Multi-objective optimisation of machine tool error mapping using automated planning

Error mapping of machine tools is a multi-measurement task that is planned based on expert knowledge. There are no intelligent tools aiding the production of optimal measurement plans. In previous work, a method of intelligently constructing measurement plans demonstrated that it is feasible to optimise the plans either to reduce machine tool downtime or the estimated uncertainty of measurement due to the plan schedule. However, production scheduling and a continuously changing environment can impose conflicting constraints on downtime and the uncertainty of measurement. In this paper, the use of the produced measurement model to minimise machine tool downtime, the uncertainty of measurement and the arithmetic mean of both is investigated and discussed through the use of twelve different error mapping instances. The multi-objective search plans on average have a 3% reduction in the time metric when compared to the downtime of the uncertainty optimised plan and a 23% improvement in estimated uncertainty of measurement metric when compared to the uncertainty of the temporally optimised plan. Further experiments on a High Performance Computing (HPC) architecture demonstrated that there is on average a 3% improvement in optimality when compared with the experiments performed on the PC architecture. This demonstrates that even though a 4% improvement is beneficial, in most applications a standard PC architecture will result in valid error mapping plan

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques

Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

Cerebrospinal fluid analysis for HIV replication and biomarkers of immune activation and neurodegeneration in long-term atazanavir/ritonavir monotherapy treated patients

Background: Cerebrospinal fluid (CSF) viral escape is a concern in ritonavir-boosted protease inhibitors monotherapy. The aim was to assess HIV-RNA, biomarkers of immune activation and neurodegeneration, and atazanavir concentrations in CSF of patients on successful long-term atazanavir/ritonavir (ATV/r) monotherapy. Methods: This is a substudy of the multicentric, randomized, open-label, noninferiority trial monotherapy once a day with atazanavir/ritonavir (NCT01511809), comparing the ongoing ATV/r along with 2 nucleoside retrotranscriptase inhibitors (NRTIs) regimen to a simplified ATV/r monotherapy. Patients with plasma HIV-RNA < 50 copies/mL after at least 96 study weeks were eligible. We assessed HIV-RNA, soluble (s)CD14, sCD163, CCL2, CXCL10, interleukin-6, and YKL40 by enzyme-linked immunosorbent assay; neopterin, tryptophan, kynurenine, and neurofilament by immunoassays; and ATV concentrations by liquid chromatography–mass spectrometry in paired plasma and CSF samples. Variables were compared with Wilcoxon rank-sum or Fisher exact test, as appropriate. Results: HIV-RNA was detected in the CSF of 1/11 patients on ATV/r monotherapy (114 copies/mL), without neurological symptoms, who was successfully reintensified with his previous 2NRTIs, and in none of the 12 patients on ATV/r + 2NRTIs. CSF biomarkers and ATV concentrations did not differ between the 2 arms. Conclusions: CSF escape was uncommon in patients on long-term ATV/r monotherapy and was controlled with reintensification

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Quantized spin waves in the metallic state of magnetoresistive manganites

High resolution spin waves measurements have been carried out in ferromagnetic (F) La(1-x)(Sr,Ca)xMnO3 with x(Sr)=0.15, 0.175, 0.2, 0.3 and x(Ca)=0.3. In all q-directions, close to the zone boundary, the spin wave spectra consist of several energy levels, with the same values in the metallic and the x\approx 1/8 ranges. Mainly the intensity varies, jumping from the lower energy levels determined in the x\approx 1/8 range to the higher energy ones observed in the metallic state. On the basis of a quantitative agreement found for x(Sr)=0.15 in a model of ordered 2D clusters, the spin wave anomalies of the metallic state can be interpreted in terms of quantized spin waves within the same 2D clusters, embedded in a 3D matrix.Comment: 4 pages, 5 figure

Extending Qualitative Spatial Theories with Emergent Spatial Concepts: An Automated Reasoning Approach

Qualitative Spatial Reasoning is an exciting research field of the Knowledge Representation and Reasoning paradigm whose application often requires the extension, refinement or combination of existent theories (as well as the associated calculus). This paper addresses the issue of the sound spatial interpretation of formal extensions of such theories; particularly the interpretation of the extension and the desired representational features. The paper shows how to interpret certain kinds of extensions of Region Connection Calculus (RCC) theory. We also show how to rebuild the qualitative calculus of these extensions.Junta de Andalucía TIC-606

Decreased Gas6 and sAxl Plasma Levels Are Associated with Hair Loss in COVID-19 Survivors

: Post-acute conditions after coronavirus disease 2019 (COVID-19) are quite common, although the underlying pathogenetic mechanisms leading to these conditions are not yet completely understood. In this prospective observational study, we aimed to test the hypothesis that Growth Arrest-Specific 6 (Gas6) and its soluble receptors, Axl (sAxl) and MerTK (sMer), might be implicated. A total of 263 subjects underwent a structured clinical evaluation one year after their hospital discharge for COVID-19, and they consented to donate a blood sample to measure their circulating Gas6, sAxl, and sMer levels. A total of 98 (37.3%) post-COVID-19 subjects complained of at least one residual physical symptom one year after their hospital discharge. Univariate analysis revealed that sAxl was marginally associated with residual symptoms, but at the level of logistic regression analysis, only the diffusing capacity of the lungs for carbon monoxide (DLCO) (OR 0.98, CI 95%: 0.96-0.99; p = 0.007) and the female sex (OR 2.49, CI 95%: 1.45-4.28; p = 0.001) were independently associated with long-lasting symptoms. A total of 69 (26.2%) subjects had hair loss. At the level of univariate analysis, Gas6, sAxl, DLCO, and the female gender were associated with its development. In a logistic regression analysis model, Gas6 (OR 0.96, CI 95%: 0.92-0.99; p = 0.015) and sAxl (OR 0.98, CI 95%; 0.97-1.0; p = 0.014), along with the female sex (OR 6.58, CI 95%: 3.39-12.78; p = 0.0001), were independent predictors of hair loss. Decreased levels of Gas6 and sAxl were associated with a history of hair loss following COVID-19. This was resolved spontaneously in most patients, although 23.7% complained of persistent hair loss one year after hospital discharge