New perspectives on the role of collective
responsibility in modern politics States are commonly
blamed for wars, called on to apologize, held liable for debts and
reparations, bound by treaties, and ...punished with sanctions. But
what does it mean to hold a state responsible as opposed to a
government, a nation, or an individual leader? Under what
circumstances should we assign responsibility to states rather than
individuals? Leviathan on a Leash demystifies the
phenomenon of state responsibility and explains why it is a
challenging yet indispensable part of modern politics. Taking
Thomas Hobbes' theory of the state as his starting point, Sean
Fleming presents a theory of state responsibility that sheds new
light on sovereign debt, historical reparations, treaty
obligations, and economic sanctions. Along the way, he overturns
longstanding interpretations of Hobbes' political thought, explores
how new technologies will alter the practice of state
responsibility as we know it, and develops new accounts of
political authority, representation, and legitimacy. He argues that
Hobbes' idea of the state offers a far richer and more realistic
conception of state responsibility than the theories prevalent
today, and demonstrates that Hobbes' Leviathan is much more than an
anthropomorphic "artificial man." Leviathan on a Leash is
essential reading for political theorists, scholars of
international relations, international lawyers, and philosophers.
This groundbreaking book recovers a forgotten understanding of
state personality in Hobbes' thought and shows how to apply it to
the world of imperfect states in which we live.
A
bstract
In this work we apply effective field theory (EFT) to observables in quarkonium production and decay that are sensitive to soft gluon radiation, in particular measurements that are ...sensitive to small transverse momentum. Within the EFT framework we study
χ
Q
decay to light quarks followed by the fragmentation of those quarks to light hadrons. We derive a factorization theorem that involves transverse momentum distribution (TMD) fragmentation functions and new quarkonium TMD shape functions. We derive renormalization group equations, both in rapidity and virtuality, which are used to evolve the different terms in the factorization theorem to resum large logarithms. This theoretical framework will provide a systematic treatment of quarkonium production and decay processes in TMD sensitive measurements.
New perspectives on the role of collective responsibility in modern politics States are commonly blamed for wars, called on to apologize, held liable for debts and reparations, bound by treaties, and ...punished with sanctions. But what does it mean to hold a state responsible as opposed to a government, a nation, or an individual leader? Under what circumstances should we assign responsibility to states rather than individuals? Leviathan on a Leash demystifies the phenomenon of state responsibility and explains why it is a challenging yet indispensable part of modern politics.Taking Thomas Hobbes' theory of the state as his starting point, Sean Fleming presents a theory of state responsibility that sheds new light on sovereign debt, historical reparations, treaty obligations, and economic sanctions. Along the way, he overturns longstanding interpretations of Hobbes' political thought, explores how new technologies will alter the practice of state responsibility as we know it, and develops new accounts of political authority, representation, and legitimacy. He argues that Hobbes' idea of the state offers a far richer and more realistic conception of state responsibility than the theories prevalent today, and demonstrates that Hobbes' Leviathan is much more than an anthropomorphic "artificial man." Leviathan on a Leash is essential reading for political theorists, scholars of international relations, international lawyers, and philosophers. This groundbreaking book recovers a forgotten understanding of state personality in Hobbes' thought and shows how to apply it to the world of imperfect states in which we live.
A
bstract
Soft functions defined in terms of matrix elements of soft fields dressed by Wilson lines are central components of factorization theorems for cross sections and decay rates in collider and ...heavy-quark physics. While in many cases the relevant soft functions are defined in terms of gluon operators, at subleading order in power counting soft functions containing quark fields appear. We present a detailed discussion of the properties of the soft-quark soft function consisting of a quark propagator dressed by two finite-length Wilson lines connecting at one point. This function enters in the factorization theorem for the Higgs-boson decay amplitude of the
h → γγ
process mediated by light-quark loops. We perform the renormalization of this soft function at one-loop order, present a conjecture for its two-loop anomalous dimension and discuss solutions to its renormalization-group evolution equation in momentum space, in Laplace space and in the “diagonal space”, where the evolution is strictly local in the momentum variable.
This study evaluated the prevalence and patterns of behavioral symptoms, including agitation/aggression (AA), psychotic symptoms (PS), anxiety/mood disorders (MD), and delirium among patients with ...Alzheimer's disease (AD) and their association with diagnosed insomnia.
A retrospective cohort analysis was conducted using the MarketScan Multi-State Medicaid Database 2016-2020.
