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This article reviews the current knowledge state on pragmatic and structural language abilities in autism and their potential relation to extralinguistic abilities and autistic traits. The focus is on questions regarding autism language profiles with varying degrees of (selective) impairment and with respect to potential comorbidity of autism and language impairment: Is language impairment in autism the co-occurrence of two distinct conditions (comorbidity), a consequence of autism itself (no comorbidity), or one possible combination from a series of neurodevelopmental properties (dimensional approach)? As for language profiles in autism, three main groups are identified, namely, (i) verbal autistic individuals without structural language impairment, (ii) verbal autistic individuals with structural language impairment, and (iii) minimally verbal autistic individuals. However, this tripartite distinction hides enormous linguistic heterogeneity. Regarding the nature of language impairment in autism, there is currently no model of how language difficulties may interact with autism characteristics and with various extralinguistic cognitive abilities. Building such a model requires carefully designed explorations that address specific aspects of language and extralinguistic cognition. This should lead to a fundamental increase in our understanding of language impairment in autism, thereby paving the way for a substantial contribution to the question of how to best characterize neurodevelopmental disorders., (© 2023. The Author(s).)

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them ( item-bound generalization ) and generalizing rules to novel instances ( category-based generalization ). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon's noisy-channel coding theory, which adds into the "formula" for rule induction the crucial dimension of time : the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity- based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity , the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization , so as to avoid exceeding the channel capacity . We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization , as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization , it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Radulescu, Kotsolakou, Wijnen, Avrutin and Grama.)

The ability to track non-adjacent dependencies (the relationship between ai and bi in an aiXbi string) has been hypothesized to support detection of morpho-syntactic dependencies in natural languages ('The princess is reluctantly kissing the frog'). But tracking such dependencies in natural languages entails being able to generalize dependencies to novel contexts ('The general is angrily berating his troops'), and also tracking co-occurrence patterns between functional morphemes like is and ing (a class of elements that often lack perceptual salience). We use the Headturn Preference Procedure to investigate (i) whether infants are capable of generalizing dependencies to novel contexts, and (ii) whether they can track dependencies between perceptually non-salient elements in an artificial grammar aXb. Results suggest that 18-month-olds extract abstract knowledge of a_b dependencies between non-salient a and b elements and use this knowledge to subsequently re-familiarize themselves with specific ai_bi combinations. However, they show no evidence of generalizing ai_bi dependencies to novel aiYbi strings., Competing Interests: The authors have declared that no competing interests exist.

In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise.

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter 𝜃 t,τ , beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.