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The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite beta and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favourable confinement properties by finding a solution to the inverse design problem, that is, obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method. 

Bilinguals experience processing costs when comprehending code-switches, yet the magnitude of the cost fluctuates depending on numerous factors. We tested whether switch costs vary based on the frequency of different types of code-switches, as estimated from natural corpora of bilingual speech and text. Spanish–English bilinguals in the U.S. read single-language and code-switched sentences in a self-paced task. Sentence regions containing code-switches were read more slowly than single-language control regions, consistent with the idea that integrating a code-switch poses a processing challenge. Crucially, more frequent code-switches elicited significantly smaller costs both within and across most classes of switch types (e.g., within verb phrases and when comparing switches at verb-phrase and noun-phrase sites). The results suggest that, in addition to learning distributions of syntactic and semantic patterns, bilinguals develop finely tuned expectations about code-switching behavior – representing one reason why code-switching in naturalistic contexts may not be particularly costly. 

Abstract The study of how bilingualism is linked to cognitive processing, including executive functioning, has historically focused on comparing bilinguals to monolinguals across a range of tasks. These group comparisons presume to capture relatively stable cognitive traits and have revealed important insights about the architecture of the language processing system that could not have been gleaned from studying monolinguals alone. However, there are drawbacks to using a group-comparison, or Traits, approach. In this theoretical review, we outline some limitations of treating executive functions as stable traits and of treating bilinguals as a uniform group when compared to monolinguals. To build on what we have learned from group comparisons, we advocate for an emerging complementary approach to the question of cognition and bilingualism. Using an approach that compares bilinguals to themselves under different linguistic or cognitive contexts allows researchers to ask questions about how language and cognitive processes interact based on dynamically fluctuating cognitive and neural states. A States approach, which has already been used by bilingualism researchers, allows for cause-and-effect hypotheses and shifts our focus from questions of group differences to questions of how varied linguistic environments influence cognitive operations in the moment and how fluctuations in cognitive engagement impact language processing.