PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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The ASA’ s Statement on p-Values: Context, Process, and Purpose.The American Statisti- cian2016;70(2):129–133
10 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present a new application of Lagrangian Perturbation Theory (LPT): the stability analysis of fluid flows. As a test case that demonstrates the framework we focus on the plane Couette flow. The incompressible Navier-Stokes equation is recast such that the particle position is the fundamental variable, expressed as a function of Lagrangian coordinates. The displacement due to the steady state flow is taken to be the zeroth order solution and the position is formally expanded in terms of a small parameter (generally, the strength of the initial perturbation). The resulting hierarchy of equations is solved analytically at first order. We find that we recover the standard result in the Eulerian frame: the plane Couette flow is asymptotically stable for all Reynolds numbers. However, it is also well established that experiments contradict this prediction. In the Eulerian picture, one of the proposed explanations is the phenomenon of `transient growth' which is related to the non-normal nature of the linear stability operator. The first order solution in the Lagrangian frame also shows this feature, albeit qualitatively. As a first step, and for the purposes of analytic manipulation, we consider only linear stability of 2D perturbations but the framework presented is general and can be extended to higher orders, other flows and/or 3D perturbations.
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astro-ph.IM 1 cond-mat.mtrl-sci 1 cs.LG 1 econ.EM 1 eess.SP 1 math.ST 1 stat.AP 1 stat.CO 1 stat.ME 1 stat.ML 1roles
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A square root form of the second-order covariance update is presented for the first time, improving numerical accuracy and efficiency in recursive estimation algorithms.
Decoupled descent enforces asymptotic tracking of test error by training error in Gaussian mixture models through bias cancellation via approximate message passing, enabling full data utilization.
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
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The reviewed method generalizes the Cash statistic C_min and likelihood-ratio ΔC to include systematic uncertainties in Poisson data, allowing simultaneous assessment of systematics level and model goodness-of-fit.
In a case study using EHR data from two health systems, multiple imputation via CART for missing systolic blood pressure and HbA1c values showed that marginal versus conditional imputation affected imputed distributions but had minimal impact on inferences about neighborhood socioeconomic status and
A commentary paper that reviews common misuses of statistical significance tests in choice modelling and advocates for greater attention to behavioural and policy relevance alongside proper uncertainty reporting.
The paper argues that statistical inference requires nuanced scientific context and that universal significance thresholds should be abandoned.
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Decoupled descent enforces asymptotic tracking of test error by training error in Gaussian mixture models through bias cancellation via approximate message passing, enabling full data utilization.
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Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
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The reviewed method generalizes the Cash statistic C_min and likelihood-ratio ΔC to include systematic uncertainties in Poisson data, allowing simultaneous assessment of systematics level and model goodness-of-fit.
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