Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decreasing the reaction time by the care-givers especially for those in care who are particularly frail or living alone. Recent advances in motion-sensor technology have enabled wearable sensors to be used efficiently for pervasive care of the elderly. In addition to fall detection, it is also important to determine the direction of a fall, which could help in the location of joint weakness or post-fall fracture. This work uses a waist-worn sensor, encompassing a 3D accelerometer and a barometric pressure sensor, for reliable fall detection and the determination of the direction of a fall. Also assessed is an efficient analysis framework suitable for on-node implementation using a low-power micro-controller that involves both feature extraction and fall detection. A detailed laboratory analysis is presented validating the practical application of the system.
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Pythonbased natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://github. com/GEM-benchmark/NL-Augmenter).
Abstract-This study investigates the spatial and directional tuning of Multi-Unit Activity (MUA) in mouse primary visual cortex and how MUA can reflect spatiotemporal structures contained in moving gratings. Analysis of multi-shank laminar electrophysiological recordings from mouse primary visual cortex indicates a directional preference for moving gratings around 180• , while preferred spatial frequency peaks around 0.02 cycles per degree, which is similar as reported in single-unit studies. Using only features from MUA, we further achieved a significant performance in decoding spatial frequency or direction of moving gratings, with average decoding performances of up to 58.54% for 8 directions, and 44% correctly identified spatial frequencies against chance level of 16.7%.
Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental human mistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguous to humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popular language models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases. The infrastructure, datacards, and robustness evaluation results are publicly available on GitHub for the benefit of researchers working on paraphrase generation, robustness analysis, and low-resource NLP. El aumento de datos es un método importante para evaluar la solidez y mejorar la diversidad del entrenamiento datos para modelos de procesamiento de lenguaje natural (NLP). इस लेख में, हम एनएल-ऑगमेंटर का प्रस्ताव करते हैं - एक नया भागी- दारी पूर्वक, पायथन में बनाया गया, लैंग्वेज (एनएल) ऑग्मेंटेशन फ्रेमवर्क जो ट्रांसफॉर्मेशन (डेटा में बदलाव करना) और फीलटर (फीचर्स के अनुसार डेटा का भाग करना) के नीरमान का समर्थन करता है।. 我们描述了NL-Augmenter框架及其初步包含的117种转换和23个过滤器,并 大致标注分类了一系列可适配的自然语言任务. این دگرگونی ها شامل نویز، اشتباهات عمدی و تصادفی انسانی، تنوع اجتماعی-زبانی، سبک معنایی معتبر، تغییرات نحوی و همچنین ساختارهای مصنوعی است که برای انسان ها مبهم است. NL-Augmenterpa allin kaynintam qawachiyku, tikrakuyninku- nata servichikuspayku, chaywanmi qawariyku modelos de lenguaje popular nisqapa allin takyasqa kayninta. Kami menemukan model yang berbeda ditantang secara berbeda pada tugas yang berbeda, dengan penurunan skor kuasi-sistematis. Infrastruktur, kartu data, dan hasil evaluasi ketahanan dipublikasikan tersedia secara gratis di GitHub untuk kepentingan para peneliti yang mengerjakan pembuatan parafrase, analisis ketahanan, dan NLP sumber daya rendah.
1Orientation tuning in mouse primary visual cortex (V1) has long been reported to have a 2 random or "salt-and-pepper" organisation, lacking the structure found in cats and primates. 3 Laminar in-vivo multi-electrode array recordings here reveal previously elusive structure in 4 the representation of visual patterns in the mouse visual cortex, with temporo-nasally drifting 5 gratings eliciting consistently highest neuronal responses across cortical layers and columns, 6 whilst upward moving gratings reliably evoked the lowest activities. We suggest this bias in 7 direction selectivity to be behaviourally relevant as objects moving into the visual field from the 8 side or behind may pose a predatory threat to the mouse whereas upward moving objects do 9 not. We found furthermore that direction preference and selectivity was affected by stimulus 10 spatial frequency, and that spatial and directional tuning curves showed high signal correlations 11 decreasing with distance between recording sites. In addition, we show that despite this bias in 12 direction selectivity, it is possible to decode stimulus identity and that spatiotemporal features 13 achieve higher accuracy in the decoding task whereas spike count or population counts are 14 sufficient to decode spatial frequencies implying different encoding strategies. 15 Pepper 17 Significance statement: 18 We show that temporo-nasally drifting gratings (i.e. opposite the normal visual flow during forward 19 movement) reliably elicit the highest neural activity in mouse primary visual cortex, whereas upward 20 moving gratings reliably evoke the lowest responses. This encoding may be highly behaviourally 21 relevant, as objects approaching from the periphery may pose a threat (e.g. predators), whereas * Corresponding author.Temporo-nasally biased moving grating selectivity in mouse V1 upward moving objects do not. This is a result at odds with the belief that mouse primary visual 23 cortex is randomly organised. Further to this biased representation, we show that direction tuning 24 depends on the underlying spatial frequency and that tuning preference is spatially correlated both 25 across layers and columns and decreases with cortical distance, providing evidence for structural 26 organisation in mouse primary visual cortex. 27Introduction 28 Visual information processing in cortical circuits is still poorly understood. Electrophysiological 29 studies in cat visual cortex area 17 revealed half a century ago that moving bars at different orientations 30 evoked responses of varying strength (Hubel and Wiesel 1962). Hubel and Wiesel discovered 31 orientation-selective neurons (Hubel and Wiesel 1962), and their organisation in orientation columns 32 (Hubel and Wiesel 1974), consisting of neurons of the same or similar preferred orientation across 33 multiple cortical layers. In addition to these orientation columns, orientation-selectivity was found 34 to be organised laterally (Bonhoeffer and Grinvald 1991), where preferred orientation progressed in 35 s...
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