56 research outputs found
Betting and Belief: Prediction Markets and Attribution of Climate Change
Despite much scientific evidence, a large fraction of the American public
doubts that greenhouse gases are causing global warming. We present a
simulation model as a computational test-bed for climate prediction markets.
Traders adapt their beliefs about future temperatures based on the profits of
other traders in their social network. We simulate two alternative climate
futures, in which global temperatures are primarily driven either by carbon
dioxide or by solar irradiance. These represent, respectively, the scientific
consensus and a hypothesis advanced by prominent skeptics. We conduct
sensitivity analyses to determine how a variety of factors describing both the
market and the physical climate may affect traders' beliefs about the cause of
global climate change. Market participation causes most traders to converge
quickly toward believing the "true" climate model, suggesting that a climate
market could be useful for building public consensus.Comment: All code and data for the model is available at
http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016
Winter Simulation Conference. IEEE Pres
Topic Modeling the President: Conventional and Computational Methods
Legal and policy scholars modeling direct actions into substantive topic classifications thus far have not employed computational methods. To compare the results of their conventional modeling methods with the computational method, we generated computational topic models of all direct actions over time periods other scholars have studied using conventional methods, and did the same for a case study of environmental-policy direct actions. Our computational model of all direct actions closely matched one of the two comprehensive empirical models developed using conventional methods. By contrast, our environmental-case-study model differed markedly from the only empirical topic model of environmental-policy direct actions using conventional methods, revealing that the conventional methods model included trivial categories and omitted important alternative topics. Provided a sufficiently large corpus of documents is used, our findings support the assessment that computational topic modeling can reveal important insights for legal scholars in designing and validating their topic models of legal text. To be sure, computational topic modeling used alone has its limitations, some of which are evident in our models, but when used along with conventional methods, it opens doors towards reaching more confident conclusions about how to conceptualize topics in law. Drawing from these results, we offer several use cases for computational topic modeling in legal research. At the front end, researchers can use the method to generate better and more complete topic-model hypotheses. At the back end, the method can effectively be used, as we did, to validate existing topic models. And at a meta-scale, the method opens windows to test and challenge conventional legal theory. Legal scholars can do all of these without the machines, but there is good reason to believe we can do it better with them in the toolkit
Application of machine learning to prediction of vegetation health
This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern
PTArcade
This is a lightweight manual for PTArcade, a wrapper of ENTERPRISE and ceffyl
that allows for easy implementation of new-physics searches in PTA data. In
this manual, we describe how to get PTArcade installed (either on your local
machine or an HPC cluster). We discuss how to define a stochastic or
deterministic signal and how PTArcade implements these signals in PTA-analysis
pipelines. Finally, we show how to handle and analyze the PTArcade output using
a series of utility functions that come together with PTArcade.Comment: 26 pages, 2 figures, 2 table
Cardiac magnetic resonance left ventricular filling pressure is linked to symptoms, signs and prognosis in heart failure
Aims
Left ventricular filling pressure (LVFP) can be estimated from cardiovascular magnetic resonance (CMR). We aimed to investigate whether CMR-derived LVFP is associated with signs, symptoms, and prognosis in patients with recently diagnosed heart failure (HF).
Methods and results
This study recruited 454 patients diagnosed with HF who underwent same-day CMR and clinical assessment between February 2018 and January 2020. CMR-derived LVFP was calculated, as previously, from long- and short-axis cines. CMR-derived LVFP association with symptoms and signs of HF was investigated. Patients were followed for median 2.9 years (interquartile range 1.5â3.6 years) for major adverse cardiovascular events (MACE), defined as the composite of cardiovascular death, HF hospitalization, non-fatal stroke, and non-fatal myocardial infarction. The mean age was 62 ± 13 years, 36% were female (n = 163), and 30% (n = 135) had raised LVFP. Forty-seven per cent of patients had an ejection fraction < 40% during CMR assessment. Patients with raised LVFP were more likely to have pleural effusions [hazard ratio (HR) 3.2, P = 0.003], orthopnoea (HR 2.0, P = 0.008), lower limb oedema (HR 1.7, P = 0.04), and breathlessness (HR 1.7, P = 0.01). Raised CMR-derived LVFP was associated with a four-fold risk of HF hospitalization (HR 4.0, P < 0.0001) and a three-fold risk of MACE (HR 3.1, P < 0.0001). In the multivariable model, raised CMR-derived LVFP was independently associated with HF hospitalization (adjusted HR 3.8, P = 0.0001) and MACE (adjusted HR 3.0, P = 0.0001).
