When second opinions hurt: a model of expert advice under career concerns

We augment the standard career concerns model by introducing (i) an action that blocks the information about the true state of the world and (ii) a second opinion/interim news after the initial consultation with the expert. In this model, the principal's action as well as the expert's message endogenously determine the observability of the states and consequently, the assessment of the expert's ability by the principal. We show that having access to better interim news could reduce the welfare of the principal due to its strategic effect on the expert's recommendation. We also discuss the implication of the results for possible delegation of decision making to another person with different decision parameters

Sin Taxes: Have Governments Gone Too Far in Their Efforts to Monetize Morality?

In June 2016, Philadelphia became the largest city in the United States to pass a soda tax, which went into effect on January 1, 2017. Soda taxes, an umbrella term for taxes that are assessed on sugar-sweetened beverages, represent the latest incarnation in a recent wave of non-traditional “sin taxes.” Sin taxes target behaviors that the government considers to be socially undesirable, and traditionally have been levied to curb consumption of alcohol and tobacco products. As state and local governments continue to face burgeoning budget deficits, legislators have increased the amount of existing sin taxes and expanded the sin tax base by taxing everything from sugar-sweetened beverages and junk food to disposable plastic bags. This Note argues that, notwithstanding the significant allure sin taxes possess as revenue generating tools, legislators must carefully evaluate each new potential “sin” independently on its own merits, and understand the inherent limitations of sin taxes, their regressive nature, and the attenuated public health justifications that accompany many non-traditional sin taxes. This Note argues that legislators should thus be wary of an unbridled expansion of sin taxes into non-traditional areas, and consider alternative methods of curbing unhealthy private behaviors, such as requiring manufacturers of sinful goods and services to affix warning labels on their offerings and improving consumer access to healthier substitutes

Technical note: application of ?-QSS to the numerical integration of kinetic equations in tropospheric chemistry

International audienceA major task in many applications of atmospheric chemistry transport problems is the numerical integration of stiff systems of Ordinary Differential Equations (ODEs) describing the chemical transformations. A faster solver that is easier to couple to the other physics in the problem is still needed. The integration method, ?-QSS, corresponding to the solver CHEMEQ2 aims at meeting the demands of a process-split, reacting-flow simulation (Mott 2000; Mott and Oran, 2001). However, this integrator has yet to be applied to the numerical integration of kinetic equations in tropospheric chemistry. A zero-dimensional (box) model is developed to test how well CHEMEQ2 works on the tropospheric chemistry equations. This paper presents the testing results. The reference chemical mechanisms herein used are Regional Atmospheric Chemistry Mechanism (RACM) (Stockwell et al., 1997) and its secondary lumped successor Regional Lumped Atmospheric Chemical Scheme (ReLACS) (Crassier et al., 2000). The box model is forced and initialized by the DRY scenarios of Protocol Ver. 2 developed by EUROTRAC (Poppe et al., 2001). The accuracy of CHEMEQ2 is evaluated by comparing the results to solutions obtained with VODE. This comparison is made with parameters of the error tolerance, relative difference with respect to VODE scheme, trade off between accuracy and efficiency, global time step for integration etc. The study based on the comparison concludes that the single-point ?-QSS approach is fast and moderately accurate as well as easy to couple to reacting flow simulation models, which makes CHEMEQ2 one of the best candidates for three-dimensional atmospheric Chemistry Transport Modelling (CTM) studies. In addition the RACM mechanism may be replaced by ReLACS mechanism for tropospheric chemistry transport modelling. The testing results also imply that the accuracy for chemistry numerical simulations is highly different from species to species. Therefore ozone is not the good choice for testing numerical ODE solvers or for evaluation of mechanisms because current tropospheric chemistry mechanisms are mainly designed for troposphere ozone prediction

Integration of surface science, nanoscience, and catalysis

This is the published version. Copyright 2010 International Union of Pure and Applied ChemistryThis article briefly reviews the development of surface science and its close relevance to nanoscience and heterogeneous catalysis. The focus of this article is to highlight the importance of nanoscale surface science for understanding heterogeneous catalysis performing at solid–gas and solid–liquid interfaces. Surface science has built a foundation for the understanding of catalysis based on the studies of well-defined single-crystal catalysts in the past several decades. Studies of catalysis on well-defined nanoparticles (NPs) significantly promoted the understanding of catalytic mechanisms to an unprecedented level in the last decade. To understand reactions performed on catalytic active sites at nano or atomic scales and thus reach the goal of catalysis by design, studies of the surface of nanocatalysts are crucial. The challenges in such studies are discussed

