Super Earth Explorer: A Coronagraphic Off-Axis Space Telescope

The Super-Earth Explorer is an Off-Axis Space Telescope (SEE-COAST) designed for high contrast imaging. Its scientific objective is to make the physico-chemical characterization of exoplanets possibly down to 2 Earth radii >. For that purpose it will analyze the spectral and polarimetric properties of the parent starlight reflected by the planets, in the wavelength range 400-1250 nmComment: Accepted in Experimental Astronom

Agarose Spot as a Comparative Method for in situ Analysis of Simultaneous Chemotactic Responses to Multiple Chemokines

yesWe describe a novel protocol to quantitatively and simultaneously compare the chemotactic responses of cells towards different chemokines. In this protocol, droplets of agarose gel containing different chemokines are applied onto the surface of a Petri dish, and then immersed under culture medium in which cells are suspended. As chemokine molecules diffuse away from the spot, a transient chemoattractant gradient is established across the spots. Cells expressing the corresponding cognate chemokine receptors migrate against this gradient by crawling under the agarose spots towards their centre. We show that this migration is chemokine-specific; meaning that only cells that express the cognate chemokine cell surface receptor, migrate under the spot containing its corresponding chemokine ligand. Furthermore, we show that migration under the agarose spot can be modulated by selective small molecule antagonists present in the cell culture medium

The instrument suite of the European Spallation Source

An overview is provided of the 15 neutron beam instruments making up the initial instrument suite of the European Spallation Source (ESS), and being made available to the neutron user community. The ESS neutron source consists of a high-power accelerator and target station, providing a unique long-pulse time structure of slow neutrons. The design considerations behind the time structure, moderator geometry and instrument layout are presented. The 15-instrument suite consists of two small-angle instruments, two reflectometers, an imaging beamline, two single-crystal diffractometers; one for macromolecular crystallography and one for magnetism, two powder diffractometers, and an engineering diffractometer, as well as an array of five inelastic instruments comprising two chopper spectrometers, an inverse-geometry single-crystal excitations spectrometer, an instrument for vibrational spectroscopy and a high-resolution backscattering spectrometer. The conceptual design, performance and scientific drivers of each of these instruments are described. All of the instruments are designed to provide breakthrough new scientific capability, not currently available at existing facilities, building on the inherent strengths of the ESS long-pulse neutron source of high flux, flexible resolution and large bandwidth. Each of them is predicted to provide world-leading performance at an accelerator power of 2 MW. This technical capability translates into a very broad range of scientific capabilities. The composition of the instrument suite has been chosen to maximise the breadth and depth of the scientific impact o

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

A domain decomposition solver to compute the barotropic component of an OGCM in the parallel processing field

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 22419, issue : a.1995 n.112 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Entropy-Assisted Emotion Recognition of Valence and Arousal Using XGBoost Classifier

Part 5: Machine Learning - Regression - ClassificationInternational audienceEmotion recognition is an essential function to realize human-machine interaction devices. Physiological signals which can be collected easily and continuously by wearable sensors are good inputs for emotion analysis. How to effectively process physiological signals, extract critical features, and choose machine learning model for emotion classification has been a big challenge. In this paper, an entropy-based processing scheme for emotion recognition framework is proposed, which includes entropy domain feature extraction and prediction by XGBoost classifier. We experiment on AMIGOS database and the experimental results show that the proposed scheme for multi-modal analysis outperforms conventional processing approaches. It achieves approximately 80% and 68% accuracy of prediction for two affect dimensions, valence and arousal. For one modality case, we found that galvanic skin response (GSR) channel is the most potential modality for prediction, which leads to best performances

Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered