Occurrence and mineral chemistry of high pressure phases, Portrillo basalt, southcentral New Mexico

Inclusions of clinopyroxenite, kaersutiteclinopyroxenite, kaersutite-rich inclusions, wehrlite and olivine-clinopyroxenite together with megacrysts of feldspar, kaersutite and spinel are found loose on the flanks of cinder cones, as inclusions within lava flows and within the cores of volcanic bombs in the Quaternary alkali-olivine basalt of the West Potrillo Mountains, southcentral New Mexico. Based on petrological and geochemical evidence the megacysts are interpreted to be phenocrysts which formed at great depth rather that xenocrysts of larger crystal aggregates. These large crystals are believed to have formed as stable phases at high temperature and pressure and have partially reacted with the basalt to produce subhedral to anhedral crystal boundaries. It can be demonstrated that the mafic and ultramafic crystal aggregates were derived from an alkali-basalt source rock generated in the mantle. The inclusions are believed to represent a cumulus body or bodies injected within the lower crust or upper mantle

An Enhanced Spectroscopic Census of the Orion Nebula Cluster

We report new spectral types or spectral classification constraints for over 600 stars in the Orion Nebula Cluster (ONC) based on medium resolution R~ 1500-2000 red optical spectra acquired using the Palomar 200" and Kitt Peak 3.5m telescopes. Spectral types were initially estimated for F, G, and early K stars from atomic line indices while for late K and M stars, constituting the majority of our sample, indices involving TiO and VO bands were used. To ensure proper classification, particularly for reddened, veiled, or nebula-contaminated stars, all spectra were then visually examined for type verification or refinement. We provide an updated spectral type table that supersedes Hillenbrand (1997), increasing the percentage of optically visible ONC stars with spectral type information from 68% to 90%. However, for many objects, repeated observations have failed to yield spectral types primarily due to the challenges of adequate sky subtraction against a bright and spatially variable nebular background. The scatter between our new and our previously determined spectral types is approximately 2 spectral sub-classes. We also compare our grating spectroscopy results with classification based on narrow-band TiO filter photometry from Da Rio et al. (2012, finding similar scatter. While the challenges of working in the ONC may explain much of the spread, we highlight several stars showing significant and unexplained bona fide spectral variations in observations taken several years apart; these and similar cases could be due to a combination of accretion and extinction changes. Finally, nearly 20% of ONC stars exhibit obvious Ca II triplet emission indicative of strong accretion.Comment: Accepted to the Astronomical Journal; 37 pages, including 11 Figures and 3 Tables (one long table not reproduced here but available upon request or from the journal

Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Separate holograms of horizontally (HH) and vertically (HV) polarized responses obtained by the APQ-102 side-looking radar were processed through an optical correlator and the resulting image was recorded on positive film from which black and white negative and positive prints were made. Visual comparison of the HH and HV images reveals a distinct dark band in the imagery which covers about 30% of the radar strip. Preliminary evaluaton of the flight line 1 date indicates that various features on the HH and HV images seem to have different response levels. The amount of sidelap due to the look angle between flight lines 1 and 2 is negligible. NASA mission #425 to obtain flightlines of NS-001 MSS data and supporting aerial photography was successfully flown. Flight line 3 data are of very good quality and virtually cloud-free. Results of data analysis for selection of test fields and for evaluation of waveband combination and spatial resolution are presented

Four-quark states from functional methods

The discovery of four-quark states attracted a lot of attention from the theoretical as well as the experimental side. To study their properties from QCD we use a functional framework which combines (truncated) Dyson-Schwinger and Bethe-Salpeter equations in Landau gauge. This approach allows us to extract qualitative results for mass spectra, decay widths and wavefunctions of candidates for bound as well as resonant four-quark states. Furthermore, we can investigate the possible internal structure of such states. We report on recent developments and results using this functional framework and give an overview about the current status as well as future developments.Comment: 5 pages, 2 figures; contribution to the proceedings of the FAIR next generation scientists workshop (FAIRNESS), 7th edition, 23-27 May 2022, Paralia, Greec

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented

Current-induced two-level fluctuations in pseudo spin-valves (Co/Cu/Co) nanostructures

Two-level fluctuations of the magnetization state of pseudo spin-valve pillars Co(10 nm)/Cu(10 nm)/Co(30 nm) embedded in electrodeposited nanowires (~40 nm in diameter, 6000 nm in length) are triggered by spin-polarized currents of 10^7 A/cm^2 at room temperature. The statistical properties of the residence times in the parallel and antiparallel magnetization states reveal two effects with qualitatively different dependences on current intensity. The current appears to have the effect of a field determined as the bias field required to equalize these times. The bias field changes sign when the current polarity is reversed. At this field, the effect of a current density of 10^7 A/cm^2 is to lower the mean time for switching down to the microsecond range. This effect is independent of the sign of the current and is interpreted in terms of an effective temperature for the magnetization.Comment: 4 pages, 5 figures, revised version, to be published in Phys. Rev. Let

Informed pair selection for self-paced metric learning in Siamese neural networks.

Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning in SNNs is not reliant on explicit class knowledge; instead they require knowledge about the relationship between pairs. Though often ignored, we have found that appropriate pair selection is crucial to maximising training efficiency, particularly in scenarios where examples are limited. In this paper, we study the role of informed pair selection and propose a 2-phased strategy of exploration and exploitation. Random sampling provides the needed coverage for exploration, while areas of uncertainty modeled by neighbourhood properties of the pairs drive exploitation. We adopt curriculum learning to organise the ordering of pairs at training time using similarity knowledge as a heuristic for pair sorting. The results of our experimental evaluation show that these strategies are key to optimising training

Rashba spin-orbit coupling and spin precession in carbon nanotubes

The Rashba spin-orbit coupling in carbon nanotubes and its effect on spin-dependent transport properties are analyzed theoretically. We focus on clean non-interacting nanotubes with tunable number of subbands $N$. The peculiar band structure is shown to allow in principle for Datta-Das oscillatory behavior in the tunneling magnetoresistance as a function of gate voltage, despite the presence of multiple bands. We discuss the conditions for observing Datta-Das oscillations in carbon nanotubes.Comment: 12 pages, published versio

Learning to compare with few data for personalised human activity recognition.

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related (but different) new tasks. Unlike task-specific model training, a meta-learner’s training instance - referred to as a meta-instance - is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN), which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model-agnostic machine-learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners