Rearrangement on Lattices with Pick-n-Swaps: Optimality Structures and Efficient Algorithms

We propose and study a class of rearrangement problems under a novel pick-n-swap prehensile manipulation model, in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices of variable dimensions in a time-optimal manner. We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully labeled, where each item has a unique label, or partially labeled, where multiple items may be of the same type) and different lattice dimensions. Focusing on the most practical setting of one and two dimensions, we develop low polynomial time cycle-following based algorithms that optimally perform rearrangements on 1D lattices under both fully- and partially-labeled settings. On the other hand, we show that rearrangement on 2D and higher dimensional lattices becomes computationally intractable to optimally solve. Despite their NP-hardness, we prove that efficient cycle-following based algorithms remain asymptotically optimal for 2D fully- and partially-labeled settings, in expectation, using the interesting fact that random permutations induce only a small number of cycles. We further improve these algorithms to provide 1.x-optimality when the number of items is small. Simulation studies corroborate the effectiveness of our algorithms.Comment: To appear in R:SS 202

Generalized Weiszfeld algorithms for Lq optimization

In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L₂ cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L₂ optimization.This research has been funded by National ICT Australia

A JKO splitting scheme for Kantorovich-Fisher-Rao gradient flows

In this article we set up a splitting variant of the JKO scheme in order to handle gradient flows with respect to the Kantorovich-Fisher-Rao metric, recently introduced and defined on the space of positive Radon measure with varying masses. We perform successively a time step for the quadratic Wasserstein/Monge-Kantorovich distance, and then for the Hellinger/Fisher-Rao distance. Exploiting some inf-convolution structure of the metric we show convergence of the whole process for the standard class of energy functionals under suitable compactness assumptions, and investigate in details the case of internal energies. The interest is double: On the one hand we prove existence of weak solutions for a certain class of reaction-advection-diffusion equations, and on the other hand this process is constructive and well adapted to available numerical solvers.Comment: Final version, to appear in SIAM SIM

Functional, randomized and smoothed multivariate quantile regions

A notion of multivariate depth, resp. quantile region, was introduced in [Chernozhukov et al., 2017], based on a mass transportation approach. In [Faugeras and Ruschendorf, 2017], this approach was generalized by dening quantiles as Markov morphisms carrying suitable algebraic, ordering and topological structures over probability measures. In addition, a copula step was added to the mass transportation step. Empirical versions of these depth areas do not give exact level depth regions. In this paper, we introduce randomized depth regions by means of a formulation by depth functions, resp. by randomized quantiles sets. These versions attain the exact level and also provide the corresponding consistency property. We also investigate in the case of continuous marginals a smoothed version of the empirical copula and compare its behavior with the unsmoothed version. Extensive simulations illustrate the resulting randomized depth areas and show that they give a valid representation of the central depth areas of a multivariate distribution, and thus are a valuable tool for their analysis

Minimal convex extensions and finite difference discretization of the quadratic Monge-Kantorovich problem

We present an adaptation of the MA-LBR scheme to the Monge-Amp{\`e}re equation with second boundary value condition, provided the target is a convex set. This yields a fast adaptive method to numerically solve the Optimal Transport problem between two absolutely continuous measures, the second of which has convex support. The proposed numerical method actually captures a specific Brenier solution which is minimal in some sense. We prove the convergence of the method as the grid stepsize vanishes and we show with numerical experiments that it is able to reproduce subtle properties of the Optimal Transport problem

