Improved Hamiltonian for Minkowski Yang-Mills Theory

I develop an improved Hamiltonian for classical, Minkowski Yang-Mills theory, which evolves infrared fields with corrections from lattice spacing $a$ beginning at $O(a^4)$. I use it to investigate the response of Chern-Simons number to a chemical potential, and to compute the maximal Lyapunov exponent. Both quantities have small $a$ limits, in both cases within $10\%$ of the limit found using the unimproved (Kogut Susskind) Hamiltonian. For the maximal Lyapunov exponent the limits differ by about $5 \%$, significant at about $5 \sigma$, indicating that while a small $a$ limit exists, its value is corrupted by lattice artefacts. For the response of Chern-Simons number the statistics are not good enough to resolve $5 \%$ differences, but it seems possible in analogy with the Lyapunov exponent that the final answer depends on the lattice regulation.Comment: Latex, 33 pages plus 2 .epsi figures included with psfig. Revised to include new data which weakens some original conclusion

Simulation of Cavity Flow by the Lattice Boltzmann Method

A detailed analysis is presented to demonstrate the capabilities of the lattice Boltzmann method. Thorough comparisons with other numerical solutions for the two-dimensional, driven cavity flow show that the lattice Boltzmann method gives accurate results over a wide range of Reynolds numbers. Studies of errors and convergence rates are carried out. Compressibility effects are quantified for different maximum velocities, and parameter ranges are found for stable simulations. The paper's objective is to stimulate further work using this relatively new approach for applied engineering problems in transport phenomena utilizing parallel computers.Comment: Submitted to J. Comput. Physics, late

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Lattice structures for optimal design and robust implementation of two-channel perfect-reconstruction QMF banks

A lattice structure and an algorithm are presented for the design of two-channel QMF (quadrature mirror filter) banks, satisfying a sufficient condition for perfect reconstruction. The structure inherently has the perfect-reconstruction property, while the algorithm ensures a good stopband attenuation for each of the analysis filters. Implementations of such lattice structures are robust in the sense that the perfect-reconstruction property is preserved in spite of coefficient quantization. The lattice structure has the hierarchical property that a higher order perfect-reconstruction QMF bank can be obtained from a lower order perfect-reconstruction QMF bank, simply by adding more lattice sections. Several numerical examples are provided in the form of design tables

Precision Pointing Control System (PPCS) system design and analysis

The precision pointing control system (PPCS) is an integrated system for precision attitude determination and orientation of gimbaled experiment platforms. The PPCS concept configures the system to perform orientation of up to six independent gimbaled experiment platforms to design goal accuracy of 0.001 degrees, and to operate in conjunction with a three-axis stabilized earth-oriented spacecraft in orbits ranging from low altitude (200-2500 n.m., sun synchronous) to 24 hour geosynchronous, with a design goal life of 3 to 5 years. The system comprises two complementary functions: (1) attitude determination where the attitude of a defined set of body-fixed reference axes is determined relative to a known set of reference axes fixed in inertial space; and (2) pointing control where gimbal orientation is controlled, open-loop (without use of payload error/feedback) with respect to a defined set of body-fixed reference axes to produce pointing to a desired target