3,621 research outputs found
LANDSAT-D investigations in snow hydrology
Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover
Cellular neural networks, Navier-Stokes equation and microarray image reconstruction
Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the NavierāStokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time
Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach
Background: In this study, we quantified age-related changes in the time-course of face processing
by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our
approach does not rely on peak measurements and can provide a more sensitive measure of
processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded
discrimination task between two faces. The phase spectrum of these faces was manipulated
parametrically to create pictures that ranged between pure noise (0% phase information) and the
undistorted signal (100% phase information), with five intermediate steps.
Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was
higher, in younger than older observers. ERPs from each subject were entered into a single-trial
general linear regression model to identify variations in neural activity statistically associated with
changes in image structure. The earliest age-related ERP differences occurred in the time window
of the N170. Older observers had a significantly stronger N170 in response to noise, but this age
difference decreased with increasing phase information. Overall, manipulating image phase
information had a greater effect on ERPs from younger observers, which was quantified using a
hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus
parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at
multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower
processing in older observers starting around 120 ms after stimulus onset. This age-related delay
increased over time to reach a maximum around 190 ms, at which latency younger observers had
around 50 ms time lead over older observers.
Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual
system sensitivity to image structure, the current study demonstrates that older observers
accumulate face information more slowly than younger subjects. Additionally, the N170 appears to
be less face-sensitive in older observers
Eco-morphodynamic carbon pumping by the largest rivers in the Neotropics
AbstractThe eco-morphodynamic activity of large tropical rivers in South and Central America is analyzed to quantify the carbon flux from riparian vegetation to inland waters. We carried out a multi-temporal analysis of satellite data for all the largest rivers in the Neotropics (i.e, width > 200 m) in the period 2000ā2019, at 30 m spatial resolution. We developed a quantification of a highly efficient Carbon Pump mechanism. River morphodynamics is shown to drive carbon export from the riparian zone and to promote net primary production by an integrated process through floodplain rejuvenation and colonization. This pumping mechanism alone is shown to account for 8.9 million tons/year of carbon mobilization in these tropical rivers. We identify signatures of the fluvial eco-morphological activity that provide proxies for the carbon mobilization capability associated with river activity. We discuss river migrationācarbon mobilization nexus and effects on the carbon intensity of planned hydroelectric dams in the Neotropics. We recommend that future carbon-oriented water policies on these rivers include a similar analysis
Generative models for natural images
Nous traitons de modeĢles geĢneĢratifs construits avec des reĢseaux de neurones dans le contexte de la modeĢlisation dāimages. De nos jours, trois types de modeĢles sont particulieĢrement preĢdominants: les modeĢles aĢ variables latentes, tel que lāauto-encodeur variationnel (VAE), les modeĢles autoreĢgressifs, tel que le reĢseau de neurones reĢcurrent pixel (PixelRNN), et les modeĢles geĢneĢratifs antagonistes (GANs), qui sont des modeĢles aĢ transformation de bruit entraineĢs aĢ lāaide dāun adversaire. Cette theĢse traite de chacun de ces modeĢles.
Le premier chapitre couvre la base des modeĢles geĢneĢratifs, ainsi que les reĢseaux de neurones pro- fonds, qui constituent la technologie principalement utiliseĢe aĢ lāheure actuelle pour lāimpleĢmentation de modeĢles statistiques puissants.
Dans le deuxieĢme chapitre, nous impleĢmentons un auto-encodeur variationnel avec un deĢcodeur auto-reĢgressif. Cela permet de se libeĢrer de lāhypotheĢse dāindeĢpendance des dimensions de sortie du deĢcodeur variationnel, en modeĢlisant une distribution jointe tracĢ§able aĢ la place, et de doter le modeĢle auto-reĢgressif dāun code latent. De plus, notre impleĢmentation a un couĢt computationnel significativement reĢduit, si on le compare aĢ un modeĢle purement auto-reĢgressif ayant les meĢmes hypotheĢses de modeĢlisation et la meĢme performance. Nous deĢcrivons lāespace latent de facĢ§on hieĢrarchique, et montrons de manieĢre qualitative la deĢcomposition seĢmantique des causes latente induites par ce design. Finalement, nous preĢsentons des reĢsultats obtenus avec des jeux de donneĢes standards et deĢmontrant que la performance de notre impleĢmentation est fortement compeĢtitive.
Dans le troisieĢme chapitre, nous preĢsentons une proceĢdure dāentrainement ameĢlioreĢe pour une variante reĢcente de modeĢles geĢneĢratifs antagoniste. Le Ā«Wasserstein GANĀ» minimise la distance, mesureĢe avec la meĢtrique de Wasserstein, entre la distribution reĢelle et celle geĢneĢreĢe par le modeĢle, ce qui le rend plus facile aĢ entrainer quāun GAN avec un objectif minimax. Cependant, en fonction des parameĢtres, il preĢsente toujours des cas dāeĢchecs avec certain modes dāentrainement. Nous avons deĢcouvert que le coupable est le coupage des poids, et nous le remplacĢ§ons par une peĢnaliteĢ sur la norme des gradients. Ceci ameĢliore et stabilise lāentrainement, et ce sur diffeĢrents types du parameĢtres (incluant des modeĢles de langue sur des donneĢes discreĢtes), et permet de geĢneĢrer des eĢchantillons de haute qualiteĢs sur CIFAR-10 et LSUN bedrooms.
