MCMC inference for Markov Jump Processes via the Linear Noise Approximation

Bayesian analysis for Markov jump processes is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding thus its applicability is limited to a small class of problems. In this paper we describe the application of Riemann manifold MCMC methods using an approximation to the likelihood of the Markov jump process which is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient while the convergence rate and mixing of the chains allows for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology

Computing and deflating eigenvalues while solving multiple right hand side linear systems in Quantum Chromodynamics

We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive definite matrix while solving a linear system of equations with Conjugate Gradient (CG). Traditionally, all the CG iteration vectors could be saved and recombined through the eigenvectors of the tridiagonal projection matrix, which is equivalent theoretically to unrestarted Lanczos. Our algorithm capitalizes on the iteration vectors produced by CG to update only a small window of vectors that approximate the eigenvectors. While this window is restarted in a locally optimal way, the CG algorithm for the linear system is unaffected. Yet, in all our experiments, this small window converges to the required eigenvectors at a rate identical to unrestarted Lanczos. After the solution of the linear system, eigenvectors that have not accurately converged can be improved in an incremental fashion by solving additional linear systems. In this case, eigenvectors identified in earlier systems can be used to deflate, and thus accelerate, the convergence of subsequent systems. We have used this algorithm with excellent results in lattice QCD applications, where hundreds of right hand sides may be needed. Specifically, about 70 eigenvectors are obtained to full accuracy after solving 24 right hand sides. Deflating these from the large number of subsequent right hand sides removes the dreaded critical slowdown, where the conditioning of the matrix increases as the quark mass reaches a critical value. Our experiments show almost a constant number of iterations for our method, regardless of quark mass, and speedups of 8 over original CG for light quark masses.Comment: 22 pages, 26 eps figure

DESIGN OF A STATED RANKING EXPERIMENT TO STUDY INTERACTIVE FREIGHT BEHAVIOUR: AN APPLICATION TO ROME'S LTZ

City logistics policies require an understanding of several issues (e.g. freight distribution context, preferences and relationship among agents) seldom accounted for in current research. Policies run the risk of producing unsatisfactory results because behavioural and contextual aspects are not considered. The acquisition of relevant data is crucial to test hypothesis and forecast agents' reactions to policy changes. Despite recent methodological advances in modelling interactive behaviour the development of apt survey instruments is still lacking to test innovative policies acceptability. This paper expands and innovate the methodological literature by describing a stated ranking experiment to study freight agent interactive behaviour and discusses the experimental design implemented to incorporate agent-specific priors when efficient design techniques are employed.urban freight distribution, group decision making, agent-specific interaction, stated preference, stated ranking experiments

Deflation for inversion with multiple right-hand sides in QCD

Most calculations in lattice Quantum Chromodynamics (QCD) involve the solution of a series of linear systems of equations with exceedingly large matrices and a large number of right hand sides. Iterative methods for these problems can be sped up significantly if we deflate approximations of appropriate invariant spaces from the initial guesses. Recently we have developed eigCG, a modification of the Conjugate Gradient (CG) method, which while solving a linear system can reuse a window of the CG vectors to compute eigenvectors almost as accurately as the Lanczos method. The number of approximate eigenvectors can increase as more systems are solved. In this paper we review some of the characteristics of eigCG and show how it helps remove the critical slowdown in QCD calculations. Moreover, we study scaling with lattice volume and an extension of the technique to nonsymmetric problems

Therapeutic brain cancer targeting by gene therapy and immunomodulation : a translational study

The hypothesis pertinent to this thesis is that glioma tumours can be therapeutically targeted by gene and/or immunotherapy in order to eliminate or delay tumour recurrence leading to significant morbidity and mortality. In our gene therapeutic approach, described in Chapter 2, we observed that chronic expression of the C-terminal fusion of IsK with EGFP (enhanced green fluorescent protein) led to cell death of more than 50% of transfected U87-MG human astrocytoma cells as early as 2 days after transfection. Our results are consistent with activation of apoptotic pathways following IsK-mediated increase in K+ efflux. However, we abandoned the gene therapy approach because of the more attractive immunotherapeutic intervention strategies for of brain tumours, which is currently emerging as a highly potential clinical option as reviewed in Chapter 3. Interestingly, as described in Chapter 4, we found a strong therapeutic antitumour efficacy for the innate immune response modifier Resiquimod, even as a stand-alone treatment, eventually leading to immunological memory against secondary tumour challenges. In parallel, we observed that cyclophosphamide treatment, although effective as chemotherapeutic agent, may be deleterious to maintenance of long-term antitumour immune memory. Our data also demonstrates that immunotherapeutic parenteral treatment of established glioma tumours by Resiquimod, as defined in the protocol, significantly improves anti-brain tumour immunity in a way that leads to immune memory, which is superior to cyclophosphamide treatment alone. Our studies have thereby identified a promising novel antitumour immunotherapy which may lead to clinical benefit. In Chapter 5, we describe our finding that, in multiple rat glioma models, a certain composition of antigens derived from syngeneic tumour cells and their lysates when therapeutically co-administered with allogeneic cells and their lysates is able to confer anti-tumour immune responses and tumour regression. For the syngeneic C6 model in SD rats therapeutic injections of allogeneic cells alone were sufficient to trigger tumour regression. This immunization approach may prove useful as a postsurgery adjuvant therapy in future cancer treatment protocols, or even as a stand-alone therapeutic tumour vaccination. In another syngeneic rat glioma model, described in Chapter 6, we found that for regression of CNS-1 glioma tumours in Lewis rats specific innate immune response stimulating substances were required as immunological adjuvants. In our hands BCG and IL-2, the Toll-Like receptor (TLR) 7/8 activator Resiquimod, and the cytokine granulocyte-macrophage colony stimulating factor (GM-CSF), showed potent activity. Finally, as described in Chapter 7, we demonstrate that our prototype therapeutic vaccine, when co-delivered in a specific regimen together with the cytokine GM-CSF as immunological adjuvant, is able to arrest progression of glioma tumour growth, when therapeutically administered following low-dose cyclophosphamide. GM-CSF is an attractive vaccine adjuvant because of its proven immune modulatory effects and low toxicity profile. The safe pharmacological use of GM-CSF in patients is well-established, which makes it feasible for clinical use. The use of GM-CSF has been included in the first clinical studies that have been approved for an Investigational New Drug application (IND) for Single patient use in the U.S..</p

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

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

Multiple-inlet BIPV/T Modeling: Wind Effects and Fan Induced Suction

AbstractBuilding Integrated Photovoltaic/Thermal (BIPV/T) collectors take up the role of energy and heat production, while acting as a rain-screen cladding. A multiple inlet BIPV/T system counters the effect of high temperature stratification on the PV layer, by enhancing the convection inside the air channel with the introduction of more than one openings for the intake of fresh air that break up the surface boundary layer. To investigate the uniformity of heat extraction from the PV panels, the fluid mechanics of the system are studied separately from the thermal effects. A numerical flow distribution model, which incorporates wind effects, is introduced for the optimal design of multiple inlet systems so as to have flow rates through each inlet that maximize the heat extracted from the PV panels