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1. Fostering inclusion and diversity through research, teaching, mentoring, and outreach

   https://doi.org/10.1091/mbc.E19-07-0379  

   Torres, Jorge Z. (November 2019, Molecular Biology of the Cell)

   I am deeply humbled and honored to receive the American Society for Cell Biology (ASCB) Prize for Excellence in Inclusivity. Thank you to the ASCB for recognizing the contributions of faculty to inclusion and diversity in STEM and the importance of this for the advancement of science. Thank you to the Howard Hughes Medical Institute (HHMI) for your generous support of inclusivity. The prize money will be used to fund outreach activities aimed at increasing inclusion in science and to create research opportunities for students from underrepresented groups in the sciences. In this essay, I share bits of my life’s story that I hope will resonate with a broad audience, especially students from underrepresented groups in STEM, and that drive my passion for inclusion and diversity. I provide points of consideration for students to enhance their preparation for science careers and for faculty to improve the current landscape of inclusion and diversity in STEM. 

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2. Synthesizing JIT Compilers for In-Kernel DSLs

   https://doi.org/10.1007/978-3-030-53291-8_29  

   Van Geffen, Jacob; Nelson, Luke; Dillig, Isil; Wang, Xi; Torlak, Emina (January 2020, Computer Aided Verification)

   Modern operating systems allow user-space applications to submit code for kernel execution through the use of in-kernel domain specific languages (DSLs). Applications use these DSLs to customize system policies and add new functionality. For performance, the kernel executes them via just-in-time (JIT) compilation. The correctness of these JITs is crucial for the security of the kernel: bugs in in-kernel JITs have led to numerous critical issues and patches. This paper presents JitSynth, the first tool for synthesizing verified JITs for in-kernel DSLs. JitSynth takes as input interpreters for the source DSL and the target instruction set architecture. Given these interpreters, and a mapping from source to target states, JitSynth synthesizes a verified JIT compiler from the source to the target. Our key idea is to formulate this synthesis problem as one of synthesizing a per-instruction compiler for abstract register machines. Our core technical contribution is a new compiler metasketch that enables JitSynth to efficiently explore the resulting synthesis search space. To evaluate JitSynth, we use it to synthesize a JIT from eBPF to RISC-V and compare to a recently developed Linux JIT. The synthesized JIT avoids all known bugs in the Linux JIT, with an average slowdown of 1.82x in the performance of the generated code. We also use JitSynth to synthesize JITs for two additional source-target pairs. The results show that JitSynth offers a promising new way to develop verified JITs for in-kernel DSLs. 

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3. A turbulent-entropic instability and the fragmentation of star-forming clouds

   https://doi.org/10.1093/mnras/staa230  

   Keto, Eric; Field, George B; Blackman, Eric G (March 2020, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT The kinetic energy of supersonic turbulence within interstellar clouds is subject to cooling by dissipation in shocks and subsequent line radiation. The clouds are therefore susceptible to a condensation process controlled by the specific entropy. In a form analogous to the thermodynamic entropy, the entropy for supersonic turbulence is proportional to the log of the product of the mean turbulent velocity and the size scale. We derive a dispersion relation for the growth of entropic instabilities in a spherical self-gravitating cloud and find that there is a critical maximum dissipation time-scale, about equal to the crossing time, that allows for fragmentation and subsequent star formation. However, the time-scale for the loss of turbulent energy may be shorter or longer, for example, with rapid thermal cooling or the injection of mechanical energy. Differences in the time-scale for energy loss in different star-forming regions may result in differences in the outcome, for example, in the initial mass function. 

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4. Profiles of readiness: Using a blended framework to explore what it takes for faculty to be ready to change instructional practice

   https://doi.org/10.1080/10899995.2021.1878573  

   Hauk, Shandy; St John, Kristen; Jones, Megan (February 2021, Journal of Geoscience Education)

   null (Ed.)

   In answer to calls for research about professional change, this study addressed the question: What is involved in college science faculty readiness for change in instructional practice? The setting was a professional development experience in oceanography/marine science and paleoclimatology among 32 faculty from 2- and 4-year colleges. Ten of the 32 participated in interviews, and all provided survey responses and documents used in the study. Cycles of inductive analysis generated three example case stories to illustrate a new model for exploring faculty readiness for change in teaching. The model blends results from the health sciences on readiness for behavioral change with research on the personal, external, professional, and consequence domains of a professional change environment. The blended model attends to how an instructor draws on the domains to (a) see an instructional challenge as requiring intentional action to be resolved; (b) notice new significance (for the instructor) in some aspect of instructional practice; (c) feel able to manage instructional stressors/challenges; (d) have commitment to initiate/sustain change; and (e) perceive adequate support in undertaking change. Profiles of instructional readiness for change are represented by composite cases named Lee, Pat, and Chris. In the case of Lee, factor (c) drove change efforts; for Pat, factors (a) and (b) were in the forefront; and for Chris it was factors (d) and (e). The three cases are valuable both as sketches of the blended model in use and as touchstones for future research and development related to postsecondary faculty professional learning. 

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5. Multi-output stochastic emulation with applications to seismic response correlation estimation

   Yi, Sang-ri; Taflanidis, Alexandros A (April 2025, Structural safety)

   Stochastic emulation techniques represent a specialized surrogate modeling branch that is appropriate for applications for which the relationship between input and output is stochastic in nature. Their objective is to address the stochastic uncertainty sources by directly predicting the output distribution for a given input. An example of such application, and the focus of this contribution, is the estimation of structural response (engineering demand parameter) distribution in seismic risk assessment. In this case, the stochastic uncertainty originates from the aleatoric variability in the seismic hazard description. Note that this is a different uncertainty-source than the potential parametric uncertainty associated with structural characteristics or explanatory variables for the seismic hazard (for example, intensity measures), that are treated as the parametric input in surrogate modeling context. The key challenge in stochastic emulation pertains to addressing heteroscedasticity in the output variability. Relevant approaches to-date for addressing this challenge have focused on scalar outputs. In contrast, this paper focuses on the multi-output stochastic emulation problem and presents a methodology for predicting the output correlation matrix, while fully addressing heteroscedastic characteristics. This is achieved by introducing a Gaussian Process (GP) regression model for approximating the components of the correlation matrix, and coupling this approximation with a correction step to guarantee positive definite properties for the resultant predictions. For obtaining the observation data to inform the GP calibration, different approaches are examined, relying-or-not on the existence of replicated samples for the response output. Such samples require that, for a portion of the training points, simulations are repeated for the same inputs and different descriptions of the stochastic uncertainty. This information can be readily used to obtain observation for the response statistics (correlation or covariance in this instance) to inform the GP development. An alternative approach is to use as observations noisy covariance samples based on the sample deviations from a primitive mean approximation. These different observation variants lead to different GP variants that are compared within a comprehensive case study. A computational framework for integrating the correlation matrix approximation within the stochastic emulation for the marginal distribution approximation of each output component is also discussed, to provide the joint response distribution approximation. 

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