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1. Biometric data transformation for cryptographic domains and its application: poster

   https://doi.org/10.1145/3317549.3326302  

   Ali, Shoukat; Karabina, Koray; Karagoz, Emrah (May 2019, 12th WISEC 2019: Miami, Florida, USA)

   null (Ed.)
2. Biolistics-mediated transformation of hornworts and its application to study pyrenoid protein localization

   https://doi.org/10.1093/jxb/erae243  

   Lafferty, Declan_J; Robison, Tanner_A; Gunadi, Andika; Schafran, Peter_W; Gunn, Laura_H; Van_Eck, Joyce; Li, Fay-Wei; Lunn, ed., John (May 2024, Journal of Experimental Botany)

   Abstract Hornworts are a deeply diverged lineage of bryophytes and a sister lineage to mosses and liverworts. Hornworts have an array of unique features that can be leveraged to illuminate not only the early evolution of land plants, but also alternative paths for nitrogen and carbon assimilation via cyanobacterial symbiosis and a pyrenoid-based CO2-concentrating mechanism (CCM), respectively. Despite this, hornworts are one of the few plant lineages with limited available genetic tools. Here we report an efficient biolistics method for generating transient expression and stable transgenic lines in the model hornwort, Anthoceros agrestis. An average of 569 (±268) cells showed transient expression per bombardment, with green fluorescent protein expression observed within 48–72 h. A total of 81 stably transformed lines were recovered across three separate experiments, averaging six lines per bombardment. We followed the same method to transiently transform nine additional hornwort species, and obtained stable transformants from one. This method was further used to verify the localization of Rubisco and Rubisco activase in pyrenoids, which are central proteins for CCM function. Together, our biolistics approach offers key advantages over existing methods as it enables rapid transient expression and can be applied to widely diverse hornwort species. 

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3. Formal Methods Analysis of the Secure Remote Password Protocol

   Sherman, Alan T.; Lanus, Erin; Liskov, Moses; Zieglar, Edward; Chang, Richard; Golaszewski, Enis; Wnuk-Fink, Ryan; Bonyadi, Cyrus J.; Yaksetig, Mario; Blumenfel, Ian (October 2020, Festschrift in Honour of Professor Andre Scedrov, Vivek Nigam, Editor, LNCS, Springer)

   We analyze the Secure Remote Password (SRP) protocol for structural weaknesses using the Cryptographic Protocol Shapes Analyzer (CPSA) in the first formal analysis of SRP (specifically, Version 3). SRP is a widely deployed Password Authenticated Key Exchange (PAKE) protocol used in 1Password, iCloud Keychain, and other products. As with many PAKE protocols, two participants use knowledge of a preshared password to authenticate each other and establish a session key. SRP aims to resist dictionary attacks, not store plaintext-equivalent passwords on the server, avoid patent infringement, and avoid export controls by not using encryption. Formal analysis of SRP is challenging in part because existing tools provide no simple way to reason about its use of the mathematical expression “v + g b mod q”. Modeling v + g b as encryption, we complete an exhaustive study of all possible execution sequences of SRP. Ignoring possible algebraic attacks, this analysis detects no major structural weakness, and in particular no leakage of any secrets. We do uncover one notable weakness of SRP, which follows from its design constraints. It is possible for a malicious server to fake an authentication session with a client, without the client’s participation. This action might facilitate an escalation of privilege attack, if the client has higher privileges than does the server. We conceived of this attack before we used CPSA and confirmed it by generating corresponding execution shapes using CPSA. 

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4. Robust High-Dimensional Regression with Coefficient Thresholding and Its Application to Imaging Data Analysis

   https://doi.org/10.1080/01621459.2022.2142590  

   Liu, Bingyuan; Zhang, Qi; Xue, Lingzhou; Song, Peter X-K; Kang, Jian (January 2024, Journal of the American Statistical Association)
5. A Stability Analysis of Neural Networks and Its Application to Tsunami Early Warning

   https://doi.org/10.1029/2024JH000223  

   Rim, Donsub; Suri, Sanah; Hong, Sanghyun; Lee, Kookjin; LeVeque, Randall J (December 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Neural networks (NNs) enable precise modeling of complicated geophysical phenomena but can be sensitive to small input changes. In this work, we present a new method for analyzing this instability in NNs. We focus our analysis on adversarial examples, test‐time inputs with carefully crafted human‐imperceptible perturbations that expose the worst‐case instability in a model's predictions. Our stability analysis is based on a low‐rank expansion of NNs on a fixed input, and we apply our analysis to a NN model for tsunami early warning which takes geodetic measurements as the input and forecasts tsunami waveforms. The result is an improved description of local stability that explains adversarial examples generated by a standard gradient‐based algorithm, and allows the generation of other comparable examples. Our analysis can predict whether noise in the geodetic input will produce an unstable output, and identifies a potential approach to filtering the input that enable more robust forecasting 

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