Minimalist Solution to Williamson County

Williamson County Regional Planning Commission v. Hamilton Bank of Johnson County relegated Fifth Amendment takings claims to a second-class of federal rights. Before a takings plaintiff can sue in federal court, she must first seek compensation through an “adequate state procedure.” Many federal courts have held that requirement to mean a takings litigant must first seek compensation through state courts if that state provides an inverse condemnation proceeding. However, if a takings litigant sues in state court, she will be unable to sue in federal court because of issue preclusion. This effectively shuts the federal courthouse door to many property owners. Only two Supreme Court justices have shown any interest in revisiting Williamson County . Thus, land use attorneys who are concerned about federal court access for takings plaintiffs should craft a case that would attract the Supreme Court’s attention. This Article argues that land use lawyers should present the Court with a case in which the property owner has used a non-judicial procedure to seek compensation (such as asking for compensation from a county board). The Court could then rule that such a non-judicial procedure is an “adequate state procedure” that satisfies Williamson County’ s requirements. This ruling would minimize the negative effects that Williamson County has wrought on takings plaintiffs

Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model

Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.Comment: 22 pages, 7 Figures, 1 Tabl

Handling Qualities Evaluations of Low Complexity Model Reference Adaptive Controllers for Reduced Pitch and Roll Damping Scenarios

National Aeronautics and Space Administration (NASA) researchers have conducted a series of flight experiments designed to study the effects of varying levels of adaptive controller complexity on the performance and handling qualities of an aircraft under various simulated failure or damage conditions. A baseline, nonlinear dynamic inversion controller was augmented with three variations of a model reference adaptive control design. The simplest design consisted of a single adaptive parameter in each of the pitch and roll axes computed using a basic gradient-based update law. A second design was built upon the first by increasing the complexity of the update law. The third and most complex design added an additional adaptive parameter to each axis. Flight tests were conducted using NASA s Full-scale Advanced Systems Testbed, a highly modified F-18 aircraft that contains a research flight control system capable of housing advanced flight controls experiments. Each controller was evaluated against a suite of simulated failures and damage ranging from destabilization of the pitch and roll axes to significant coupling between the axes. Two pilots evaluated the three adaptive controllers as well as the non-adaptive baseline controller in a variety of dynamic maneuvers and precision flying tasks designed to uncover potential deficiencies in the handling qualities of the aircraft, and adverse interactions between the pilot and the adaptive controllers. The work was completed as part of the Integrated Resilient Aircraft Control Project under NASA s Aviation Safety Program

Microbial community management in aquaculture

Microbial community management in aquaculture creates benefits at the nutritional as well as at health level for cultured species. In addition, in case of biofloc application, it allows to link species at different trophic levels, making bioflocs the potential link in integrated multispecies aquaculture

Towards Training the Extended Voltage Manifold Computer (EVMC) using Particle Swarm Optimization

poster abstractExtended Analog Computers (EAC) have been explored as a substrate for unconventional computing techniques since the early 1990s. A particular strength of the technique is the near instantaneous speed it solves computational problems. However, application of the EAC and specific EAC classes, as the Extended Voltage Manifold Computer (EVMC), to real-world problems await the development of methods to program EACs. A property of the EVMC is that each output voltage can be described by a class of radial basis functions (RBF). Linking multiple EVMCs, a neural network called a radial basis function network (RBFN) can be implemented. The specific aim of this work is to develop the means to train EVMCs and networks of EVMC based RBFNs. The strategy employed in the present work is to develop a method using EVMCs implemented as finite element method (FEM) simulations to define the error state-space and error gradient of the untrained EVMC manifold. Once defined the EVMC simulation can be recursively configured to reduce the error in a Hebbian sense. Furthermore, particle swarm optimization (PSO) is being explored to improve the speed of convergence. FEM simulations were constructed using COMSOL Multiphysics to model EVMC manifolds in different states. In parallel, a particle swarm optimizer was altered to demonstrate training of simple RBF manifolds. Examination of FEM simulations verified the kernel function as hyperbolic and radially based. These preliminary findings indicated that the EVMC can be accurately modeled and manipulated using COMSOL, and PSO can be used once the error manifold is defined. From this we can take the possibility of improving the speed of training the EVMC via PSO. The next step to verify this possibility is to combine the COMSOL and Python codes to confirm the EVMC can be trained