Patients aged ≥50 with newly diagnosed AD (N = 56,904) were identified during 2017-2019 and categorized into insomnia and non-insomnia groups based on billing codes recorded in medical and pharmacy claims.
The index date was defined as the earliest date of diagnosis/medication of insomnia. The new diagnosis of AD had to be established within 12 months before (baseline) or 3 months after the index date. Point prevalence of behavioral symptoms was estimated during baseline and the 12-month follow-up period. Propensity score matching was performed to match patients with and without insomnia. Multivariable conditional logistic regression was used to assess the risk of diagnosis of behavioral symptoms among insomnia and non-insomnia groups.
The study cohort included 7808 patients with newly diagnosed AD (mean age = 79.4, SD = 9.6 years). The point prevalence of behavioral symptoms was as follows: among those with insomnia (n = 3904), in the baseline, AA = 9.0%, PS = 12.5%, and MD = 57.8%, and during the follow-up, AA = 13.9%, PS = 16.3%, and MD = 72.1%; among those without insomnia (n = 3904), in the baseline, AA = 6.2%, PS = 9.2%, and MD = 41.4%; and during the follow-up, AA = 7.4%, PS = 10.4%, and MD = 49.2%. The likelihood of being diagnosed with any behavioral symptoms in the follow-up period was significantly higher among patients with insomnia than those without (adjusted odds ratio OR, 2.7; 95% confidence interval CI, 2.4-3.1).
In patients with AD, prevalence of behavioral symptoms and likelihood of being diagnosed with behavioral symptoms were significantly higher among patients with diagnosed insomnia. Further investigation is needed to understand the relationship between insomnia and behavioral symptoms in patients with AD.
Cross‐validated principal component regression (PCR) is widely used in day‐to‐day operational forecasting systems for seasonal river runoff volume in western North America. Complexities are ...increasing in both predictor datasets (including climate‐science products) and in predictive models employed instead of linear regression within the PCR framework (including artificial intelligence), potentially complicating cross‐validation for model evaluation. We explored these issues with 300 modeling experiments on two high‐impact and hydroclimatically diverse basins in the western United States, the Truckee River (Sierra Nevada) and Rio Grande headwaters (southern Rockies), using five different PCR and PCR‐like machine learning models. The results suggest out‐of‐sample error is satisfactorily estimated by applying cross‐validation to only the final, supervised learning, step of PCR/PCR‐like procedures. The outcome facilitates streamlined algorithms and potentially reduced computational times for more complex emerging model architectures and datasets; provides reassurance around a possible inability to perform genuinely complete cross‐validation when predictors include certain complex and externally sourced data sources; and may reflect mitigation of overtraining by geophysical process‐informed model development protocols normally used during feature selection in operational water supply forecast (WSF). The results provide practical guidance helping support the design of next‐generation WSF models.
Research Impact Statement: Seasonal river forecast models are used to manage water in the western US. Our study found alternative ways to measure their accuracy, facilitating design of next‐generation prediction systems.
Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, flood control, recreational ...uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existing system; uncertainty estimates that seamlessly handle complex (heteroscedastic, non-Gaussian) prediction errors; integration of physical hydrometeorological process knowledge and domain-specific expert experience; ability to accommodate nonlinearity, model selection uncertainty and equifinality, and predictor multicollinearity and high dimensionality; and relatively easy, low-cost implementation. Some methods satisfied some of these requirements but none met all, leading us to develop a novel, interdisciplinary, and pragmatic prediction metasystem through a carefully considered synthesis of well-established, off-the-shelf components and approaches, spanning supervised and unsupervised machine learning, nonparametric statistical modeling, ensemble learning, and evolutionary optimization, focusing on maintaining but radically updating the principal components regression framework widely used for WSF. Testing this integrated multi-method prediction engine demonstrated its value for river forecasting; USDA adoption is a landmark for transitioning machine learning from research into practice in this field. Its ability to handle all the foregoing design criteria and requirements, which are not unique to WSF, suggests potential for extension to complex probabilistic prediction problems in other fields.
Seasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and ...temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA‐enabled accuracy gains are most consistent and explainable for short‐lead, late‐season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring‐summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late‐season snowpack. fSCA also improved accuracy for long‐lead, early‐season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring‐summer snowmelt. The AI‐based hydrologic model generally outperformed the statistical model and, in some cases, better‐capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data‐driven hydrologic models using genetic algorithm‐based predictor selection could help guide new SNOTEL site selection.
