Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral imagery

The rapid expansion of remote sensing and information collection capabilities demands methods to highlight interesting or anomalous patterns within an overabundance of data. This research addresses this issue for hyperspectral imagery (HSI). Two new reconstruction based HSI anomaly detectors are outlined: one using principal component analysis (PCA), and the other a form of non-linear PCA called logistic principal component analysis. Two very effective, yet relatively simple, modifications to the autonomous global anomaly detector are also presented, improving algorithm performance and enabling receiver operating characteristic analysis. A novel technique for HSI anomaly detection dubbed multiple PCA is introduced and found to perform as well or better than existing detectors on HYDICE data while using only linear deterministic methods. Finally, a response surface based optimization is performed on algorithm parameters such as to affect consistent desired algorithm performance

One place doesn't fit all: improving the effectiveness of sustainability standards by accounting for place

Includes bibliographical references (pages 8-10).The growing interest in incentivizing sustainable agricultural practices is supported by a large network of voluntary production standards, which aim to offer farmers and ranchers increased value for their product in support of reduced environmental impact. To be effective with producers and consumers alike, these standards must be both credible and broadly recognizable, and thus are typically highly generalizable. However, the environmental impact of agriculture is strongly place-based and varies considerably due to complex biophysical, socio-cultural, and management-based factors, even within a given sector in a particular region. We suggest that this contradiction between the placeless generality of standards and the placed-ness of agriculture renders many sustainability standards ineffective. In this policy and practice review, we examine this contradiction through the lens of beef production, with a focus on an ongoing regional food purchasing effort in Denver, Colorado, USA. We review the idea of place in the context of agricultural sustainability, drawing on life cycle analysis and diverse literature to find that recognition of place-specific circumstances is essential to understanding environmental impact and improving outcomes. We then examine the case of the Good Food Purchasing Program (GFPP), a broad set of food-purchasing standards currently being implemented for institutional purchasing in Denver. The GFPP was created through a lengthy stakeholder-inclusive process for use in Los Angeles, California, USA, and has since been applied to many cities across the country. The difference between Los Angeles' process and that of applying the result of Los Angeles' process to Denver is instructive, and emblematic of the flaws of generalizable sustainability standards themselves. We then describe the essential elements of a place-based approach to agricultural sustainability standards, pointing toward a democratic, process-based, and outcome-oriented strategy that results in standards that enable rather than hinder the creativity of both producers and consumers. Though prescription is anathema to our approach, we close by offering a starting point for the development of standards for beef production in Colorado that respect the work of people in place

Understanding and Managing Stress

Leaders, particularly those who lead in dangerous contexts, are a powerful force in managing and alleviating the effects of stress. This chapter discusses how to leverage that force, describing stress management practices above and beyond the stalwarts of individual fitness, sleep, and good health habits. Theory along with the context of real world cases are presented to make leaders aware of the nature and effects of the decisions to be made while preparing for or leading in dangerous situations and how to assess and respond to critical incidents. The main lesson is that leaders must know their people, know the crucible in which they operate, establish a culture of catharsis, and know that they are a principle source of resilience

Understanding and Managing Stress

Leaders, particularly those who lead in dangerous contexts, are a powerful force in managing and alleviating the effects of stress. This chapter discusses how to leverage that force, describing stress management practices above and beyond the stalwarts of individual fitness, sleep, and good health habits. Theory along with the context of real world cases are presented to make leaders aware of the nature and effects of the decisions to be made while preparing for or leading in dangerous situations and how to assess and respond to critical incidents. The main lesson is that leaders must know their people, know the crucible in which they operate, establish a culture of catharsis, and know that they are a principle source of resilience

Randomized trial of polychromatic blue-enriched light for circadian phase shifting, melatonin suppression, and alerting responses.