Fully differential photo-electron spectra of hydrogen and helium atoms

The ability to probe and manipulate electron dynamics and correlations on their characteristic time scales would open up many technological and scientific possibilities. While modern laser technology already allows to do that in principle, a lot of theoretical ground work is still missing. This thesis focuses on the elementary effect of laser strong field ionization of the two simplest systems: The Hydrogen and Helium atoms. To that end, the time-dependent Schroedinger equation is solved numerically, and photo-electron spectra are extracted using the highly efficient tSurff technique. We implemented both the one and two particle versions of tSurff together with several other numerical techniques in a new parallelizable C++ code. We provide details on the employed methods and algorithms, and study numerical efficiency properties of various approaches. We propose a description of the electric field interaction in a mixture of length and velocity gauge for the correct and most efficient implementation of a coupled channels approach, which can be used to compute accurate single ionization photo-electron spectra from true multi-electron systems, even molecules. We provide extensive numerical data for a detailed study of the Hydrogen atom in an Attoclock experimental setup, where it is found that the involved strong field tunnel ionization processes can be considered instantaneous. In particular, there appear no tunneling delays, which can be used as a calibration for experiments with more complicated targets. Similarly, it is investigated whether discrepancies between theory and experimental data for the longitudinal photo-electron momentum spread, resulting from photo-ionization of Helium in elliptically polarized laser pulses, can be explained by non-adiabatic effects, and a related consistency problem in current laser intensity calibration methods is pointed out. We further show that Fano resonance line shapes of doubly excited states in the Helium atom, prominently appearing in single ionization spectra generated by short wavelength laser pulses, can be controlled by an external long wavelength streaking field. The resulting line shapes are still characterized by the general Fano situation, but with a complex - rather than real - Fano parameter. We provide a theoretical description of this two color process and prove numerically that the entire doubly excited state series exhibits synchronized line shape modifications as the specifics of the involved states are unimportant. Finally, we compute fully differential double ionization spectra and suggest a measure of correlation that is directly applicable to experimental data. We confirm literature results at short wavelengths, and achieve to compute five-fold differential double ionization photo-electron spectra at infrared wavelengths from the Helium atom, thereby reproducing a characteristic several orders of magnitude enhancement of double emission due to correlation effects

High performance graph analysis on parallel architectures

PhD ThesisOver the last decade pharmacology has been developing computational methods to enhance drug development and testing. A computational method called network pharmacology uses graph analysis tools to determine protein target sets that can lead on better targeted drugs for diseases as Cancer. One promising area of network-based pharmacology is the detection of protein groups that can produce better e ects if they are targeted together by drugs. However, the e cient prediction of such protein combinations is still a bottleneck in the area of computational biology. The computational burden of the algorithms used by such protein prediction strategies to characterise the importance of such proteins consists an additional challenge for the eld of network pharmacology. Such computationally expensive graph algorithms as the all pairs shortest path (APSP) computation can a ect the overall drug discovery process as needed network analysis results cannot be given on time. An ideal solution for these highly intensive computations could be the use of super-computing. However, graph algorithms have datadriven computation dictated by the structure of the graph and this can lead to low compute capacity utilisation with execution times dominated by memory latency. Therefore, this thesis seeks optimised solutions for the real-world graph problems of critical node detection and e ectiveness characterisation emerged from the collaboration with a pioneer company in the eld of network pharmacology as part of a Knowledge Transfer Partnership (KTP) / Secondment (KTS). In particular, we examine how genetic algorithms could bene t the prediction of protein complexes where their removal could produce a more e ective 'druggable' impact. Furthermore, we investigate how the problem of all pairs shortest path (APSP) computation can be bene ted by the use of emerging parallel hardware architectures as GPU- and FPGA- desktop-based accelerators. In particular, we address the problem of critical node detection with the development of a heuristic search method. It is based on a genetic algorithm that computes optimised node combinations where their removal causes greater impact than common impact analysis strategies. Furthermore, we design a general pattern for parallel network analysis on multi-core architectures that considers graph's embedded properties. It is a divide and conquer approach that decomposes a graph into smaller subgraphs based on its strongly connected components and computes the all pairs shortest paths concurrently on GPU. Furthermore, we use linear algebra to design an APSP approach based on the BFS algorithm. We use algebraic expressions to transform the problem of path computation to multiple independent matrix-vector multiplications that are executed concurrently on FPGA. Finally, we analyse how the optimised solutions of perturbation analysis and parallel graph processing provided in this thesis will impact the drug discovery process.This research was part of a Knowledge Transfer Partnership (KTP) and Knowledge Transfer Secondment (KTS) between e-therapeutics PLC and Newcastle University. It was supported as a collaborative project by e-therapeutics PLC and Technology Strategy boar