Aphanitic buildup from the onset of the Mulde Event (Homerian, middle Silurian) at Whitman’s Hill, Herefordshire, UK: ultrastructural insights into proposed microbial fabrics; pp. 287–292

A microbial origin has been proposed for matrix-supported, low-diversity buildups reported from different palaeocontinents during the onset of the Mulde positive carbon isotope excursion. We have investigated a small aphanitic buildup from the Lower Quarried Limestone Member of the Much Wenlock Limestone Formation, exposed at Whitman's Hill (Herefordshire), corresponding to the central part of the Midland Platform (UK). Up to 50% of the rock volume in this buildup consists of mottled micrite. The SEM studies revealed that the micrite is largely detrital and does not show features characteristic of calcareous cyanobacteria or leiolites. The aphanitic character of the buildup is suggested to be controlled by the depositional rate, and the widespread occurrence of matrix-supported reefs in this interval to be driven by a mid-Homerian rapid eustatic transgression

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Palaeozoic stromatoporoid diagenesis: a synthesis

Supplementary Information: the online version contains supplementary material available at https://doi.org/10.1007/s10347-021-00628-x.Nuffield Foundation; Deutsche Forschungsgemeinschaft (grant no JA 2718/3-1);Nuffield Foundation; Deutsche Forschungsgemeinschaft (grant no JA 2718/3-1)