DeepSleep: A ballistocardiographic deep learning approach for classifying sleep stages

Current techniques for tracking sleep are either obtrusive (Polysomnography) or low in accuracy (wearables). In this early work, we model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from a commercially available pressure-sensitive sensor sheet. We present DeepSleep, a hybrid deep neural network architecture comprising of CNN and LSTM layers. We further employed a 2-phase training strategy to build a pre-trained model and to tackle the limited dataset size. Our model results in a classification accuracy of 74%, 82%, 77% and 63% using Dozee BCG, MIT-BIH’s ECG, Dozee’s ECG and Fitbit’s PPG datasets, respectively. Furthermore, our model shows a positive correlation (r = 0.43) with the SATED perceived sleep quality scores. We show that BCG signals are effective for long-term sleep monitoring, but currently not suitable for medical diagnostic purposes

Yield of forage, grain and biomass in eight hybrids of maize with different sowing dates and environmental conditions

The aim was to evaluate yield of forage, grain and biomass and fibre content of eight hybrids of maize (Rio-Grande, Arrayan, Genex 778, Narro 2010, Advance 2203, DAS 2358, P4082W and HT9150W) during two sowing seasons (spring/summer) for two consecutive years at La Laguna in Torreon, Mexico. Once the grain progression of the kernel milk line was ⅓, green forage yield (GFY), dry matter (DM), neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined. When the corncobs were fully mature, grain yield (GY) and biomass production (TBP) were determined. Weather conditions were recorded during the experiment. The results indicated that maximum temperature was higher and rainfall lower in the summer sowing and second year. Spring sowing had significantly higher yields of GFY, DM, GY and TBP compared to summer sowing. The first year of study showed significantly higher yields regarding GFY, GY and TBP, but FDN, FDA, DM content compared to the second year. The best hybrid for GFY and DM was Rio-Grande; for FDN and FDA was Advance 2203; for GY was HT9150W and finally for TBP was Arrayan. Regardless of the hybrid used and the sowing season, production of maize depended on external factors such as maximum temperature and rainfall; therefore, producers need to consider sowing in spring to avoid the negative effect of high temperatures on plant development.The aim was to evaluate yield of forage, grain and biomass and fibre content of eight hybrids of maize (Rio-Grande, Arrayan, Genex 778, Narro 2010, Advance 2203, DAS 2358, P4082W and HT9150W) during two sowing seasons (spring/summer) for two consecutive years at La Laguna in Torreon, Mexico. Once the grain progression of the kernel milk line was ⅓, green forage yield (GFY), dry matter (DM), neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined. When the corncobs were fully mature, grain yield (GY) and biomass production (TBP) were determined. Weather conditions were recorded during the experiment. The results indicated that maximum temperature was higher and rainfall lower in the summer sowing and second year. Spring sowing had significantly higher yields of GFY, DM, GY and TBP compared to summer sowing. The first year of study showed significantly higher yields regarding GFY, GY and TBP, but FDN, FDA, DM content compared to the second year. The best hybrid for GFY and DM was Rio-Grande; for FDN and FDA was Advance 2203; for GY was HT9150W and finally for TBP was Arrayan. Regardless of the hybrid used and the sowing season, production of maize depended on external factors such as maximum temperature and rainfall; therefore, producers need to consider sowing in spring to avoid the negative effect of high temperatures on plant development

Reconstructing the parameter space of non-analytical cosmological fixed points

Dynamical system theory is a widely used technique in the analysis of cosmological models. Within this framework, the equations describing the dynamics of a model are recast in terms of dimensionless variables, which evolve according to a set of autonomous first-order differential equations. The fixed points of this autonomous set encode the asymptotic evolution of the model. Usually, these points can be written as analytical expressions for the variables in terms of the parameters of the model, which allows a complete characterization of the corresponding parameter space. However, a thoroughly analytical treatment is impossible in some cases. In this work, we give an example of a dark energy model, a scalar field coupled to a vector field in an anisotropic background, where not all the fixed points can be analytically found. Then, we put forward a general scheme that provides a numerical description of the parameter space. This allows us to find interesting accelerated attractors of the system with no analytical representation. This work may serve as a template for the numerical analysis of highly complicated dynamical systems.Comment: 13 pages, 13 figures, 1 table. Changes match the published versio

Video Analysis Tools for Annotating User-Generated Content from Social Events

In this presentation we present how low-level metadata extraction tools have been applied in the context of a pan-European project called Together Anywhere, Together Anytime (TA2). The TA2 project studies new forms of computer-mediated social communications between spatially and temporally distant people. In particular, we concentrate on automatic video analysis tools in an asynchronous community-based video sharing environment called MyVideos, in which users can experience and share personalized music concert videos within their social grou

Self‐Assembled Nanofibrilar Networks: Boosting Hydrogelation Efficiency by Replacement of a Pyridine Moiety by a Quinoline One

This is the pre-peer reviewed version of the following article: Self‐Assembled Nanofibrilar Networks: Boosting Hydrogelation Efficiency by Replacement of a Pyridine Moiety by a Quinoline One, which has been published in final form at https://doi.org/10.1002/cnma.201800219. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsA new molecular hydrogelator consisting of a L‐Valine derivative with appended quinoline units behaves as a supramolecular superhydrogelator forming gels at such low concentrations as 0.8 mM (0.05% w/w). On the other hand, an analogue compound containing a pyridine moiety is found to be a poor gelator, forming gels at 19 mM. The gels are pH sensitive because of the protonation of the heterocycle and show microcrystallinity. The rheological properties and biocompatibility are also reported

Evaluating Viewer-Side Enrichment of Television Content

The research area of interactive digital television is in the midst of a significant revival. Unlike the first generation of digital television – which focused on the concerns of producers and broadcasters, and limited the end-user impact – the current generation of digital television research is closely linked to the role of the user in selecting, producing and distributing content. This paper presents the rationale for evaluating new interaction paradigms with television content: micro-level navigation and selection of content, direct recommendation of (pieces of) content, and enrichment of content while watching. The rationale is composed of four steps: system design, business analysis, prototype implementation, and user studies

Uncovering perceived identification accuracy of in-vehicle biometric sensing

Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustere

CorrFeat: Correlation-based feature extraction algorithm using skin conductance and pupil diameter for emotion recognition

To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods