All-Optical Reinforcement Learning in Solitonic X-Junctions

L'etologia ha dimostrato che gruppi di animali o colonie possono eseguire calcoli complessi distribuendo semplici processi decisionali ai membri del gruppo. Ad esempio, le colonie di formiche possono ottimizzare le traiettorie verso il cibo eseguendo sia un rinforzo (o una cancellazione) delle tracce di feromone sia spostarsi da una traiettoria ad un'altra con feromone più forte. Questa procedura delle formiche possono essere implementati in un hardware fotonico per riprodurre l'elaborazione del segnale stigmergico. Presentiamo qui innovative giunzioni a X completamente integrate realizzate utilizzando guide d'onda solitoniche in grado di fornire entrambi i processi decisionali delle formiche. Le giunzioni a X proposte possono passare da comportamenti simmetrici (50/50) ad asimmetrici (80/20) utilizzando feedback ottici, cancellando i canali di uscita inutilizzati o rinforzando quelli usati.Ethology has shown that animal groups or colonies can perform complex calculation distributing simple decision-making processes to the group members. For example ant colonies can optimize the trajectories towards the food by performing both a reinforcement (or a cancellation) of the pheromone traces and a switch from one path to another with stronger pheromone. Such ant's processes can be implemented in a photonic hardware to reproduce stigmergic signal processing. We present innovative, completely integrated X-junctions realized using solitonic waveguides which can provide both ant's decision-making processes. The proposed X-junctions can switch from symmetric (50/50) to asymmetric behaviors (80/20) using optical feedbacks, vanishing unused output channels or reinforcing the used ones

Shape analysis on homogeneous spaces: a generalised SRVT framework

Shape analysis is ubiquitous in problems of pattern and object recognition and has developed considerably in the last decade. The use of shapes is natural in applications where one wants to compare curves independently of their parametrisation. One computationally efficient approach to shape analysis is based on the Square Root Velocity Transform (SRVT). In this paper we propose a generalised SRVT framework for shapes on homogeneous manifolds. The method opens up for a variety of possibilities based on different choices of Lie group action and giving rise to different Riemannian metrics.Comment: 28 pages; 4 figures, 30 subfigures; notes for proceedings of the Abel Symposium 2016: "Computation and Combinatorics in Dynamics, Stochastics and Control". v3: amended the text to improve readability and clarify some points; updated and added some references; added pseudocode for the dynamic programming algorithm used. The main results remain unchange

1º Premio Concurso de Anteproyecto para el edificio del Instituto de Estudio del Medio Ambiente (I.E.M.A.) de la Universidad de Mendoza.

Al análisis del Programa y de las Premisas de proyecto solicitados en Bases y Condiciones Generales del concurso, prosiguió un pormenorizado estudio de todos los aspectos que forman parte de la realidad y se los fue incorporando al mencionado Programa con todas sus posibles variantes, tratando de dosificar, jerarquizar y armonizar el hecho arquitectónico en cada uno de sus componentes. Todo ello bajo el convencimiento de que en un completo y pormenorizado proceso de diseño vale tanto la construcción con materiales y técnicas apropiadas por costo y facilidad de mantenimiento, como la creación de espacios construidos dignos del hombre.La organización de espacios contemporáneos nos obliga a pensar en el mañana. El hombre necesita de espacios cálidos e íntimos donde lo individual y lo grupal no puede ser definido con precisión de ley. Por ello se considera que: al ser toda construcción la manifestación estética de la organización imperante, las condiciones para su proyecto deben tener en cuenta las siguientes determinantes: CREACIÓN DE ESPACIOS A ESCALA HUMANA, CRECIMIENTO, FLEXIBILIDAD Y CAMBIO

Influence of growth stage on efficacy, absorption and translocation of glyphosate in Eragrostis plana.

Resumo

Non-negative data-driven mapping of structural connections with application to the neonatal brain

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation

Identificación y caracterización de refugios de quirópteros en la Ciudad de Corrientes, Argentina

Los estudios sobre quirópteros y sus relaciones ecológicas son escasos en el nordeste argentino. Los objetivos del trabajo fueron localizar y caracterizar refugios, así como identificar las especies de murciélagos halladas, para establecer datos sobre la actual ecología urbana de estos mamíferos en la Ciudad de Corrientes. Mediante observaciones de los investigadores y entrevistas personalizadas realizadas al azar en diferentes barrios, se localizaron refugios durante el período marzo 2010 a mayo 2012. Se evaluó la altura de los refugios y se los clasificó en naturales y artificiales, estos últimos habitados o no por el hombre. Se identificaron especies arbóreas utilizadas como refugios, se estimó la distancia entre éstas y las viviendas humanas y se estableció género y especie de murciélagos capturados. La captura, realizada en forma manual y por trampa balde, fue menor durante el período invernal. Se caracterizaron 38 refugios, 53% naturales y 47% artificiales, sin encontrarse diferencias significativas entre ambos (p>0,05). Los refugios fueron más numerosos en viviendas habitadas por el hombre (92,31%) que en aquéllas deshabitadas. Se identificaron siete especies arbóreas, Fraxinus americana fue la más frecuentemente utilizada como refugio, quizás por su abundancia en la ciudad y por sus características favorables para los insectos. Los refugios naturales fueron hallados a menos de 10 m de distancia de edificaciones y a no más de 5 m de altura con respecto al suelo. Se capturaron 76 murciélagos pertenecientes a 3 familias y 9 especies, 93% insectívoras. Se hallaron dos especies de la familia Molossidae compartiendo refugio, Eumops patagonicus y Molossus rufus. Tanto los refugios naturales como los artificiales se encontraron en áreas con presencia de vegetación, luz artificial y alta densidad de insectos. La escasa proximidad de los refugios con viviendas humanas demuestra la eficiente adaptabilidad de diferentes géneros y especies de quirópteros de la Ciudad de Corrientes

Confound modelling in UK Biobank brain imaging

© 2020 Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds

Non-negative data-driven mapping of structural connections with application to the neonatal brain

© 2020 Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation

Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity