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1. Linearized binary regression

   https://doi.org/10.1109/CISS.2018.8362200  

   Lan, Andrew S.; Chiang, Mung; Studer, Christoph (March 2018, 52nd Annual Conference on Information Sciences and Systems (CISS))

   Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established binary regression methods. Concretely, we demonstrate that linearizing the probit model in combination with linear estimators performs on par with state-of-the-art nonlinear regression methods, such as posterior mean or maximum aposteriori estimation, for a broad range of real-world regression problems. We derive exact, closed-form, and nonasymptotic expressions for the mean-squared error of our linearized estimators, which clearly separates them from nonlinear regression methods that are typically difficult to analyze. We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements. 

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2. Binary Subspace Chirps

   https://doi.org/10.1109/TIT.2022.3186872  

   Pllaha, Tefjol; Tirkkonen, Olav; Calderbank, Robert (June 2022, IEEE Transactions on Information Theory)

   Binary Chirps (BCs) are 2^m dimensional complex vectors employed in deterministic compressed sensing and in random/unsourced multiple access in wireless networks. The vectors are obtained by exponentiating codewords from a 2nd order Reed-Muller code defined over Z4, the ring of integers modulo 4. We doubled the size of the BC codebook, without compromising performance in wireless multiple access. 

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3. OGLE-2019-BLG-0304: Competing Interpretations between a Planet–binary Model and a Binary-source + Binary-lens Model

   https://doi.org/10.3847/1538-3881/ac1c09  

   Han, Cheongho; Udalski, Andrzej; Lee, Chung-Uk; Kim, Doeon; Ryu, Yoon-Hyun; Albrow, Michael D.; Chung, Sun-Ju; Gould, Andrew; Hwang, Kyu-Ha; Kil Jung, Youn; et al (October 2021, The Astronomical Journal)
4. Long Short-Term Memory with Spin-Based Binary and Non-Binary Neurons

   https://doi.org/10.1109/MWSCAS47672.2021.9531773  

   Sheikhfaal, Shadi; Vangala, Meghana Reddy; Adepegba, Adedoyin; DeMara, Ronald F. (August 2021, IEEE International Midwest Symposium on Circuits and Systems (MWSCAS))

   null (Ed.)
5. Local Binary Pattern Networks

   https://doi.org/10.1109/WACV45572.2020.9093550  

   Lin, Jeng-Hau; Lazarow, Justin; Yang, Yunfan; Hong, Dezhi; Gupta, Rajesh K.; Tu, Zhuowen (March 2020, IEEE Winter Conference on Applications of Computer Vision)

   Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available memory (in few KB to MBytes), and energy. Under these constraints, we explore a novel approach to character recognition using local binary pattern networks, or LBPNet, that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet has its advantage for characters whose features are composed of structured strokes and distinctive outlines. LBPNet uses local binary comparisons and random projections in place of conventional convolution (or approximation of convolution) operations, providing an important means to improve memory efficiency as well as inference speed. We evaluate LBPNet on a number of character recognition benchmark datasets as well as several object classification datasets and demonstrate its effectiveness and efficiency. 

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