Parallel Metropolis chains with cooperative adaptation

Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC scheme where parallel MCMC chains interact, adapting cooperatively the parameters of their proposal functions. Furthermore, the novel algorithm distributes the computational effort adaptively, rewarding the chains which are providing better performance and, possibly even stopping other ones. These extinct chains can be reactivated if the algorithm considers necessary. Numerical simulations shows the benefits of the novel scheme

Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called {\it orthogonal MCMC} (O-MCMC), where a set of "vertical" parallel MCMC chains share information using some "horizontal" MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters

Complete intersection singularities of splice type as universal abelian covers

It has long been known that every quasi-homogeneous normal complex surface singularity with Q-homology sphere link has universal abelian cover a Brieskorn complete intersection singularity. We describe a broad generalization: First, one has a class of complete intersection normal complex surface singularities called "splice type singularities", which generalize Brieskorn complete intersections. Second, these arise as universal abelian covers of a class of normal surface singularities with Q-homology sphere links, called "splice-quotient singularities". According to the Main Theorem, splice-quotients realize a large portion of the possible topologies of singularities with Q-homology sphere links. As quotients of complete intersections, they are necessarily Q-Gorenstein, and many Q-Gorenstein singularities with Q-homology sphere links are of this type. We conjecture that rational singularities and minimally elliptic singularities with Q-homology sphere links are splice-quotients. A recent preprint of T Okuma presents confirmation of this conjecture.Comment: Published by Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol9/paper17.abs.htm

Detection of Critical Structures in Laparoscopic Cholecystectomy Using Label Relaxation and Self-supervision

Laparoscopic cholecystectomy can be subject to complications such as bile duct injury, which can seriously harm the patient or even result in death. Computer-assisted interventions have the potential to prevent such complications by highlighting the critical structures (cystic duct and cystic artery) during surgery, helping the surgeon establish the Critical View of Safety and avoid structure misidentification. A method is presented to detect the critical structures, using state of the art computer vision techniques. The proposed label relaxation dramatically improves performance for segmenting critical structures, which have ambiguous extent and highly variable ground truth labels. We also demonstrate how pseudo-label self-supervision allows further detection improvement using unlabelled data. The system was trained using a dataset of 3,050 labelled and 3,682 unlabelled laparoscopic cholecystectomy frames. We achieved an IoU of .65 and presence detection F1 score of .75. The model’s outputs were further evaluated qualitatively by three expert surgeons, providing preliminary confirmation of our method’s benefits. This work is among the first to perform detection of critical anatomy during laparoscopic cholecystectomy, and demonstrates the great promise of computer-assisted intervention to improve surgical safety and workflow

Current-induced spin polarization in InGaAs and GaAs epilayers with varying doping densities

The current-induced spin polarization and momentum-dependent spin-orbit field were measured in In$_{x}$Ga$_{1-x}$As epilayers with varying indium concentrations and silicon doping densities. Samples with higher indium concentrations and carrier concentrations and lower mobilities were found to have larger electrical spin generation efficiencies. Furthermore, current-induced spin polarization was detected in GaAs epilayers despite the absence of measurable spin-orbit fields, indicating that the extrinsic contributions to the spin polarization mechanism must be considered. Theoretical calculations based on a model that includes extrinsic contributions to the spin dephasing and the spin Hall effect, in addition to the intrinsic Rashba and Dresselhaus spin-orbit coupling, are found to qualitatively agree with the experimental results.Comment: 16 pages, 8 figure

Towards real-time multiple surgical tool tracking

Surgical tool tracking is an essential building block for computer-assisted interventions (CAI) and applications like video summarisation, workflow analysis and surgical navigation. Vision-based instrument tracking in laparoscopic surgical data faces significant challenges such as fast instrument motion, multiple simultaneous instruments and re-initialisation due to out-of-view conditions or instrument occlusions. In this paper, we propose a real-time multiple object tracking framework for whole laparoscopic tools, which extends an existing single object tracker. We introduce a geometric object descriptor, which helps with overlapping bounding box disambiguation, fast motion and optimal assignment between existing trajectories and new hypotheses. We achieve 99.51% and 75.64% average accuracy on ex-vivo robotic data and in-vivo laparoscopic sequences respectively from the Endovis’15 Instrument Tracking Dataset. The proposed geometric descriptor increased the performance on laparoscopic data by 32%, significantly reducing identity switches, false negatives and false positives. Overall, the proposed pipeline can successfully recover trajectories over long-sequences and it runs in real-time at approximately 25–29 fps

Linear latent force models using Gaussian processes.

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics

Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution II: dynamics.

BackgroundTo accurately describe gene expression and computationally model animal transcriptional networks, it is essential to determine the changing locations of cells in developing embryos.ResultsUsing automated image analysis methods, we provide the first quantitative description of temporal changes in morphology and gene expression at cellular resolution in whole embryos, using the Drosophila blastoderm as a model. Analyses based on both fixed and live embryos reveal complex, previously undetected three-dimensional changes in nuclear density patterns caused by nuclear movements prior to gastrulation. Gene expression patterns move, in part, with these changes in morphology, but additional spatial shifts in expression patterns are also seen, supporting a previously proposed model of pattern dynamics based on the induction and inhibition of gene expression. We show that mutations that disrupt either the anterior/posterior (a/p) or the dorsal/ventral (d/v) transcriptional cascades alter morphology and gene expression along both the a/p and d/v axes in a way suggesting that these two patterning systems interact via both transcriptional and morphological mechanisms.ConclusionOur work establishes a new strategy for measuring temporal changes in the locations of cells and gene expression patterns that uses fixed cell material and computational modeling. It also provides a coordinate framework for the blastoderm embryo that will allow increasingly accurate spatio-temporal modeling of both the transcriptional control network and morphogenesis