Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

On Coupling FCA and MDL in Pattern Mining

International audiencePattern Mining is a well-studied field in Data Mining and Machine Learning. The modern methods are based on dynamically updating models, among which MDL-based ones ensure high-quality pattern sets. Formal concepts also characterize patterns in a condensed form. In this paper we study MDL-based algorithm called Krimp in FCA settings and propose a modified version that benefits from FCA and relies on probabilistic assumptions that underlie MDL. We provide an experimental proof that the proposed approach improves quality of pattern sets generated by Krimp

Three Eruptions Observed by Remote Sensing Instruments Onboard Solar Orbiter

On February 21 and March 21 – 22, 2021, the Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter observed three prominence eruptions. The eruptions were associated with coronal mass ejections (CMEs) observed by Metis, Solar Orbiter’s coronagraph. All three eruptions were also observed by instruments onboard the Solar–TErrestrial RElations Observatory (Ahead; STEREO-A), the Solar Dynamics Observatory (SDO), and the Solar and Heliospheric Observatory (SOHO). Here we present an analysis of these eruptions. We investigate their morphology, direction of propagation, and 3D properties. We demonstrate the success of applying two 3D reconstruction methods to three CMEs and their corresponding prominences observed from three perspectives and different distances from the Sun. This allows us to analyze the evolution of the events, from the erupting prominences low in the corona to the corresponding CMEs high in the corona. We also study the changes in the global magnetic field before and after the eruptions and the magnetic field configuration at the site of the eruptions using magnetic field extrapolation methods. This work highlights the importance of multi-perspective observations in studying the morphology of the erupting prominences, their source regions, and associated CMEs. The upcoming Solar Orbiter observations from higher latitudes will help to constrain this kind of study better

Apraxia of speech and cerebellar mutism syndrome: a case study

Background Cerebellar mutism syndrome (CMS) or posterior fossa syndrome (PFS) consists of a constellation of neuropsychiatric, neuropsychological and neurogenic speech and language deficits. It is most commonly observed in children after posterior fossa tumor surgery. The most prominent feature of CMS is mutism, which generally starts after a few days after the operation, has a limited duration and is typically followed by motor speech deficits. However, the core speech disorder subserving CMS is still unclear. Case presentation This study investigates the speech and language symptoms following posterior fossa medulloblastoma surgery in a 12-year-old right-handed boy. An extensive battery of formal speech (DIAS = Diagnostic Instrument Apraxia of Speech) and language tests were administered during a follow-up of 6 weeks after surgery. Although the neurological and neuropsychological (affective, cognitive) symptoms of this patient are consistent with Schmahmann’s syndrome, the speech and language symptoms were markedly different from what is typically described in the literature. In-depth analyses of speech production revealed features consistent with a diagnosis of apraxia of speech (AoS) while ataxic dysarthria was completely absent. In addition, language assessments showed genuine aphasic deficits as reflected by distorted language production and perception, wordfinding difficulties, grammatical disturbances and verbal fluency deficits. Conclusion To the best of our knowledge this case might be the first example that clearly demonstrates that a higher level motor planning disorder (apraxia) may be the origin of disrupted speech in CMS. In addition, identification of non-motor linguistic disturbances during follow-up add to the view that the cerebellum not only plays a crucial role in the planning and execution of speech but also in linguistic processing. Whether the cerebellum has a direct or indirect role in motor speech planning needs to be further investigated

SunPy - Python for Solar Physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy

Constructing a national higher education brand for the UK: positional competition and promised capitals

This article examines national branding of UK higher education, a strategic intent and action to collectively brand UK higher education with the aim to attract prospective international students, using a Bourdieusian approach to understanding promises of capitals. We trace its development between 1999 and 2014 through a sociological study, one of the first of its kind, from the 'Education UK' and subsumed under the broader 'Britain is GREAT' campaign of the Coalition Government. The findings reveal how a national higher education brand is construed by connecting particular representations of the nation with those of prospective international students and the higher education sector, which combine in the brand with promises of capitals to convert into positional advantage in a competitive environment. The conceptual framework proposed here seeks to connect national higher education branding to the concept of the competitive state, branded as a nation and committed to the knowledge economy

Self-justification for opportunistic purchasing behavior in strategic supplier relationships

Purpose this study aims to get a deeper understanding of one of the antecedents of opportunistic behavior in strategic supplier relationships at the individual level of analysis. The authors specifically focus on self-justification, which could be seen as a mechanism that relaxes the moral scruples of purchasing professionals and, hence, facilitates actual opportunistic behavior.design/methodology/approachthe critical incident technique was deployed to interview purchasing professionals in the netherlands about their personal opportunistic behavior in strategic supplier relationships. This resulted in rich autobiographical accounts of 29 critical incidents of opportunistic behavior. The data were analyzed through the lens of the self-justification theory.findingsthe study identified a set of self-justification strategies underlying opportunistic purchasing behavior in strategic supplier relationships. Opportunistic professionals tended to deploy six strategies: acknowledgement, denial, rationalization, attributional egotism, sense of entitlement and ego aggrandizement.research limitations/implicationsthis study is limited to dutch industrial purchasers and was exploratory by nature. Future research could extend the perspective to other sectors, cultures and professional roles.practical implicationsthe study draws attention to radically new interventions at the individual level of analysis. To understand and minimize opportunistic behavior in strategic supplier relationships, organizations should acknowledge and address the important issue of self-justifications of purchasing professionals.originality/valuein contrast to the existing literature at the firm level of analysis, this study sheds new light on the antecedents of buyer opportunism from an alternative theoretical perspective at the individual level of analysis. The authors do not draw on the narrow perspective of personality psychology, but rather focus on the role of self-justification as an antecedent of buyer opportunism in strategic supplier relationships

An Efficient Algorithm for Computing Entropic Measures of Feature Subsets

International audienceEntropic measures such as conditional entropy or mutual information have been used numerous times in pattern mining, for instance to characterize valuable itemsets or approximate functional dependencies. Strangely enough the fundamental problem of designing efficient algorithms to compute entropy of subsets of features (or mutual information of feature subsets relatively to some target feature) has received little attention compared to the analog problem of computing frequency of itemsets. The present article proposes to fill this gap: it introduces a fast and scalable method that computes entropy and mutual information for a large number of feature subsets by adopting the divide and conquer strategy used by FP-growth-one of the most efficient frequent itemset mining algorithm. In order to illustrate its practical interest, the algorithm is then used to solve the recently introduced problem of mining reliable approximate functional dependencies. It finally provides empirical evidences that in the context of non-redundant pattern extraction, the proposed method outperforms existing algorithms for both speed and scalability