Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.     

Facile Synthesis of Amine-Functionalized Eu3+-Doped La(OH)3 Nanophosphors for Bioimaging

Here, we report a straightforward synthesis process to produce colloidal Eu³⁺-activated nanophosphors (NPs) for use as bioimaging probes. In this procedure, poly(ethylene glycol) serves as a high-boiling point solvent allowing for nanoscale particle formation as well as a convenient medium for solvent exchange and subsequent surface modification. The La(OH)₃:Eu³⁺ NPs produced by this process were ~3.5 nm in diameter as determined by transmission electron microscopy. The NP surface was coated with aminopropyltriethoxysilane to provide chemical functionality for attachment of biological ligands, improve chemical stability and prevent surface quenching of luminescent centers. Photoluminescence spectroscopy of the NPs displayed emission peaks at 597 and 615 nm (λ_ex = 280 nm). The red emission, due to ⁵D₀ → ⁷F₁ and ⁵D₀ → ⁷F₂ transitions, was linear with concentration as observed by imaging with a conventional bioimaging system. To demonstrate the feasibility of these NPs to serve as optical probes in biological applications, an in vitro experiment was performed with HeLa cells. NP emission was observed in the cells by fluorescence microscopy. In addition, the NPs displayed no cytotoxicity over the course of a 48-h MTT cell viability assay. These results suggest that La(OH)₃:Eu³⁺ NPs possess the potential to serve as a luminescent bioimaging probe.     

Undecidable properties of flat term rewrite systems

Reachability, joinability and confluence properties are known to be undecidable for flat term rewrite systems (TRS). We give shorter and conceptually simpler proofs of these results. We also prove undecidability of weak normalization and unique normalization properties for flat TRS. The first author was supported by Spanish Ministry of Education and Science by the FORMALISM project (TIN2007-66523) and by the LOGICTOOLS-2 project (TIN2007-68093-C02-01). The second author was supported by Spanish Ministry of Education and Science by the FORMALISM project (TIN2007-66523).     

The role of K+ and H+ transport systems during glucose‐ and H2O2‐induced cell death in Saccharomyces cerevisiae

Glucose, in the absence of additional nutrients, induces programmed cell death in yeast. This phenomenon is independent of yeast metacaspase (Mca1/Yca1) and of calcineurin, requires ROS production and it is concomitant with loss of cellular K⁺ and vacuolar collapse. K⁺ is a key nutrient protecting the cells and this effect depends on the Trk1 uptake system and is associated with reduced ROS production. Mutants with decreased activity of plasma membrane H⁺‐ATPase are more tolerant to glucose‐induced cell death and exhibit less ROS production. A triple mutant ena1‐4 tok1 nha1, devoid of K⁺ efflux systems, is more tolerant to both glucose‐ and H₂O₂‐induced cell death. We hypothesize that ROS production, activated by glucose and H⁺‐ATPase and inhibited by K⁺ uptake, triggers leakage of K⁺, a process favoured by K⁺ efflux systems. Loss of cytosolic K⁺ probably causes osmotic lysis of vacuoles. The nature of the ROS‐producing system sensitive to K⁺ and H⁺ transport is unknown. Copyright © 2010 John Wiley & Sons, Ltd.     

Performance Analysis of Multiuser Detectors for Uplink Quasi-synchronous MC-CDMA Systems

This paper analyses and compares the average bit error rate (BER) of different multiuser detectors (MUD) in the uplink of a multicarrier code- division multiple access (MC-CDMA) system. In particular, maximum likelihood, zero-forcing, minimum mean-square error and interference cancellation-based multiuser detectors have been analysed for the special case of uncorrelated subcarriers. The derived BER expressions are based upon previous results on diversity combining and also on recent findings on multiple input multiple output (MIMO) architectures. The subcarrier correlation is considered in the context of physical parameters currently under discussion for future wireless systems to give an indication up to what extent the assumption of uncorrelated subcarrier fading is plausible.     

Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and in situ measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island.     

Emptiness and Finiteness for Tree Automata with Global Reflexive Disequality Constraints

In recent years, several extensions of tree automata have been considered. Most of them are related with the capability of testing equality or disequality of certain subterms of the term evaluated by the automaton. In particular, tree automata with global constraints are able to test equality and disequality of subterms depending on the state to which they are evaluated. The emptiness problem is known decidable for this kind of automata, but with a non-elementary time complexity, and the finiteness problem remains unknown. In this paper, we consider the particular case of tree automata with global constraints when the constraint is a conjunction of disequalities between states, and the disequality predicate is forced to be reflexive. This restriction is significant in the context of XML definitions with monadic key constraints. We prove that emptiness and finiteness are decidable in triple exponential time for this kind of automata.     

BER analysis of group-orthogonal multicarrier code-division multiplex systems

Group-orthogonal multi-carrier code division multiple access (GO-MC-CDMA) has recently been proposed as a promising technique for the uplink segment of wireless systems. In this paper we propose and analyze a related scheme, group-orthogonal multi-carrier code division multiplexing (GO-MC-CDM), suitable for the downlink segment of the future generation of wireless systems. The proposed system is shown to offer a similar bit error rate (BER) performance as the downlink version of GO-MC-CDMA at a fraction of its computational complexity. An analytical expression for the BER when using maximum likelihood (ML) detection is derived providing valuable insight into the parameters affecting the system performance and providing a basis for its optimization. Simulation results using parameters and (correlated) channel models aiming at the next generation of wireless systems are provided confirming the analytically derived results.     

Generalization of the possibility of eliminating the filtration step in the determination of acid-soluble chloride content in cement and concrete by potentiometric titration

In this work, it is demonstrated that it is possible to eliminate the filtration step in the determination of the acid-soluble chloride content of cement and concrete by potentiometric titration, irrespective of the method used for detecting the end-point of the titration. Extensive analytical results are presented corresponding to several types of cement and to a broad range of chloride concentrations in concrete. Some requirements to avoid errors in such analytical determinations are discussed.     

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Surface roughness plays a key role in the performance of machined components??specially dies and moulds??manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors, cutting conditions, dynamic factors and lubricant type. An intensive comparison with an artificial neural network approach for the same dataset is included, to reveal the improvements of the new technique over other well-established ones for this industrial problem. This comparison shows that ensemble learning can by-pass the time-consuming task of tuning neural network parameters and can also improve prediction model accuracy, both of which are features that could lead to greater use of these kinds of prediction models in real workshops. Finally, a methodology, based on this new approach, is presented, in order to illustrate how the prediction model can be used in workshops to optimize cutting conditions in terms of their surface quality and productivity.