535 research outputs found

    Realization of a feedback controlled flashing ratchet

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    A flashing ratchet transports diffusive particles using a time-dependent, asymmetric potential. Particle speed is predicted to increase when a feedback algorithm based on particle positions is used. We have experimentally realized such a feedback ratchet using an optical line trap, and observed that use of feedback increases velocity by up to an order of magnitude. We compare two different feedback algorithms for small particle numbers, and find good agreement with simulations. We also find that existing algorithms can be improved to be more tolerant to feedback delay times

    Transaction Costs in Payment of Environmental Service Contracts

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    Citation: Jeffrey M. Peterson, Craig M. Smith, John C. Leatherman, Nathan P. Hendricks, John A. Fox; Transaction Costs in Payment for Environmental Service Contracts, American Journal of Agricultural Economics, Volume 97, Issue 1, 1 January 2015, Pages 219–238, https://doi.org/10.1093/ajae/aau071Payment for environmental service contracts commonly require actions beyond adoption of a practice, such as undergoing specified enrollment procedures, granting consent to being monitored, and paying penalties for violations. These provisions are a bundle of attributes a landholder must accept with contract enrollment, leading to transaction costs in the contracting process. This article develops a principal–agent framework to study the links between these transaction costs and the well-known information asymmetries between the landholders and the government agency offering contracts. Using stated choice data collected from a sample of farmers, we estimate a mixed logit model to quantify the contribution of different contract attributes on contract willingness-to-accept (WTA). More stringent provisions in contracts were found to raise individual WTA by widely differing amounts across farmers, but the average effects imply that overall contract supply is sensitive to stringency. From a series of microsimulations based on the estimated model, we find that transaction costs create a significant drain on the cost-effectiveness of contracting from the agency’s point of view, similar in magnitude to the inefficiency created by hidden information. Although stringent contractual terms raise program expenditures, they may be justified if they raise compliance rates enough to offset the added cost. We also simulate an implicit frontier to trace out the change in compliance needed to justify a given increase in stringency. For environmental benefits in the range of previous estimates, this analysis suggests that stringent terms would need to substantially raise compliance rates to be cost effective

    Adaptive patch foraging in deep reinforcement learning agents

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    Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.Comment: Published in Transactions on Machine Learning Research (TMLR). See: https://openreview.net/pdf?id=a0T3nOP9s

    miR-196b target screen reveals mechanisms maintaining leukemia stemness with therapeutic potential.

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    We have shown that antagomiR inhibition of miRNA miR-21 and miR-196b activity is sufficient to ablate MLL-AF9 leukemia stem cells (LSC) in vivo. Here, we used an shRNA screening approach to mimic miRNA activity on experimentally verified miR-196b targets to identify functionally important and therapeutically relevant pathways downstream of oncogenic miRNA in MLL-r AML. We found Cdkn1b (p27Kip1) is a direct miR-196b target whose repression enhanced an embryonic stem cell–like signature associated with decreased leukemia latency and increased numbers of leukemia stem cells in vivo. Conversely, elevation of p27Kip1 significantly reduced MLL-r leukemia self-renewal, promoted monocytic differentiation of leukemic blasts, and induced cell death. Antagonism of miR-196b activity or pharmacologic inhibition of the Cks1-Skp2–containing SCF E3-ubiquitin ligase complex increased p27Kip1 and inhibited human AML growth. This work illustrates that understanding oncogenic miRNA target pathways can identify actionable targets in leukemia

    Protocol for a Mixed-Method Investigation of the Impact of the COVID-19 Pandemic and Gambling Practices, Experiences and Marketing in the UK:The "Betting and Gaming COVID-19 Impact Study"

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    The COVID-19 pandemic led to unprecedented restrictions on people’s movements and interactions, as well as the cancellation of major sports events and social activities, directly altering the gambling landscape. There is urgent need to provide regulators, policy makers and treatment providers with evidence on the patterns and context of gambling during COVID-19 and its aftermath. This protocol describes a study addressing the following three questions: (1) How has COVID-19 changed gambling practices and the risk factors for, and experience of, gambling harms? (2) What is the effect of COVID-19 on gambling marketing? (3) How has COVID-19 changed high risk groups’ gambling experiences and practices? This mixed-method study focuses on two groups, namely young adults and sports bettors. In workpackage-1, we will extend an existing longitudinal survey of gambling in young adults (aged 16–24 years) (first wave conducted June–August 2019), adding COVID-19-related questions to the second wave (July–August 2020) and extending to a third wave in 2021; and undertake a survey of sports bettors in the UK (baseline n = 4000, ~July–August 2020), with follow-ups in ~October–November 2020 and ~February-March 2021. In workpackage-2, we will examine changes in expenditure on paid-for gambling advertising from January 2019 to July 2021 and undertake a mixed-method content analysis of a random sample of paid-for gambling advertising (n ~ 200) and social media marketing (n ~ 100) during the initial COVID-19 “lockdown”. Workpackage-3 will involve qualitative interviews with a purposive sample of (a) young adults (aged 18–24 years) and (b) sports bettors
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