Marsbot: A monopod robot capable of achieving three-dimensional dynamic and stable jumping

Continuous, stable and accurate jumping in three-dimensional (3D) environment is one of difficulties of jumping robot research. In this paper, a monopod jumping robot Marsbot whose center of mass (CoM) can accurately reach desired positions in 3D space is designed. Based on the spring loaded inverted pendulum (SLIP) dynamics model, the take-off velocity control of Marsbot was realized, which made controllable continuous jump possible. Based on the reaction wheel pendulum (RWP) dynamics model and the law of conservation of angular momentum, the real-time 3D attitude control of Marsbot with three inertial tails was realized. By integrating SLIP model, RWP model and the post-landing steering strategy proposed in this paper, a continuous jump algorithm for CoM of Marsbot to accurately reach desired 3D positions is proposed. This paper proposes the air target grasping strategy of Marsbot (the author will elaborate on this issue in another article): When the robot jumps to the desired grasping position in the air, the robotic arm can quickly and stably grasp the target object such as tree branches, so that the robot can perch or hang on the target object, and get a good view from high place. The simulation results show that Marsbot can achieve continuous, stable, accurate jumping and realize air perching/observation operations through the above schemes. The simulation results also verify feasibility of the 3D jump dynamics model and its control algorithm proposed in this paper.

Speeding up Inference of Homologous Recombination in Bacteria

Bacteria reproduce clonally but most species recombine frequently, so that the ancestral process is best captured using an ancestral recombination graph. This graph model is often too complex to be used in an inferential setup, but it can be approximated for example by the ClonalOrigin model. Inference in the ClonalOrigin model is performed via a Reversible-Jump Markov Chain Monte Carlo algorithm, which attempts to jointly explore: the recombination rate, the number of recombination events, the departure and arrival points on the clonal genealogy for each recombination event, and the range of genomic sites affected by each recombination event. However, the Reversible-Jump algorithm usually performs poorly due to the complexity of the target distribution since it needs to explore spaces of different dimensions. Recent developments in Bayesian computation methodology have provided ways to improve existing methods and code, but are not well-known outside the statistics community. We show how exploiting one of these new computational methods can lead to faster inference under the ClonalOrigin model.

OPTIMAL AUXILIARY PRIORS AND REVERSIBLE JUMP PROPOSALS FOR A CLASS OF VARIABLE DIMENSION MODELS

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm with an optimal choice of auxiliary priors and as a reversible jump algorithm with optimal proposal distributions. The method is primarily motivated by and applied to a Bayesian nonparametric model for conditional densities based on mixtures of a variable number of experts. The mixture of experts model outperforms standard parametric and nonparametric alternatives in out of sample performance comparisons in an application to Engel curve estimation. The proposed MCMC algorithm makes estimation of this model practical.

The Lightning Jump Algorithm for Nowcasting Convective Rainfall in Catalonia

Previous studies in Catalonia (NE Iberian Peninsula) showed a direct relationship between the Lightning Jump (LJ) and severe weather, from the study of different events, occurring in the last few years in this region. This research goes a step beyond by studying the relationship between LJ and heavy rainfall, considering different criteria. It selects those episodes exceeding the 40 mm/h threshold, dividing them between those with or without LJ occurrence (3760 and 14,238 cases, respectively). The time and distance criteria (<150 km and <50 min, respectively) allow the detection of rainfall episodes with LJ, to establish an accurate relationship between the jump and the heavy rain occurrence. Then, lightning and radar data are analyzed, considering monthly and hourly distributions. Skill scores for the period 2013–2018 showed good results, especially in summer, with values of POD ≃ 90% and FAR ≃ 10%

“Dense diffusion” in colloidal glasses: short-ranged long-time self-diffusion as a mechanistic model for relaxation dynamics

Individual particle dynamics are monitored during the colloidal glass transition, using a novel size-jump algorithm to quench from liquid to glass.

Tracking niche change through time: simultaneous inference of environmental niche evolution and estimation of contemporary niches

The environmental niche of a species is often studied from two different perspectives. Niche modelling correlates a species’ niche to known observations from contemporary species distribution. Niche evolution applies phylogenetic methods on summary statistics of species distribution to infer the patterns of changing niche over time. Even though the two areas of research are fundamentally linked and are typically both based on the same species distribution data, modelling the contemporary niche and inferring niche history are never combined in the same analytical framework. Here we provide a new way to combine both niche modelling and niche evolution, using distribution data and phylogenies. Doing so allows us to simultaneously infer the history of niche evolution and estimate the environmental niche of each tip species on the phylogeny. It also avoids some limitations of the separate methods, such as the failure of environmental niche models to account for the role of history in shaping current species distribution, and the use of unrealistic models of change in phylogenetic comparative methods. The novelty of our method is that it explicitly models three fundamental processes of niche evolution − adaptation, speciation, and dispersal − applying a reversible jump algorithm to infer the occurrences of these processes on phylogeny. Simulations demonstrate high accuracy of our method for estimating the environmental niche of tip species and reasonable power to identify the occurrences of the three processes on phylogeny. We also prove the efficacy of our approach using real-world data. We show that our method of combining niche modelling with niche evolution is far more effective at predicting salt tolerance in Australian Acacia species than using environmental niche modelling alone. We also show that we can correctly infer periods of evolutionary change in response to changing climate: our method identified two peak times in drought adaptation events in the evolutionary history of Australian Acacia, which correspond to the two drying periods in Australia during the Cenozoic. Therefore, we believe our method provides a promising approach to integrate different research areas in understanding species distribution.

Computing dimensions of spaces of Arakelov divisors of number fields

The function [Formula: see text] for a number field is analogous to the dimension of the Riemann–Roch spaces at divisors on an algebraic curve. We provide a method to compute this function for number fields with unit group of rank at most 2, even with large discriminant. This method is based on using LLL-reduced bases, the “jump algorithm” and Poisson summation formula.