Post-conflict justice is an integral component in maintaining stability and building peace in the aftermath of civil conflict. Despite its instrumental function, scholars routinely find that policymakers’ choice of justice is shaped by the structural conditions of the post-conflict environment, with outright victories leading to retributive forms of justice and negotiated outcomes yielding restorative forms of justice. However, existing literature conflates ceasefires and peace agreements into a single phenomenon, thereby overlooking the independent effects of each outcome. Leveraging the dual sovereignty framework, this article argues the conflation of negotiated outcomes is problematic because peace agreements and ceasefires generate different post-conflict environments. Relative to ceasefires, peace agreements lead to a reduction in the degree of dual sovereignty because they resolve a conflict’s incompatibility, thereby encouraging efforts to move society beyond war through restorative forms of justice. Due to the persistent threat of recurrent war generated by high levels of dual sovereignty, policymakers following ceasefires will be inclined to pursue retributive forms of justice that may target political opponents or potential defectors to bolster organizational strength. Statistical analyses confirm the underlying expectation that ceasefires and peace agreements yield different post-conflict justice outcomes. Peace agreements, relative to ceasefires, are more likely to be followed by the implementation of amnesties and reparations, whereas ceasefires exhibit a greater probability of yielding purges in the post-conflict environment.

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This work investigates event-based sensor (EBS) imaging system’s read-out bandwidth performance under linear motion, with and without hardware stabilization techniques. We implement three image stabilization methods using hardware rotation to cancel the sensor platform’s linear motion and recapture lost EBS performance. We successfully demonstrated the methods, showing a bandwidth reduction of over an order of magnitude in two scenes, 10 scene variations, and five EBS velocities. This work demonstrates the benefits of stabilization with EBS to reduce bandwidth requirements versus unstabilized EBS systems.

The Dynamic Vision Sensor (DVS) is an imaging sensor that processes the incident irradiance image and outputs temporal log irradiance changes in the image, such as those generated by moving target(s) and/or the moving sensor platform. From a static platform, this enables the DVS to cancel out background clutter and greatly decrease the sensor bandwidth required to track temporal changes in a scene. However, the sensor bandwidth advantage is lost when imaging a scene from a moving platform due to platform motion causing optical flow in the background. Imaging from a moving platform has been utilized in many recently reported applications of this sensor. However, this approach inherently outputs background clutter generated from optical flow, and as such this approach has limited spatio-temporal resolution and is of limited utility for target tracking applications. In this work we present a new approach to moving target tracking applications with the DVS. Essentially, we propose modifying the incident image to cancel out optical flow due to platform motion, thereby removing background clutter and recovering the bandwidth performance advantage of the DVS. We propose that such improved performance can be accomplished by integrating a hardware tracking and stabilization subsystem with the DVS. Representative simulation scenarios are used to quantify the performance of the proposed approach to clutter cancellation and improved sensor bandwidth.