Finalement, dans le quatrieĢme chapitre, nous consideĢrons lāusage de modeĢles geĢneĢratifs modernes comme modeĢles de normaliteĢ dans un cadre de deĢtection hors-distribution Ā«zero-shotĀ». Nous avons eĢvalueĢ certains des modeĢles preĢceĢdemment preĢsenteĢs dans la theĢse, et avons trouveĢ que les VAEs sont les plus prometteurs, bien que leurs performances laissent encore un large place aĢ lāameĢlioration. Cette partie de la theĢse constitue un travail en cours.
Nous concluons en reĢpeĢtant lāimportance des modeĢles geĢneĢratifs dans le deĢveloppement de lāintelligence artificielle et mentionnons quelques deĢfis futurs.We discuss modern generative modelling of natural images based on neural networks. Three varieties of such models are particularly predominant at the time of writing: latent variable models such as variational autoencoders (VAE), autoregressive models such as pixel recurrent neural networks (PixelRNN), and generative adversarial networks (GAN), which are noise-transformation models trained with an adversary. This thesis touches on all three kinds.
The first chapter covers background on generative models, along with relevant discussions about deep neural networks, which are currently the dominant technology for implementing powerful statistical models.
In the second chapter, we implement variational autoencoders with autoregressive decoders. This removes the strong assumption of output dimensions being conditionally independent in variational autoencoders, instead tractably modelling a joint distribution, while also endowing autoregressive models with a latent code. Additionally, this model has significantly reduced computational cost compared to that of a purely autoregressive model with similar modelling assumptions and performance. We express the latent space as a hierarchy, and qualitatively demonstrate the semantic decomposition of latent causes induced by this design. Finally, we present results on standard datasets that demonstrate strongly competitive performance.
In the third chapter, we present an improved training procedure for a recent variant on generative adversarial networks. Wasserstein GANs minimize the Earth-Moverās distance between the real and generated distributions and have been shown to be much easier to train than with the standard minimax objective of GANs. However, they still exhibit some failure modes in training for some settings. We identify weight clipping as a culprit and replace it with a penalty on the gradient norm. This improves training further, and we demonstrate stability on a wide variety of settings (including language models over discrete data), and samples of high quality on the CIFAR-10 and LSUN bedrooms datasets.
Finally, in the fourth chapter, we present work in development, where we consider the use of modern generative models as normality models in a zero-shot out-of-distribution detection setting. We evaluate some of the models we have discussed previously in the thesis, and find that VAEs are the most promising, although their overall performance leaves a lot of room for improvement.
We conclude by reiterating the significance of generative modelling in the development of artificial intelligence, and mention some of the challenges ahead
PORE-LEVEL FLUID MIGRATION IN RESERVOIR SANDSTONES
The void space properties of a set of gas reservoir sandstone samples have been
measured. The properties include porosity, absolute gas permeability, electrical resistivity
formation factor and tortuosity. The mineralogy of each sandstone was determined by
scanning electron microscopy and energy dispersive x-ray analysis. Mercury intrusion and
extrusion data have been measured for most of the sandstone samples. A new procedure
for measuring the degree and range of void size correlations within resin-filled sandstones
has been developed. Image analysis of backscattered electron micrographs of these samples
supplies void size and positional information. A "semi-variogram" study of void size and
coordinate data ascertains the degree and range of void size correlation. Measurable
correlation has been found in two sandstone samples, but was absent from four others.
Diffusion coefficients of methane, iso-butane and n-butane through dry sandstones
have been measured using an adaptation of a non-steady state method, using a redesigned
apparatus. A repeatability and error analysis of diffusion coefficient measurement has also
been performed. A correlation between diffusion coefficients, absolute permeability,
porosity and formation factor was detected for sandstones containing little clay. The
diffusion coefficients measured for clay affected sandstones did not correlate with any
petrophysical properties of these samples.
A computer model capable of simulating porous media has been previously
developed. It consists of a 10x10x10 network of cubic pores and cylindrical throats, and
simulates die mercury intrusion curve. The void size distribution is modified until both
simulated and experimental curves closely match. New void size distribution input and
curve fit algorithms have been developed to increase the speed and accuracy of die
simulations and a new modelling procedure allows the modelling of samples with void size
correlation. The model is capable of simulating porosity, permeability, tortuosity and
mercury extrusion. Each of the reservoir sandstones has been modelled and their
characteristic properties simulated. Successful simulations were obtained for all relatively
clay-free reservoir sandstones. Clay affected sandstone simulations were less successful due
to the high complexity of these samples.
A study into formation damage witiiin reservoir sandstones was also undertaken.
The effect of colloidal particulate void space penetration is measured and simulated.British Gas,
Michael Road Research Station,
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