William (Bill) Peterson's contributions to ocean science, management, and policy

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Schwing, F. B., Sissenwine, M. J., Batchelder, H., Dam, H. G., Gomez-Gutierrez, J., Keister, J. E., Liu, H., & Peterson, J. O. William (Bill) Peterson's contributions to ocean science, management, and policy. Progress in Oceanography, 182, (2020): 102241, doi:10.1016/j.pocean.2019.102241.In addition to being an esteemed marine ecologist and oceanographer, William T. (Bill) Peterson was a dedicated public servant, a leader in the ocean science community, and a mentor to a generation of scientists. Bill recognized the importance of applied science and the need for integrated “big science” programs to advance our understanding of ecosystems and to guide their management. As the first US GLOBEC program manager, he was pivotal in transitioning the concept of understanding how climate change impacts marine ecosystems to an operational national research program. The scientific insight and knowledge generated by US GLOBEC informed and advanced the ecosystem-based management approaches now being implemented for fishery management in the US. Bill held significant leadership roles in numerous international efforts to understand global and regional ecological processes, and organized and chaired a number of influential scientific conferences and their proceedings. He was passionate about working with and training young researchers. Bill’s academic affiliations, notably at Stony Brook and Oregon State Universities, enabled him to advise, train, and mentor a host of students, post-doctoral researchers, and laboratory technicians. Under his collegial guidance they became critical independent thinkers and diligent investigators. His former students and colleagues carry on Bill Peterson’s legacy of research that helps us understand marine ecosystems and informs more effective resource stewardship and conservation

Protection against High-Fat-Diet-Induced Obesity in MDM2 C305F Mice Due to Reduced p53 Activity and Enhanced Energy Expenditure

The RPL11-MDM2 interaction constitutes a p53 signaling pathway activated by deregulated ribosomal biosynthesis in response to stress. Mice bearing an MDM2C305F mutation that disrupts RPL11-MDM2 binding were analyzed on a high-fat diet (HFD). The Mdm2C305F/C305F mice, although phenotypically indistinguishable from WT mice when fed normal chow, demonstrated decreased fat accumulation along with improved insulin sensitivity and glucose tolerance after prolonged HFD feeding. We found that HFD increases expression of c-MYC and RPL11 in both WT and Mdm2C305F/C305F mice; however, p53 was only induced in WT but not in Mdm2C305F/C305F mice. Reduced p53 activity in HFD-fed Mdm2C305F/C305F mice resulted in higher levels of p53 down-regulated targets GLUT4 and SIRT1, leading to increased biosynthesis of NAD+, and increased energy expenditure. Our study reveals a role for the RPL11-MDM2-p53 pathway in fat storage during nutrient excess and suggests that targeting this pathway may be a potential treatment for obesity

Dwarf AGNs from Variability for the Origins of Seeds (DAVOS): Optical Variability of Broad-line Dwarf AGNs from the Zwicky Transient Facility

We study the optical variability of a sample of candidate low-mass (dwarf ang Seyfert) active galactic nuclei (AGNs) using Zwicky Transient Facility g-band light curves. Our sample is compiled from broad-line AGNs in dwarf galaxies reported in the literature with single-epoch virial black hole (BH) masses in the range $M_{\rm{BH}} \sim 10^{4}$--$10^{8}\ M_{\odot}$. We measure the characteristic ``damping'' timescale of the optical variability $\tau_{\rm{DRW}}$, beyond which the power spectral density flattens, of a final sample of 79 candidate low-mass AGNs with high-quality light curves. Our results provide further confirmation of the $M_{\rm{BH}} - \tau_{\rm{DRW}}$ relation from Burke et al. 2022 within $1\sigma$ agreement, adding 78 new low-mass AGNs to the relation. The agreement suggests that the virial BH mass estimates for these AGNs are generally reasonable. We expect that the optical light curve of an accreting intermediate-mass black hole (IMBH) to vary with a rest-frame damping timescale of $\sim$ tens of hours, which could enable detection and direct mass estimation of accreting IMBHs in wide-field time-domain imaging surveys with sufficient cadence like with the Vera C. Rubin Observatory.Comment: 9 pages plus 6 appendix, 7 figure

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction. Many protein structures of interest remain out of reach for both computational prediction and experimental determination. DeepContact learns patterns of co-evolution across thousands of experimentally determined structures, identifying conserved local motifs and leveraging this information to improve protein residue-residue contact predictions. DeepContact extracts additional information from the evolutionary couplings using its knowledge of co-evolution and structural space, while also converting coupling scores into probabilities that are comparable across protein sequences and alignments. Keywords: contact prediction; convolutional neural networks; deep learning; protein structure prediction; structure prediction; co-evolution; evolutionary couplingsNational Institutes of Health (U.S.) (Grant R01GM081871

The role of haptic communication in dyadic collaborative object manipulation tasks

Intuitive and efficient physical human-robot collaboration relies on the mutual observability of the human and the robot, i.e. the two entities being able to interpret each other's intentions and actions. This is remedied by a myriad of methods involving human sensing or intention decoding, as well as human-robot turn-taking and sequential task planning. However, the physical interaction establishes a rich channel of communication through forces, torques and haptics in general, which is often overlooked in industrial implementations of human-robot interaction. In this work, we investigate the role of haptics in human collaborative physical tasks, to identify how to integrate physical communication in human-robot teams. We present a task to balance a ball at a target position on a board either bimanually by one participant, or dyadically by two participants, with and without haptic information. The task requires that the two sides coordinate with each other, in real-time, to balance the ball at the target. We found that with training the completion time and number of velocity peaks of the ball decreased, and that participants gradually became consistent in their braking strategy. Moreover we found that the presence of haptic information improved the performance (decreased completion time) and led to an increase in overall cooperative movements. Overall, our results show that humans can better coordinate with one another when haptic feedback is available. These results also highlight the likely importance of haptic communication in human-robot physical interaction, both as a tool to infer human intentions and to make the robot behaviour interpretable to humans