Multi-step ultraviolet index forecasting using long short-term memory networks

The ultraviolet index is an international standard metric for measuring the strength of the ultraviolet radiation reaching Earth’s surface at a particular time, at a particular place. Major health problems may arise from an overexposure to such radiation, including skin cancer or premature ageing, just to name a few. Hence, the goal of this work is to make use of Deep Learning models to forecast the ultraviolet index at a certain area for future timesteps. With the problem framed as a time series one, candidate models are based on Recurring Neural Networks, a particular class of Artificial Neural Networks that have been shown to produce promising results when handling time series. In particular, candidate models implement Long Short-Term Memory networks, with the models’ input ranging from uni to multi-variate. The used dataset was collected by the authors of this work. On the other hand, the models’ output follows a recursive multi-step approach to forecast several future timesteps. The obtained results strengthen the use of Long Short-Term Memory networks to handle time series problems, with the best candidate model achieving high performance and accuracy for ultraviolet index forecasting.This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units project scope UIDB/00319/2020 and DSAIPA/AI/0099/2019. The work of Bruno Fernandes is also supported by a Portuguese doctoral grant, SFRH/BD/130125/2017, issued by FCT in Portugal

Articulatory evidence for feedback and competition in speech production.

We report an experimental investigation of slips of the tongue using a Word Order Competition (WOC) paradigm in which context (entirely non-lexical, mixed) and competitor (whether a possible phoneme substitution would result in a word or not) were crossed. Our primary analysis uses electropalatographic (EPG) records to measure articulatory variation, and reveals that the articulation of onset phonemes is affected by two factors. First, onsets with real word competitors are articulated more similarly to the competitor onset than when the competitor would result in a non-word. Second, onsets produced in a non-lexical context vary more from the intended onset than when the context contains real words. We propose an account for these findings that incorporates feedback between phonological and lexical representations in a cascading model of speech production, and argue that measuring articulatory variation can improve our understanding of the cognitive processes involved in speech productio

On the effects of the ocean on atmospheric CFC-11 lifetimes and emissions

The ocean is a reservoir for CFC-11, a major ozone-depleting chemical. Anthropogenic production of CFC-11 dramatically decreased in the 1990s under the Montreal Protocol, which stipulated a global phase out of production by 2010. However, studies raise questions about current overall emission levels and indicate unexpected increases of CFC-11 emissions of about 10 Gg ⋅ yr−1 after 2013 (based upon measured atmospheric concentrations and an assumed atmospheric lifetime). These findings heighten the need to understand processes that could affect the CFC-11 lifetime, including ocean fluxes. We evaluate how ocean uptake and release through 2300 affects CFC-11 lifetimes, emission estimates, and the long-term return of CFC-11 from the ocean reservoir. We show that ocean uptake yields a shorter total lifetime and larger inferred emission of atmospheric CFC-11 from 1930 to 2075 compared to estimates using only atmospheric processes. Ocean flux changes over time result in small but not completely negligible effects on the calculated unexpected emissions change (decreasing it by 0.4 ± 0.3 Gg ⋅ yr−1). Moreover, it is expected that the ocean will eventually become a source of CFC-11, increasing its total lifetime thereafter. Ocean outgassing should produce detectable increases in global atmospheric CFC-11 abundances by the mid-2100s, with emission of around 0.5 Gg ⋅ yr−1; this should not be confused with illicit production at that time. An illustrative model projection suggests that climate change is expected to make the ocean a weaker reservoir for CFC-11, advancing the detectable change in the global atmospheric mixing ratio by about 5 yr

Filled pauses in Hungarian: Their phonetic form and function

Filled pauses are natural occurrences in spontaneous speech and they may turn up at any level of the speech planning process and in a number of functions. The aim of this paper is to find out whether the diverse functions of filled pauses correlate with diverse articulations resulting in diverse acoustic structures. Spontaneous narratives are used as research material. The duration of the filled pauses and the frequency values of their first two formants are analyzed. The most frequent form, schwa, shows function-dependent realizations as confirmed by the durational values and by the second formant values of these vowel-like sounds

On Not Remembering Disfluencies

Disfluencies - repetitions and reformulations midsentence in normal spontaneous speech - are problematic for both psychological and computational models of speech understanding. Much effort is being applied to finding ways of adapting computational systems to detect and delete disfluencies. The input to such systems is usually an accurate transcription. We present results of an experiment in which human listeners are asked to give verbatim transcriptions of disfluent and fluent utterances. These suggest that listeners are seldom able to identify all the words "deleted" in disfluencies. While all types suffer, identification rates for repetitions are even worse than for other types. We attribute the results to difficulties in recall or coding for recall items which can not be identified with certainty. This inability seems to make human speech recognition more robust than current computational models. 1. BACKGROUND Human listeners are reasonably accurate in transcribing fluent speech ..