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1. Lions, tigers and bears: Conveying a superordinate category without a superordinate label.

   Rissman, L.; Lupyan, G. (July 2021, Proceedings of the Annual Meeting of the Cognitive Science Society)

   null (Ed.)

   We asked whether categories expressed through lists of salient exemplars (e.g., car, truck, boat, etc.) convey the same meaning as categories expressed through conventional superordinate nouns (e.g., vehicles). We asked English speakers to list category members, with one group given superordinate labels like vehicles and the other group given only a list of salient exemplars. We found that the responses of the group given labels were more related, more typical, and less diverse than the responses of the group given exemplars. This result suggests that when people do not see a superordinate label, the categories that they infer are less well aligned across participants. In addition, categories inferred based on exemplars may be broader in general than categories given by superordinate labels. 

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2. Certified Hermite matrices from approximate roots

   https://doi.org/10.1016/j.jsc.2022.12.001  

   Ayyildiz Akoglu, Tulay; Szanto, Agnes (July 2023, Journal of Symbolic Computation)

   Let I = f1,..., fm ⊂ Q[x1,..., xn] be a zero dimensional radical ideal defined by polynomials given with exact rational coefficients. Assume that we are given approximations {z1,..., zk} ⊂ Cn for the common roots {ξ1,..., ξk} = V (I) ⊆ Cn. In this paper we show how to construct and certify the rational entries of Hermite matrices for I from the approximate roots {z1,..., zk}. When I is non-radical, we give methods to construct and certify Hermite matrices for √ I from the approximate roots. Furthermore, we use signatures of these Hermite matrices to give rational certificates of non-negativity of a given polynomial over a (possibly positive dimensional) real variety, as well as certificates that there is a real root within an ε distance from a given point z ∈ Qn. 

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3. Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model

   https://doi.org/10.18653/v1/2022.cmcl-1.6  

   Futrell, Richard (January 2022, Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics)

   We investigate how to use pretrained static word embeddings to deliver improved estimates of bilexical co-occurrence probabilities: conditional probabilities of one word given a single other word in a specific relationship. Such probabilities play important roles in psycholinguistics, corpus linguistics, and usage-based cognitive modeling of language more generally. We propose a log-bilinear model taking pretrained vector representations of the two words as input, enabling generalization based on the distributional information contained in both vectors. We show that this model outperforms baselines in estimating probabilities of adjectives given nouns that they attributively modify, and probabilities of nominal direct objects given their head verbs, given limited training data in Arabic, English, Korean, and Spanish. 

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4. Data Structures for Density Estimation

   Aamand, Anders; Andoni, Alexandr; Chen, Justin; Indyk, Piotr; Narayanan, Shyam; Silwal, Sandeep (January 2023, International Conference on Machine Learning)

   We study statistical/computational tradeoffs for the following density estimation problem: given kdistributionsv1,...,vk overadiscretedomain of size n, and sampling access to a distribution p, identify vi that is “close” to p. Our main result is the first data structure that, given a sublinear (in n) number of samples from p, identifies vi in time sublinear in k. We also give an improved version of the algorithm of (Acharya et al., 2018) that reports vi in time linear in k. The experimental evaluation of the latter algorithm shows that it achieves a significant reduction in the number of operations needed to achieve a given accuracy compared to prior work. 

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5. Quantizers with Parameterized Distortion Measures

   Guo, J.; Walk, P.; Jafarkhani, H. (March 2019, Data Compression Conference (DCC-19))

   In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are additively and multiplicatively weighted for each reproduction point resulting in a heterogeneous quantization problem, as used for example in deployment problems of sensor networks. Whereas, normally in such problems, the average distortion is minimized for given weights (parameters), we will optimize the quantization problem over all weights, i.e., we tune or control the distortion functions in our favor. For a uniform source distribution in one-dimension, we derive the unique minimizer, given as the uniform scalar quantizer with an optimal common weight. By numerical simulations, we demonstrate that this result extends to two-dimensions where asymptotically the parameter optimized quantizer is the hexagonal lattice with common weights. As an application, we will determine the optimal deployment of unmanned aerial vehicles (UAVs) to provide a wireless communication to ground terminals under a minimal communication power cost. Here, the optimal weights relate to the optimal flight heights of the UAVs. 

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