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The Seebeck coefficient of Ni–Fe, the metallic alloy proposed for active cooling applications, shows a higher Seebeck coefficient compared to its constituent elements and demonstrates agreement between ML predictions and experimental results. 

Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3–5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations. 

Abstract Creating materials that do not exist in nature can lead to breakthroughs in science and technology. Magnetic skyrmions are topological excitations that have attracted great attention recently for their potential applications in low power, ultrahigh density memory. A major challenge has been to find materials that meet the dual requirement of small skyrmions stable at room temperature. Here we meet both these goals by developing epitaxial FeGe films with excess Fe using atomic layer molecular beam epitaxy (MBE) far from thermal equilibrium. Our atomic layer design permits the incorporation of 20% excess Fe while maintaining a non-centrosymmetric crystal structure supported by theoretical calculations and necessary for stabilizing skyrmions. We show that the Curie temperature is well above room temperature, and that the skyrmions have sizes down to 15 nm as imaged by Lorentz transmission electron microscopy (LTEM) and magnetic force microscopy (MFM). The presence of skyrmions coincides with a topological Hall effect-like resistivity. These atomically tailored materials hold promise for future ultrahigh density magnetic memory applications. 

Assessing child growth in terms of speech and language is a crucial indicator of long term learning ability and life-long progress. Since the preschool classroom provides a potent opportunity for monitoring growth in young children’s interactions, analyzing such data has come into prominence for early childhood researchers. The foremost task of any analysis of such naturalistic recordings would involve parsing and tagging the interactions between adults and young children. An automated tagging system will provide child interaction metrics and would be important for any further processing. This study investigates the language environment of 3-5 year old children using a CRSS based diarization strategy employing an i-vector-based baseline that captures adult-to-child or childto- child rapid conversational turns in a naturalistic noisy early childhood setting. We provide analysis of various loss functions and learning algorithms using Deep Neural Networks to separate child speech from adult speech. Performance is measured in terms of diarization error rate, Jaccard error rate and shows good results for tagging adult vs children’s speech. Distinction between primary and secondary child would be useful for monitoring a given child and analysis is provided for the same. Our diarization system provides insights into the direction for preprocessing and analyzing challenging naturalistic daylong child speech recordings.