It is vital that we develop appropriate statistical models to represent and extract valuable insights from these large datasets, since the bridges constitute critical infrastructure within modern transportation networks. The process of monitoring engineered systems via streaming data is typically referred to as Structural Health Monitoring (SHM) and while successful applications have been emerging in recent years, a number of challenges remain for practical implementation [5]. During model design, these concerns usually centre around low variance data: that is, measurements are not available for the entire range of expected operational, environmental, and damage conditions. Consider a bridge following construction, this will have a relatively small dataset that should only be associated with normal operation. On the other hand, a structure with historical data might still not experience low-probability events - such as extreme weather or landslides. An obvious solution considers sharing data (or information) between structures; this has been the focus of a large body of recent work [6-8].

In this paper, we propose a model to schedule the next 24 hours of operations in a bulk cargo port to unload bulk cargo trains onto stockpiles. It is a problem that includes multiple parts such as splitting long trains into shorter ones and the routing of bulk material through a configurable network of conveyors to the stockpiles. Managing such trains (up to three kilometers long) also requires specialized equipment. The real world nature of the problem specification implies the necessity to manage heterogeneous data. Indeed, when new equipment is added (e.g. dumpers) or a new type of wagon comes in use, older or different equipment will still be in use as well. All these details need to be accounted for. In fact, avoiding a full deadlock of the facility after a new but ineffective schedule is produced. In this paper, we provide a detailed presentation of this real world problem and its associated data. This allows us to propose an effective constraint programming model to solve this problem. We also discuss the model design and the different implementations of the propagators that we used in practice. Finally, we show how this model, coupled with a large neighborhood search, was able to find 24 hour schedules efficiently.

--Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones are constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to- end. These trained meta-weights are then used as initializers to the network in a test environment and fine-tuned for the last few fully connected layers. V ariation in drone dynamics and environmental characteristics is carried out to show robustness of the approach. Using NVIDIA GPU profiler it was shown that the energy consumption and training latency is reduced by 3.7x and 1.8x respectively without significant degradation in the performance in terms of average distance traveled before crash i.e. The approach is also tested on a real environment using DJI T ello drone and similar results were reported. The video of the drone with proposed approach will be uploaded to Y ouTube. VER the past decade, Unmanned aerial vehicle (UA V) are emerging as a new form of IoT devices being used in varied applications such as reconnaissance, surveying, rescuing and mapping. Irrespective of the application, navigating autonomously is one of the key desirable features of UA Vs both indoors and outdoors.

It is obvious that there are significant differences between groups (i.e.