Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Exploring unfrequent events with accelerated molecular dynamics simulations: from photochemistry to drug design

La meva incorporació al grup de recerca del Prof. McCammon (University of California San Diego) en qualitat d’investigador post doctoral amb una beca Beatriu de Pinós, va tenir lloc el passat 1 de desembre de 2010; on vaig dur a terme les meves tasques de recerca fins al darrer 1 d’abril de 2012. El Prof. McCammon és un referent mundial en l’aplicació de simulacions de dinàmica molecular (MD) en sistemes biològics d’interès humà. La contribució més important del Prof. McCammon en la simulació de sistemes biològics és el desenvolupament del mètode de dinàmiques moleculars accelerades (AMD). Les simulacions MD convencionals, les quals estan limitades a l’escala de temps del nanosegon (~10-9s), no son adients per l’estudi de sistemes biològics rellevants a escales de temps mes llargues (μs, ms...). AMD permet explorar fenòmens moleculars poc freqüents però que son clau per l’enteniment de molts sistemes biològics; fenòmens que no podrien ser observats d’un altre manera. Durant la meva estada a la “University of California San Diego”, vaig treballar en diferent aplicacions de les simulacions AMD, incloent fotoquímica i disseny de fàrmacs per ordinador. Concretament, primer vaig desenvolupar amb èxit una combinació dels mètodes AMD i simulacions Car-Parrinello per millorar l’exploració de camins de desactivació (interseccions còniques) en reaccions químiques fotoactivades. En segon lloc, vaig aplicar tècniques estadístiques (Replica Exchange) amb AMD en la descripció d’interaccions proteïna-lligand. Finalment, vaig dur a terme un estudi de disseny de fàrmacs per ordinador en la proteïna-G Rho (involucrada en el desenvolupament de càncer humà) combinant anàlisis estructurals i simulacions AMD. Els projectes en els quals he participat han estat publicats (o estan encara en procés de revisió) en diferents revistes científiques, i han estat presentats en diferents congressos internacionals. La memòria inclosa a continuació conté més detalls de cada projecte esmentat.

Distributed all-atom simulations of intrinsically unstructured proteins

The Computational Biophysics Group at the Universitat Pompeu Fabra (GRIB-UPF) hosts two unique computational resources dedicated to the execution of large scale molecular dynamics (MD) simulations: (a) the ACMD molecular-dynamics software, used on standard personal computers with graphical processing units (GPUs); and (b) the GPUGRID. net computing network, supported by users distributed worldwide that volunteer GPUs for biomedical research. We leveraged these resources and developed studies, protocols and open-source software to elucidate energetics and pathways of a number of biomolecular systems, with a special focus on flexible proteins with many degrees of freedom. First, we characterized ion permeation through the bactericidal model protein Gramicidin A conducting one of the largest studies to date with the steered MD biasing methodology. Next, we addressed an open problem in structural biology, the determination of drug-protein association kinetics; we reconstructed the binding free energy, association, and dissaciociation rates of a drug like model system through a spatial decomposition and a Makov-chain analysis. The work was published in the Proceedings of the National Academy of Sciences and become one of the few landmark papers elucidating a ligand-binding pathway. Furthermore, we investigated the unstructured Kinase Inducible Domain (KID), a 28-peptide central to signalling and transcriptional response; the kinetics of this challenging system was modelled with a Markovian approach in collaboration with Frank Noe’s group at the Freie University of Berlin. The impact of the funding includes three peer-reviewed publication on high-impact journals; three more papers under review; four MD analysis components, released as open-source software; MD protocols; didactic material, and code for the hosting group.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

Global Prospects for Dairy in Argentina and Chile: Evidence from Field Visits and Model Simulations, August 2006

We assess the international competitiveness of the dairy industries in Argentina and Chile, combining recent market intelligence gathered from field visits with quantitative simulations of global policy reform scenarios. Both countries exhibit strong potential for export growth but face significant internal and external barriers to expanding their dairy industries. Global policy reforms would resolve some of the international obstacles to their expansion. Argentina has great potential, but it is handicapped by its current macroeconomic policies, trade policy distortions, and the uncertainty associated with policy implementation. Chile is more limited in terms of natural capacity for expansion, but it has a positive trade and investment environment.

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paperexplores the consistency of some of its standard techniques. We focus in particular on prevailingwholesale electricity trading simulation methods. We include different supply and demandrepresentations and propose the Experience-Weighted Attractions method to include severalbehavioural algorithms. We compare the results across assumptions and to economic theorypredictions. The match is good under best-response and reinforcement learning but not underfictitious play. The simulations perform well under flat and upward-slopping supply bidding,and also for plausible demand elasticity assumptions. Learning is influenced by the number ofbids per plant and the initial conditions. The overall conclusion is that agent-based simulationassumptions are far from innocuous. We link their performance to underlying features, andidentify those that are better suited to model wholesale electricity markets.

Spread spectrum magnetic resonance imaging.

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s(2)MRI performs better than state-of-the-art variable density k-space under-sampling approaches.

Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

Quantitative comparison of simulations of seismic wave propagation in heterogeneous poro-elastic media involving fluid-solid interfaces and in equivalent visco-elastic solids

There is increasing evidence to suggest that the presence of mesoscopic heterogeneities constitutes the predominant attenuation mechanism at seismic frequencies. As a consequence, centimeter-scale perturbations of the subsurface physical properties should be taken into account for seismic modeling whenever detailed and accurate responses of the target structures are desired. This is, however, computationally prohibitive since extremely small grid spacings would be necessary. A convenient way to circumvent this problem is to use an upscaling procedure to replace the heterogeneous porous media by equivalent visco-elastic solids. In this work, we solve Biot's equations of motion to perform numerical simulations of seismic wave propagation through porous media containing mesoscopic heterogeneities. We then use an upscaling procedure to replace the heterogeneous poro-elastic regions by homogeneous equivalent visco-elastic solids and repeat the simulations using visco-elastic equations of motion. We find that, despite the equivalent attenuation behavior of the heterogeneous poro-elastic medium and the equivalent visco-elastic solid, the seismograms may differ due to diverging boundary conditions at fluid-solid interfaces, where there exist additional options for the poro-elastic case. In particular, we observe that the seismograms agree for closed-pore boundary conditions, but differ significantly for open-pore boundary conditions. This is an interesting result, which has potentially important implications for wave-equation-based algorithms in exploration geophysics involving fluid-solid interfaces, such as, for example, wave field decomposition.