Conclusions
Raised CMR-derived LVFP is strongly associated with symptoms and signs of HF. In addition, raised CMR-derived LVFP is independently associated with subsequent HF hospitalization and MACE
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the legal
community has given rise to the question: what types of legal reasoning can
LLMs perform? To enable greater study of this question, we present LegalBench:
a collaboratively constructed legal reasoning benchmark consisting of 162 tasks
covering six different types of legal reasoning. LegalBench was built through
an interdisciplinary process, in which we collected tasks designed and
hand-crafted by legal professionals. Because these subject matter experts took
a leading role in construction, tasks either measure legal reasoning
capabilities that are practically useful, or measure reasoning skills that
lawyers find interesting. To enable cross-disciplinary conversations about LLMs
in the law, we additionally show how popular legal frameworks for describing
legal reasoning -- which distinguish between its many forms -- correspond to
LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary.
This paper describes LegalBench, presents an empirical evaluation of 20
open-source and commercial LLMs, and illustrates the types of research
explorations LegalBench enables.Comment: 143 pages, 79 tables, 4 figure
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers find interesting. To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoningâwhich distinguish between its many formsâcorrespond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary. This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables
SGC-CAMKK2-1: A Chemical Probe for CAMKK2
The serine/threonine protein kinase calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) plays critical roles in a range of biological processes. Despite its importance, only a handful of inhibitors of CAMKK2 have been disclosed. Having a selective small molecule tool to interrogate this kinase will help demonstrate that CAMKK2 inhibition can be therapeutically beneficial. Herein, we disclose SGC-CAMKK2-1, a selective chemical probe that targets CAMKK2
The NANOGrav 15-year Data Set: Evidence for a Gravitational-Wave Background
We report multiple lines of evidence for a stochastic signal that is
correlated among 67 pulsars from the 15-year pulsar-timing data set collected
by the North American Nanohertz Observatory for Gravitational Waves. The
correlations follow the Hellings-Downs pattern expected for a stochastic
gravitational-wave background. The presence of such a gravitational-wave
background with a power-law-spectrum is favored over a model with only
independent pulsar noises with a Bayes factor in excess of , and this
same model is favored over an uncorrelated common power-law-spectrum model with
Bayes factors of 200-1000, depending on spectral modeling choices. We have
built a statistical background distribution for these latter Bayes factors
using a method that removes inter-pulsar correlations from our data set,
finding (approx. ) for the observed Bayes factors in the
null no-correlation scenario. A frequentist test statistic built directly as a
weighted sum of inter-pulsar correlations yields (approx. ). Assuming a fiducial
characteristic-strain spectrum, as appropriate for an ensemble of binary
supermassive black-hole inspirals, the strain amplitude is (median + 90% credible interval) at a reference frequency of
1/(1 yr). The inferred gravitational-wave background amplitude and spectrum are
consistent with astrophysical expectations for a signal from a population of
supermassive black-hole binaries, although more exotic cosmological and
astrophysical sources cannot be excluded. The observation of Hellings-Downs
correlations points to the gravitational-wave origin of this signal.Comment: 30 pages, 18 figures. Published in Astrophysical Journal Letters as
part of Focus on NANOGrav's 15-year Data Set and the Gravitational Wave
Background. For questions or comments, please email [email protected]
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Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.
Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (nâ=â143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (nâ=â152), or no hydrocortisone (nâ=â108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (nâ=â137), shock-dependent (nâ=â146), and no (nâ=â101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707
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