Plain Language Summary
Western US operational water supply forecasts (WSFs) are predictions, typically issued at the start of every month from January through spring, of upcoming spring‐summer flow volume for a given point on a river, performed by service‐delivery organizations having strict accountabilities to end users around reliably delivering this information. WSFs use mathematical models of watershed hydrology that heavily leverage on‐the‐ground data on winter mountain snowpack, source of much of the spring‐summer runoff. Past research shows that snow measurements from air and space can improve WSF accuracy, but operational WSF models often don't directly ingest such data for practical reasons. Here, we consider new, NASA satellite‐derived snow data that is uniquely suited to WSF operations, partly because coverage in space and time is gap‐free, and test it in the largest stand‐alone operational WSF system in the American West, run by the US Department of Agriculture. Overall, outcomes demonstrate the benefits of this satellite data in statistical and AI‐based hydrologic models used in standard WSF applications in the western US, and guide scientists and engineers around when and where to use those data. Such WSF improvements will be critical to successful water management going forward in this increasingly water‐stressed region.
Key Points
Improvements to operational water supply forecasts (WSFs), based heavily on mountain snow data, are critical to western US water management
We test a new satellite remote sensing snow product, having no spatial or temporal coverage gaps, for ability to improve USDA WSF models
Results argue for operational implementation and give practical guidance around when and where such data may provide the most benefit
Rivers are essential to civilization and even life itself, yet how many of us truly understand how they work? Why do rivers run where they do? Where do their waters actually come from? How can the ...same river flood one year and then dry up the next? Where the River Flows takes you on a majestic journey along the planet's waterways, providing a scientist's reflections on the vital interconnections that rivers share with the land, the sky, and us. Sean Fleming draws on examples ranging from common backyard creeks to powerful and evocative rivers like the Mississippi, Yangtze, Thames, and Congo. Each chapter looks at a particular aspect of rivers through the lens of applied physics, using abundant graphics and intuitive analogies to explore the surprising connections between watershed hydrology and the world around us. Fleming explains how river flows fluctuate like stock markets, what "digital rainbows" can tell us about climate change and its effects on water supply, how building virtual watersheds in silicon may help avoid the predicted water wars of the twenty-first century, and much more. Along the way, you will learn what some of the most exciting ideas in science—such as communications theory, fractals, and even artificial life—reveal about the life of rivers. Where the River Flows offers a new understanding of the profound interrelationships that rivers have with landscapes, ecosystems, and societies, and shows how startling new insights are possible when scientists are willing to think outside the disciplinary box.
Nursing home residents have been disproportionately affected by the COVID-19 pandemic. Despite recognition as a priority group for receipt of the COVID-19 vaccine, vaccine uptake and COVID-19 cases, ...hospitalizations, and deaths in nursing home facilities were variable across nursing homes. This study has 2 objectives: (1) to describe nursing facility characteristics associated with higher vs lower vaccination rates and (2) to estimate facility characteristics associated with COVID-19 cases, hospitalizations, and deaths, stratified by vaccination rate.
Cross-sectional study.
Facility-level data from 12,811 US nursing home facilities.
Using the CMS's Nursing Home COVID-19 Public File, we analyzed nursing home COVID-19 vaccination rates and outcomes from June 13, 2021, to September 19, 2021. We performed multivariable logistic regressions and identified facility characteristics associated with increased vaccination uptake and COVID-19 outcomes.
Nursing homes with average vaccination rates ≤80% experienced higher total average COVID-19 cases, hospitalizations, and deaths compared to facilities with >80% average vaccination rates during the Delta surge. Moreover, facility factors, such as higher average age of residents, proportion of non-white residents, nurse staffing hours, and occupancy rates, were variably associated with increased risk of COVID-19 outcomes.
Facilities with higher resident vaccination rates experienced lower average COVID-19 cases, hospitalizations, and deaths in US nursing homes. Access to vaccines may play a role in mitigating harm associated with infectious diseases. Additionally, facility factors associated with increased adverse outcomes were variably associated with increased odds of COVID-19 outcomes, often, irrespective of vaccination level. As the COVID-19 pandemic continues to evolve and as the possibility of other infectious disease variants emerge, this research provides insight into facility factors, including vaccine uptake, that may mitigate adverse outcomes.
PDF
