There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Its functionality and modeling capabilities continue to be extended, while its performance is enhanced. An overview of recent developments in diverse areas of the toolkit is presented. These include performance optimization for complex setups; improvements for the propagation in fields; new options for event biasing; and additions and improvements in geometry, physics processes and interactive capabilities

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Geant4 developments and applications

(Abridged) The Large Area Telescope (Fermi/LAT, hereafter LAT), the primary instrument on the Fermi Gamma-ray Space Telescope (Fermi) mission, is an imaging, wide field-of-view, high-energy gamma-ray telescope, covering the energy range from below 20 MeV to more than 300 GeV. This paper describes the LAT, its pre-flight expected performance, and summarizes the key science objectives that will be addressed. On-orbit performance will be presented in detail in a subsequent paper. The LAT is a pair-conversion telescope with a precision tracker and calorimeter, each consisting of a 4x4 array of 16 modules, a segmented anticoincidence detector that covers the tracker array, and a programmable trigger and data acquisition system. Each tracker module has a vertical stack of 18 x,y tracking planes, including two layers (x and y) of single-sided silicon strip detectors and high-Z converter material (tungsten) per tray. Every calorimeter module has 96 CsI(Tl) crystals, arranged in an 8 layer hodoscopic configuration with a total depth of 8.6 radiation lengths. The aspect ratio of the tracker (height/width) is 0.4 allowing a large field-of-view (2.4 sr). Data obtained with the LAT are intended to (i) permit rapid notification of high-energy gamma-ray bursts (GRBs) and transients and facilitate monitoring of variable sources, (ii) yield an extensive catalog of several thousand high-energy sources obtained from an all-sky survey, (iii) measure spectra from 20 MeV to more than 50 GeV for several hundred sources, (iv) localize point sources to 0.3 - 2 arc minutes, (v) map and obtain spectra of extended sources such as SNRs, molecular clouds, and nearby galaxies, (vi) measure the diffuse isotropic gamma-ray background up to TeV energies, and (vii) explore the discovery space for dark matter.

The Large Area Telescope on the Fermi Gamma-ray Space Telescope Mission

Polyphenols are secondary metabolites of plants and are generally involved in defense against ultraviolet radiation or aggression by pathogens. In the last decade, there has been much interest in the potential health benefits of dietary plant polyphenols as antioxidant. Epidemiological studies and associated meta-analyses strongly suggest that long term consumption of diets rich in plant polyphenols offer protection against development of cancers, cardiovascular diseases, diabetes, osteoporosis and neurodegenerative diseases. Here we present knowledge about the biological effects of plant polyphenols in the context of relevance to human health.

/pdf/plant-polyphenols-as-dietary-antioxidants-in-human-health-29nh0evfx2.pdf

Plant Polyphenols as Dietary Antioxidants in Human Health and Disease

Transfer Learning is a critical topic of research in the BCI field. Its goal is to reuse data gathered in a previous session (source session) in order to reduce, or completely bypass, calibration in a new session (target session). Although many methods have been proposed to tackle this problem, little is known about what characteristics of the datasets should be taken into account in order to ensure good performance. In this paper, we perform an exploratory analysis to study the influence of some simple descriptors of the source and target datasets over the classification scores obtained with Transfer Learning. We observe that the discriminability of the data points in the target session plays an important role in determining how well the Transfer Learning will work, as opposed to that of the source session, which has no statistically significant role in most cases.

https://hal.archives-ouvertes.fr/hal-02321580/document

"When does it work ?" : An exploratory analysis of Transfer Learning for BCI.

Carbamazepine, venlafaxine, tramadol, and their main metabolites: Toxicological effects on zebrafish embryos and larvae.

Influência do regime hídrico e da rega no comportamento da casta Touriga Nacional na Região do Dão

Pectus Carinatum (PC) is a chest deformity consisting on the anterior protrusion of the sternum and adjacent costal cartilages. Non-operative corrections, such as the orthotic compression brace, require previous information of the patient chest surface, to improve the overall brace fit. This paper focuses on the validation of the Kinect scanner for the modelling of an orthotic compression brace for the correction of Pectus Carinatum. To this extent, a phantom chest wall surface was acquired using two scanner systems – Kinect and Polhemus FastSCAN – and compared through CT. The results show a RMS error of 3.25mm between the CT data and the surface mesh from the Kinect sensor and 1.5mm from the FastSCAN sensor.

Assessment of 3D Scanners for Modeling Pectus Carinatum Corrective Bar

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics. Author summary A central problem in computational neural modeling is estimating model parameters that can account for observed activity patterns. While several techniques exist to perform parameter inference in special classes of abstract neural models, there are comparatively few approaches for large scale biophysically detailed neural models. In this work, we describe challenges and solutions in applying a deep learning based statistical framework to estimate parameters in a biophysically detailed large scale neural model, and emphasize the particular difficulties in estimating parameters for time series data. Our example uses a multi-scale model designed to connect human MEG/EEG recordings to the underlying cell and circuit level generators. Our approach allows for crucial insight into how cell-level properties interact to produce measured neural activity, and provides guidelines for diagnosing the quality of the estimate and uniqueness of predictions for different MEG/EEG biomarkers.

/pdf/methods-and-considerations-for-estimating-parameters-in-3afegbmv.pdf

Pedro Rodrigues

Papers

"When does it work ?" : An exploratory analysis of Transfer Learning for BCI.

Carbamazepine, venlafaxine, tramadol, and their main metabolites: Toxicological effects on zebrafish embryos and larvae.

Influência do regime hídrico e da rega no comportamento da casta Touriga Nacional na Região do Dão

Assessment of 3D Scanners for Modeling Pectus Carinatum Corrective Bar

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference