Renormalization of One-Pion Exchange and Power Counting

The renormalization of the chiral nuclear interactions is studied. In leading order, the cutoff dependence is related to the singular tensor interaction of the one-pion exchange potential. In S waves and in higher partial waves where the tensor force is repulsive this cutoff dependence can be absorbed by counterterms expected at that order. In the other partial waves additional contact interactions are necessary. The implications of this finding for the effective-field-theory program in nuclear physics are discussed.Comment: 19 pages, 18 figure

A Software Package for Unsupervised Pattern Recognition and Synoptic Representation of Results: Application to Volcanic Tremor Data of Mt Etna

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form

Complementary Methods for Volcanic Seismic Source Discrimination

ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks

Identification of activity regimes by unsupervised pattern classification of volcanic tremor data. Case studies from Mt. Etna

The monitoring of the seismic background signal – commonly referred to as volcanic tremor - has become a key tool for volcanic surveillance, particularly when field surveys are unsafe and/or visual observations are hampered by bad weather conditions. Indeed, it could be demonstrated that changes in the state of activity of the volcano show up in the volcanic tremor signature, such as amplitude and frequency content. Hence, the analysis of the characteristics of volcanic tremor leads us to pass from a mere monoparametric vision of the data to a multivariate one, which can be tackled with modern concepts of multivariate statistics. For this aim we present a recently developed software package which combines various concepts of unsupervised classification, in particular cluster analysis and Kohonen maps. Unsupervised classification is based on a suitable definition of similarity between patterns rather than on a-priori knowledge of their class membership. It aims at the identification of heterogeneities within a multivariate data set, thus permitting to focalize critical periods where significant changes in signal characteristics are encountered. The application of the software is demonstrated on sample sets derived from Mt. Etna during eruptions in 2001, 2006 and 2007-8

Regimes of Volcanic Activity at Mt. Etna in 2007-2009 inferred from Unsupervised Pattern Recognition on Volcanic Tremor Data

Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Particularly, volcano-seismic data sets cover decades long local recordings, temporally encompassing different styles of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Intense earthquakes swarms have often heralded effusive activity. However, from the seismic point of view, volcanic tremor has proved to be one of the most reliable indicators of impending eruptive activity. Indeed, changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. Some of us (Langer and Messina) have recently developed a new software for the classification of volcanic tremor data, combining Self Organizing Maps (also known as Kohonen Maps) along with Cluster and Fuzzy Analysis. This software allows us to analyse the background seismic radiation at permanent broadband stations located at various distance from the summit craters to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal. The information embedded in the spectrum is interpreted to assign a specific state of the volcano. An application of this new software is proposed here on the eruptive events at Etna of 2007-2009, which consisted of 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, followed by lava emissions on the upper east flank of the volcano, with start on 13 May 2008 and end on 6 July 2009. In the study period the source of volcanic tremor was always shallow (less than 3 km) and within the volcano edifice. The upraise of magma to the surface was fast and associated with changes of volcanic tremor features, which covered time windows of variable duration from several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well

Complementary Methods for Volcanic Seismic Source Discrimination

ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks

3D Visualization of Human Blood Vascular Networks Using Single-Domain Antibodies Directed against Endothelial Cell-Selective Adhesion Molecule (ESAM)

High-quality three-dimensional (3D) microscopy allows detailed, unrestricted and non-destructive imaging of entire volumetric tissue specimens and can therefore increase the diagnostic accuracy of histopathological tissue analysis. However, commonly used IgG antibodies are oftentimes not applicable to 3D imaging, due to their relatively large size and consequently inadequate tissue penetration and penetration speed. The lack of suitable reagents for 3D histopathology can be overcome by an emerging class of single-domain antibodies, referred to as nanobodies (Nbs), which can facilitate rapid and superior 2D and 3D histological stainings. Here, we report the generation and experimental validation of Nbs directed against the human endothelial cell-selective adhesion molecule (hESAM), which enables spatial visualization of blood vascular networks in whole-mount 3D imaging. After analysis of Nb binding properties and quality, selected Nb clones were validated in 2D and 3D imaging approaches, demonstrating comparable staining qualities to commercially available hESAM antibodies in 2D, as well as rapid and complete staining of entire specimens in 3D. We propose that the presented hESAM-Nbs can serve as novel blood vessel markers in academic research and can potentially improve 3D histopathological diagnostics of entire human tissue specimens, leading to improved treatment and superior patient outcomes

Aborted eruptions at Mt. Etna (Italy) in spring 2007 unveiled by an integrated study of gas emission and volcanic tremor

In spring 2007, a sequence of paroxysmal episodes took place at the Southeast Crater of Mt. Etna, Italy. Eruptive activity, characterised by Strombolian explosions, lava fountains, emission of lava flows and tephra, were all associated with an outstanding increase in the amplitude of volcanic tremor. In periods of quiescence between the eruptive episodes, recurring phases of seismic unrest were observed in forms of small temporary enhancements of the volcanic tremor amplitude, even though none of them culminated in eruptive activity. Here, we present the results of an integrated geophysical and geochemical data analysis encompassing records of volcanic tremor, thermal data, plume SO2 flux and radon over two months.We conclude that between February and April 2007, magma triggered repeated episodes of gas pulses and rock fracturing, but failed to reach the surface. Our multidisciplinary study allowed us to unveil these ‘aborted’ eruptions by investigating the long-temporal evolution of gas measurements along with seismic radiation. Short-term changes were additionally highlighted using a method of pattern classification based on Kohonen Maps and Fuzzy Clustering applied to volcanic tremor and radon data

Pressurization and depressurization phases inside the plumbing system of Mount Etna volcano: Evidence from a multiparametric approach

During 2013 Mount Etna volcano experienced intense eruptive activity at the summit craters, foremost at the New Southeast Crater and to a minor degree at the Voragine and Bocca Nuova (BN), which took place in two cycles, February-April and September-December. In this work, we mainly focus on the period between these cycles, applying a multiparametric approach. The period from the end of April to 5 September showed a gradual increase in the amplitude of long-period (LP) events and volcanic tremor, a slight inflation testified by both tilt and GPS data, and a CO2 flux increase. Such variations were interpreted as due to a gradual pressurization of the plumbing system, from the shallowest part, where LP and volcanic tremor are located, down to about 3-9km below sea level, pressure source depths obtained by both geodetic and CO2 data. On 5 September, at the same time as a large explosion at BN, we observed an instantaneous variation of the aforementioned signals (decrease in amplitude of LP events and volcanic tremor, slight deflation, and CO2 flux decrease) and the activation of a new infrasonic source located at BN. In the light of it, the BN explosion probably caused the instantaneous end of the pressurization, and the opening of a new vent at BN, that has become a new steady source of infrasonic events. This apparently slight change in the plumbing system also led to the gradual resumption of activity at the New Southeast Crater, culminating with the second lava fountain cycle of 2013