CSIS's The Future of Military Engines looks at the state of the U.S. military engine industrial base and the choices confronting policymakers at the Department of Defense (DoD). The military engine industrial base is closely tied to the industrial base for commercial engines. U.S. engine providers use many of the same facilities and largely the same supply chain for military and commercial engines. The ability to leverage commercial supply chains is critical because supply chain quality underlies the performance advantage of U.S. military engines, both for individual aircraft and military aircraft fleets. International competitors such as Russia and China are seeking to overtake the U.S. in engines. However, the current U.S. advantage is sustainable if it is treated as a national priority. Many military aircraft, especially fighters, require engines with important differences from commercial aircraft. They fly different flight profiles and perform different jobs. These differences mean that while DoD can leverage the commercial engine industrial base, it must also make investments to sustain the industrial base’s unique military components. In the next few years, DoD investment in military engines is projected to decrease significantly, particularly for R&D. This presents a challenge as military-unique engineering skills are highly perishable. Four major policy choices confront DoD as it formulates its investment approach to military engines going forward: 1) Priority, 2) Resources, 3) Business Model, and 4) Competition. The DoD is at an inflection point for engine investment, and the time for choosing on these four key policy questions will come in the next few years.

"On October 28, 2016, the Center for Strategic and International Studies (CSIS) hosted a daylong conference, including senior defense and intelligence policymakers, military leaders, strategists, regional experts, international and industry partners, and others, to discuss the Defense Department's Third Offset Strategy. In order to understand what the Third Offset Strategy is, it is first necessary to understand the challenges and trends it is addressing. Technological superiority has been a foundation of U.S. military dominance for decades. However, the assumption of U.S. technological superiority as the status quo has been challenged in recent years as near-peer competitors have sought a variety of asymmetric capabilities to counter the overwhelming conventional military advantages possessed by the United States. This report summarizes the discussions and analysis of the Third Offset that took place at CSIS"--Publisher's web site.

The popularity of wearable cameras is steadily increasing, both for entertainment and productivity purposes. Understanding the footage and inferring the body pose of the camera wearer can be of great importance in many fields, like medicine or robotics. It could help in monitoring rehabilitating patients at home from the hospital, in determining the acts of a law enforcement agent from the body camera or, in robotics, simplifying imitation learning based on video input or robot to worker coordination by estimating the posture of the operator. In this project, we aim to build a human motion dataset acquired indoors and outdoors using a GoPro and MVN Awinda, a movement tracking system based on inertial sensors that provide the 3D human pose. Next, the dataset has been used to train a Deep Neural Network to classify a sequence of frames in the task that it's being performed (walking, running, ...). Finally, a model for estimating the 3D pose of the camera wearer at each frame is proposed based on the same structure than the first network. The dataset is made of 300,000 frames, captured from seven different people, each one perform-ing 5-6 tasks in different scenarios, both indoors and outdoors. Each sequence of video frames has a synchronized sequence of 3D poses associated to it. Every 3D pose is composed of 23 segments. The vast majority of the code created and used in this project can be found in the GitHub repository for the project: https://github.com/BielColl/Human-Motion-Dataset-in-the-Wild-MAT-. Due to its size, the dataset built is not posted.