AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.Comment: Accepted for Field and Service Robotics conference 2017 (FSR 2017

Water-wise Landscape Ideas for Existing Landscapes

This fact sheet outlines five easy ways to convert an existing landscape to a water-wise landscape without substantial renovation for those who do not have the time, resources, or expertise to renovate the existing landscape completely

How Much Water Do Landscape Trees Require in Utah? An Irrigation Calculator

Trees, undoubtedly, are the most valuable plant in a landscape and must be prioritized for irrigation in drought conditions. Grass easily recovers from a period of long drought; hence, it must be placed last on the priority list for irrigation. Many resources are available online that explain ways to irrigate landscape trees. However, growers and homeowners in Utah are still confused about quantifying and applying the appropriate amount of water a landscape tree needs. This fact sheet includes a simple calculator designed to determine the amount of water a landscape tree needs in Utah’s hot and dry summer months

Cryptocurrency Price Prediction using Neural Networks and Deep Learning Techniques

Over the last several years, new kinds of currency, such as cryptocurrencies, have been constantly emerging. In terms of market value, cryptocurrency is extremely volatile, with a slew of unknowns that make it difficult to forecast and analyze future pricing. With the ability to predict crypto prices, one can make a prediction for stocks since the popular coin; Bitcoin affects stock prices. Although machine learning has been successful in predicting stock market prices using a variety of time series models, it has been limited in its use in predicting cryptocurrency prices. The reason for this is obvious: cryptocurrency values are influenced by a variety of factors such as technological advancements, internal competitiveness, market pressure to produce, economic troubles, security concerns, political factors, and so on. This research proposes three recurrent neural networks (RNN) algorithms for predicting the values of three different cryptocurrencies: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The three models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (bi-LSTM) will be analyzed depending on the mean absolute percentage error (MAPE)

La Teoria de juegos y la sucesión en las empresas familiares

El paso del control ejecutivo de la empresa familiar por el fundador a la generación siguiente es una fase critica. Esta tesis extiende el uso de la teoría del juegos para proveer un entendimiento del papel que tienen la familia, el fundador y el ambiente cultural en la selección del sucesor. Los juegos usados incluyen explícitamente, y por primera vez, los factores emocionales relacionados con la dimensión familiar de la empresa familiar. En relación al impacto de la familia, la tesis se enfoca en la competencia fraternal que puede erosionar la armonía familiar y arriesgar la continuidad de la empresa. Los resultados destacan el costo emocional de conflicto, en importante para la definición del sucesor y esencial para explicar la ventaja del primer que se mueva. Esta tesis contribuye demostrando analíticamente la importancia del fundador adoptar una aproximación activa en el proceso de sucesión. Los resultados muestran que eso es esencial para asegurar la continuidad intergeneracional de la empresa y la asignación de su candidato preferido como sucesor. Los factores emocionales son determinantes para el resultado del sucesor y son evidentes en algunos ambientes culturales, como en India. Los resultados enfatizan que la discrepancia cultural entre las generaciones puede comprometer la sucesión y la armonía familiar. La tesis complementa la teoría de juego con economía experimental, lo que es completamente original en esta área. Para incluir la deficiencia de comunicación, se usa un juego de información completa e imperfecta. Los resultados confirman que las conclusiones teóricas son verdaderas en el laboratorio.The passing of the family firm’s executive control from the founder to the next generation is a critical stage for the family firm. This thesis extends the use of game theory to provide insights on the role the family, the founder and the cultural setting have on successor selection in family firms. The games used explicitly include, for the first time, the emotional factors related to the family dimension of the family firm. In terms of the impact of the family on successor selection, the thesis focuses on sibling competition which can erode family harmony and risk the firm’s continuity. The findings highlight that the emotional cost of conflict, triggered by the succession race, plays a key role on the definition of the successor and is essential in explaining the first mover advantage. This thesis contributes by analytically demonstrating the importance of the founder adopting an activist approach to the succession process. The results show that the founder’s approach is essential to ensure firm intergenerational continuity and secure the appointment of his preferred successor candidate. The emotional factors are determinant for the successor outcome and are even more evident in certain cultural settings, such as India. The results emphasize that the younger generation’s cultural misalignment can jeopardize intergenerational succession and risk family harmony. The thesis complements game theory with experimental economics, which is completely original in this field. A game of complete and imperfect information is used to extend the application to families with communication deficiency. The results confirm that the theoretical conclusions hold true in the laboratory

Effectiveness of lockdown as COVID-19 intervention: official and computed cases in Nepal

Introduction: COVID-19 was first reported on 31 December 2019 from China. It was first confirmed in Nepal on 23 January 2020. Government enforced first wave of nationwide ‘lockdown’ for one week on 24 March 2020 and fourth from 16 April for 12 days. This paper aims to compute effectiveness of lockdown on COVID-19 cases in Nepal. Method: Doubling times were calculated using official COVID-19 records first, and then, the number of COVID-19 cases based on various doubling time scenarios staring 23 January 2020 were computed and compared with the official cases of Nepal. All the calculations were done in Microsoft Excel. Result: Doubling time was 60-day between first and second case, 5-day between 2nd and 5th, 15-day between 5th and 12th and again 5-day between 12th to 30th cases. Doubling time increased to 15-day after the lockdown. Estimated doubling time was 28 days till March, 21 days till 12 April and 18 days till 17 April 2020 and it is expected to reach 15 days on 24 April 2020. Conclusion: The reported COVID-19 cases doubling time was 5, 15 and 5 days in Nepal after the lockdown. The doubling time increased due to lockdown. Keyword: COVID-19, doubling time, Nepa

