Plane Cremona maps: saturation and regularity of the base ideal

One studies plane Cremona maps by focusing on the ideal theoretic and homological properties of its homogeneous base ideal ("indeterminacy locus"). The {\em leitmotiv} driving a good deal of the work is the relation between the base ideal and its saturation. As a preliminary one deals with the homological features of arbitrary codimension 2 homogeneous ideals in a polynomial ring in three variables over a field which are generated by three forms of the same degree. The results become sharp when the saturation is not generated in low degrees, a condition to be given a precise meaning. An implicit goal, illustrated in low degrees, is a homological classification of plane Cremona maps according to the respective homaloidal types. An additional piece of this work relates the base ideal of a rational map to a few additional homogeneous "companion" ideals, such as the integral closure, the $\boldsymbol\mu$-fat ideal and a seemingly novel ideal defined in terms of valuations.Comment: New version only 36 pages, one typo correcte

Teaching computers to see from space: deep learning and Sentinel 2

An outline of progress in the first year of research activities under my PhD. This is an outline of how and why Deep Learning can be used with remote sensing data for water contents analysis and classification, results from proof of concept experiments are described and future research activities are explained. A recording of the presentation and associated questions is available at https://1drv.ms/v/s!AsHRpsQE0ig4jPcrly23In5Tbqd10

Classification and Segmentation of Blooms and Plumes from High Resolution Satellite Imagery Using Deep Learning

Recent launches of high-resolution satellite sensors mean Earth Observation data are available at an unprecedented spatial and temporal scale. As data collection intensifies, our ability to inspect and investigate individual scenes for harmful algal or cyanobacterial blooms becomes limited, particularly for global monitoring. Algal Blooms and River Plumes are visible to trained experts in high resolution satellite imagery from Red-Green-Blue composites. Therefore, computer-assisted detection and classification of these events would provide invaluable information to experts and the general public on everyday water use. Advances in image recognition through Deep Learning techniques offer solutions that can accurately detect, classify and segment objects across thousands of images in a fraction of the time a human operator would require, while inspecting these images with much greater detail. Deep Learning techniques that jointly leverage spectral-spatial data are well characterised as a solution to land classification problems and have been shown to be accurate when using multi- or hyper-spectral data such as collected by the Sentinel-2 MultiSpectral Instrument. This work develops on state-of-the-art natural image segmentation algorithms to evaluate the capability of Deep Learning to detect and outline the presence of Algal Blooms or River Plumes in Sentinel 2 MSI data. The challenges in the application of these approaches are highlighted in the availability of suitable training and benchmark data, the use of atmospheric correction and neural network architecture design for utilisation of spectral data. Current Deep Learning network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines. Finally a training dataset and associated Deep Learning method is presented for use in future work relating to water contents classification

Deep Learning For Feature Tracking In Optically Complex Waters

PosterEnvironmental monitoring and early warning of water quality from space is now feasible at unprecedented spatial and temporal resolution following the latest generation of satellite sensors. The transformation of this data through classification into labelled, tracked event information is of critical importance to offer a searchable dataset. Advances in image recognition techniques through Deep Learning research have been successfully applied to satellite remote sensing data. Deep Learning approaches that leverage optical satellite data are now being developed for remotely sensed multi- and hyperspectral reflectance. The combination of spectral with spatial feature extracting Deep Learning networks promises a significant improvement in the accuracy of classifiers using remotely sensed data. This project aims to re-tool and optimise spectral-spatial Convolutional Neural Networks originally developed for land classification as a novel approach to identifying and labelling dynamic features in waterbodies, such as algal blooms and sediment plumes in high-resolution satellite sensors

Neuromodulation as a cognitive enhancement strategy in healthy older adults: promises and pitfalls

Increases in life expectancy have been followed by an upsurge of age-associated cognitive decline. Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have risen as promising approaches to prevent or delay such cognitive decline. However, consensus has not yet been reached about their efficacy in improving cognitive functioning in healthy older adults. Here we review the effects of TMS and tDCS on cognitive abilities in healthy older adults. Despite considerable variability in the targeted cognitive domains, design features and outcomes, the results generally show an enhancement or uniform benefit across studies. Most studies employed tDCS, suggesting that this technique is particularly well-suited for cognitive enhancement. Further work is required to determine the viability of these techniques as tools for long-term cognitive improvement. Importantly, the combination of TMS/tDCS with other cognitive enhancement strategies may be a promising strategy to alleviate the cognitive decline associated with the healthy aging process

