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1. Production and perception of focus in Sümi

   Teo, Amos; Baese-Berk, Melissa (August 2019, Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019)

   This paper investigates the homophony/polysemy between a morphological agentive marker and a contrastive focus marker in Sümi, a Tibeto-Burman language of Northeast India. Both are realized by a phrasal suffix -no that attaches to grammatical subjects, but the interpretation of the suffix varies by clause type. The present study examines whether transitive and intransitive subjects in contrastive focus receive any special prosodic marking that is recognizable to native listeners. The study has implications for understanding the development of agentive/focus marking in Sümi, as well as other languages of the Himalayas, and in New Guinea and Australia where similar homophony/polysemy between agentive and focus markers has been found. 

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2. The interface of explicit and implicit second-language knowledge: A longitudinal study

   https://doi.org/10.1017/S1366728922000773  

   Kim, Kathy MinHye; Godfroid, Aline (January 2023, Bilingualism: Language and Cognition)

   Abstract The aim of our study was to examine the longitudinal associations between two forms of second language (L2) knowledge (i.e., explicit and implicit knowledge) and the activity types that facilitate different processing mechanisms (i.e., form- and meaning-focused processing). L2 English speakers completed two tests of explicit knowledge (untimed written grammaticality judgment test and metalinguistic knowledge test) and three tests of implicit knowledge (timed written grammaticality judgment test, oral production, and elicited imitation) at the beginning and the end of a semester of university-level study. To track engagement in the activity types, participants completed self-reported language exposure logs across five days throughout the semester. The results from an autoregressive cross-lag analysis suggest L2 explicit and implicit knowledge influenced each other reciprocally over time. Neither activity type predicted knowledge development. We conclude that language acquisition is a developmental process typified by a dynamic, synergistic interface between explicit and implicit knowledge. 

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3. English‐learning infants developing sensitivity to vowel phonotactic cues to word segmentation

   https://doi.org/10.1111/desc.13564  

   Katsuda, Hironori; Sundara, Megha (September 2024, Developmental Science)

   Abstract Previous research has shown that when domain‐general transitional probability (TP) cues to word segmentation are in conflict with language‐specific stress cues, English‐learning 5‐ and 7‐month‐olds rely on TP, whereas 9‐month‐olds rely on stress. In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP cues to word segmentation vis‐a‐vis language‐specific vowel phonotactic (VP) cues—English words do not end in lax vowels. These cues were either consistent or conflicting. When these cues were in conflict, 10‐month‐olds relied on the VP cues, whereas 5‐month‐olds relied on TP. These findings align with statistical bootstrapping accounts, where infants initially use domain‐general distributional information for word segmentation, and subsequently discover language‐specific patterns based on segmented words. Research HighlightsResearch indicates that when transitional probability (TP) conflicts with stress cues for word segmentation, English‐learning 9‐month‐olds rely on stress, whereas younger infants rely on TP.In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP versus vowel phonotactic (VP) cues for word segmentation.When these cues conflicted, 10‐month‐olds relied on VPs, whereas 5‐month‐olds relied on TP.These findings align with statistical bootstrapping accounts, where infants first utilize domain‐general distributional information for word segmentation, and then identify language‐specific patterns from segmented words. 

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4. Quantum Social Computing Approaches for Influence Maximization

   https://doi.org/10.1109/GLOBECOM48099.2022.10000698  

   Dinh, Thang; Nguyen, An; Nguyen, Uyen; Nguyen, Giang (January 2023, GLOBECOM 2022 - 2022 IEEE Global Communications Conference)

   Influence Maximization (IM), which seeks a small set of important nodes that spread the influence widely into the network, is a fundamental problem in social networks. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, finding near-optimal solutions for IM is difficult due to its NP-completeness. In this paper, we propose the first social quantum computing approaches for IM, aiming to retrieve near-optimal solutions. We propose a two-phase algorithm that 1) converts IM into a Max-Cover instance and 2) provides efficient quadratic unconstrained binary optimization formulations to solve the Max-Cover instance on quantum annealers. Our experiments on the state-of-the-art D-Wave annealer indicate better solution quality compared to classical simulated annealing, suggesting the potential of applying quantum annealing to find high-quality solutions for IM. 

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5. Astrid: accurate selectivity estimation for string predicates using deep learning

   https://doi.org/10.14778/3436905.3436907  

   Shetiya, Suraj; Thirumuruganathan, Saravanan; Koudas, Nick; Das, Gautam (December 2020, Proceedings of the VLDB Endowment)

   null (Ed.)

   Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results. 

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