More Like this

1. High Production Rate of High Purity, High Fidelity Nafion Nanofibers via Needleless Electrospinning

   https://doi.org/10.1021/acsapm.9b00681  

   Hwang, Monica; Karenson, Muizz O.; Elabd, Yossef A. (August 2019, ACS Applied Polymer Materials)
2. High-performance symbolic-numerics via multiple dispatch

   https://doi.org/10.1145/3511528.3511535  

   Gowda, Shashi; Ma, Yingbo; Cheli, Alessandro; Gwóźzdź, Maja; Shah, Viral B.; Edelman, Alan; Rackauckas, Christopher (September 2021, ACM Communications in Computer Algebra)

   As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the best performance out of their machine without deep knowledge of code optimization. Naturally, users need different term types either to have different algebraic properties for them, or to use efficient data structures. To this end, we developed Symbolics.jl, an extendable symbolic system which uses dynamic multiple dispatch to change behavior depending on the domain needs. In this work we detail an underlying abstract term interface which allows for speed without sacrificing generality. We show that by formalizing a generic API on actions independent of implementation, we can retroactively add optimized data structures to our system without changing the pre-existing term rewriters. We showcase how this can be used to optimize term construction and give a 113x acceleration on general symbolic transformations. Further, we show that such a generic API allows for complementary term-rewriting implementations. Exploiting this feature, we demonstrate the ability to swap between classical term-rewriting simplifiers and e-graph-based term-rewriting simplifiers. We illustrate how this symbolic system improves numerical computing tasks by showcasing an e-graph ruleset which minimizes the number of CPU cycles during expression evaluation, and demonstrate how it simplifies a real-world reaction-network simulation to halve the runtime. Additionally, we show a reaction-diffusion partial differential equation solver which is able to be automatically converted into symbolic expressions via multiple dispatch tracing, which is subsequently accelerated and parallelized to give a 157x simulation speedup. Together, this presents Symbolics.jl as a next-generation symbolic-numeric computing environment geared towards modeling and simulation. 

   more » « less
3. Functionally Graded Adhesives via High-Energy Irradiation

   https://doi.org/10.1021/acsapm.0c01137  

   Xia, Weiqing; Najafian, Sara; Cassano, Alessandro G.; Stapleton, Scott E.; Schmidt, Daniel F. (January 2021, ACS Applied Polymer Materials)

   null (Ed.)
4. Revealing local order via high energy EELS

   https://doi.org/10.1016/j.mtnano.2022.100298  

   Hart, J.L.; Lang, A.C.; Li, Y.; Shahrezaei, S.; Alix-Williams, D.D.; Falk, M.L.; Mathaudhu, S.N.; Frenkel, A.I.; Taheri, M.L. (March 2023, Materials Today Nano)

   Short range order (SRO) is critical in determining the performance of many important engineering materials. However, accurate characterization of SRO with high spatial resolution – which is needed for the study of individual nanoparticles and at material defects and interfaces – is often experimentally inaccessible. Here, we locally quantify SRO via scanning transmission electron microscopy with extended energy loss fine structure analysis. Specifically, we use novel instrumentation to perform electron energy loss spectroscopy out to 12 keV, accessing energies which are conventionally only possible using a synchrotron. Our data is of sufficient energy resolution and signal-to-noise ratio to perform quantitative extended fine structure analysis, which allows determination of local coordination environments. To showcase this technique, we investigate a multicomponent metallic glass nanolaminate and locally quantify the SRO with <10 nm spatial resolution; this measurement would have been impossible with conventional synchrotron or electron microscopy methods. We discuss the nature of SRO within the metallic glass phase, as well as the wider applicability of our approach for determining processing–SRO–property relationships in complex materials. 

   more » « less
5. High-Effort Crowds: Limited Liability via Tournaments

   https://doi.org/10.1145/3543507.3583334  

   Zhang, Yichi; Schoenebeck, Grant (April 2023, ACM)

   We consider the crowdsourcing setting where, in response to the assigned tasks, agents strategically decide both how much effort to exert (from a continuum) and whether to manipulate their reports. The goal is to design payment mechanisms that (1) satisfy limited liability (all payments are non-negative), (2) reduce the principal’s cost of budget, (3) incentivize effort and (4) incentivize truthful responses. In our framework, the payment mechanism composes a performance measurement, which noisily evaluates agents’ effort based on their reports, and a payment function, which converts the scores output by the performance measurement to payments. Previous literature suggests applying a peer prediction mechanism combined with a linear payment function. This method can achieve either (1), (3) and (4), or (2), (3) and (4) in the binary effort setting. In this paper, we suggest using a rank-order payment function (tournament). Assuming Gaussian noise, we analytically optimize the rank-order payment function, and identify a sufficient statistic, sensitivity, which serves as a metric for optimizing the performance measurements. This helps us obtain (1), (2) and (3) simultaneously. Additionally, we show that adding noise to agents’ scores can preserve the truthfulness of the performance measurements under the non-linear tournament, which gives us all four objectives. Our real-data estimated agent-based model experiments show that our method can greatly reduce the payment of effort elicitation while preserving the truthfulness of the performance measurement. In addition, we empirically evaluate several commonly used performance measurements in terms of their sensitivities and strategic robustness. 

   more » « less