CEO turnover in public and private organizations: analysis of the relevance of different performance horizons

Purpose – This paper investigates how past performance changes, prior CEO replacements and changes in the chairperson impact CEO turnover in public and large private businesses. Design/methodology/approach – We analyze 1,679 CEO replacements documented in a sample of 1,493 Spanish public and private firms during 1998–2004 by computing dynamic binary choice models that control for endogeneity in CEO turnovers. Findings – The results reveal that different performance horizons (short- and long-term) explain the dissimilar rate of CEO turnover between public and private firms. Private firms exercise monitoring patience and path dependency characterizes the evaluation of CEOs, while public companies’ short-termism leads to higher CEO turnover rates as a reaction to poor short-term economic results, and alternative controls—ownership and changes in the chairperson—improve the monitoring of management. Originality/value – Our results show the importance of controlling for path dependency to examine more accurately top executives’ performance. The findings confirm that exposure to market controls affects the functioning of internal controls in evaluating CEOs and shows a short-term performance horizon that could be behind the recent moves of public firms going private or restraining shareholders’ power.Peer ReviewedPostprint (published version

A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Silicon-based three-dimensional microstructures for radiation dosimetry in hadrontherapy

In this work, we propose a solid-state-detector for use in radiation microdosimetry. This device improves the performance of existing dosimeters using customized 3D-cylindrical microstructures etched inside silicon. The microdosimeter consists of an array of micro-sensors that have 3D-cylindrical electrodes of 15 μm diameter and a depth of 5 μm within a silicon membrane, resulting in a well-defined micrometric radiation sensitive volume. These microdetectors have been characterized using an 241Am source to assess their performance as radiation detectors in a high-LET environment. This letter demonstrates the capability of this microdetector to be used to measure dose and LET in hadrontherapy centers for treatment plan verification as part of their patient-specific quality control program

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Pneumonia is an infectious and deadly disease which strikes over millions of people. Usually, chest X-rays are used by radiotherapist to diagnose pneumonia. In this paper, a Computer- Aided Diagnosis (CAD) system for pneumonia detection in chest X-ray images is proposed. This system is based on Convolutional Neural Networks (CNNs) which are able to classify the image into two classes (pneumonia or normal). Experimental results show that the proposed system obtained an accuracy rate of 98.59%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The Arabidopsis Synaptotagmin1 is enriched in endoplasmic reticulum-plasma membrane contact sites and confers cellular resistance to mechanical stresses

Eukaryotic endoplasmic reticulum (ER)-plasma membrane (PM) contact sites are evolutionarily conserved microdomains that have important roles in specialized metabolic functions such as ER-PM communication, lipid homeostasis, and Ca2+ influx. Despite recent advances in knowledge about ER-PM contact site components and functions in yeast (Saccharomyces cerevisiae) and mammals, relatively little is known about the functional significance of these structures in plants. In this report, we characterize the Arabidopsis (Arabidopsis thaliana) phospholipid binding Synaptotagmin1 (SYT1) as a plant ortholog of the mammal extended synaptotagmins and yeast tricalbins families of ER-PM anchors. We propose that SYT1 functions at ER-PM contact sites because it displays a dual ER-PM localization, it is enriched in microtubule-depleted regions at the cell cortex, and it colocalizes with Vesicle-Associated Protein27-1, a known ER-PM marker. Furthermore, biochemical and physiological analyses indicate that SYT1 might function as an electrostatic phospholipid anchor conferring mechanical stability in plant cells. Together, the subcellular localization and functional characterization of SYT1 highlights a putative role of plant ER-PM contact site components in the cellular adaptation to environmental stresses

Teaching Materials for Active Methodologies in University Education

In the present university educational model, teaching is less teacher-centered (instruction) and more student-centered (learning). In this context, it is essential to have adequate teaching materials to support the student, both in the learning process and in the evaluation stage of the subjects. Thus, the formative evaluation (it allows knowing the progress of the students, offering proposals for additional learning) becomes more important than merely summative evaluation (with the sole purpose of obtaining a grade). This article presents a teaching-learning proposal based on the experience accumulated by the authors over several years, in which different teaching innovations have been applied. These innovations were intended to facilitate learning, improve results, increase motivation, foster collaborative work and student involvement, among other objectives. It has been found that active methodologies improve learning results through student motivation and involvement. For their part, teaching materials play a fundamental role in guiding and orienting the students’ autonomous learning process and, at the same time, facilitating collaborative and participatory dynamics in the classroom

Convection-induced severe winds over Menorca Island 28th October 2018. Under the watch of a forecaster

Póster presentado en: 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering

Knowing student learning styles represents an effective way to design the most suitable methodology for our students so that performance can improve with less effort for both students and teachers. However, a methodology is usually set in teaching guides according to the previous academic year's information without any knowledge of our current audience. In this work, a new software for learning styles and grade analysis based on the Honey-Alonso Learning Styles Questionnaire has been proposed. This tool proposes the average learning style profiles of a given course by clustering student learning styles and analyzes the possible relation between grades and learning style profiles. By using that program, three different courses from Computer Sciences Engineering degrees during an academic year have been analyzed. The obtained results in our specific context exhibit that possible relation. This information could be useful to understand how students approach learning materials

A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes

[EN] Background and objective Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become the inputs to shallow and deep Gaussian process classifiers which achieve an accurate prediction of cancer. Results We have used a real and unique dataset. The dataset is composed of 60 Whole Slide Images. For a five fold cross validation, shallow and deep Gaussian Processes obtain area under ROC curve values higher than 0.98. They outperform current state of the art patch based shallow classifiers and are very competitive to the best performing deep learning method. Models were also compared on 17 Whole Slide test Images using the FROC curve. With the cost of one false positive, the best performing method, the one layer Gaussian process, identifies 83.87% (sensitivity) of all annotated cancer in the Whole Slide Image. This result corroborates the quality of the extracted features, no more than a layer is needed to achieve excellent generalization results. Conclusion Two new descriptors to extract morphological features from histological images have been proposed. They collect very relevant information for cancer detection. From these descriptors, shallow and deep Gaussian Processes are capable of extracting the complex structure of prostate histological images. The new space/descriptor/classifier paradigm outperforms state-of-art shallow classifiers. Furthermore, despite being much simpler, it is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external databaseThis work was supported by the Ministerio de Economia y Competitividad through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA CorporationEsteban, AE.; López-Pérez, M.; Colomer, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2019). A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes. Computer Methods and Programs in Biomedicine. 178:303-317. https://doi.org/10.1016/j.cmpb.2019.07.003S30331717