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Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go.sup.1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning.sup.3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex.sup.6. Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space.sup.7,8 and is critical for integrating self-motion (path integration).sup.6,7,9 and planning direct trajectories to goals (vector-based navigation).sup.7,10,11. Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types.sup.12. We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments--optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation.sup.7,10,11, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.Grid-like representations emerge spontaneously within a neural network trained to self-localize, enabling the agent to take shortcuts to destinations using vector-based navigation., Author(s): Andrea Banino [sup.1] [sup.2] [sup.3] , Caswell Barry [sup.2] , Benigno Uria [sup.1] , Charles Blundell [sup.1] , Timothy Lillicrap [sup.1] , Piotr Mirowski [sup.1] , Alexander Pritzel [sup.1] [...]

ANALYZING ELECTRON MICROSCOPY WITH DEEP LEARNING A significantly faster way to find defects in electron microscopy images is now possible. The Multinode Evolutionary Neural Networks for Deep Learning (MENNDL) creates [...]

Author(s): Andre Esteva (corresponding author) [1]; Brett Kuprel (corresponding author) [1]; Roberto A. Novoa (corresponding author) [2, 3]; Justin Ko [2]; Susan M. Swetter [2, 4]; Helen M. Blau [5]; [...]

A hybrid machine-learning model, integrating an artificial neural network (ANN) and a support vector machine (SVM) model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE). The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans., 1. Introduction Highway maintenance activities usually require lane closures, frequently disrupt traffic operations, and increase delays because of restricted capacity. According to an urban mobility report [1], traffic congestion in [...]

UAV based traffic monitoring holds distinct advantages over traditional traffic sensors, such as loop detectors, as UAVs have higher mobility, wider field of view, and less impact on the observed traffic. For traffic monitoring from UAV images, the essential but challenging task is vehicle detection. This paper extends the framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections. Experimental results show that Faster R-CNN can achieve promising car detection results compared with other methods. Our tests further demonstrate that Faster R-CNN is robust to illumination changes and cars' in-plane rotation. Besides, the detection speed of Faster R-CNN is insensitive to the detection load, that is, the number of detected cars in a frame; therefore, the detection speed is almost constant for each frame. In addition, our tests show that Faster R-CNN holds great potential for parking lot car detection. This paper tries to guide the readers to choose the best vehicle detection framework according to their applications. Future research will be focusing on expanding the current framework to detect other transportation modes such as buses, trucks, motorcycles, and bicycles., 1. Introduction Unmanned aerial vehicles (UAVs) hold promise of great value for transportation research, particularly for traffic data collection (e.g., [1-5]). UAVs have many advantages over ground based traffic sensors [...]

Byline: Maryam. Farhadian, Mohsen. Aliabadi, Ebrahim. Darvishi Background: Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from [...]

A SIMPLE RELIABILITY MODEL FOR A HUMAN MISSION TO MARS USING MONTE CARLO ANALYSIS. GAYLE HAGEWOOD AND J. WAYNE MCCAIN, ATHENS STATE UNIVERSITY. The planet Mars has been the inspiration [...]