In precision crop protection, (target-orientated) object detection in image processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection drones) to the right place to apply the pesticide. Unnecessary application of non-target areas could be avoided. Deep learning algorithms dominantly use in modern computer vision tasks which require high computing time, memory footprint, and power consumption. Based on the Edge Artificial Intelligence, we investigate the main three paths that lead to dealing with this problem, including hardware accelerators, efficient algorithms, and model compression. Finally, we integrate them and propose a solution based on a light deep neural network (DNN), called Ag-YOLO, which can make the crop protection UAV have the ability to target detection and autonomous operation.

After loading in the data from the train and test data folders and setting up our simple base model, we decided it would be worth the effort to figure out how to upload the data in the TFRecords format. TFRecords is a binary storage format specifically designed to expedite performance and training time of models built in Tensor Flow. In essence, data in the TFRecords format is optimized for use with Tensorflow in various aspects. Despite the previously mentioned advantages of using this data format, getting the data into a format that is ready to feed into a model is not straightforward. Doing so requires defining functions to read the files and decode the images contained in those files. It is also logical to augment the data (flip, randomly change brightness, add saturation, etc.) in this step since the images will eventually be reshaped into arrays.

At Lyft, our mission is to improve people's lives with the world's best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization. We care deeply about delivering the best transportation experience; this means the best experience for the passenger and the best experience for the driver. We believe this quality of service can only be achieved with a deep understanding of our world, our cities, our streets… how they evolve, how they breathe. We embrace the powerful positive impact autonomous transportation will bring to our everyday lives and with our ambition, we will become a leader in the development and operation of such vehicles.

The innovation in technology has been a direct response to demands and expectations from humans. Emerging technologies like artificial intelligence continues to play a vital role in helping the world in numerous ways. Even artificial intelligence is evolving through many iterations. Recently, Gartner has considered'Formative AI' in its Hype Cycle for Emerging Technologies 2020. So, what is Formative AI? Let's learn more about it.

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding.

SRCNN proposes a 3 layer CNN for image super-resolution. It is one of the first papers to apply deep neural networks for the task of image super-resolution. The SRCNN architecture is composed of three components: Feature extractor, non-linear mapping, reconstruction. The model is trained to minimize the pixel-wise MSE between the reconstructed and ground truth image. A variety of model architectures and hyperparameter is tested and traded for performance and speed in the paper.

Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately, current approaches are only available for the most basic geometries and fall short when the underlying space has a nontrivial topology, limiting their applicability for most real-world data. Using fundamental ideas from differential geometry and geometric control theory, we describe how the recently introduced Neural ODEs and continuous normalizing flows can be extended to arbitrary smooth manifolds. We propose a general methodology for parameterizing vector fields on these spaces and demonstrate how gradient-based learning can be performed. Additionally, we provide a scalable unbiased estimator for the divergence in this generalized setting. Experiments on a diverse selection of spaces empirically showcase the defined framework's ability to obtain reparameterizable samples from complex distributions.

OpenWorm Foundation, Boston, MA USA Abstract While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks. Introduction How can Artificial Neural Networks (ANNs) emulate the "lifelike" nature of Biological Neural Networks (BNNs)?

Multiple imputation (MI) is the state-of-the-art approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is the most widely used MI method, but it lacks theoretical foundation and is computationally intensive. Recently, MI methods based on deep learning models have been developed with encouraging results in small studies. However, there has been limited research on systematically evaluating their performance in realistic settings comparing to MICE, particularly in large-scale surveys. This paper provides a general framework for using simulations based on real survey data and several performance metrics to compare MI methods. We conduct extensive simulation studies based on the American Community Survey data to compare repeated sampling properties of four machine learning based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation network, and multiple imputation using denoising autoencoders. We find the deep learning based MI methods dominate MICE in terms of computational time; however, MICE with classification trees consistently outperforms the deep learning MI methods in terms of bias, mean squared error, and coverage under a range of realistic settings.

The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years. Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing; and a TensorFlow-based runtime for running simulations on accelerated hardware. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem, complemented by a small set of simple use cases that demonstrate how RecSim NG can help both researchers and practitioners easily develop and train novel algorithms for recommender systems.