Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization

This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting the algorithm, we prove a contraction estimate that guarantees the tracking performance of the algorithm. Two variants of this algorithm are investigated. The first one can be used to solve nonlinear programming problems while the second variant is aimed to treat online parametric nonlinear programming problems. The local convergence of these variants is proved. An application to a large-scale benchmark problem that originates from nonlinear model predictive control of a hydro power plant is implemented to examine the performance of the algorithms.Comment: This manuscript consists of 25 pages and 7 figure

Hidden Broad Line Seyfert 2 Galaxies in the CfA and 12micron Samples

We report the results of a spectropolarimetric survey of the CfA and 12micron samples of Seyfert 2 galaxies (S2s). Polarized (hidden) broad line regions (HBLRs) are confirmed in a number of galaxies, and several new cases (F02581-1136, MCG -3-58-7, NGC 5995, NGC 6552, NGC 7682) are reported. The 12micron S2 sample shows a significantly higher incidence of HBLR (50%) than its CfA counterpart (30%), suggesting that the latter may be incomplete in hidden AGNs. Compared to the non-HBLR S2s, the HBLR S2s display distinctly higher radio power relative to their far-infrared output and hotter dust temperature as indicated by the f25/f60 color. However, the level of obscuration is indistinguishable between the two types of S2. These results strongly support the existence of two intrinsically different populations of S2: one harboring an energetic, hidden S1 nucleus with BLR, and the other, a ``pure S2'', with weak or absent S1 nucleus and a strong, perhaps dominating starburst component. Thus, the simple purely orientation-based unification model is not applicable to all Seyfert galaxies.Comment: 5 pages with embedded figs, ApJ Letters, in pres

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

A molecular perspective on the limits of life: Enzymes under pressure

From a purely operational standpoint, the existence of microbes that can grow under extreme conditions, or "extremophiles", leads to the question of how the molecules making up these microbes can maintain both their structure and function. While microbes that live under extremes of temperature have been heavily studied, those that live under extremes of pressure have been neglected, in part due to the difficulty of collecting samples and performing experiments under the ambient conditions of the microbe. However, thermodynamic arguments imply that the effects of pressure might lead to different organismal solutions than from the effects of temperature. Observationally, some of these solutions might be in the condensed matter properties of the intracellular milieu in addition to genetic modifications of the macromolecules or repair mechanisms for the macromolecules. Here, the effects of pressure on enzymes, which are proteins essential for the growth and reproduction of an organism, and some adaptations against these effects are reviewed and amplified by the results from molecular dynamics simulations. The aim is to provide biological background for soft matter studies of these systems under pressure.Comment: 16 pages, 8 figure

Advanced Fabrication and Properties of Aligned Carbon Nanotube Composites: Experiments and Modeling

Aligned carbon nanotube (CNT) composites have attracted a lot of interest due to their superb mechanical and physical properties. This article presents a brief overview of the synthesis approaches of aligned CNT composites. The three major methods for fabricating aligned CNT fibers are first reviewed, including wet-spinning, dry-spinning and floating catalysts. The obtained CNT fibers, however, have limited mechanical and physical properties due to their porous structure and poor CNT alignment within the fibers. Appropriate treatments are required to densify the fibers to enhance their properties. The main approaches for the densification of CNT fibers are then discussed. To further enhance load transfer within CNT fibers, polymer infiltration is always used. Typical studies on polymer infiltration of CNT fibers are reviewed, and the properties of the obtained composites indicate the superiority of this composite fabrication method over the conventional dispersion method. Since aligned CNT composites are usually obtained in structures of long fiber or thin film, it is difficult to measure the thermal conductivity of these composites. An off-lattice Monte Carlo model is developed to accurately predict the thermal conductivity of aligned CNT composites

Fuzzy controller for better tennis ball robot

This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport. It is found that it is more economically efficient by using the sensorless fuzzy control algorithm to replace the high-resolution optical encoders traditionally used in two main servo motors. From our simulation and practical experiment, the tennis ball robot can provide accurate speed and various directions as expected

Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields

We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly mixing random fields. Our approach is based on the Lindeberg's method rather than on Bernstein's small-block-large-block technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. Our method allows us to consider only minimal conditions on the bandwidth parameter and provides a simple criterion on the (non-uniform) strong mixing coefficients which do not depend on the bandwith.Comment: 16 page