268 research outputs found
The multi-fractal structure of contrast changes in natural images: from sharp edges to textures
We present a formalism that leads very naturally to a hierarchical
description of the different contrast structures in images, providing precise
definitions of sharp edges and other texture components. Within this formalism,
we achieve a decomposition of pixels of the image in sets, the fractal
components of the image, such that each set only contains points characterized
by a fixed stregth of the singularity of the contrast gradient in its
neighborhood. A crucial role in this description of images is played by the
behavior of contrast differences under changes in scale. Contrary to naive
scaling ideas where the image is thought to have uniform transformation
properties \cite{Fie87}, each of these fractal components has its own
transformation law and scaling exponents. A conjecture on their biological
relevance is also given.Comment: 41 pages, 8 figures, LaTe
Multifractal wavelet filter of natural images
Natural images are characterized by the multiscaling properties of their
contrast gradient, in addition to their power spectrum. In this work we show
that those properties uniquely define an {\em intrinsic wavelet} and present a
suitable technique to obtain it from an ensemble of images. Once this wavelet
is known, images can be represented as expansions in the associated wavelet
basis. The resulting code has the remarkable properties that it separates
independent features at different resolution level, reducing the redundancy,
and remains essentially unchanged under changes in the power spectrum. The
possible generalization of this representation to other systems is discussed.Comment: 4 pages, 4 figures, RevTe
The effect of hydrogen sulphide on ammonium bisulphite when used as an oxygen scavenger in aqueous solutions
Peer reviewedPostprin
Computational Models to Detect Radiation in Urban Environments
Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This study presents a computational model to detect radioactive sources in urban environments, which uses signal processing techniques to identify radiation signatures. Moreover, the model uses artificial neural networks to identify types of radiation sources, classifying them as innocuous or harmful, and discerning between weapons-grade material and radioactive isotopes used in medical/industrial settings
Developing Computational Models to Detect Radiation in Urban Environments
The main objective of this project is to detect, characterize, and locate radioactive sources in urban environments using computational models based on machine learning and statistical techniques. The project explores multiple approaches such as signal processing methods, and neural networks. Unnatural radiation sources, such as Uranium or Plutonium, can present a risk to the population if they remain undetected by radiological search and response teams. Moreover, the computational model being developed must be capable of identifying the type of radiation source, classifying it as innocuous (i.e., isotopes used in medical and industrial settings) or harmful (nuclear weapons). The project is currently supported by the Pacific Northwest National Laboratory (PNNL), in collaboration with the Department of Mathematics at Embry-Riddle
Numerical simulation of a binary communication channel: Comparison between a replica calculation and an exact solution
The mutual information of a single-layer perceptron with Gaussian inputs
and deterministic binary outputs is studied by numerical simulations. The
relevant parameters of the problem are the ratio between the number of output
and input units, , and those describing the two-point
correlations between inputs. The main motivation of this work refers to the
comparison between the replica computation of the mutual information and an
analytical solution valid up to . The most relevant results
are: (1) the simulation supports the validity of the analytical prediction, and
(2) it also verifies a previously proposed conjecture that the replica solution
interpolates well between large and small values of .Comment: 6 pages, 8 figures, LaTeX fil
Implementation of Machine Learning Methods for Ionospheric Scintillation Data Analysis
The ionosphere is a region in the Earth’s upper atmosphere, where atoms are ionized due to solar radiation. The behavior of the ionosphere depends on time and location, and it is highly influenced by solar activity. The ionization process creates layers of free electrons at different altitudes, which can cause fluctuations in electromagnetic waves crossing the region. The effect of ionospheric events on radio signals can be measured using Global Navigation Satellite Systems (GNSS) receivers, in terms of ionospheric scintillation and Total Electron Content (TEC). The GNSS team at the Space Physics Research Lab (SPRL) studies ionospheric events using multi-frequency GNSS receivers (NovAtel GPStation-6) capable measuring high and low rate scintillation data as well as TEC values from three different GNSS systems (GPS, GALILEO, and GLONASS).
The purpose of this project is to develop a machine learning algorithm, using recurrent neural networks, to detect ionospheric events in low-rate scintillation data. Recurrent neural networks are often used for time-series applications, including forecasting and prediction. The model is being trained using data collected by the GNSS receivers in multiple locations (including Daytona Beach), with a focus on high-latitude data from the Canadian High Artic Ionospheric Network (CHAIN). The machine learning model will be integrated with the Embry-Riddle Ionospheric Scintillation Algorithm (EISA), an existing model capable of processing ionospheric data. EISA was developed by the GNSS team at SPRL. The updated model will allow the team to automate the process of ionospheric event detection, which is currently done manually. Upon this implementation, EISA will become an end-to-end model for ionospheric data collection, processing, and modelling
Auto and crosscorrelograms for the spike response of LIF neurons with slow synapses
An analytical description of the response properties of simple but realistic
neuron models in the presence of noise is still lacking. We determine
completely up to the second order the firing statistics of a single and a pair
of leaky integrate-and-fire neurons (LIFs) receiving some common slowly
filtered white noise. In particular, the auto- and cross-correlation functions
of the output spike trains of pairs of cells are obtained from an improvement
of the adiabatic approximation introduced in \cite{Mor+04}. These two functions
define the firing variability and firing synchronization between neurons, and
are of much importance for understanding neuron communication.Comment: 5 pages, 3 figure
Periodically rippled graphene: growth and spatially resolved electronic structure
We studied the growth of an epitaxial graphene monolayer on Ru(0001). The
graphene monolayer covers uniformly the Ru substrate over lateral distances
larger than several microns reproducing the structural defects of the Ru
substrate. The graphene is rippled with a periodicity dictated by the
difference in lattice parameter between C and Ru. The theoretical model predict
inhomogeneities in the electronic structure. This is confirmed by measurements
in real space by means of scanning tunnelling spectroscopy. We observe electron
pockets at the higher parts of the ripples.Comment: 5 page
A Novel Mathematical Approach for Analysis of Integrated Cell–Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance
A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogeneous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogeneous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy
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