Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction

In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of modules, some of which are learned, and others heuristic. While the agent is not "end-to-end" in the ML sense, end-to-end interaction is a vital part of the agent's learning mechanism. We describe how the design of the agent works together with the design of multiple annotation interfaces to allow crowd-workers to assign credit to module errors from end-to-end interactions, and to label data for individual modules. Over multiple automated human-agent interaction, credit assignment, data annotation, and model re-training and re-deployment, rounds we demonstrate agent improvement

Remote sensing based estimation of forest biophysical variables using machine learning algorithm

Leaf Area Index (LAI), Fraction of Intercepted Photosynthetically Active Radiation (fIPAR) and forest Aboveground Biomass (AGB) are important regulatory parameters for several functions of the forest canopy. An accurate information about the spatial variability of these biophysical variables is vital to capture the variability in estimates of gross primary productivity, carbon exchange and microclimate in terrestrial ecosystems. The present study aims at developing predictive models for generating spatial distribution of LAI, fIPAR and AGB by integrating remote sensing imagery and field data using random forest (RF) regression algorithm. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral and texture variables were derived using Sentinel-2 data of 10 April 2017. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (I_o), below canopy (I), and diameter at breast height (dbh) were taken. fIPAR and AGB were calculated. RF regression algorithm was used to optimize the variables to select the best predictor variables. Three models, using only spectral variables, only texture variables and both spectral and texture variables were tested. For all three biophysical variables, the models using both spectral and texture variables gave better results. The best predictor variables were used to map the spatial distribution of LAI, fIPAR and AGB. On validation, the models were able to predict LAI with R^2=0.83, %RMSE = 13.25%, fIPAR with R^2=0.87, %RMSE = 13.24%, and AGB with R^2=0.85, %RMSE = 12.17%. The estimated biophysical parameters showed high interdependence (LAI-fIPAR R2= 0.71, LAI-AGB R^2=0.75 and fIPAR-AGB R^2= 0.74). The results showed that RF can be effectively applied to predict the spatial distribution of forest biophysical variables like LAI, fIPAR and AGB with adequate accuracy

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks

Towards Sustainable Transport In Bangkok

High road congestion in developing countries leads to inefficient energy consumption and pollution, and dealing with such issues in cities like Bangkok is complex as per the underdeveloped transport infrastructure and typically poor and fragmented prior urban planning. In reviewing the literature, these problems are evidenced and discussed and have led to fewer passengers travelling through the public transport system in cities like Bangkok in Thailand as compared to those favoring private cars, which is a sub-optimal and unsustainable transport mode when compared to say, London in the UK. Developing country scenarios such as such requires a low-cost investment strategy. Thus, steps towards integrated passenger and goods transport services to improve public transport and logistics are presented in this paper. The strategy proposed aims to utilize the existing and emerging resources and technology synergy with improving transport system ICT frameworks to promote more innovative and sustainable transport. Research indicates that the factors that promote integrated passengers and goods transport schemes can be categorized as those which: 1) Require organizational cooperation efforts such as the cooperation between the public and private organizations (electric train and ride-hailing services), 2) Technology that could enable information sharing and technological solutions to tackle sustainability issues and synergy with the improvement of current transport systems, and 3) Infrastructure and resource sharing to utilize current resources such as electric trains and stations are assumed to act as a consolidation and distribution centers with the synergy of technology. Investigation of data in this research found that integrated passengers and goods transportation to public transport scheme will be best to apply during off-peak hours to promote resource utilization, as there is spare capacity for electric trains during that time. GPS taxi probe data records also indicate that commuters would seem to be adopting ride-hailing services in conjunction with electric trains for travel from the outer area of Bangkok, which lacks public transport accessibility, and that the density of demand is less than in the center of Bangkok. However, the usefulness of integrated passenger and goods transport services to public transport schemes will increase if travel and parcel delivery services can be integrated with services from both ride-hailing applications and the electric train system. We conclude with observations and future work, for example, that ride-hailing services may be overlooked because transport fares are currently too high when using ride-hailing together with electric trains, and ICT intervention to increase the efficiency of the former and journey management to reduce on-peak pressure of the latter may be beneficial.</p

Coupling Earth observation and eddy covariance data in light-use efficiency based model for estimation of forest productivity

The light use efficiency (LUE) approach is a well-established method for estimating gross primary productivity (GPP) over large areas using Earth observation data. The present study aims to determine maximum light use efficiency (LUEmax) values specific to the northwest Himalayan foothills of India. It also aims to estimate the spatio-temporal variability of GPP from 2001 to 2020 using remote sensing data in combination with eddy covariance data in the LUE-based model. The model was parameterized using different sets of default and calculated parameters. The study showed that the use of PFT-specific LUEmax and temperatures increased the accuracy of the model predictions. On validation, the LUE-based model predicted GPP showed R2 = 0.82 for moist deciduous and R2 = 0.83 for dry deciduous PFTs. The study revealed that with rigorous model parameterization, RS data can be used in an LUE-based model to achieve accurate spatio-temporal estimates of GPP