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1. Effect of similarity and training experiences on new vocabulary learning

   Waller, M; Yurovsky, D; Nozari, N (June 2024, Proceedings of the 45th Annual Conference of the Cognitive Science Society.)

   Samuelson, L K; Frank, S; Toneva, M; Mackey, A; Hazeltine, E (Ed.)

   In two experiments (N = 179), we studied the effect of contextual similarity and training mode on new vocabulary learning. Adult participants were trained on blocks of items that were semantically similar, phonologically similar, or unrelated to one another. Each participant was trained through passive exposure, active comprehension, or active production of the new vocabulary. Exp 1 trained items in clusters of 9, whereas Exp 2 trained the same number of items in clusters of 3. Exp 2 also assessed delayed retention 48-72 hours after training. Results showed a robust and negative impact of semantic similarity and production mode on vocabulary learning. A detrimental effect of phonological similarity was only observed in the delayed test. These results suggest that adding the challenge of resolving similarity-induced competition and articulating the word-form negatively impacts the quick acquisition of new vocabulary. 

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2. Production training and contextual similarity hurt the comprehension of new vocabulary

   https://doi.org/10.1080/23273798.2024.2429127  

   Waller, Megan; Yurovsky, Daniel; Nozari, Nazbanou (March 2025, Language, Cognition and Neuroscience)

   In two experiments (N = 179), we studied the effects of contextual similarity and training mode on the comprehension of new vocabulary. Participants were trained on new vocabulary in blocks of semantically similar, phonologically similar, or unrelated items. Each participant was trained through passive exposure, active comprehension, or active production. Same number of items were trained in clusters of 9 in Experiment 1 and clusters of 3 in Experiment 2, manipulating difficulty during training. Results showed a detrimental and persistent effect of semantic similarity, and a less robust effect of phonological similarity, both of which grew larger over time. We also found a negative and largely independent influence of production mode on learning, which, contrary to the similarity effect, shrank with time. Neither effect was modulated by difficulty at training time. These findings shed further light on the factors influencing new vocabulary learning and open new avenues for larger-scale and classroom-level studies. 

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3. Protein2Vec: Aligning Multiple PPI Networks with Representation Learning

   Jianliang Gao, Ling Tian (October 2019, IEEE/ACM transactions on computational biology and bioinformatics)

   Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. Existing methods of PPI network alignment often try to improve the coverage ratio of the alignment result by aligning all proteins from different species. However, there is a fundamental biological premise that needs to be considered carefully: not every protein in a species can, nor should, find its homologous proteins in other species. In this work, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we apply a representation learning method on the networks that can generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against previous multiple alignment methods 

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4. Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

   https://doi.org/10.1038/s41598-021-90070-4  

   Broderick, Scott; Dongol, Ruhil; Zhang, Tianmu; Rajan, Krishna (December 2021, Scientific Reports)

   null (Ed.)

   Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds. 

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5. Algorithms and complexity of range clustering

   https://doi.org/10.1002/net.21854  

   Hochbaum, Dorit S. (September 2018, Networks)

   We introduce a novel criterion in clustering that seeks clusters with limitedrangeof values associated with each cluster's elements. In clustering or classification the objective is to partition a set of objects into subsets, called clusters or classes, consisting of similar objects so that different clusters are as dissimilar as possible. We propose a number of objective functions that employ the range of the clusters as part of the objective function. Several of the proposed objectives mimic objectives based on sums of similarities. These objective functions are motivated by image segmentation problems, where the diameter, or range of values associated with objects in each cluster, should be small. It is demonstrated that range‐based problems are in general easier, in terms of their complexity, than the analogous similarity‐sum problems. Several of the problems we present could therefore be viable alternatives to existing clustering problems which are NP‐hard, offering the advantage of efficient algorithms. 

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