The Complexity of Reasoning about Spatial Congruence

In the recent literature of Artificial Intelligence, an intensive research effort has been spent, for various algebras of qualitative relations used in the representation of temporal and spatial knowledge, on the problem of classifying the computational complexity of reasoning problems for subsets of algebras. The main purpose of these researches is to describe a restricted set of maximal tractable subalgebras, ideally in an exhaustive fashion with respect to the hosting algebras. In this paper we introduce a novel algebra for reasoning about Spatial Congruence, show that the satisfiability problem in the spatial algebra MC-4 is NP-complete, and present a complete classification of tractability in the algebra, based on the individuation of three maximal tractable subclasses, one containing the basic relations. The three algebras are formed by 14, 10 and 9 relations out of 16 which form the full algebra

Integrated region- and pixel-based approach to background modelling

In this paper a new probabilistic method for background modelling is proposed, aimed at the application in video surveillance tasks using a monitoring static camera. Recently, methods employing Time-Adaptive, Per Pixel, Mixture of Gaussians (TAPPMOG) modelling have become popular due to their intrinsic appealing properties. Nevertheless, they are not able per se to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, we propose to integrate this kind of pixel-based information with higher level region-based information, that permits to manage also sudden changes of the background. These pixel- and regionbased modules are naturally and effectively embedded in a probabilistic Bayesian framework called particle filtering, that allows a multi-object tracking. Experimental comparison with a classic pixel-based approach reveals that the proposed method is really effective in recovering from situations of sudden global illumination changes of the background, as well as limited non-uniform changes of the scene illumination.

Human-centric light sensing and estimation from RGBD images: the invisible light switch

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices

ACOUSTIC RANGE IMAGE SEGMENTATION BY EFFECTIVE MEAN SHIFT

Image perception in underwater environment is a difficult task for a human operator, and data segmentation becomes a crucial step toward an higher level interpretation and recognition of the observing scenarios. This paper contributes to the related state of the art, by fitting the mean shift clustering paradigm to the segmentation of acoustical range images, providing a segmentation approach in which whatever parameter tuning is absent. Moreover, the method exploits actively the connectivity information provided by the range map, by using reverse projection as acceleration technique. Therefore, the method is able to produce, starting from raw range data, meaningful segmented clouds of points in a fully automatic and efficient fashion. 1

Protecting the environment: A multi-agent approach to environmental monitoring

In this paper we discuss a transition model from commonly adopted models of data gathering, transfer and management for environmental monitoring towards more sophisticated ones based on Artificial Intelligence and IoT. The transition model is based on the paradigm of multiple agent systems. The adoption of this transition model is motivated by the need to improve effectiveness, efficiency and interoperability of environmental monitoring by simultaneously guaranteeing its sustainability in economic term

A knowledge-intensive methodology for explainable sales prediction

Sales prediction in food market is a complex issue that has been addressed in the recent past with machine learning techniques. Although some promising results, an experimental work that we describe in this paper shows some drawbacks of the above mentioned data-driven method and habilitates the definition of a novel methodology, strongly involving a piori knowledg

Organic vs conventional stockless arable systems: a multidisciplinary approach to soil quality evaluation

Soil quality in Mediterranean conventional and organic stockless arable systems was assessed by a multidisciplinary approach. At the end of the first cycle of a 5-year crop rotation (2002–2006) in the Mediterranean Arable Systems Comparison Trial (MASCOT) long-term experiment, the effects of organic and conventional management systems were evaluated by using soil chemical, biochemical and biological parameters. Chemical and biochemical parameters linked to soil C cycle, arbuscular mycorrhizal fungi (AMF) and microarthropod communities were analysed according to a comparative approach. Results suggested a higher soil carbon sequestration in the organic respect to the conventional system, as shown by the values of total organic C (9.5 and 7.8 g kg1, for organic and conventional system, respectively) and potentially mineralisable C (277 and 254 mg kg1, for organic and conventional system, respectively). AMF population, AMF root colonisation and diversity of microarthropod population were slightly influenced by management system. On the other hand, mites/collembolans ratio was higher in conventionally than in organically managed soil (2.67 and 1.30, respectively), indicating as organic managed soils were more disturbed than conventional ones, probably as the consequence of the more frequent soil tillage performed for mechanical weeds control. The overall results demonstrated that, even in the short-term, the implementation of organically managed stockless systems in Mediterranean areas determined significant changes of some attributes for soil quality evaluation

Lipid peroxidation and protein oxidation in patients affected by Hodgkin's lymphoma.

A dysregulation of the redox homoeostasis has been reported in various neoplastic disorders. Malondialdehyde/4-hydroxy-2,3-nonenal (MDA/HNE) and protein carbonyl groups represent in vivo indexes of lipid peroxidation and protein oxidation, respectively, suitable to investigate radical-mediated physio-pathological conditions. We evaluated MDA/HNE and protein carbonyl groups in sera of untreated Hodgkin's lymphoma (HL) patients in advanced disease stages, in order to quantify the oxidative stress. HL patients displayed significantly higher levels of both MDA/HNE and protein carbonyl groups as compared with healthy controls. This is the first evidence that a strong increase in HL is one of the most common haematological malignancies, representing approximately 30% of all lymphomas in the circulating protein carbonyl content in HL. These findings may contribute to a better definition of the redox homoeostasis dysregulation in HL

Dynamics of Crossover from a Chaotic to a Power Law State in Jerky Flow

We study the dynamics of an intriguing crossover from a chaotic to a power law state as a function of strain rate within the context of a recently introduced model which reproduces the crossover. While the chaotic regime has a small set of positive Lyapunov exponents, interestingly, the scaling regime has a power law distribution of null exponents which also exhibits a power law. The slow manifold analysis of the model shows that while a large proportion of dislocations are pinned in the chaotic regime, most of them are pushed to the threshold of unpinning in the scaling regime, thus providing insight into the mechanism of crossover.Comment: 5 pages, 3 figures. In print in Phy. Rev. E Rapid Communication