A General Approach for Predicting the Behavior of the Supreme Court of the United States

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but distinct from arXiv:1407.6333, and the results herein supersede arXiv:1407.6333. Source code available at https://github.com/mjbommar/scotus-predict-v

Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models

Simulating a binary black hole (BBH) coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from non-spinning BBH coalescences with mass ratios in $[1, 10]$ and durations corresponding to about $15$ orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms {\em not} used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic ${}_{-2}Y_{\ell m}$ waveform modes resolved by the NR code up to $\ell=8.$ We compare our surrogate model to Effective One Body waveforms from $50$-$300 M_\odot$ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).Comment: Updated to published version, which includes a section comparing the surrogate and effective-one-body models. The surrogate is publicly available for download at http://www.black-holes.org/surrogates/ . 6 pages, 6 figure

A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers

We present the first surrogate model for gravitational waveforms from the coalescence of precessing binary black holes. We call this surrogate model NRSur4d2s. Our methodology significantly extends recently introduced reduced-order and surrogate modeling techniques, and is capable of directly modeling numerical relativity waveforms without introducing phenomenological assumptions or approximations to general relativity. Motivated by GW150914, LIGO's first detection of gravitational waves from merging black holes, the model is built from a set of $276$ numerical relativity (NR) simulations with mass ratios $q \leq 2$, dimensionless spin magnitudes up to $0.8$, and the restriction that the initial spin of the smaller black hole lies along the axis of orbital angular momentum. It produces waveforms which begin $\sim 30$ gravitational wave cycles before merger and continue through ringdown, and which contain the effects of precession as well as all $\ell \in \{2, 3\}$ spin-weighted spherical-harmonic modes. We perform cross-validation studies to compare the model to NR waveforms \emph{not} used to build the model, and find a better agreement within the parameter range of the model than other, state-of-the-art precessing waveform models, with typical mismatches of $10^{-3}$. We also construct a frequency domain surrogate model (called NRSur4d2s_FDROM) which can be evaluated in $50\, \mathrm{ms}$ and is suitable for performing parameter estimation studies on gravitational wave detections similar to GW150914.Comment: 34 pages, 26 figure

P2519: The Impact of Transcatheter Aortic Valve Implantation on Quality of Life: A Mixed Methods Study

Objective: To provide an in-depth understanding of patients' views about the impact of transcatheter aortic valve implantation on self-reported quality of life. Background: Transcatheter aortic valve implantation is considered to be the gold standard of care for inoperable patients diagnosed with severe symptomatic aortic stenosis. Mid- to long-term clinical outcomes are favourable and questionnaire data indicates improvements in quality of life but an in-depth understanding of how quality of life is altered by the intervention is missing. Methods: A mixed methods study design with a total of 89 in-depth qualitative interviews conducted with participants (39% male; mean age 81.7 years), 1 and 3 months post TAVI, recruited from a regional centre in England. Data were triangulated with questionnaire data (SF-36 and EQ5D-VAS) collected, pre, 1 and 3 months post implantation. Results: Participants' accounts were characterised by four key themes; shortened life, extended life, limited life and changed life. Quality of life was changed through two mechanisms. Most participants reported a reduced symptom burden and all explained that their life expectancy was improved. Questionnaire data supported interview data with gradual improvements in mean EQ-5D scores and SF-36 physical and mental domain scores at 1 and 3 months compared to baseline. Conclusion: Findings suggest that TAVI was of variable benefit, producing considerable improvements in either mental or physical health in many participants, while a smaller proportion continued to deteriorate

Expert Consensus on Sizing and Positioning of SAPIEN 3/Ultra in Bicuspid Aortic Valves

Vàlvula aòrtica bicúspide; Talla del bicúspide; SAPIEN 3/UltraVálvula aórtica bicúspide; Talla del bicúspide; SAPIEN 3/UltraBicuspid aortic valve; Bicuspid sizing; SAPIEN 3/UltraSevere aortic stenosis patients with bicuspid anatomy have been excluded from the major transcatheter aortic valve replacement (TAVI) randomized clinical trials. As a result, there is no official recommendation on bicuspid TAVI. A panel of bicuspid experts was created to fill this gap. In this consensus statement, an algorithm is proposed to guide the choice of surgery or TAVI within this complex patient population, depending on aortic dilatation, age, surgical risk score, and anatomy. A step-by-step guide for sizing and positioning of the SAPIEN 3/Ultra TAVI bioprostheses is presented. Annular sizing remains the primary strategy in most bicuspid patients. However, some anatomies may require sizing at the supra-annular level, for which patients the panel recommends the circle method, a dedicated sizing and positioning approach for SAPIEN 3/Ultra. The consensus provides valuable pre-operative insights on the interactions between SAPIEN 3/Ultra and the bicuspid anatomy; understanding the valve–anatomy relationship is critical to avoid complications and to optimize outcomes for patients.The journal’s Rapid Service Fee was supported by Edwards Lifesciences

Dispersity effects in polymer self-assemblies : a matter of hierarchical control

Advanced applications of polymeric self-assembled structures require a stringent degree of control over such aspects as functionality location, morphology and size of the resulting assemblies. A loss of control in the polymeric building blocks of these assemblies can have drastic effects upon the final morphology or function of these structures. Gaining precise control over various aspects of the polymers, such as chain lengths and architecture, blocking efficiency and compositional distribution is a challenge and, hence, measuring the intrinsic mass and size dispersity within these areas is an important aspect of such control. It is of great importance that a good handle on how to improve control and accurately measure it is achieved. Additionally dispersity of the final structure can also play a large part in the suitability for a desired application. In this Tutorial Review, we aim to highlight the different aspects of dispersity that are often overlooked and the effect that a lack of control can have on both the polymer and the final assembled structure