Constrained LQR Using Online Decomposition Techniques

This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints. Second, the MPC problem is decomposed into smaller subproblems of fixed dimension independent of the horizon length. Third, using the fast alternating minimization algorithm to solve the subproblems, the horizon length is estimated online, by adding or removing subproblems based on a periodic check on the state of the last subproblem to determine whether it belongs to a given control invariant set. We show that the estimated horizon length is bounded and that the control sequence computed using the proposed algorithm is an optimal solution of the CLQR problem. Compared to state-of-the-art algorithms proposed to solve the CLQR problem, our design solves at each iteration only unconstrained least-squares problems and simple gradient calculations. Furthermore, our technique allows the horizon length to decrease online (a useful feature if the initial guess on the horizon is too conservative). Numerical results on a planar system show the potential of our algorithm.Comment: This technical report is an extended version of the paper titled "Constrained LQR Using Online Decomposition Techniques" submitted to the 2016 Conference on Decision and Contro

Lipoplatin Formulation Review Article

Patented platform technologies have been used for the liposomal encapsulation of cisplatin (Lipoplatin) into tumor-targeted 110 nm (in diameter) nanoparticles. The molecular mechanisms, preclinical and clinical data concerning lipoplatin, are reviewed here. Lipoplatin has been successfully administered in three randomized Phase II and III clinical trials. The clinical data mainly include non-small-cell lung cancer but also pancreatic, breast, and head and neck cancers. It is anticipated that lipoplatin will replace cisplatin as well as increase its potential applications. For the first time, a platinum drug has shown superiority to cisplatin, at least in non-squamous non-small-cell lung cancer as reported in a Phase III study which documented a simultaneous lowering of all of the side effects of cisplatin

Primary Malignant Fibrous Histiocytoma of the Lung: A Case Report

Primary malignant fibrous histiocytoma (MFH) of the lung is extremely rare although it is among the most common soft tissue sarcomas in adults. Surgery is the primary mode of therapy, with high rates of local and distant recurrence, while radiation therapy appears to be a very useful adjunct, decreasing local relapse. We report a case of primary malignant fibrous histiocytoma of the lung. Fourteen years after surgical resection, the patient is still alive although with multiple metastatic lesions throughout his body

Application of neural networks to synchro-Compton blazar emission models

Jets from supermassive black holes in the centers of active galaxies are the most powerful persistent sources of electromagnetic radiation in the Universe. To infer the physical conditions in the otherwise out-of-reach regions of extragalactic jets we usually rely on fitting of their spectral energy distribution (SED). The calculation of radiative models for the jet non-thermal emission usually relies on numerical solvers of coupled partial differential equations. In this work machine learning is used to tackle the problem of high computational complexity in order to significantly reduce the SED model evaluation time, which is needed for SED fitting with Bayesian inference methods. We compute SEDs based on the synchrotron self-Compton model for blazar emission using the radiation code ATHE${\nu}$A, and use them to train Neural Networks exploring whether these can replace the original computational expensive code. We find that a Neural Network with Gated Recurrent Unit neurons can effectively replace the ATHE${\nu}$A leptonic code for this application, while it can be efficiently coupled with MCMC and nested sampling algorithms for fitting purposes. We demonstrate this through an application to simulated data sets and with an application to observational data. We offer this tool in the community through a public repository. We present a proof-of-concept application of neural networks to blazar science. This is the first step in a list of future applications involving hadronic processes and even larger parameter spaces.Comment: 12 pages, submitted, comments are welcome, code will be soon available at https://github.com/tzavellas/blazar_m

Mitigating the impact of errors in travel time reporting on mode choice modelling

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple \u2013 sometimes incongruent \u2013 sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements

Algebraic-matrix calculation of vibrational levels of triatomic molecules

We introduce an accurate and efficient algebraic technique for the computation of the vibrational spectra of triatomic molecules, of both linear and bent equilibrium geometry. The full three-dimensional potential energy surface (PES), which can be based on entirely {\it ab initio} data, is parameterized as a product Morse-cosine expansion, expressed in bond-angle internal coordinates, and includes explicit interactions among the local modes. We describe the stretching degrees of freedom in the framework of a Morse-type expansion on a suitable algebraic basis, which provides exact analytical expressions for the elements of a sparse Hamiltonian matrix. Likewise, we use a cosine power expansion on a spherical harmonics basis for the bending degree of freedom. The resulting matrix representation in the product space is very sparse and vibrational levels and eigenfunctions can be obtained by efficient diagonalization techniques. We apply this method to carbonyl sulfide OCS, hydrogen cyanide HCN, water H$_2$O, and nitrogen dioxide NO$_2$. When we base our calculations on high-quality PESs tuned to the experimental data, the computed spectra are in very good agreement with the observed band origins.Comment: 11 pages, 2 figures, containg additional supporting information in epaps.ps (results in tables, which are useful but not too important for the paper