Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and laborintensive. Traditionally, tissue slicing for spatial sequencing involves parallel axisaligned sections, often yielding redundant or correlated information. We propose
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Abstract structured batch experimental design , a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studiesâ€”one to construct a spatiallyresolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumorâ€”our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and costefficient design strategies, allowing spatial genomics studies to be deployed by smaller, resourceconstrained labs. 
Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase diagrams does not neatly fit into traditional active learning approaches due to their global nature. Specifically, the thermodynamic stability of a material is not simply a function of its own energy, but rather requires energetic information from all other competing compositions and phases. Here we present Convex hullaware Active Learning (CAL), a novel Bayesian algorithm that chooses experiments to minimize the uncertainty in the convex hull. CAL prioritizes compositions that are close to or on the hull, leaving significant uncertainty in other compositions that are quickly determined to be irrelevant to the convex hull. The convex hull can thus be predicted with significantly fewer observations than approaches that focus solely on energy. Intrinsic to this Bayesian approach is uncertainty quantification in both the convex hull and all subsequent predictions (e.g., stability and chemical potential). By providing increased search efficiency and uncertainty quantification, CAL can be readily incorporated into the emerging paradigm of uncertaintybased workflows for thermodynamic prediction.more » « lessFree, publiclyaccessible full text available February 23, 2025

Markov chain Monte Carlo (MCMC) is an established approach for uncertainty quantification and propagation in scientific applications. A key challenge in apply ing MCMC to scientific domains is computation: the target density of interest is often a function of expensive computations, such as a highfidelity physical simulation, an intractable integral, or a slowlyconverging iterative algorithm. Thus, using an MCMC algorithms with an expensive target density becomes impractical, as these expensive computations need to be evaluated at each iteration of the algorithm. In practice, these computations often approximated via a cheaper, low fidelity computation, leading to bias in the resulting target density. Multifidelity MCMC algorithms combine models of varying fidelities in order to obtain an ap proximate target density with lower computational cost. In this paper, we describe a class of asymptotically exact multifidelity MCMC algorithms for the setting where a sequence of models of increasing fidelity can be computed that approximates the expensive target density of interest. We take a pseudomarginal MCMC approach for multifidelity inference that utilizes a cheaper, randomizedfidelity unbiased estimator of the target fidelity constructed via random truncation of a telescoping series of the lowfidelity sequence of models. Finally, we discuss and evaluate the proposed multifidelity MCMC approach on several applications, including logGaussian Cox process modeling, Bayesian ODE system identification, PDEconstrained optimization, and Gaussian process parameter inference.more » « less

Many probabilistic modeling problems in machine learning use gradientbased optimization in which the objective takes the form of an expectation. These problems can be challenging when the parameters to be optimized determine the probability distribution under which the expectation is being taken, as the na\"ive Monte Carlo procedure is not differentiable. Reparameterization gradients make it possible to efficiently perform optimization of these Monte Carlo objectives by transforming the expectation to be differentiable, but the approach is typically limited to distributions with simple forms and tractable normalization constants. Here we describe how to differentiate samples from slice sampling to compute \textit{slice sampling reparameterization gradients}, enabling a richer class of Monte Carlo objective functions to be optimized. Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions; it only requires a density function that can be evaluated pointwise up to a normalization constant, making it applicable to a variety of inference problems and unnormalized models. Our approach is based on the observation that when the slice endpoints are known, the sampling path is a deterministic and differentiable function of the pseudorandom variables, since the algorithm is rejectionfree. We evaluate the method on synthetic examples and apply it to a variety of applications with reparameterization of unnormalized probability distributions.more » « less

null (Ed.)Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as mobile phones or industrial sensors. In these scenarios it may be beneficial to trade the cost of collecting an environmental measurement against the quality or "fidelity" of this measurement and how the measurement affects changepoint estimation. For instance, one might decide between inertial measurements or GPS to determine changepoints for motion. A Bayesian approach to changepoint detection is particularly appealing because we can represent our posterior uncertainty about changepoints and make active, costsensitive decisions about data fidelity to reduce this posterior uncertainty. Moreover, the total cost could be dramatically lowered through active fidelity switching, while remaining robust to changes in data distribution. We propose a multifidelity approach that makes costsensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints. We evaluate this framework on synthetic, video, and audio data and show that this informationbased approach results in accurate predictions while reducing total cost.more » « less