Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts

Miscibility Gap Closure, Interface Morphology, and Phase Microstructure of 3D Li_<i>x</i>FePO₄ Nanoparticles from Surface Wetting and Coherency Strain

We study the mesoscopic effects which modify phase-segregation in Li_<i>x</i>FePO₄ nanoparticles using a multiphysics phase-field model implement on a high performance cluster. We simulate 3D spherical particles of radii from 3 to 40 nm and examine the equilibrium microstructure and voltage profiles as they depend on size and overall lithiation. The model includes anisotropic, concentration-dependent elastic moduli, misfit strain, and facet dependent surface wetting within a Cahn–Hilliard formulation. We find that the miscibility gap vanishes for particles of radius ∼5 nm, and the solubility limits change with overall particle lithiation. Surface wetting stabilizes minority phases by aligning them with energetically beneficial facets. The equilibrium voltage profile is modified by these effects in magnitude, and the length and slope of the voltage plateau during two-phase coexistence

petsc: Portable, Extensible Toolkit for Scientific Computation

Version of Firedrake in the Firedrake paper. This release is specifically created to document the version of Firedrake used in a particular set of experiments. Please do not cite this as a general source for Firedrake or any of its dependencies. Instead, refer to http://www.firedrakeproject.org/publications.htm

firedrakeproject/petsc: Portable, Extensible Toolkit for Scientific Computation

Version of Firedrake used in 'Solver composition across the PDE/linear algebra barrier'. This release is specifically created to document the version of Firedrake used in a particular set of experiments. Please do not cite this as a general source for Firedrake or any of its dependencies. Instead, refer to http://www.firedrakeproject.org/publications.htm

Model Predictive Control for Scheduling and Routing in a Solid Waste Management System

Solid waste collection and hauling account for the greater part of the total cost in modern solid waste management systems. In a recent initiative, 3,300 Swedish recycling containers have been fitted with level sensors and wireless communication equipment thereby giving waste collection operators access to real-time information on the status of each container. In a previous study (Johansson, 2006), analytical modeling and discrete-event simulation have been used to evaluate different scheduling and routing policies utilizing the real-time data, and it has been shown that dynamic scheduling and routing policies exist that have lower operating costs, shorter collection and hauling distances, and reduced labor hours compared to the static policy with fixed routes and pre-determined pick-up frequencies employed by many waste collection operators today. This study aims at further refining the scheduling and routing policies by employing a model predictive control (MPC) framework on the system. In brief, the MPC controller should minimize an objective cost function consisting of fixed and variable collection and hauling costs for a fixed future horizon by calculating a sequence of tactical scheduling and routing decisions that satisfies system constraints using a receding horizon strategy

Deep learning generates synthetic cancer histology for explainability and education

Abstract Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology

firedrakeproject/petsc: Portable, Extensible Toolkit for Scientific Computation

&lt;p&gt;This release is specifically created to document the version of petsc used in a particular set of experiments using Firedrake. Please do not cite this as a general source for Firedrake or any of its dependencies. Instead, refer to https://www.firedrakeproject.org/citing.html&lt;/p&gt