under varying experimental parameters (contact time and solid to liquid ratio). Data are compared to a standard water leach (WL) to ascertain whether the WL can be used as a simple proxy for SLF leaching. The main findings are: PTE concentrations reach steady-state dissolution by 24 h, and a relatively short contact time (10 min) approximates maximum dissolution; PTE dissolution is comparatively stable at low solid to liquid ratios (1:100 to 1:1000); inclusion of commonly used macromolecules has element-specific effects, and addition of a lung surfactant has little impact on extraction efficiency. These observations indicate that a WL can be used to approximate lung bioaccessible PTEs in an eruption response situation. This is a useful step towards standardizing in vitro methods to determine the soluble-element hazard from inhaled ash

Concept drift detection using online histogram-based bayesian classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Geologically recent areas as one key target for identifying active volcanism on Venus

The recently selected NASA VERITAS and DAVINCI missions, the ESA EnVision, the Roscosmos Venera-D will open a new era in the exploration of Venus. One of the key targets of the future orbiting and in situ investigations of Venus is the identification of volcanically active areas on the planet. The study of the areas characterized by recent or ongoing volcano-tectonic activity can inform us on how volcanism and tectonism are currently evolving on Venus. Following this key target, Brossier et al. (2022, https://doi.org/10.1029/2022GL099765) extend the successful approach and methodology used by previous works to Ganis Chasma in Atla Regio. Here we comment on the main results published in Brossier et al. (2022, https://doi.org/10.1029/2022GL099765) and discuss the important implications of their work for the future orbiting and in situ investigation of Venus. Their results add further lines of evidence indicating possibly recent volcanism on Venus

Mount Etna as a terrestrial laboratory to investigate recent volcanic activity on Venus by future missions:A comparison with Idunn Mons, Venus

The recently selected missions to Venus have opened a new era for the exploration of this planet. These missions will provide information about the chemistry of the atmosphere, the geomorphology, local-to-regional surface composition, and the rheology of the interior. One key scientific question to be addressed by these future missions is whether Venus remains volcanically active, and if so, how its volcanism is currently evolving. Hence, it is fundamental to analyze appropriate terrestrial analog sites for the study of possibly active volcanism on Venus. To this regard, we propose Mount Etna - one of the most active and monitored volcanoes on Earth - as a suitable terrestrial laboratory for remote and in-situ investigations to be performed by future missions to Venus. Being characterized by both effusive and explosive volcanic products, Mount Etna offers the opportunity to analyze multiple eruptive styles, both monitoring active volcanism and identifying the possible occurrence of pyroclastic activity on Venus. We directly compare Mount Etna with Idunn Mons, one of the most promising potentially active volcanoes of Venus. Despite the two structures show a different topography, they also show some interesting points of comparison, and in particular: a) comparable morpho-structural setting, since both volcanoes interact with a rift zone, and b) morphologically similar volcanic fields around both Mount Etna and Idunn Mons. Given its ease of access, we also propose Mount Etna as an analog site for laboratory spectroscopic studies to identify the signatures of unaltered volcanic deposits on Venus

SIMBIO-SYS : Scientific Cameras and Spectrometer for the BepiColombo Mission

The SIMBIO-SYS (Spectrometer and Imaging for MPO BepiColombo Integrated Observatory SYStem) is a complex instrument suite part of the scientific payload of the Mercury Planetary Orbiter for the BepiColombo mission, the last of the cornerstone missions of the European Space Agency (ESA) Horizon + science program. The SIMBIO-SYS instrument will provide all the science imaging capability of the BepiColombo MPO spacecraft. It consists of three channels: the STereo imaging Channel (STC), with a broad spectral band in the 400-950 nm range and medium spatial resolution (at best 58 m/px), that will provide Digital Terrain Model of the entire surface of the planet with an accuracy better than 80 m; the High Resolution Imaging Channel (HRIC), with broad spectral bands in the 400-900 nm range and high spatial resolution (at best 6 m/px), that will provide high-resolution images of about 20% of the surface, and the Visible and near-Infrared Hyperspectral Imaging channel (VIHI), with high spectral resolution (6 nm at finest) in the 400-2000 nm range and spatial resolution reaching 120 m/px, it will provide global coverage at 480 m/px with the spectral information, assuming the first orbit around Mercury with periherm at 480 km from the surface. SIMBIO-SYS will provide high-resolution images, the Digital Terrain Model of the entire surface, and the surface composition using a wide spectral range, as for instance detecting sulphides or material derived by sulphur and carbon oxidation, at resolutions and coverage higher than the MESSENGER mission with a full co-alignment of the three channels. All the data that will be acquired will allow to cover a wide range of scientific objectives, from the surface processes and cartography up to the internal structure, contributing to the libration experiment, and the surface-exosphere interaction. The global 3D and spectral mapping will allow to study the morphology and the composition of any surface feature. In this work, we describe the on-ground calibrations and the results obtained, providing an important overview of the instrument performances. The calibrations have been performed at channel and at system levels, utilizing specific setup in most of the cases realized for SIMBIO-SYS. In the case of the stereo camera (STC), it has been necessary to have a validation of the new stereo concept adopted, based on the push-frame. This work describes also the results of the Near-Earth Commissioning Phase performed few weeks after the Launch (20 October 2018). According to the calibration results and the first commissioning the three channels are working very well.Peer reviewe

Concept drift detection using online histogram-based bayesian classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Two Novel Mutations in 2 Families With Benign Familial Neonatal Convulsions

Benign familial neonatal convulsion is a rare autosomal dominant inherited epilepsy syndrome characterized by unprovoked seizures in the first few days of life, normal psychomotor development, and a positive intergenerational family history of neonatal seizures. Over 90% of the affected individuals have inherited causal mutations in KCNQ2 , which encodes for the potassium voltage-gated channel subfamily Q, member 2. Mutations in KCNQ2 are also associated with a severe neonatal encephalopathy phenotype associated with poor seizure control and neurodevelopmental deficits. The authors report the clinical presentations, response to medication, and intrafamilial phenotypic variability in 2 families with benign familial neonatal convulsions, carrying previously unreported heterozygous missense mutations, c.1066C>G (p.Leu356Val) and c.1721G < A (p.Gly574Asp), in KCNQ2 . The cases reported herein suggest that inherited missense mutations in KCNQ2 can be associated with an intermediate phenotype and illustrate the challenges associated with prognosis and counselling for individuals with inherited missense mutations in KCNQ2

Stereoisomers of hydroxymethanes: Probing structural and spectroscopic features upon substitution

International audienc

A general representation of dynamical systems for reservoir computing

Dynamical systems are capable of performing computation in a reservoir computing paradigm. This paper presents a general representation of these systems as an artificial neural network (ANN). Initially, we implement the simplest dynamical system, a cellular automaton. The mathematical fundamentals behind an ANN are maintained, but the weights of the connections and the activation function are adjusted to work as an update rule in the context of cellular automata. The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an initial step towards a general framework for dynamical systems. It aims to evolve such systems to optimize their usage in reservoir computing and to model physical computing substrates