1,358 research outputs found
The QM9 Benchmark
e3nn is an artificial neural network which operates on atomic coordinates and achieves equivariance to the special euclidean group in three dimensions by using spherical harmonics as features. The main experiment is to benchmark the model against a standard chemical data set called QM9, on which e3nn achieves state of the art performance on three of twelve regression targets. Along with empirical results, this thesis presents theoretical argumentation for why e3nn outperforms its closest relatives, SchNet and Cormorant, on some regression targets. Significant background regarding machine learning, quantum chemistry, and the special euclidean group is also presented
Finding symmetry breaking order parameters with Euclidean neural networks
Curie's principle states that “when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them.” We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions as simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites
Balancing Simulation-based Inference for Conservative Posteriors
Conservative inference is a major concern in simulation-based inference. It
has been shown that commonly used algorithms can produce overconfident
posterior approximations. Balancing has empirically proven to be an effective
way to mitigate this issue. However, its application remains limited to neural
ratio estimation. In this work, we extend balancing to any algorithm that
provides a posterior density. In particular, we introduce a balanced version of
both neural posterior estimation and contrastive neural ratio estimation. We
show empirically that the balanced versions tend to produce conservative
posterior approximations on a wide variety of benchmarks. In addition, we
provide an alternative interpretation of the balancing condition in terms of
the divergence
Peregrine: Sequential simulation-based inference for gravitational wave signals
The current and upcoming generations of gravitational wave experiments
represent an exciting step forward in terms of detector sensitivity and
performance. For example, key upgrades at the LIGO, Virgo and KAGRA facilities
will see the next observing run (O4) probe a spatial volume around four times
larger than the previous run (O3), and design implementations for e.g. the
Einstein Telescope, Cosmic Explorer and LISA experiments are taking shape to
explore a wider frequency range and probe cosmic distances. In this context,
however, a number of very real data analysis problems face the gravitational
wave community. For example, it will be crucial to develop tools and strategies
to analyse (amongst other scenarios) signals that arrive coincidentally in
detectors, longer signals that are in the presence of non-stationary noise or
other shorter transients, as well as noisy, potentially correlated, coherent
stochastic backgrounds. With these challenges in mind, we develop peregrine, a
new sequential simulation-based inference approach designed to study broad
classes of gravitational wave signal. In this work, we describe the method and
implementation, before demonstrating its accuracy and robustness through direct
comparison with established likelihood-based methods. Specifically, we show
that we are able to fully reconstruct the posterior distributions for every
parameter of a spinning, precessing compact binary coalescence using one of the
most physically detailed and computationally expensive waveform approximants
(SEOBNRv4PHM). Crucially, we are able to do this using only 2\% of the waveform
evaluations that are required in e.g. nested sampling approaches. Finally, we
provide some outlook as to how this level of simulation efficiency and
flexibility in the statistical analysis could allow peregrine to tackle these
current and future gravitational wave data analysis problems.Comment: 14 pages, 5 figures. Code: peregrine available at
https://github.com/undark-lab/peregrine-publi
Healing relationships and the existential philosophy of Martin Buber
The dominant unspoken philosophical basis of medical care in the United States is a form of Cartesian reductionism that views the body as a machine and medical professionals as technicians whose job is to repair that machine. The purpose of this paper is to advocate for an alternative philosophy of medicine based on the concept of healing relationships between clinicians and patients. This is accomplished first by exploring the ethical and philosophical work of Pellegrino and Thomasma and then by connecting Martin Buber's philosophical work on the nature of relationships to an empirically derived model of the medical healing relationship. The Healing Relationship Model was developed by the authors through qualitative analysis of interviews of physicians and patients. Clinician-patient healing relationships are a special form of what Buber calls I-Thou relationships, characterized by dialog and mutuality, but a mutuality limited by the inherent asymmetry of the clinician-patient relationship. The Healing Relationship Model identifies three processes necessary for such relationships to develop and be sustained: Valuing, Appreciating Power and Abiding. We explore in detail how these processes, as well as other components of the model resonate with Buber's concepts of I-Thou and I-It relationships. The resulting combined conceptual model illuminates the wholeness underlying the dual roles of clinicians as healers and providers of technical biomedicine. On the basis of our analysis, we argue that health care should be focused on healing, with I-Thou relationships at its core
Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
Sampling-based inference techniques are central to modern cosmological data
analysis; these methods, however, scale poorly with dimensionality and
typically require approximate or intractable likelihoods. In this paper we
describe how Truncated Marginal Neural Ratio Estimation (TMNRE) (a new approach
in so-called simulation-based inference) naturally evades these issues,
improving the efficiency, scalability, and trustworthiness
of the inferred posteriors. Using measurements of the Cosmic Microwave
Background (CMB), we show that TMNRE can achieve converged posteriors using
orders of magnitude fewer simulator calls than conventional Markov Chain Monte
Carlo (MCMC) methods. Remarkably, the required number of samples is effectively
independent of the number of nuisance parameters. In addition, a property
called \emph{local amortization} allows the performance of rigorous statistical
consistency checks that are not accessible to sampling-based methods. TMNRE
promises to become a powerful tool for cosmological data analysis, particularly
in the context of extended cosmologies, where the timescale required for
conventional sampling-based inference methods to converge can greatly exceed
that of simple cosmological models such as CDM. To perform these
computations, we use an implementation of TMNRE via the open-source code
\texttt{swyft}.Comment: v2: accepted journal version. v1: 37 pages, 13 figures.
\texttt{swyft} is available at https://github.com/undark-lab/swyft, and
demonstration code for cosmological examples is available at
https://github.com/acole1221/swyft-CM
Balancing Simulation-based Inference for Conservative Posteriors
peer reviewedConservative inference is a major concern in simulation-based inference. It
has been shown that commonly used algorithms can produce overconfident
posterior approximations. Balancing has empirically proven to be an effective
way to mitigate this issue. However, its application remains limited to neural
ratio estimation. In this work, we extend balancing to any algorithm that
provides a posterior density. In particular, we introduce a balanced version of
both neural posterior estimation and contrastive neural ratio estimation. We
show empirically that the balanced versions tend to produce conservative
posterior approximations on a wide variety of benchmarks. In addition, we
provide an alternative interpretation of the balancing condition in terms of
the divergence
Defining and Measuring the Patient-Centered Medical Home
The patient-centered medical home (PCMH) is four things: 1) the fundamental tenets of primary care: first contact access, comprehensiveness, integration/coordination, and relationships involving sustained partnership; 2) new ways of organizing practice; 3) development of practices’ internal capabilities, and 4) related health care system and reimbursement changes. All of these are focused on improving the health of whole people, families, communities and populations, and on increasing the value of healthcare
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