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1. Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs

   https://doi.org/10.1145/3622856  

   Renda, Alex; Ding, Yi; Carbin, Michael (October 2023, Proceedings of the ACM on Programming Languages)

   Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy. 

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2. Assume(), Capture(), Verify(), Establish(): A Vocabulary for Static Program Analysis

   https://doi.org/10.1109/QRS-C60940.2023.00067  

   Mohammadi, Hessamaldin; Ghardallou, Wided; Brick, Elijah; Mili, Ali (October 2023, IEEE)

   We propose a set of functions that a user can invoke to analyze a program written in a C-like language: Assume() refers to a label in the source code or to a program part, and enables the user to make an assumption about the state of the program at some label or the function of some program part; Capture() refers to a label or a program part and returns an assertion about the state of the program at the label or the function of the program part; Verify() refers to a label or a program part and tests a unary assertion about the state of the program at the label or a binary assertion about the function of the program part; Establish() refers to a label or a program part and modifies the program code to make Verify() return TRUE at that label or program part, if it did not originally. We discuss the foundations of this tool as well as a preliminary implementation. 

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3. Polynomial Chaos-Based Controller Design for Uncertain Linear Systems With State and Control Constraints

   https://doi.org/10.1115/1.4038800  

   Nandi, Souransu; Migeon, Victor; Singh, Tarunraj; Singla, Puneet (July 2018, Journal of Dynamic Systems, Measurement, and Control)

   For linear dynamic systems with uncertain parameters, design of controllers which drive a system from an initial condition to a desired final state, limited by state constraints during the transition is a nontrivial problem. This paper presents a methodology to design a state constrained controller, which is robust to time invariant uncertain variables. Polynomial chaos (PC) expansion, a spectral expansion, is used to parameterize the uncertain variables permitting the evolution of the uncertain states to be written as a polynomial function of the uncertain variables. The coefficients of the truncated PC expansion are determined using the Galerkin projection resulting in a set of deterministic equations. A transformation of PC polynomial space to the Bernstein polynomial space permits determination of bounds on the evolving states of interest. Linear programming (LP) is then used on the deterministic set of equations with constraints on the bounds of the states to determine the controller. Numerical examples are used to illustrate the benefit of the proposed technique for the design of a rest-to-rest controller subject to deformation constraints and which are robust to uncertainties in the stiffness coefficient for the benchmark spring-mass-damper system. 

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4. A stepped-sine curve-fit algorithm for finding cantilever resonance shifts in AFM

   https://doi.org/10.23919/ACC.2019.8814403  

   Kang, Zhixin; Saygin, Verda; Brown, Keith A.; Andersson, Sean (July 2019, 2019 American Control Conference (ACC))

   Atomic force microscopes (AFMs) are used not only to image with nanometer-scale resolution, but also to nanofabricate structures on a surface using methods such as dip-pen nanolithography (DPN). DPN involves using the tip of the AFM to deposit a small amount of material on the surface. Typically, this process is done in open loop, leading to large variations in the amount of material transferred. One of the first steps to closing this loop is to be able to accurately and rapidly measure the amount of deposition. This can be done by measuring the change in the resonance frequency of the cantilever before and after a write as that shift is directly related to the change in mass on the cantilever. Currently, this is done using a thermal-based system identification, a technique which uses the natural Brownian excitation of the cantilever as a white noise excitation combined with a fast Fourier transform to extract a Bode plot. However, thermal-based techniques do not have a good signal to noise ratio at typical cantilever resonance frequencies and thus do not provide the needed resolution in the DPN application. Here we develop a scheme that iteratively uses a stepped-sine approach. At each step of the iteration, three frequencies close to the approximate location of the resonance are injected and used to fit a model of the magnitude of the transfer function. The identified peak is used to select three new frequencies in a smaller range in a binary search to reduce the uncertainty of the measured resonance peak location. The scheme is demonstrated through simulation and shown to produce an accuracy of better than 0.5 Hz on a cantilever with a 14 kHz resonance in a physically realistic noise scenario. 

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5. Microstructure classification and the polycrystalline microstructure state space

   https://doi.org/10.1016/j.actamat.2025.121219  

   Miley, Dylan; Suwandi, Ethan; Schweinhart, Benjamin; Mason, Jeremy K (September 2025, Acta Materialia)

   A cornerstone of materials science is that material properties are determined by their microstructure. While the community has already developed a wide variety of approaches to describe microstructure, most of these are tailored to specific material systems or classes. This work proposes a way to quantitatively measure the similarity of microstructures based on the geometry of the grain boundary network, a feature which is fundamental to and characteristic of all polycrystalline materials. Specifically, a distance on all single-phase polycrystalline microstructures is proposed such that two microstructures that are close with regard to the distance have grain boundary networks that are statistically similar in all geometric respects below a user-specified length scale. Given a pair of micrographs, the distance is approximated by sampling windows from the micrographs, defining a distance between pairs of windows, and finding a window matching that minimizes the sum of the pairwise window distances. The approach is used to compare a variety of synthetic microstructures and to develop a procedure to query a proof-of-concept database suitable for general single-phase polycrystalline microstructures. 

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