Avoiding negative depth in inverse depth bearing-only SLAM

In this paper we consider ways to alleviate negative estimated depth for the inverse depth parameterisation of bearing-only SLAM. This problem, which can arise even if the beacons are far from the platform, can cause catastrophic failure of the filter.We consider three strategies to overcome this difficulty: applying inequality constraints, the use of truncated second order filters, and a reparameterisation using the negative logarithm of depth. We show that both a simple inequality method and the use of truncated second order filters are succesful. However, the most robust peformance is achieved using the negative log parameterisation. ©2008 IEEE

The common state filter for SLAM

This paper presents the Common State Filter (CSF), a novel and efficient suboptimal Multiple Hypothesis SLAM (MHSLAM) method for Kalman Filter-based SLAM algorithms. Conventional MHSLAM algorithms require the entire vehicle and map state to be copied for each hypothesis. The CSF, by contrast, maintains a single, common instance of the vast majority of the map and only copies the map portion that varies substantially across different hypotheses. We demonstrate the performance of the algorithm on the Victoria Park data set. ©2008 IEEE

The importance of screening in children who snore

It is important to screen for OSAS in children who snore, as early treatment of OSAS can prevent neurocognitive, behavioural, cardiovascular and metabolic consequences. Paediatricians should always investigate sleep habits and the possible presence of snoring, respiratory efforts or pauses during routine examination of children. These instruments may be effectively used to identify patients with OSAS, and the specificity and positive predictive value may be increased by adding other screening instruments such as nocturnal pulse oximetry [10]. The sleep questionnaires are instruments that can be used to screen patient candidates for a PSG study for suspected OSAS, and to identify those with a mild form of SDB, enabling early treatment

PILCO: A Model-Based and Data-Efficient Approach to Policy Search

In this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks. Copyright 2011 by the author(s)/owner(s)

A Probabilistic Perspective on Gaussian Filtering and Smoothing

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing these moments. Based on this insight, we derive the cubature Kalman smoother and propose a novel robust filtering and smoothing algorithm based on Gibbs sampling

Identification and expression analysis of CBF/DREB1 and COR15 genes in mutants of Brassica oleracea var. botrytis with enhanced proline production and frost resistance.

Frost resistant mutants of Brassica oleracea var. botrytis were investigated for the presence of CBF/DREB1 and COR15a gene products and induced frost resistance. Total RNA of clones was isolated after 3 h, 6 h, 24 h and 14 d acclimation at 4 °C and proteins and free proline were isolated after 14 d acclimation. cDNA was produced using RT-PCR and the first CBF gene in B. oleracea detected and did quantify. Through SDS-PAGE and Western blotting, the COR15a protein was detected for the first time in B. oleracea. The results confirmed the first report of the presence of BoCBF/DREB1 in B. oleracea and this only appeared under cold acclimation. The sequence analysis of predicted amino acids revealed a very high homology (90%) with CBF sequences of other Brassica species (BnCBF5/DREB1, BrDREB1 and BjDREB1B) and homology reduced to 67% when compared to plants other than Brassicas. BoCBF/DREB1 transcript levels increased up to 24 h acclimation and then declined. Some mutants showed BoCBF/DREB1 expression at 3 h while others only after 6 h and 24 h acclimation. The genotypes showed positive significant correlation between BoCBF/DREB1 expression and frost resistance (R(2) = 0.9343). The proline level under acclimation increased about 8 fold and demonstrated positive and significant correlation with BoCBF/DREB1 expression. Proline also showed positive and significant correlation with frost resistance under cold acclimation but very not under non-acclimation. All clones were positive for COR15a protein after 14 d cold acclimation and expression correlated with frost resistance. Under non-acclimation COR15a was constitutively expressed in 3 mutants

Occurrence of 'Antrobathynella stammeri' (Jakobi, 1954) (Crustacea: Syncarida: Bathynellidae) in the hyporheic zones of two English karst rivers

This paper reports the first records of Antrobathynella stammeri in the British Isles since 1985. Numerous individuals were observed in hyporheic water extracted from the River Skirfare (Yorkshire, UK), whilst, in an independent study, a single confirmed specimen was recorded from hyporheic water pumped from the River Lathkill (Derbyshire, UK). The latter observation is the first record of the superorder Syncarida in the Peak District and provides an important geographical link between previously recorded individuals in Scotland and north-west England and records from the south of England. These records suggest that bathynellids may be more abundant in the UK than previously thought, highlighting the need for more effective sampling of subterranean habitats to determine the conservation status of such rarely seen species

Model-Based Reinforcement Learning with Continuous States and Actions

Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment