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   https://doi.org/10.1002/zaac.201800378  

   Choffel, Marisa A.; Hamann, Danielle M.; Joke, Jordan A.; Cordova, Dmitri Leo; Johnson, David C. (December 2018, Zeitschrift für anorganische und allgemeine Chemie)
2. The Nuanced Relationship Between Creative Cognition and the Interaction Between Executive Functioning and Intelligence

   https://doi.org/10.1002/jocb.493  

   Taylor, Christa L.; Zaghi, Arash E. (February 2021, The Journal of Creative Behavior)

   Abstract The current study addresses gaps in our understanding of the relationship between creative cognition, intelligence (IQ), and executive functioning (EF). Undergraduate students completed an IQ test, verbal and figural divergent thinking (DT) tests, and a self‐assessment of EF, across four study sessions. Participant data (N = 199) were analyzed using linear regression and PROCESS moderation models. Results demonstrated that EF interacts with IQ to predict figural and verbal DT in distinct ways, with different patterns emerging from different methods of scoring DT. Using traditional DT scoring,Gf(but notGc) significantly moderated the relationship between EF and scores on both verbal and figural DT tasks. Low EF was associated with diminished DT scores for those with lowGfscores, unrelated for those with relatively higherGf, and enhanced scores for those with the highestGf. Using originality ratio scores, low EF was associated with diminished originality in verbal DT responses for those with low IQ (bothGfandGc), unrelated for those with relatively higher IQ, and enhanced originality for those with the highestGc(but notGf) scores. Thus, there are several nuances in the way that EF interacts with IQ to predict DT. 

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3. Tensions between the early and late Universe

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4. On the Duality Between the BSC and Quantum PSC

   https://doi.org/10.1109/ISIT45174.2021.9518034  

   Rengaswamy, Narayanan; Pfister, Henry D. (July 2021, 2021 IEEE International Symposium on Information Theory (ISIT))

   null (Ed.)

   In 2018, Renes [IEEE Trans. Inf. Theory, vol. 64, no. 1, pp. 577-592 (2018)] developed a general theory of channel duality for classical-input quantum-output channels. His result shows that a number of well-known duality results for linear codes on the binary erasure channel can be extended to general classical channels at the expense of using dual problems which are intrinsically quantum mechanical. One special case of this duality is a connection between coding for error correction on the quantum pure-state channel (PSC) and coding for wiretap secrecy on the classical binary symmetric channel (BSC). Similarly, coding for error correction on the BSC is related to wire-tap secrecy on the PSC. While this result has important implications for classical coding, the machinery behind the general duality result is rather challenging for researchers without a strong background in quantum information theory. In this work, we leverage prior results for linear codes on PSCs to give an alternate derivation of the aforementioned special case by computing closed-form expressions for the performance metrics. The noted prior results include the optimality of square-root measurement for linear codes on the PSC and the Fourier duality of linear codes. 

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5. Understanding and Mitigating the Tradeoff between Robustness and Accuracy

   Aditi Raghunathan, Aditi; Michael, Sang; Xie, Michael Xie; Yang, Fanny; Duchi, John; Liang, Percy (June 2022, Proceedings of the 37th International Conference on Machine Learning, PMLR)

   Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). Previous explanations for this tradeoff rely on the assumption that no predictor in the hypothesis class has low standard and robust error. In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor has zero standard and robust error. In particular, we show that the standard error could increase even when the augmented perturbations have noiseless observations from the optimal linear predictor. We then prove that the recently proposed robust self-training (RST) estimator improves robust error without sacrificing standard error for noiseless linear regression. Empirically, for neural networks, we find that RST with different adversarial training methods improves both standard and robust error for random and adversarial rotations and adversarial l_infty perturbations in CIFAR-10. 

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