On the Equivalence of the Digital Waveguide and Finite Difference Time Domain Schemes

It is known that the digital waveguide (DW) method for solving the wave equation numerically on a grid can be manipulated into the form of the standard finite-difference time-domain (FDTD) method (also known as the ``leapfrog'' recursion). This paper derives a simple rule for going in the other direction, that is, converting the state variables of the FDTD recursion to corresponding wave variables in a DW simulation. Since boundary conditions and initial values are more intuitively transparent in the DW formulation, the simple means of converting back and forth can be useful in initializing and constructing boundaries for FDTD simulations.Comment: v1: 6 pages; v2: 7 pages, generally more polished, more examples, expanded discussion; v3: 15 pages, added state space formulation, analysis of inputs and boundary conditions, translation of passive boundary conditions; v4: various typos fixe

Deformable bearing seat

A deformable bearing seat is described for seating a bearing assembly in a housing. The seat includes a seating surface in the housing having a first predetermined spheroidal contour when the housing is in an undeformed mode. The seating surface is deformable to a second predetermined spherically contoured surface when the housing is in a deformed mode. The seat is particularly adaptable for application to a rotating blade and mounting ring assembly in a gas turbine engine

Quantitative determination of fatty acids in the University of Vermont\u27s CREAM herd followed over the course of six months

Holstein and Jersey cows were used to determine how the fatty acid profile of their milk differed with regard to breed of cow and season over the course of six months. Milk was collected from each cow monthly between November 2011 and April 2012 and analyzed for components and fatty acid composition. Data were analyzed using linear mixed models with a repeated measures design with breed, month, and breed × month as the main effects. Holstein cows produced more milk than Jersey cows (79.98 vs. 50.48 lbs), and significant differences were seen between both breeds (P\u3c0.001) and months (P=0.014) of the study. Jersey cows produced more fat (5.20 vs. 3.91%, P\u3c0.001) and protein in their milk (3.83 vs. 3.12%, P\u3c0.001) when compared to Holstein cows. Jersey cows produced more saturated fatty acids than Holstein cows (72.9% vs. 70.7%, breed P=0.004, month P\u3c0.001). Vaccenic acid was produced in similar levels by both breeds, but Holsteins produced slightly higher levels (0.92%) than Jerseys (0.81%, breed P=0.0016, month P\u3c0.001). Holstein cows produced higher levels of conjugated linoleic acids in their milk (0.49%) when compared to Jersey cows (0.41%, P\u3c0.001). Holstein cows produced higher levels of linoleic acid (1.60% vs. 1.43%, P\u3c0.001, month P=0.005) when compared to Jersey cows. Linolenic acid levels produced by both breeds were similar for Holstein (0.31%) and Jersey cows (0.29%, month P=0.007). From the analysis of concentrations of both selected fatty acids as well as groups of major fatty acids, there is evidence that the fatty acid profile of milk differs with respect to both breed of cow, as well as month of the year

The development philosophy for SNAP mechanisms

Hardware development for SNAP reactor control mechanis

Decomposable Principal Component Analysis

We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. For this purpose, we reformulate the problem in the sparse inverse covariance (concentration) domain and solve the global eigenvalue problem using a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We demonstrate the application of our methodology in the context of decentralized anomaly detection in the Abilene backbone network. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA

Scalable Hash-Based Estimation of Divergence Measures

We propose a scalable divergence estimation method based on hashing. Consider two continuous random variables $X$ and $Y$ whose densities have bounded support. We consider a particular locality sensitive random hashing, and consider the ratio of samples in each hash bin having non-zero numbers of Y samples. We prove that the weighted average of these ratios over all of the hash bins converges to f-divergences between the two samples sets. We show that the proposed estimator is optimal in terms of both MSE rate and computational complexity. We derive the MSE rates for two families of smooth functions; the H\"{o}lder smoothness class and differentiable functions. In particular, it is proved that if the density functions have bounded derivatives up to the order $d/2$, where $d$ is the dimension of samples, the optimal parametric MSE rate of $O(1/N)$ can be achieved. The computational complexity is shown to be $O(N)$, which is optimal. To the best of our knowledge, this is the first empirical divergence estimator that has optimal computational complexity and achieves the optimal parametric MSE estimation rate.Comment: 11 pages, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spai

L0 Sparse Inverse Covariance Estimation

Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex $l_1$ norm. However, the best estimator performance is not always achieved with this penalty. The most natural sparsity promoting "norm" is the non-convex $l_0$ penalty but its lack of convexity has deterred its use in sparse maximum likelihood estimation. In this paper we consider non-convex $l_0$ penalized log-likelihood inverse covariance estimation and present a novel cyclic descent algorithm for its optimization. Convergence to a local minimizer is proved, which is highly non-trivial, and we demonstrate via simulations the reduced bias and superior quality of the $l_0$ penalty as compared to the $l_1$ penalty