Technical Report: Compressive Temporal Higher Order Cyclostationary Statistics

The application of nonlinear transformations to a cyclostationary signal for the purpose of revealing hidden periodicities has proven to be useful for applications requiring signal selectivity and noise tolerance. The fact that the hidden periodicities, referred to as cyclic moments, are often compressible in the Fourier domain motivates the use of compressive sensing (CS) as an efficient acquisition protocol for capturing such signals. In this work, we consider the class of Temporal Higher Order Cyclostationary Statistics (THOCS) estimators when CS is used to acquire the cyclostationary signal assuming compressible cyclic moments in the Fourier domain. We develop a theoretical framework for estimating THOCS using the low-rate nonuniform sampling protocol from CS and illustrate the performance of this framework using simulated data

Shape matching and clustering in design

Generalising knowledge and matching patterns is a basic human trait in re-using past experiences. We often cluster (group) knowledge of similar attributes as a process of learning and or aid to manage the complexity and re-use of experiential knowledge [1, 2]. In conceptual design, an ill-defined shape may be recognised as more than one type. Resulting in shapes possibly being classified differently when different criteria are applied. This paper outlines the work being carried out to develop a new technique for shape clustering. It highlights the current methods for analysing shapes found in computer aided sketching systems, before a method is proposed that addresses shape clustering and pattern matching. Clustering for vague geometric models and multiple viewpoint support are explored

Hadronic B Decays to Charmless VT Final States

Charmless hadronic decays of B mesons to a vector meson (V) and a tensor meson (T) are analyzed in the frameworks of both flavor SU(3) symmetry and generalized factorization. We also make comments on B decays to two tensor mesons in the final states. Certain ways to test validity of the generalized factorization are proposed, using $B \to VT$ decays. We calculate the branching ratios and CP asymmetries using the full effective Hamiltonian including all the penguin operators and the form factors obtained in the non-relativistic quark model of Isgur, Scora, Grinstein and Wise.Comment: 27 pages, no figures, LaTe

Structural diversity of neuronal calcium sensor proteins and insights for activation of retinal guanylyl cyclase by GCAP1.

Neuronal calcium sensor (NCS) proteins, a sub-branch of the calmodulin superfamily, are expressed in the brain and retina where they transduce calcium signals and are genetically linked to degenerative diseases. The amino acid sequences of NCS proteins are highly conserved but their physiological functions are quite different. Retinal recoverin controls Ca(2) (+)-dependent inactivation of light-excited rhodopsin during phototransduction, guanylyl cyclase activating proteins 1 and 2 (GCAP1 and GCAP2) promote Ca(2) (+)-dependent activation of retinal guanylyl cyclases, and neuronal frequenin (NCS-1) modulates synaptic activity and neuronal secretion. Here we review the molecular structures of myristoylated forms of NCS-1, recoverin, and GCAP1 that all look very different, suggesting that the attached myristoyl group helps to refold these highly homologous proteins into different three-dimensional folds. Ca(2) (+)-binding to both recoverin and NCS-1 cause large protein conformational changes that ejects the covalently attached myristoyl group into the solvent exterior and promotes membrane targeting (Ca(2) (+)-myristoyl switch). The GCAP proteins undergo much smaller Ca(2) (+)-induced conformational changes and do not possess a Ca(2) (+)-myristoyl switch. Recent structures of GCAP1 in both its activator and Ca(2) (+)-bound inhibitory states will be discussed to understand structural determinants that control their Ca(2) (+)-dependent activation of retinal guanylyl cyclases

Calibration of Distributionally Robust Empirical Optimization Models

We study the out-of-sample properties of robust empirical optimization problems with smooth $\phi$-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative "robustness parameter" $\delta$ that controls the size of the deviations from the nominal model. Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution, we show that the first-order benefit of ``little bit of robustness" (i.e., $\delta$ small, positive) is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller. One implication is that substantial variance (sensitivity) reduction is possible at little cost if the robustness parameter is properly calibrated. To this end, we introduce the notion of a robust mean-variance frontier to select the robustness parameter and show that it can be approximated using resampling methods like the bootstrap. Our examples show that robust solutions resulting from "open loop" calibration methods (e.g., selecting a $90\%$ confidence level regardless of the data and objective function) can be very conservative out-of-sample, while those corresponding to the robustness parameter that optimizes an estimate of the out-of-sample expected reward (e.g., via the bootstrap) with no regard for the variance are often insufficiently robust.Comment: 51 page

Silicon resistor to measure temperature during rapid thermal annealing

A resistor composed of a piece of Si wafer and two thin silver wires attached to it, can reliably sense the temperature during rapid thermal annealing (RTA). As constant electric current passes through the Si piece, the resistivity change of Si with temperature produces a voltage signal that can be readily calibrated and converted to an actual temperature of the samples. An accuracy better than ±10 °C is achieved between 300° and 600 °C