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CORENBLIT, Dov, BAAS, Andreas, BALKE, Thorsten, BOUMA, Tjeerd, FROMARD, François, GARÓFANO-GÓMEZ, Virginia, GONZÁLEZ, Eduardo, GURNELL, Angela M., HORTOBÁGYI, Borbála and JULIEN, Frédéric, 2015. Engineer pioneer plants respond to and affect geomorphic constraints similarly along water–terrestrial interfaces world-wide. Global Ecology and Biogeography. 2015. Vol. 24, no. 12, p. 1363–1376. In which ways are object-oriented programming and functional programming similar? In which ways are they different?, [no date]. Quora. Online. Available from: https://www.quora.com/In-which-ways-are-object-oriented-programming-and-functional-programming-similar-In-which-ways-are-they-different [Accessed 30 May 2022]. Answer (1 of 3): They’re much more similar in theory than in practice. Similarities In theory… Both place a high value on abstraction. Unlike in structured programming (procedural programming), the abstractions are central, not control structures or a strict, linear sequence of statement evalu... JIANG, Jay J. and CONRATH, David W., 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. arXiv:cmp-lg/9709008. Online. 20 September 1997. Available from: http://arxiv.org/abs/cmp-lg/9709008 [Accessed 31 March 2020]. This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task. arXiv: cmp-lg/9709008 JOHNSON, Jeff, DOUZE, Matthijs and JÉGOU, Hervé, 2017. Billion-scale similarity search with GPUs.. Online. 28 February 2017. arXiv. arXiv:1702.08734. [Accessed 5 September 2023]. Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data-parallel tasks, prior approaches are bottlenecked by algorithms that expose less parallelism, such as k-min selection, or make poor use of the memory hierarchy. We propose a design for k-selection that operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5x faster than prior GPU state of the art. We apply it in different similarity search scenarios, by proposing optimized design for brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation enables the construction of a high accuracy k-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility. arXiv:1702.08734 [cs] PAPAGEORGOPOULOS, Nikos, 2023. Comparative analysis of trajectory similarity techniques for vessels in real time: a case study on maritime traffic monitoring. Online. Master’s Thesis. Πανεπıotaστ\acute\etaμıotao Πεıotaραıota\acuteømegaς. Available from: https://dione.lib.unipi.gr/xmlui/handle/unipi/15539 [Accessed 20 February 2024].

PHUC, Do and PHUNG, Nguyen Thi Kim, 2010. Visualization of the Similar Protein Structures Using SOM Neural Network and Graph Spectra. In: NGUYEN, Ngoc Thanh, LE, Manh Thanh and ŚWIĄTEK, Jerzy (eds.), Intelligent Information and Database Systems. Online. Berlin, Heidelberg: Springer Berlin Heidelberg. p. 258–267. Lecture Notes in Computer Science. ISBN 978-3-642-12100-5. [Accessed 4 March 2024].

RACHKOVSKIJ, Dmitri A., 2021. Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences.. Online. 31 December 2021. arXiv. arXiv:2112.15475. Available from: http://arxiv.org/abs/2112.15475 [Accessed 4 March 2024]. Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical applications and emerging neuromorphic and nanoscale hardware. HDC/VSA operate with hypervectors, i.e., distributed vector representations of large fixed dimension (usually > 1000). One of the key ingredients of HDC/VSA are the methods for encoding data of various types (from numeric scalars and vectors to graphs) into hypervectors. In this paper, we propose an approach for the formation of hypervectors of sequences that provides both an equivariance with respect to the shift of sequences and preserves the similarity of sequences with identical elements at nearby positions. Our methods represent the sequence elements by compositional hypervectors and exploit permutations of hypervectors for representing the order of sequence elements. We experimentally explored the proposed representations using a diverse set of tasks with data in the form of symbolic strings. Although our approach is feature-free as it forms the hypervector of a sequence from the hypervectors of its symbols at their positions, it demonstrated the performance on a par with the methods that apply various features, such as subsequences. The proposed techniques were designed for the HDC/VSA model known as Sparse Binary Distributed Representations. However, they can be adapted to hypervectors in formats of other HDC/VSA models, as well as for representing sequences of types other than symbolic strings. arXiv:2112.15475 [cs] RACHKOVSKIJ, Dmitri A., 2022. Representation of spatial objects by shift-equivariant similarity-preserving hypervectors. Neural Computing and Applications. December 2022. Vol. 34, no. 24, p. 22387–22403. DOI 10.1007/s00521-022-07619-1. RACHKOVSKIJ, Dmitri A. and KLEYKO, Denis, 2022. Recursive binding for similarity-preserving hypervector representations of sequences. In: 2022 International Joint Conference on Neural Networks (IJCNN). Online. IEEE. 2022. p. 1–8. Available from: https://ieeexplore.ieee.org/abstract/document/9892462/ [Accessed 4 March 2024]. SELF-SABOTAGING BEHAVIOR: THE EFFECT OF GOAL PROXIMITY, PERCEIVED COMPETITOR SIMILARITY, AND COMPETITOR’S SEX ON SELF-SABOTAGING BEHAVIOR IN HIGH FEAR OF SUCCESS, HIGH SCHOOL STUDENTS - ProQuest, [no date]. . Online. Available from: https://www.proquest.com/openview/816a818908670cab1dcc3e1d6e436482/1?pq-origsite=gscholar&cbl=18750&diss=y [Accessed 4 September 2023]. Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform. STEFFENS, Timo, 2005a. Similarity-based Opponent Modelling using Imperfect Domain Theories. CIG. 2005. Vol. 5, p. 285–291. STEFFENS, Timo, 2005b. Knowledge-Intensive Similarity-based Opponent Modeling. Reasoning, Representation, and Learning in Computer Games. 2005. P. 101. SUN, Yizhou, HAN, Jiawei, YAN, Xifeng, YU, Philip S. and WU, Tianyi, 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment. 2011. Vol. 4, no. 11, p. 992–1003. TAKTAK, Mariem, TRIKI, Slim and KAMOUN, Anas, 2017. SAX-based representation with longest common subsequence dissimilarity measure for time series data classification. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). Online. IEEE. 2017. p. 821–828. Available from: https://ieeexplore.ieee.org/abstract/document/8308374/ [Accessed 4 March 2024].