Spin-3/2 baryons from an anisotropic lattice QCD action

The mass spectrum of baryons in the spin-3/2 sector is computed in quenched lattice QCD using a tadpole-improved anisotropic action. Both isospin 1/2 and 3/2 (the traditional decuplet) are considered, as well as members that contain strange quarks. States with positive and negative parities are isolated by parity projection, while states with spin-3/2 and spin-1/2 are separated by spin projection. The extent to which spin projection is needed is examined. The issue of optimal interpolating field is also investigated. The results are discussed in relation to previous calculations and experiment.Comment: modified version to appear in Phys Rev

The feasibility of utilizing remotely sensed data to assess and monitor oceanic gamefish

There are no author-identified significant results in this report

Particle physics methodologies applied to time-of-flight positron emission tomography with silicon-photomultipliers and inorganic scintillators

Positron emission tomography, or PET, is a medical imaging technique which has been used in clinical environments for over two decades. With the advent of fast timing detectors and scintillating crystals, it is possible to envisage improvements to the technique with the inclusion of time-of-flight capabilities. In this context, silicon photomultipliers coupled to fast inorganic LYSO crystals are investigated as a possible technology choice. As part of the ENVISION collaboration a range of photon detectors were investigated experimentally, leading to the selection of specific devices for use in a first prototype detector, currently being commissioned at the Rutherford Appleton Laboratory. In order to characterise the design of the prototype a GEANT4 simulation has been developed describing coupled systems of silicon photomultipliers and LYSO scintillators. Very good agreement is seen between the timing response of the experimental and simulated systems. Results of the simulation for a range of detector array arrangements are presented and a number of optimisations proposed for the final prototype design. Without the results provided here a detector system including only 3x3x5 mm3 crystals would have been adopted. A 3x3x5 mm3 crystal geometry is shown to provide little-to-no timing advantage over an identical system with 3x3x10 mm3 crystals, where detection efficiency is improved by approximately a factor of three. Additionally an investigation is presented which explores the impact of using events where gamma-ray photons are scattered internally within the detector array. It is shown that including such events could increase the signal achievable with one-to-one coupled detector arrays systems for PET by approximately 60%, with only minor reductions in coincidence timing resolution

LANDSAT menhaden and thread herring resources investigation, Gulf of Mexico

The author has identified the following significant results. The most significant achievements thus far include the successful charting of high probability fishing areas from LANDSAT MSS data and the successful simulation of an operational satellite system to provide tactical information for the commercial harvest of menhaden

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Calibration of the SNO+ experiment

The main goal of the SNO+ experiment is to perform a low-background and high-isotope-mass search for neutrinoless double-beta decay, employing 780 tonnes of liquid scintillator loaded with tellurium, in its initial phase at 0.5% by mass for a total mass of 1330 kg of (130)Te. The SNO+ physics program includes also measurements of geo- and reactor neutrinos, supernova and solar neutrinos. Calibrations are an essential component of the SNO+ data-taking and analysis plan. The achievement of the physics goals requires both an extensive and regular calibration. This serves several goals: the measurement of several detector parameters, the validation of the simulation model and the constraint of systematic uncertainties on the reconstruction and particle identification algorithms. SNO+ faces stringent radiopurity requirements which, in turn, largely determine the materials selection, sealing and overall design of both the sources and deployment systems. In fact, to avoid frequent access to the inner volume of the detector, several permanent optical calibration systems have been developed and installed outside that volume. At the same time, the calibration source internal deployment system was re-designed as a fully sealed system, with more stringent material selection, but following the same working principle as the system used in SNO. This poster described the overall SNO+ calibration strategy, discussed the several new and innovative sources, both optical and radioactive, and covered the developments on source deployment systems.Peer Reviewe

LANDSAT menhaden and thread herring resources investigation

The author has identified the following significant results. The relationship between the distribution of menhaden and selected oceanographic parameters (water color, turbidity, and possibly chlorophyll concentrations) was established. Similar relationships for thread herring were not established nor were relationships relating to the abundance of either species. Use of aircraft and LANDSAT remote sensing instruments to measure or infer a set of basic oceanographic parameters was evaluated. Parameters which could be accurately inferred included surface water temperature, salinity, and color. Water turbidity (Secchi disk) was evaluated as marginally inferrable from the LANDSAT MSS data and chlorophyll-a concentrations as less than marginal. These evaluations considered the parameters only as experienced in the two test areas using available sensors and statistical techniques

Fisheries Utilization of Remotely Sensed Data

The Fisheries Engineering Laboratory has conducted experiments in conjunction with ERTS-1 and Skylab-3 overflights, and is initiating an experiment using LANDSAT data acquisition systems. Data analyses have demonstrated relationships between remotely sensed oceanographic conditions and the distribution and abundance of specific living marine resources. These correlations have been used as the basis for predictive models which, when validated and refined, may benefit the fishing industry and the biological community