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1. Structure Sensitive Tier Projection: Applications and Formal Properties

   De Santo, Aniello; Graf, Thomas (August 2019, FG 2019)

   The subregular approach has revealed that the phonological surface patterns found in natural language are much simpler than previously assumed. Most patterns belong to the subregular class of tier-based strictly local languages (TSL), which characterizes them as the combination of a strictly local dependency with a tier-projection mechanism that masks out irrelevant segments. Some non-TSL patterns have been pointed out in the literature, though. We show that these outliers can be captured by rendering the tier projection mechanism sensitive to the surrounding structure. We focus on a specific instance of these structure-sensitive TSL languages: input-local TSL (ITSL), in which the tier projection may distinguish between identical segments that occur in different local contexts in the input string. This generalization of TSL establishes a tight link between tier-based language classes and ISL transductions, and is motivated by several natural language phenomena. 

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2. Universal Features in Phonological Neighbor Networks

   https://doi.org/10.3390/e20070526  

   Brown, Kevin; Allopenna, Paul; Hunt, William; Steiner, Rachael; Saltzman, Elliot; McRae, Ken; Magnuson, James (July 2018, Entropy)

   Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that “universal” features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority. 

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3. Feedback in the Time-Invariant String Kernel model of spoken word recognition

   Magnuson, J.; You, H. (July 2018, Proceedings of the Cognitive Science Society)

   The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hanngan et al., 2013) is an interactive activation model like TRACE (McClelland & Elman, 1986). However, it uses orders of magnitude fewer nodes and connections because it replaces TRACE's time-specific duplicates of phoneme and word nodes with time-invariant nodes based on a string kernel representation (essentially a phoneme-by-phoneme matrix, where a word is encoded as by all ordered open diphones it contains; e.g., cat has /kæ/, /æt/, and /kt/). Hannagan et al. (2013) showed that TISK behaves similarly to TRACE in the time course of phonological competition and even word-specific recognition times. However, the original implementation did not include feedback from words to diphone nodes, precluding simulation of top-down effects. Here, we demonstrate that TISK can be easily adapted to lexical feedback, affording simulation of top-down effects as well as allowing the model to demonstrate graceful degradation given noisy inputs. 

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4. An SBV relaxation of the Cross-Newell energy for modeling stripe patterns and their defects

   https://doi.org/10.3934/dcdss.2022101  

   Venkataramani, Shankar C. (January 2022, Discrete and Continuous Dynamical Systems - S)

   We investigate stripe patterns formation far from threshold using a combination of topological, analytic, and numerical methods. We first give a definition of the mathematical structure of 'multi-valued' phase functions that are needed for describing layered structures or stripe patterns containing defects. This definition yields insight into the appropriate 'gauge symmetries' of patterns, and leads to the formulation of variational problems, in the class of special functions with bounded variation, to model patterns with defects. We then discuss approaches to discretize and numerically solve these variational problems. These energy minimizing solutions support defects having the same character as seen in experiments. 

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5. Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset

   https://doi.org/10.1029/2023EF004077  

   Wang, Jian; Liu, Desheng (June 2024, Earth's Future)

   Abstract Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. 

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