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1. A classification of incompleteness statements

   https://doi.org/10.4153/S000843952510091X  

   Towsner, Henry; Walsh, James (July 2025, Canadian Mathematical Bulletin)

   Abstract For which choices of$$X,Y,Z\in \{\Sigma ^1_1,\Pi ^1_1\}$$does no sufficiently strongX-sound andY-definable extension theory prove its ownZ-soundness? We give a complete answer, thereby delimiting the generalizations of Gödel’s second incompleteness theorem that hold within second-order arithmetic. 

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2. Classification Using Hyperdimensional Computing: A Review

   https://doi.org/10.1109/mcas.2020.2988388  

   Ge, Lulu; Parhi, Keshab K. (June 2020, IEEE circuits and systems magazine)

   Hyperdimensional (HD) computing is built upon its unique data type referred to as hypervectors. The dimension of these hypervectors is typically in the range of tens of thousands. Proposed to solve cognitive tasks, HD computing aims at calculating similarity among its data. Data transformation is realized by three operations, including addition, multiplication and permutation. Its ultra-wide data representation introduces redundancy against noise. Since information is evenly distributed over every bit of the hypervectors, HD computing is inherently robust. Additionally, due to the nature of those three operations, HD computing leads to fast learning ability, high energy efficiency and acceptable accuracy in learning and classification tasks. This paper introduces the background of HD computing, and reviews the data representation, data transformation, and similarity measurement. The orthogonality in high dimensions presents opportunities for flexible computing. To balance the tradeoff between accuracy and efficiency, strategies include but are not limited to encoding, retraining, binarization and hardware acceleration. Evaluations indicate that HD computing shows great potential in addressing problems using data in the form of letters, signals and images. HD computing especially shows significant promise to replace machine learning algorithms as a light-weight classifier in the field of internet of things (IoTs). 

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3. A New Classification of the Dictyostelids

   https://doi.org/10.1016/j.protis.2017.11.001  

   Sheikh, Sanea; Thulin, Mats; Cavender, James C.; Escalante, Ricardo; Kawakami, Shin-ichi; Lado, Carlos; Landolt, John C.; Nanjundiah, Vidyanand; Queller, David C.; Strassmann, Joan E.; et al (February 2018, Protist)
4. FReCS: A First Responder Classification System

   Adesokan, Ademola; Madria, Sanjay; Nguyen, Long (September 2024, Springer)
5. A refined reweighing technique for nondiscriminatory classification

   https://doi.org/10.1371/journal.pone.0308661  

   Liang, Yuefeng; Hsieh, Cho-Jui; Lee, Thomas (August 2024, PLOS ONE)

   Discrimination-aware classification methods remedy socioeconomic disparities exacerbated by machine learning systems. In this paper, we propose a novel data pre-processing technique that assigns weights to training instances in order to reduce discrimination without changing any of the inputs or labels. While the existing reweighing approach only looks into sensitive attributes, we refine the weights by utilizing both sensitive and insensitive ones. We formulate our weight assignment as a linear programming problem. The weights can be directly used in any classification model into which they are incorporated. We demonstrate three advantages of our approach on synthetic and benchmark datasets. First, discrimination reduction comes at a small cost in accuracy. Second, our method is more scalable than most other pre-processing methods. Third, the trade-off between fairness and accuracy can be explicitly monitored by model users. Code is available athttps://github.com/frnliang/refined_reweighing. 

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