Bubble popper: considering body contact in games

Exertion games, digital games that involve physical effort, are becoming more popular. Although some of these games support social experiences, they rarely consider or support body contact. We believe overlooking body contact as part of social play experiences limits opportunities to design engaging exertion games. To explore this opportunity, we present Bubble Popper, an exertion game that considers and facilitates body contact. Bubble Popper, which uses very simple technology, also demonstrates that considering and facilitating body contact can be achieved without the need to sense body contact. Through reflecting on our design and analyzing observations of play we are able to articulate what impact physical space layout in relation to digital game elements, and physical disparity between input and digital display can have on body contact. Our results aid game designers in creating engaging exertion game experiences by guiding them when considering body contact, ultimately helping players benefiting from more engaging exertion games

Late extensional shear zones and associated recumbent folds in the Alpujarride subduction complex, Betic Cordillera, southern Spain

The existence in the Alpujarride Complex (Betic Cordillera, southern Spain) of a relatively continuous extensional event (following crustal thickening) is based on detailed structural studies and is consistent with the P-T paths and geochronological data established for the Alpujarride rocks. According to our research, the Alpujarride Complex contains two large-scale shear zones accommodating early Miocene extension. The shear zones contain km-scale recumbent folds, some with sheath fold geometry, and megaboudinage structures, and are closely associated with detachment faults.Large-scale folds and boudins cause dome-like undulations in the detachments, which are inferred to overlap in time with the deformation in the shear zones. One shear zone in the eastern part of the orogen is top-N; the other, in the western part, is top-E. The change in the shear direction may represent a temporal evolution in the direction of shear, possibly related to a change in the subduction direction in space and time

Satellite Chartography of Atmospheric Methane and carbon monoxide from SCIAMACHY onboard ENVISAT

The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO_2 in combination with modeled column-averaged CO_2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget

Assessing Methane Emissions from Global Space-Borne Observations

In the past two centuries, atmospheric methane has more than doubled and now constitutes 20% of the anthropogenic climate forcing by greenhouse gases. Yet its sources are not well quantified, introducing uncertainties in its global budget. We retrieved the global methane distribution by using spaceborne near-infrared absorption spectroscopy. In addition to the expected latitudinal gradient, we detected large-scale patterns of anthropogenic and natural methane emissions. Furthermore, we observed unexpectedly high methane concentrations over tropical rainforests, revealing that emission inventories considerably underestimated methane sources in these regions during the time period of investigation (August through November 2003)

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

ESTIMATION OF BALLISTIC PARAMETERS OF GUN PROPELLANTS THROUGH CLOSED VESSEL EXPERIMENT MODELING

Closed vessels have being used for the regression of lumped ballistic parameters for decades. However, if material and energy balances are coupled with burning rate empirical correlations, several uncorrelated parameters can be estimated, which describe more accurately the thermochemical behavior of the gases generated, even if the chemical composition of the propellant is unknown (as when the propellant is aged, for instance). This research presents such approach leading to a system of differential equations which are integrated to produce a theoretical pressure profile in the vessel, highly dependent on the choice of empirical parameters. Such parameters are manipulated according to the Maximum Likelihood statistical procedure, which leads to the best set of parameters to describe the propellant

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

Detrital zircon provenance and lithofacies associations of montmorillonitic sands in the maastrichtian ripley formation: Implications for mississippi embayment paleodrainage patterns and paleogeography

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. We provide new detrital zircon evidence to support a Maastrichtian age for the establishment of the present-day Mississippi River drainage system. Fieldwork conducted in Pontotoc County, Mississippi, targeted two sites containing montmorillonitic sand in the Maastrichtian Ripley Formation. U-Pb detrital zircon (DZ) ages from these sands (n = 649) ranged from Mesoarchean (~2870 Ma) to Pennsylvanian (~305 Ma) and contained ~91% Appalachian-derived grains, including Appalachian–Ouachita, Gondwanan Terranes, and Grenville source terranes. Other minor source regions include the Mid-Continent Granite–Rhyolite Province, Yavapai–Mazatzal, Trans-Hudson/Penokean, and Superior. This indicates that sediment sourced from the Appalachian Foreland Basin (with very minor input from a northern or northwestern source) was being routed through the Mississippi Embayment (MSE) in the Maastrichtian. We recognize six lithofacies in the field areas interpreted as barrier island to shelf environments. Statistically significant differences between DZ populations and clay mineralogy from both sites indicate that two distinct fluvial systems emptied into a shared back-barrier setting, which experienced volcanic ash input. The stratigraphic positions of the montmorillonitic sands suggest that these deposits represent some of the youngest Late Cretaceous volcanism in the MSE

Sensitivity of shelf sea marine ecosystems to temporal resolution of meteorological forcing

Phytoplankton phenology and the length of the growing season have implications that cascade through trophic levels and ultimately impact the global carbon flux to the seafloor. Coupled hydrodynamic‐ecosystem models must accurately predict timing and duration of phytoplankton blooms in order to predict the impact of environmental change on ecosystem dynamics. Meteorological conditions, such as solar irradiance, air temperature and wind‐speed are known to strongly impact the timing of phytoplankton blooms. Here, we investigate the impact of degrading the temporal resolution of meteorological forcing (wind, surface pressure, air and dew point temperatures) from 1‐24 hours using a 1D coupled hydrodynamic‐ecosystem model at two contrasting shelf‐sea sites: one coastal intermediately stratified site (L4) and one offshore site with constant summer stratification (CCS). Higher temporal resolutions of meteorological forcing resulted in greater wind stress acting on the sea surface increasing water column turbulent kinetic energy. Consequently, the water column was stratified for a smaller proportion of the year producing a delayed onset of the spring phytoplankton bloom by up to 6 days, often earlier cessation of the autumn bloom, and shortened growing season of up to 23 days. Despite opposing trends in gross primary production between sites, a weakened microbial loop occurred with higher meteorological resolution due to reduced dissolved organic carbon production by phytoplankton caused by differences in resource limitation: light at CCS and nitrate at L4. Caution should be taken when comparing model runs with differing meteorological forcing resolutions. Recalibration of hydrodynamic‐ecosystem models may be required if meteorological resolution is upgraded

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases