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1. Now You See It, Now You Don't: A Large-scale Analysis of Early Domain Deletions

   Barron, Timothy; Miramirkhani, Najmeh; Nikiforakis, Nick (January 2019, Proceedings of the 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID))
2. A “hump” in the third-order dielectric response of a highly polar liquid: Now you see it, now you don’t

   https://doi.org/10.1063/5.0248963  

   Thoms, Erik; Richert, Ranko (March 2025, The Journal of Chemical Physics)

   We have measured the linear and nonlinear dielectric responses of S-methoxypropylene carbonate, a highly polar glass-former, for which it has been reported that the “hump,” which is typical of third harmonic susceptibilities, disappears across a 5 K temperature change. To understand this unusual feature, we have measured the responses to high amplitude ac and dc electric fields at the fundamental frequency. The static limits of these results are entered into a model aimed at reproducing nonlinear dielectric susceptibility spectra using the concept of a fictive electric field. This model reproduces the “hump” in the third-harmonic response and its seeming disappearance. It is revealed that the “hump” is predominantly the result of reduced time constants, a consequence of the energy the sample absorbs from the electric field. At elevated temperatures, the “hump” only appears to vanish because its reduced amplitude submerges below the extraordinarily high level of polarization saturation of this liquid. 

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3. Now You See It, Now You Don’t: Star Formation Truncation Precedes the Loss of Molecular Gas by ∼100 Myr in Massive Poststarburst Galaxies at z ∼ 0.6

   https://doi.org/10.3847/1538-4357/ac3dfa  

   Bezanson, Rachel; Spilker, Justin S.; Suess, Katherine A.; Setton, David J.; Feldmann, Robert; Greene, Jenny E.; Kriek, Mariska; Narayanan, Desika; Verrico, Margaret (February 2022, The Astrophysical Journal)

   Abstract We use ALMA observations of CO(2–1) in 13 massive (M_*≳ 10¹¹M_⊙) poststarburst galaxies atz∼ 0.6 to constrain the molecular gas content in galaxies shortly after they quench their major star-forming episode. The poststarburst galaxies in this study are selected from the Sloan Digital Sky Survey spectroscopic samples (Data Release 14) based on their spectral shapes, as part of the Studying QUenching at Intermediate-z Galaxies: Gas, angu $\overrightarrow{L}\mathrm{ar}$momentum, and Evolution ( $\mathit{SQuIGG}\vec{L}E$) program. Early results showed that two poststarburst galaxies host large H₂reservoirs despite their low inferred star formation rates (SFRs). Here we expand this analysis to a larger statistical sample of 13 galaxies. Six of the primary targets (45%) are detected, with $M_{{\mathrm{H}}_2}\gtrsim {10}^9$M_⊙. Given their high stellar masses, this mass limit corresponds to an average gas fraction of $〈f_{{\mathrm{H}}_2}\equiv M_{{\mathrm{H}}_2}/M_{*}〉\sim 7\%$or ∼14% using lower stellar masses estimates derived from analytic, exponentially declining star formation histories. The gas fraction correlates with theD_n4000 spectral index, suggesting that the cold gas reservoirs decrease with time since burst, as found in local K+A galaxies. Star formation histories derived from flexible stellar population synthesis modeling support this empirical finding: galaxies that quenched ≲150 Myr prior to observation host detectable CO(2–1) emission, while older poststarburst galaxies are undetected. The large H₂reservoirs and low SFRs in the sample imply that the quenching of star formation precedes the disappearance of the cold gas reservoirs. However, within the following 100–200 Myr, the $\mathit{SQuIGG}\vec{L}E$galaxies require the additional and efficient heating or removal of cold gas to bring their low SFRs in line with standard H₂scaling relations. 

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4. Can You Hear Me Now? Audio and Visual Interactions That Change App Choices

   https://doi.org/10.3389/fpsyg.2020.02227  

   Gopavaram, Shakthidhar Reddy; Bhide, Omkar; Camp, L. Jean (October 2020, Frontiers in Psychology)

   null (Ed.)
5. You asked, now what? Modeling Students’ Help-Seeking and Coding actions from Re- quest to Resolution

   Zhikai Gao; Bradley Erickson; Yiqiao Xu; Collin Lynch; Sarah Heckman; Tiffany Barnes (December 2022, Journal of educational data mining)

   Nigel Bosch; Antonija Mitrovic; Agathe Merceron (Ed.)

   Demand for education in Computer Science has increased markedly in recent years. With increased demand has come to an increased need for student support, especially for courses with large programming projects. Instructors commonly provide online post forums or office hours to address this massive demand for help requests. Identifying what types of questions students are asking in those interactions and what triggers their help requests can in turn assist instructors in better managing limited help-providing resources. In this study, we aim to explore students’ help-seeking actions from the two separate approaches we mentioned before and investigate their coding actions before help requests to understand better what motivates students to seek help in programming projects. We collected students’ help request data and commit logs from two Fall offerings of a CS2 course. In our analysis, we first believe that different types of questions should be related to different behavioral patterns. Therefore, we first categorized students’ help requests based on their content (e.g., Implementation, General Debugging, or Addressing Teaching Staff (TS) Test Failures). We found that General Debugging is the most frequently asked question. Then we analyzed how the popularity of each type of request changed over time. Our results suggest that implementation is more popular in the early stage of the project cycle, and it changes to General Debugging and Addressing TS Failures in the later stage. We also calculated the accuracy of students’ commit frequency one hour before their help requests; the results show that before Implementation requests, the commit frequency is significantly lower, and before TS failure requests, the frequency is significantly higher. Moreover, we checked before any help request whether students changed their source code or test code. The results show implementation requests related to higher chances of source code changes and coverage questions related to more test code changes. Moreover, we use a Markov Chain model to show students’ action sequences before, during, and after the requests. And finally, we explored students’ progress after the office hours interaction and found that over half of the students improved the correctness of their code after 20 minutes of their office hours interaction addressing TS failures ends. 

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