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1. When generic language does not promote psychological essentialism.

   Vasilyeva, N.; Gopnik, A.; Lombrozo, T. (January 2020, Proceedings of the 42nd Annual Conference of the Cognitive Science Society)

   Denison, Stephanie; Mack, Michael; Xu, Yang; Armstrong, Blair C. (Ed.)

   Generic language (e.g., “Women are nurturing”; “Women do not like math”) is prominent in child-directed speech, and has been shown to promote essentialist beliefs about the relevant kind, supporting stereotyping and prejudice. Here we investigate a theoretically-motivated intervention to break the link between generics and essentialist assumptions. In a study with 223 3-8-year-old children who learned about novel social groups from generic language, we demonstrate that a structural construal of generics (attributing the category-property association to stable external constraints) mitigates essentialist assumptions about social categories. We discuss practical applications for reducing stereotype endorsement, and theoretical implications regarding the meaning of generic language and the development of social kind representations. 

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2. Effective Density for Inhomogeneous Quadratic Forms II: Fixed Forms and Generic Shifts

   https://doi.org/10.1093/imrn/rnad192  

   Ghosh, Anish; Kelmer, Dubi; Yu, Shucheng (August 2023, International Mathematics Research Notices)

   Abstract We establish effective versions of Oppenheim’s conjecture for generic inhomogeneous quadratic forms. We prove such results for fixed quadratic forms and generic shifts. Our results complement our previous paper [13] where we considered generic forms and fixed shifts. In this paper, we use ergodic theorems and in particular we establish a strong spectral gap with effective bounds for some representations of orthogonal groups, which do not possess Kazhdan’s property $(T)$. 

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3. Generic Refinement Types

   https://doi.org/10.1145/3704885  

   Lehmann, Nico; Kurashige, Cole; Akiti, Nikhil; Krishnakumar, Niroop; Jhala, Ranjit (January 2025, Proceedings of the ACM on Programming Languages)

   We present Generic Refinement Types: a way to write modular higher-order specifications that abstract invariants over function contracts, while preserving automatic SMT-decidable verification. We show how generic refinements let us write a variety of modular higher-order specifications, including specifications for Rust's traits which abstract over the concrete refinements that hold for different trait implementations. We formalize generic refinements in a core calculus and show how to synthesize the generic instantiations algorithmically at usage sites via a combination of syntactic unification and constraint solving. We give semantics to generic refinements via the intuition that they correspond to ghost parameters, and we formalize this intuition via a type-preserving translation into the polymorphic contract calculus to establish the soundness of generic refinements. Finally, we evaluate generic refinements by implementing them in Flux and using it for two case studies. First, we show how generic refinements let us write modular specifications for Rust's vector indexing API that lets us statically verify the bounds safety of a variety of vector-manipulating benchmarks from the literature. Second, we use generic refinements to refine Rust's Diesel ORM library to track the semantics of the database queries issued by client applications, and hence, statically enforce data-dependent access-control policies in several database-backed web applications. 

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4. Generic algebraic properties in spaces of enumerated groups

   https://doi.org/10.1090/tran/8902  

   Goldbring, Isaac; Kunnawalkam Elayavalli, Srivatsav; Lodha, Yash (June 2023, Transactions of the American Mathematical Society)

   We introduce and study Polish topologies on various spaces of countable enumerated groups, where an enumerated group is simply a group whose underlying set is the set of natural numbers. Using elementary tools and well-known examples from combinatorial group theory, combined with the Baire category theorem, we obtain a plethora of results demonstrating that several phenomena in group theory are generic. In effect, we provide a new topological framework for the analysis of various well known problems in group theory. We also provide a connection between genericity in these spaces, the word problem for finitely generated groups and model-theoretic forcing. Using these connections, we investigate a natural question raised by Osin: when does a certain space of enumerated groups contain a comeager isomorphism class? We obtain a sufficient condition that allows us to answer Osin’s question in the negative for the space of all enumerated groups and the space of left orderable enumerated groups. We document several open questions in connection with these considerations. 

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5. EDLT: Enabling Deep Learning for Generic Data Classification

   https://doi.org/10.1109/ICDM.2018.00030  

   Han, Huimei; Zhu, Xingquan; Li, Ying (November 2018, Proc. of the 18th IEEE International Conference on Data Mining (ICDM- 2018))

   This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix , we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification 

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