Examination of the Relative Influence of Vegetation, Distance from Inflow, and Elevation on Sedimentation in a Coastal Californian Wetland

Wetlands and floodplains can act as areas of sediment deposition and storage. Therefore, they have the capability to improve downstream water quality and physical habitat. However, sedimentation rates may vary greatly within even a single wetland or floodplain. Much of the knowledge on wetland sedimentation rates is based on studies in controlled wetlands, where the setting and inflow may be carefully manipulated. While wetland systems receiving unregulated inflows are far more abundant, they are not as well studied. Determining which environmental factors drive deposition patterns may allow land managers to optimize sedimentation in managed wetlands. Additionally, quantified rates of sedimentation and land accretion have become important for managers considering the likelihood of habitat conversion, such as from freshwater wetlands to brackish or salt marsh, given climate change and subsequent sea level rise. We evaluated the influence of vegetation type and density, elevation, and proximity to the point of inflow on sedimentation in a natural Californian wetland receiving unregulated inflows through model comparison and evidence ratios based on Akaike information criterion weights. In addition to generating an interpolated surface generated from 59 artificial grass mat sediment traps, we conducted a mass-balance sediment budget to act as an independent check of the total sedimentation in the wetland basin. Sedimentation values over the eight month study period ranged from 254.0 to 2875.2 g/m2, with an average of 1054.6 g/m2.We found strong evidence that distance from the point of inflow was the driving factor in depositional patterns, with vegetation also potentially playing a role. However, some of these postulated influences may have been confounded with each other; vegetation type and density were determined to be moderately correlated with distance from the point of inflow (R = 0.273 and R = 0.325, respectively). This limited our ability to conclude if vegetation was a driving influence on observed sedimentation patterns

Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning

Active Inference is a recent framework for modeling planning under uncertainty. Empirical and theoretical work have now begun to evaluate the strengths and weaknesses of this approach and how it might be improved. A recent extension - the sophisticated inference (SI) algorithm - improves performance on multi-step planning problems through recursive decision tree search. However, little work to date has been done to compare SI to other established planning algorithms. SI was also developed with a focus on inference as opposed to learning. The present paper has two aims. First, we compare performance of SI to Bayesian reinforcement learning (RL) schemes designed to solve similar problems. Second, we present an extension of SI - sophisticated learning (SL) - that more fully incorporates active learning during planning. SL maintains beliefs about how model parameters would change under the future observations expected under each policy. This allows a form of counterfactual retrospective inference in which the agent considers what could be learned from current or past observations given different future observations. To accomplish these aims, we make use of a novel, biologically inspired environment designed to highlight the problem structure for which SL offers a unique solution. Here, an agent must continually search for available (but changing) resources in the presence of competing affordances for information gain. Our simulations show that SL outperforms all other algorithms in this context - most notably, Bayes-adaptive RL and upper confidence bound algorithms, which aim to solve multi-step planning problems using similar principles (i.e., directed exploration and counterfactual reasoning). These results provide added support for the utility of Active Inference in solving this class of biologically-relevant problems and offer added tools for testing hypotheses about human cognition.Comment: 31 pages, 5 figure

A Mismatch in the Ultraviolet Spectra between Low-Redshift and Intermediate-Redshift Type Ia Supernovae as a Possible Systematic Uncertainty for Supernova Cosmology

We present Keck high-quality rest-frame ultraviolet (UV) through optical spectra of 21 Type Ia supernovae (SNe Ia) in the redshift range 0.11 < z < 0.37 and a mean redshift of 0.22 that were discovered during the Sloan Digital Sky Survey-II (SDSS-II) SN Survey. Using the broad-band photometry of the SDSS survey, we are able to reconstruct the SN host-galaxy spectral energy distributions (SEDs), allowing for a correction for the host-galaxy contamination in the SN Ia spectra. Comparison of composite spectra constructed from a subsample of 17 high-quality spectra to those created from a low-redshift sample with otherwise similar properties shows that the Keck/SDSS SNe Ia have, on average, extremely similar rest-frame optical spectra but show a UV flux excess. This observation is confirmed by comparing synthesized broad-band colors of the individual spectra, showing a difference in mean colors at the 2.4 - 4.4 sigma level for various UV colors. We further see a slight difference in the UV spectral shape between SNe with low-mass and high-mass host galaxies. Additionally, we detect a relationship between the flux ratio at 2770 and 2900 A and peak luminosity that differs from that observed at low redshift. We find that changing the UV SED of an SN Ia within the observed dispersion can change the inferred distance moduli by ~0.1 mag. This effect only occurs when the data probe the rest-frame UV. We suggest that this discrepancy could be due to differences in the host-galaxy population of the two SN samples or to small-sample statistics.Comment: 28 pages, 21 figures, accepted by AJ, spectra are available at http://www.cfa.harvard.edu/~rfoley/data

Biomechanical characteristics of lower limb gait waveforms: Associations with body fat in children