Symbolic powers of monomial ideals and Cohen-Macaulay vertex-weighted digraphs

In this paper we study irreducible representations and symbolic Rees algebras of monomial ideals. Then we examine edge ideals associated to vertex-weighted oriented graphs. These are digraphs having no oriented cycles of length two with weights on the vertices. For a monomial ideal with no embedded primes we classify the normality of its symbolic Rees algebra in terms of its primary components. If the primary components of a monomial ideal are normal, we present a simple procedure to compute its symbolic Rees algebra using Hilbert bases, and give necessary and sufficient conditions for the equality between its ordinary and symbolic powers. We give an effective characterization of the Cohen--Macaulay vertex-weighted oriented forests. For edge ideals of transitive weighted oriented graphs we show that Alexander duality holds. It is shown that edge ideals of weighted acyclic tournaments are Cohen--Macaulay and satisfy Alexander dualityComment: Special volume dedicated to Professor Antonio Campillo, Springer, to appea

Effects of TENS and methylphenidate in tuberculous meningo-encephalistis

Primary objective: Beneficial effects of transcutaneous electrical nerve stimulation (TENS) on cognition and behaviour were observed in a child with probable Herpes Simplex Encephalitis. Based on these positive findings, it was examined in the present case study whether a child who had been diagnosed to suffer from tuberculous meningitis would benefit from TENS. Furthermore, as aggression and overactive behaviour were also prominent clinical symptoms, the effects of methylphenidate were investigated. Methods and Procedures: Neuropsychological tests were used to assess attention/concentration and visuospatial and visuoconstructive memory. Behaviour, including the level of activity during 24 hours, was assessed by one observation scale and actigraphy. Experimental interventions: TENS and methylphenidate. Main outcomes and results: TENS particularly improved overall affective behaviour. Methylphenidate appeared to have the opposite effect on cognition and hardly any effect on patient's behaviour. Conclusions: TENS might improve the patient's behavioural functioning. Pros and cons for treatment effects are discussed

Remote Sensing-Driven Pacific Oyster (Crassostrea gigas) Growth Modeling to Inform Offshore Aquaculture Site Selection

Aquaculture increasingly contributes to global seafood production, requiring new farm sites for continued growth. In France, oyster cultivation has conventionally taken place in the intertidal zone, where there is little or no further room for expansion. Despite interest in moving production further offshore, more information is needed regarding the biological potential for offshore oyster growth, including its spatial and temporal variability. This study shows the use of remotely-sensed chlorophyll-a and total suspended matter concentrations retrieved from the Medium Resolution Imaging Spectrometer (MERIS), and sea surface temperature from the Advanced Very High Resolution Radiometer (AVHRR), all validated using in situ matchup measurements, as input to run a Dynamic Energy Budget (DEB) Pacific oyster growth model for a study site along the French Atlantic coast (Bourgneuf Bay, France). Resulting oyster growth maps were calibrated and validated using in situ measurements of total oyster weight made throughout two growing seasons, from the intertidal zone, where cultivation currently takes place, and from experimental offshore sites, for both spat (R2 = 0.91; RMSE = 1.60 g) and adults (R2 = 0.95; RMSE = 4.34 g). Oyster growth time series are further digested into industry-relevant indicators, such as time to achieve market weight and quality index, elaborated in consultation with local producers and industry professionals, and which are also mapped. Offshore growth is found to be feasible and to be as much as two times faster than in the intertidal zone (p < 0.001). However, the potential for growth is also revealed to be highly variable across the investigated area. Mapping reveals a clear spatial gradient in production potential in the offshore environment, with the northeastern segment of the bay far better suited than the southwestern. Results also highlight the added value of spatiotemporal data, such as satellite image time series, to drive modeling in support of marine spatial planning. The current work demonstrates the feasibility and benefit of such a coupled remote sensing modeling approach within a shellfish farming context, responding to real and current interests of oyster producers