New Perspectives Related to the Bioluminescent System in Dinoflagellates: Pyrocystis lunula, a Case Study

Pyrocystis lunula is considered a model organism due to its bioluminescence capacity linked to circadian rhythms. The mechanisms underlying the bioluminescent phenomenon have been well characterized in dinoflagellates; however, there are still some aspects that remain an enigma. Such is the case of the presence and diversity of the luciferin-binding protein (LBP), as well as the synthesis process of luciferin. Here we carry out a review of the literature in relation to the molecular players responsible for bioluminescence in dinoflagellates, with particular interest in P. lunula. We also carried out a phylogenetic analysis of the conservation of protein sequence, structure and evolutionary pattern of these key players. The basic structure of the luciferase (LCF) is quite conserved among the sequences reported to date for dinoflagellate species, but not in the case of the LBP, which has proven to be more variable in terms of sequence and structure. In the case of luciferin, its synthesis has been shown to be complex process with more than one metabolic pathway involved. The glutathione S-transferase (GST) and the P630 or blue compound, seem to be involved in this process. In the same way, various hypotheses regarding the role of bioluminescence in dinoflagellates are exposed

Legitimacy and independence of international tribunals:an analysis of the European Court of Human Rights

This paper explores the theoretical framework of judicial independence of international tribunals, with specific reference to the independence of the European Court of Human Rights. It then argues that independence is a key aspect of the legitimacy of an international tribunal and suggests that legal reforms designed to enhance the judicial independence of the European Court of Human Rights should focus on the two main structural parts of the Court, namely the judiciary and the Registry. This paper analyses a number of proposed reforms that can make the European Court of Human Rights more independent and credible. These insights are applicable to other international judicial fora

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region $X_{inf}$, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling $X_{inf}$ directly. State-of-the-art approaches to sampling $X_{inf}$ in such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in $X_{inf}$ rather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set $X_{inf}$ for a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems

A Compilation Target for Probabilistic Programming Languages

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.Comment: In Proceedings of the 31st International Conference on Machine Learning (ICML), 201

Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance

We consider a Metropolis-Hastings method with proposal kernel $\mathcal{N}(x,hG^{-1}(x))$, where $x$ is the current state. After discussing specific cases from the literature, we analyse the ergodicity properties of the resulting Markov chains. In one dimension we find that suitable choice of $G^{-1}(x)$ can change the ergodicity properties compared to the Random Walk Metropolis case $\mathcal{N}(x,h\Sigma)$, either for the better or worse. In higher dimensions we use a specific example to show that judicious choice of $G^{-1}(x)$ can produce a chain which will converge at a geometric rate to its limiting distribution when probability concentrates on an ever narrower ridge as $|x|$ grows, something which is not true for the Random Walk Metropolis.Comment: 15 pages + appendices, 4 figure