Wavelength comparisons have indicated that circadian phase-shifting and enhancement of subjective and EEG-correlates of alertness have a higher sensitivity to short wavelength visible light. The aim of the current study was to test whether polychromatic light enriched in the blue portion of the spectrum (17,000 K) has increased efficacy for melatonin suppression, circadian phase-shifting, and alertness as compared to an equal photon density exposure to a standard white polychromatic light (4000 K). Twenty healthy participants were studied in a time-free environment for 7 days. The protocol included two baseline days followed by a 26-h constant routine (CR1) to assess initial circadian phase. Following CR1, participants were exposed to a full-field fluorescent light (1 × 10 14 photons/cm 2 /s, 4000 K or 17,000 K, n = 10/condition) for 6.5 h during the biological night. Following an 8 h recovery sleep, a second 30-h CR was performed. Melatonin suppression was assessed from the difference during the light exposure and the corresponding clock time 24 h earlier during CR1. Phase-shifts were calculated from the clock time difference in dim light melatonin onset time (DLMO) between CR1 and CR2. Blue-enriched light caused significantly greater suppression of melatonin than standard light ((mean ± SD) 70.9 ± 19.6% and 42.8 ± 29.1%, respectively, p \u3c 0.05). There was no significant difference in the magnitude of phase delay shifts. Blue-enriched light significantly improved subjective alertness (p \u3c 0.05) but no differences were found for objective alertness. These data contribute to the optimization of the short wavelength-enriched spectra and intensities needed for circadian, neuroendocrine and neurobehavioral regulation

The sampling and estimation of marine paleodiversity patterns: implications of a Pliocene model

Abstract.-Data that accurately capture the spatial structure of biodiversity are required for many paleobiological questions, from assessments of changing provinciality and the role of geographic ranges in extinction and originations, to estimates of global taxonomic or morphological diversity through time. Studies of temporal changes in diversity and global biogeographic patterns have attempted to overcome fossil sampling biases through sampling standardization protocols, but such approaches must ultimately be limited by available literature and museum collections. One approach to evaluating such limits is to compare results from the fossil record with models of past diversity patterns informed by modern relationships between diversity and climatic factors. Here we use present-day patterns for marine bivalves, combined with data on the geologic ages and distributions of extant taxa, to develop a model for Pliocene diversity patterns, which is then compared with diversity patterns retrieved from the literature as compiled by the Paleobiology Database (PaleoDB). The published Pliocene bivalve data (PaleoDB) lack the first-order spatial structure required to generate the modern biogeography within the time available (,3 Myr). Instead, the published data (raw and standardized) show global diversity maxima in the Tropical West Atlantic, followed closely by a peak in the cooltemperate East Atlantic. Either today's tropical West Pacific diversity peak, double that of any other tropical region, is a purely Pleistocene phenomenon-highly unlikely given the geologic ages of extant genera and the topology of molecular phylogenies-or the paleontological literature is such a distorted sample of tropical Pliocene diversity that current sampling standardization methods cannot compensate for existing biases. A rigorous understanding of large-scale spatial and temporal diversity patterns will require new approaches that can compensate for such strong bias, presumably by drawing more fully on our understanding of the factors that underlie the deployment of diversity today

Connecting urban food plans to the countryside: leveraging Denver's food vision to explore meaningful rural-urban linkages

Includes bibliographical references (pages 14-18).Cities are increasingly turning to food policy plans to support goals related to food access, food security, the environment, and economic development. This paper investigates ways that rural farmers, communities, and economies can both support and be supported by metropolitan food-focused initiatives. Specifically, our research question asked what opportunities and barriers exist to developing food policies that support urban food goals, particularly related to local procurement, as well as rural economic development. To address this question, we described and analyzed a meeting of urban stakeholders and larger-scale rural producers related to Colorado’s Denver Food Vision and Plan. We documented and explored “findings” gleaned from a supply chain diagraming and data compilation process that were then used to inform an event that brought together diverse supply chain partners. Three findings stand out. First, facilitating dialog between urban food policymakers and rural producers to understand potential tensions, mitigate such tensions, and capitalize on opportunities is essential. Second, perceptions and expectations surrounding “good food” are nuanced—a timely finding given the number of preferred procurement programs emerging across the county. Third, critical evaluation is needed across a diverse set of value chain strategies (e.g., conventional and alternative distribution) if food policy intends to support heterogeneous producers, their communities, and urban food policy goals

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved