Nilpotent Bases for a Class of Non-Integrable Distributions with Applications to Trajectory Generation for Nonholonomic Systems

This paper develops a constructive method for finding a nilpotent basis for a special class of smooth nonholonomic distributions. The main tool is the use of the Goursat normal form theorem which arises in the study of exterior differential systems. The results are applied to the problem of finding a set of nilpotent input vector fields for a nonholonomic control system, which can then used to construct explicit trajectories to drive the system between any two points. A kinematic model of a rolling penny is used to illustrate this approach. The methods presented here extend previous work using "chained form" and cast that work into a coordinate-free setting

Unstitching Scarlet Letters?: Prosecutorial Discretion and Expungement

This Article argues that scholarly discussions about prosecutorial discretion need to extend their focus beyond the exercise of prosecutorial judgment pretrial or questions of factual and legal guilt. Given that the primary role of the prosecutoris to do “justice,” this Article calls for increased attention to the exercise of discretion after the guilt phase is complete, specifically in the context of expungement of nonconviction andconviction information. It offers a framework for exercising such discretion and, in doing so, hopes to initiate additional conversation about the role of prosecutors during the phases that follow arrest and prosecution

Conversion and verification procedure for goal-based control programs

Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System, developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a method for converting goal network control programs into linear hybrid systems is developed. The linear hybrid system can then be verified for safety in the presence of failures using existing symbolic model checkers. An example task is developed and successfully verified using HyTech, a symbolic model checking software for linear hybrid systems

Optimising workplace interventions for health and wellbeing: a commentary on the limitations of the public health perspective within the workplace health arena [forthcoming]

Purpose: This paper discusses contemporary approaches to workplace health and well-being, articulating key differences in the intervention architecture between public and workplace health contexts and implications for intervention design. Approach: Contemporary practice is discussed in light of calls for a paradigm shift in occupational health from a treatment orientation to an holistic approach focused on mitigation of the causes of ill health and the promotion of well-being. In practice, relatively few organizations have or seem able to engage with a broader perspective that encompasses challenges to health and well-being associated with contextual organizational drivers, e.g. job design/role, workload, systems of reward, leadership style and the underpinning climate. Drawing upon insights from public health and the workplace safety tradition, the scope for broadening the perspective on intervention (in terms of vectors of harm addressed, theory of change and intervention logic) is discussed. Findings: There are important differences in scope and options for intervention between public health and workplace health contexts. While there is scope to emulate public health practice, this should not constrain thinking over intervention opinions. Increased awareness of these key differences within work organizations, and an evidence-based epidemiological approach to learning has the potential to strengthen and broaden the approach to workplace health and well-being management. Originality/Value: We argue that approaches to workplace well-being interventions that selectively cross-fertilise and adapt elements of public health interventions offer promise for realising a broader change agenda and for building inherently healthy workplaces

Solving physics-driven inverse problems via structured least squares

Numerous physical phenomena are well modeled by partial differential equations (PDEs); they describe a wide range of phenomena across many application domains, from model- ing EEG signals in electroencephalography to, modeling the release and propagation of toxic substances in environmental monitoring. In these applications it is often of interest to find the sources of the resulting phenomena, given some sparse sensor measurements of it. This will be the main task of this work. Specifically, we will show that finding the sources of such PDE-driven fields can be turned into solving a class of well-known multi-dimensional structured least squares prob- lems. This link is achieved by leveraging from recent results in modern sampling theory – in particular, the approximate Strang-Fix theory. Subsequently, numerical simulation re- sults are provided in order to demonstrate the validity and robustness of the proposed framework