Assessing the Limitations and Capabilities of Lidar and Landsat 8 to Estimate the Aboveground Vegetation Biomass and Cover in a Rangeland Ecosystem Using a Machine Learning Algorithm

Remote sensing based quantification of semiarid rangeland vegetation provides the large scale observations required for monitoring native plant distribution, estimating fuel loads, modeling climate and hydrological dynamics, and measuring carbon storage. Fine scale 3-dimensional vertical structural information from airborne lidar and improved signal to noise ratio and radiometric resolution of recent satellite imagery provide opportunities for refined measurements of vegetation structure. In this study, we leverage a large number of time series Landsat 8 vegetation indices and lidar point cloud - based vegetation metrics with ground validation for scaling aboveground shrub and herb biomass and cover from small scale plot to large, regional scales in the Morley Nelson Snake River Birds of Prey National Conservation Area (NCA), Idaho. The Landsat vegetation indices were trained and linked to in-situ measurements (n = 141) with the random forest regression to impute vegetation biomass and cover across the NCA. We also validated our model with an independent dataset (n = 44), explaining up to 63% and 53% of variation in shrub cover and biomass, respectively. Forty six of the in-situ plots were used in a model to compare the performance of lidar and Landsat data in estimating vegetation characteristics. Our results demonstrate that Landsat performs better in estimating both herb (R2 ~ 0.60) and shrub cover (R2 ~ 0.75) whereas lidar performs better in estimating shrub and total biomass (R2 ~ 0.75 and 0.68, respectively). Using the lidar only model, we demonstrate that lidar metrics based on shrub height have a strong correlation with field-measured shrub biomass (R2 ~ 0.76). We also compare processing the lidar data with raster-based and point cloud-based approaches. The results are scale-dependent, with improved results of biomass estimation at coarser scales with point cloud processing. Overall, the results of this study indicate that Landsat and lidar can be efficiently utilized independently and together to estimate biomass and cover of vegetation in this semi-arid rangeland environment

Observation- and Model-Based Estimates of Particulate Dry Nitrogen Deposition to the Oceans

© Author(s) 2017. This is an Open Access article distributed under the Creative Commons Attribution License CC BY 3.0 ( https://creativecommons.org/licenses/by/3.0/ ). Published by Copernicus Publications on behalf of the European Geosciences Union.Anthropogenic nitrogen (N) emissions to the atmosphere have increased significantly the deposition of nitrate (NO3−) and ammonium (NH4+) to the surface waters of the open ocean, with potential impacts on marine productivity and the global carbon cycle. Global-scale understanding of the impacts of N deposition to the oceans is reliant on our ability to produce and validate models of nitrogen emission, atmospheric chemistry, transport and deposition. In this work,  ∼  2900 observations of aerosol NO3− and NH4+ concentrations, acquired from sampling aboard ships in the period 1995–2012, are used to assess the performance of modelled N concentration and deposition fields over the remote ocean. Three ocean regions (the eastern tropical North Atlantic, the northern Indian Ocean and northwest Pacific) were selected, in which the density and distribution of observational data were considered sufficient to provide effective comparison to model products. All of these study regions are affected by transport and deposition of mineral dust, which alters the deposition of N, due to uptake of nitrogen oxides (NOx) on mineral surfaces. Assessment of the impacts of atmospheric N deposition on the ocean requires atmospheric chemical transport models to report deposition fluxes; however, these fluxes cannot be measured over the ocean. Modelling studies such as the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), which only report deposition flux, are therefore very difficult to validate for dry deposition. Here, the available observational data were averaged over a 5° × 5° grid and compared to ACCMIP dry deposition fluxes (ModDep) of oxidised N (NOy) and reduced N (NHx) and to the following parameters from the Tracer Model 4 of the Environmental Chemical Processes Laboratory (TM4): ModDep for NOy, NHx and particulate NO3− and NH4+, and surface-level particulate NO3− and NH4+ concentrations. As a model ensemble, ACCMIP can be expected to be more robust than TM4, while TM4 gives access to speciated parameters (NO3− and NH4+) that are more relevant to the observed parameters and which are not available in ACCMIP. Dry deposition fluxes (CalDep) were calculated from the observed concentrations using estimates of dry deposition velocities. Model–observation ratios (RA, n), weighted by grid-cell area and number of observations, were used to assess the performance of the models. Comparison in the three study regions suggests that TM4 overestimates NO3− concentrations (RA, n =  1.4–2.9) and underestimates NH4+ concentrations (RA, n =  0.5–0.7), with spatial distributions in the tropical Atlantic and northern Indian Ocean not being reproduced by the model. In the case of NH4+ in the Indian Ocean, this discrepancy was probably due to seasonal biases in the sampling. Similar patterns were observed in the various comparisons of CalDep to ModDep (RA, n =  0.6–2.6 for NO3−, 0.6–3.1 for NH4+). Values of RA, n for NHx CalDep–ModDep comparisons were approximately double the corresponding values for NH4+ CalDep–ModDep comparisons due to the significant fraction of gas-phase NH3 deposition incorporated in the TM4 and ACCMIP NHx model products. All of the comparisons suffered due to the scarcity of observational data and the large uncertainty in dry deposition velocities used to derive deposition fluxes from concentrations. These uncertainties have been a major limitation on estimates of the flux of material to the oceans for several decades. Recommendations are made for improvements in N deposition estimation through changes in observations, modelling and model–observation comparison procedures. Validation of modelled dry deposition requires effective comparisons to observable aerosol-phase species' concentrations, and this cannot be achieved if model products only report dry deposition flux over the ocean.Peer reviewe