Childhood obesity is associated with musculoskeletal dysfunction and altered lower limb biomechanics during gait. Few previous studies have explored relationships between childhood obesity measured by body fat and lower limb joint waveform kinematics and kinetics. What is the association between body fat and hip, knee and ankle joint angles and moments during gait and in 7 to 11 year-old boys? Fifty-five boys participated in the study. Body fat was measured by air displacement plethysmography. Hip, knee and ankle 3D waveforms of joint angles and moments were recorded during gait. Principle component analysis was used to reduce the multidimensional nature of the waveform into components representing parts of the gait cycle. Multiple linear regression analysis determined the association between the components with body fat. Higher body fat predicted greater hip flexion, knee flexion and knee internal rotation during late stance and greater ankle external rotation in late swing/early stance. Greater hip flexion and adduction moments were found in early stance with higher body fat. In mid-stance, greater knee adduction moments were associated with high body fat. Finally, at the ankle, higher body fat was predictive of greater internal rotation moments. The study presents novel information on relationships between body fat and kinematic and kinetic waveform analysis of paediatric gait. The findings suggest altered lower limb joint kinematics and kinetics with high body fat in young boys. The findings may help to inform research in to preventing musculoskeletal comorbidities and promoting weight management. [Abstract copyright: Copyright © 2018 Elsevier B.V. All rights reserved.

Philosophical Foundations of Wisdom

Practical wisdom (hereafter simply ‘wisdom’), which is the understanding required to make reliably good decisions about how we ought to live, is something we all have reason to care about. The importance of wisdom gives rise to questions about its nature: what kind of state is wisdom, how can we develop it, and what is a wise person like? These questions about the nature of wisdom give rise to further questions about proper methods for studying wisdom. Is the study of wisdom the proper subject of philosophy or psychology? How, exactly, can we determine what wisdom is and how we can get it? In this chapter, we give an overview of some prominent philosophical answers to these questions. We begin by distinguishing practical wisdom from theoretical wisdom and wisdom as epistemic humility. Once we have a clearer sense of the target, we address questions of method and argue that producing a plausible and complete account of wisdom will require the tools of both philosophy and empirical psychology. We also discuss the implications this has for prominent wisdom research methods in empirical psychology. We then survey prominent philosophical accounts of the nature of wisdom and end with reflections on the prospects for further interdisciplinary research

On the Lack of Correlation Between [OIII]/[OII] and Lyman-Continuum Escape Fraction

We present the first results of our pilot study of 8 photometrically selected Lyman continuum (LyC) emitting galaxy candidates from the COSMOS field and focus on their optical emission line ratios. Observations were performed in the H and K bands using the Multi-Object Spectrometer for Infra-Red Exploration (MOSFIRE) instrument at the Keck Observatory, targeting the [OII], H$\beta$, and [OIII] emission lines. We find that photometrically selected LyC emitting galaxy candidates have high ionization parameters, based on their high [OIII]/[OII] ratios (O32), with an average ratio for our sample of 2.5$\pm$0.2. Preliminary results of our companion Low Resolution Imaging Spectrometer (LRIS) observations, targeting LyC and Ly$\alpha$, show that those galaxies with the largest O32 are typically found to also be Ly$\alpha$ emitters. High O32 galaxies are also found to have tentative non-zero LyC escape fractions ($f_{esc}(LyC)$) based on $u$ band photometric detections. These results are consistent with samples of highly ionized galaxies, including confirmed LyC emitting galaxies from the literature. We also perform a detailed comparison between the observed emission line ratios and simulated line ratios from density bounded H$_{\textrm{II}}$ regions modeled using the photoionization code MAPPINGS V. Estimates of $f_{esc}(LyC)$ for our sample fall in the range from 0.0-0.23 and suggest possible tension with published correlations between O32 and $f_{esc}(LyC)$, adding weight to dichotomy of arguments in the literature. We highlight the possible effects of clumpy geometry and mergers that may account for such tension.Comment: 21 pages, 11 figures, 3 tables, accepted for publication in MNRA

Calibration and Validation of Accelerometry using cut-points to Assess Physical Activity in Paediatric Clinical Groups: A Systematic Review

Regular physical activity is associated with physiological and psychosocial benefits in both healthy and clinical populations. However, little is known about tailoring the analysis of physical activity using accelerometers to the specific characteristics of chronic conditions. Whilst accelerometry is broadly used to assess physical activity, recommendations on calibration in paediatric clinical groups are warranted. The aim of this systematic review was to provide a critical overview of protocols used to calibrate accelerometry in children and adolescents with clinical conditions, as well as to develop recommendations for calibration and validation of accelerometry in such populations. The search was performed between March to July 2017 using text words and subject headings in six databases. Studies had to develop moderate-to-vigorous intensity physical activity (MVPA) cut-points for paediatric clinical populations to be included. Risk of bias was assessed using a specific checklist. A total of 540,630 titles were identified, with 323 full-text articles assessed. Five studies involving 347 participants aged 9 to 15 years were included. Twenty-four MVPA cut-points were reported across seven clinical conditions, 16 of which were developed for different models of ActiGraph, seven for Actical and one for Tritrac-R3D. Statistical approaches included mixed regression, machine learning and receiver operating characteristic analyses. Disease-specific MVPA cut-points ranged from 152 to 735 counts·15 s−1, with lower cut-points found for inherited muscle disease and higher cut-points associated with intellectual disabilities. The lower MVPA cut-points for diseases characterised by both ambulatory and metabolic impairments likely reflect the higher energetic demands associated with those conditions