Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Role of Histamine H4 Receptor ligands in Bleomycin-induced pulmonary fibrosis

Fibrosis of lung tissue is a disease where a chronic inflammatory process determines a pathological remodelling of lung parenchyma. The animal model obtained by intra-tracheal administration of bleomycin in C57BL/6 mice is one of the most validated murine model. Bleomycin stimulates oxidative stress and the production of pro-inflammatory mediators. Histamine H4R have recently been implicated in inflammation and immune diseases. This study was focused to investigate the effects of H4R ligands in the modulation of inflammation and in the reduction of lung fibrosis in C57BL/6 mice treated with bleomycin. C57BL/6 mice were treated with vehicle, JNJ7777120 (JNJ, selective H4R antagonist) or ST-1006 (partial H4R agonist), ST-994 (H4R neutral antagonist) and ST-1012 (inverse H4R agonist) at equimolar doses, released by micro-osmotic pumps for 21 days. Airway resistance to inflation was assayed and lung samples were processed to measure malondialdehyde (TBARS); 8-hydroxy-2'-deoxyguanosine (8OHdG); myeloperoxidase (MPO); COX-2 expression and activity as markers of oxidative stress and inflammation. Fibrosis and airway remodelling were evaluated throughout transforming growth factor-β (TGF-β), percentage of positive Goblet cells, smooth muscle layer thickness determination. Our results indicated that JNJ, ST-994 and ST-1012 decreased inflammation and oxidative stress markers, i.e. the number of infiltrating leukocytes evaluated as lung tissue MPO, COX-2 expression and activity, TBARS and 8OHdG production. They also reduced the level of TGF-β, a pro-fibrotic cytokine, collagen deposition, thickness of smooth muscle layer, Goblet cells hyperplasia; resulting in a decrease of airway functional impairment. The results here reported clearly demonstrated that H4R ligands have a beneficial effect in a model of lung fibrosis in the mouse, thus indicating that H4R antagonists or inverse agonists could be a novel therapeutic strategy for lung inflammatory diseases

ADHOC: a tool for performing effective feature selectionProceedings Eighth IEEE International Conference on Tools with Artificial Intelligence

The paper introduces ADHOC, a tool that integrates statistical methods and machine learning techniques to perform effective feature selection. Feature selection plays a central role in the data analysis process since redundant and irrelevant features often degrade the performance of induction algorithms, both in speed and predictive accuracy. ADHOC combines the advantages of both filter and feedback approaches to feature selection to enhance the understanding of the given data and increase the efficiency of the feature selection process. We report results of extensive experiments on real world data which demonstrate the effectiveness of ADHOC as data reduction technique as well as feature selection method. ADHOC has been employed in the analysis of several corporate databases. In particular, it is currently used to support the difficult task of early estimation of the cost of software projects

Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations

An integrated low-cost system for at-home rehabilitation

reserved4We show here how integrating novel natural user interfaces, like Microsoft Kinect, with a fully adappatient's current statustive game engine, a system that can be used for rehabilitation at home can be built. A wide variety of game scenarios, a balanced scoring system, quantitative and qualitative exercise evaluation, automatic gameplay level adaptation to patient's current status, and audiovisual feed-back are all implemented inside the Intelligent Game Engine for Rehabilitation here introduced, and are aimed at maximizing patient's motivation and rehabilitation effectiveness. The system is integrated into a multi-level platform that provides continuous monitoring by the hospital and it has been developed inside the framework of the EU funded Rewire project.Nunzio Alberto Borghese; Michele Pirovano; Renato Mainetti; Pier Luca LanziNunzio Alberto, Borghese; Pirovano, Michele; Renato, Mainetti; Lanzi, PIER LUC