Mediterranean developed coasts: what future for the foredune restoration?

The feasibility and efficacy of soft engineering foredune restoration approaches still lack insight from research and monitoring activities, especially in areas where dunes are under persisting human disturbance. We evaluated the efficacy of Mediterranean foredune restoration in dune areas freely accessible to tourists. Foredunes were reconstructed using only sand already available at nearby places and consolidated through the plantation of seedlings of native ecosystem engineer species and foredune focal species. We monitored transplanted and spontaneous seedlings for one year to assess their mortality and growth in relation to the distance from the closest beach access, either formal or informal, as proxy of human disturbance.We also tested whether species differing in their ecology (i.e., affinity to a given habitat) and growth form showed different response to human disturbance. The relationship between seedling mortality and growth and the distance from the closest beach access was tested through Generalized Linear Mixed Models. We found a clear spatial pattern of seedling survival and growth, which decreased as the proximity to the closest beach access increased. Only invasive alien plants and erect leafy species showed to better perform at lower distances from beach accesses. In dune areas with a strong tourist vocation, foredune restoration should be coupled with the implementation of integrated management plans aiming at optimising the relationship between protection and use. Management plans should not only rely on passive conservation measures; rather they should include educational activities to stimulate a pro-environmental behaviour, increase the acceptance of behaviour rules and no entry zones, and actively engage stakeholders in long-term conservation

Testing the performance of an innovative markerless technique for quantitative and qualitative gait analysis

Gait abnormalities such as high stride and step frequency/cadence (SF-stride/second, CAD-step/second), stride variability (SV) and low harmony may increase the risk of injuries and be a sentinel of medical conditions. This research aims to present a new markerless video-based technology for quantitative and qualitative gait analysis. 86 healthy individuals (mead age 32 years) performed a 90 s test on treadmill at self-selected walking speed. We measured SF and CAD by a photoelectric sensors system; then, we calculated average \ub1 standard deviation (SD) and within-subject coefficient of variation (CV) of SF as an index of SV. We also recorded a 60 fps video of the patient. With a custom-designed web-based video analysis software, we performed a spectral analysis of the brightness over time for each pixel of the image, that reinstituted the frequency contents of the videos. The two main frequency contents (F1 and F2) from this analysis should reflect the forcing/dominant variables, i.e., SF and CAD. Then, a harmony index (HI) was calculated, that should reflect the proportion of the pixels of the image that move consistently with F1 or its supraharmonics. The higher the HI value, the less variable the gait. The correspondence SF-F1 and CAD-F2 was evaluated with both paired t-Test and correlation and the relationship between SV and HI with correlation. SF and CAD were not significantly different from and highly correlated with F1 (0.893 \ub1 0.080 Hz vs. 0.895 \ub1 0.084 Hz, p < 0.001, r2 = 0.99) and F2 (1.787 \ub1 0.163 Hz vs. 1.791 \ub1 0.165 Hz, p < 0.001, r2 = 0.97). The SV was 1.84% \ub1 0.66% and it was significantly and moderately correlated with HI (0.082 \ub1 0.028, p < 0.001, r2 = 0.13). The innovative video-based technique of global, markerless gait analysis proposed in our study accurately identifies the main frequency contents and the variability of gait in healthy individuals, thus providing a time-efficient, low-cost means to quantitatively and qualitatively study human locomotion

The (un)suitability of automatic evaluation metrics for text simplification

In order to simplify sentences, several rewriting operations can be performed such as replacing complex words per simpler synonyms, deleting unnecessary information, and splitting long sentences. Despite this multi-operation nature, evaluation of automatic simplification systems relies on metrics that moderately correlate with human judgements on the simplicity achieved by executing specific operations (e.g. simplicity gain based on lexical replacements). In this article, we investigate how well existing metrics can assess sentence-level simplifications where multiple operations may have been applied and which, therefore, require more general simplicity judgements. For that, we first collect a new and more reliable dataset for evaluating the correlation of metrics and human judgements of overall simplicity. Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new dataset (and other existing data) to analyse the variation of the correlation between metrics’ scores and human judgements across three dimensions: the perceived simplicity level, the system type and the set of references used for computation. We show that these three aspects affect the correlations and, in particular, highlight the limitations of commonly-used operation-specific metrics. Finally, based on our findings, we propose a set of recommendations for automatic evaluation of multi-operation simplifications, suggesting which metrics to compute and how to interpret their scores

ASSET : a dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations

In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed

EASSE: easier automatic sentence simplification evaluation

We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus). Finally, EASSE generates easy-to-visualise reports on the various metrics and features above and on how a particular SS output fares against reference simplifications. Through experiments, we show that these functionalities allow for better comparison and understanding of the performance of SS systems

Solution of the End Problem of a Liquid-Filled Cylindrical Acoustic Waveguide Using a Biorthogonality Principle

This paper treats the forced motion of an isothermal, Newtonian liquid in a sem

Comparison between parameter-efficient techniques and full fine-tuning: a case study on multilingual news article classification

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning. We focus specifically on multilingual text classification tasks (genre, framing, and persuasion techniques detection; with different input lengths, number of predicted classes and classification difficulty), some of which have limited training data. In addition, we conduct in-depth analyses of their efficacy across different training scenarios (training on the original multilingual data; on the translations into English; and on a subset of English-only data) and different languages. Our findings provide valuable insights into the applicability of parameter-efficient fine-tuning techniques, particularly for multilabel classification and non-parallel multilingual tasks which are aimed at analysing input texts of varying length