Average Common Submatrix: A New Image Distance Measure

Abstract. A new information-theoretic distance measure for images is proposed. The measure is based on the concept of average common sub-matrix by considering the pixel matrices associated with the images. An algorithm to compute such a value is described, and its computational complexity analyzed. Experimental results show that the measure is able to discriminate images by correctly reflecting human perception. Furthermore, comparison with state-of-the-art informationtheoretic measures, points out that the new measure outperforms these measures in terms of retrieval precision

Low Order Grey-box Models for Short-term Thermal Behavior Prediction in Buildings

Abstract Low order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1 °C for a prediction horizon of 3-hours

Celector®: An Innovative Technology for Quality Control of Living Cells

Among the in vitro and ex vivo models used to study human cancer biology, cancer cell lines are widely utilized. The standardization of a correct tumor model including the stage of in vitro testing would allow for the development of new high-efficiency drug systems. The poor correlation between preclinical in vitro and in vivo data and clinical trials is still an open issue, hence the need for new systems for the quality control (QC) of these cell products. In this work, we present a new technology, Celector®, capable of the label-free analysis and separation of cells based on their physical characteristics with full preservation of their native properties. Two types of cancer cell lines were used: HL60 as cells growing in suspension and SW620 as adherent cells. Cell lines in general show a growth variability depending on the passage and method of culture. Celector® highlights physical differences that can be correlated to cell viability. This work demonstrates the use of Celector® as an analytical platform for the QC of cells used for drug screening, with fundamental improvement of preclinical tests. Cells with a stable doubling time under analysis can be collected and used as standardized systems for high-quality drug monitoring

Brain Derived Neurotrophic Factor (BDNF) Expression Is Regulated by MicroRNAs miR-26a and miR-26b Allele-Specific Binding

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays an essential role in neuronal development and plasticity. MicroRNA (miRNAs) are small non-coding RNAs of about 22-nucleotides in length regulating gene expression at post-transcriptional level. In this study we explore the role of miRNAs as post-transcriptional inhibitors of BDNF and the effect of 3′UTR sequence variations on miRNAs binding capacity. Using an in silico approach we identified a group of miRNAs putatively regulating BDNF expression and binding to BDNF 3′UTR polymorphic sequences. Luciferase assays demonstrated that these miRNAs (miR-26a1/2 and miR-26b) downregulates BDNF expression and that the presence of the variant alleles of two single nucleotide polymorphisms (rs11030100 and rs11030099) mapping in BDNF 3′UTR specifically abrogates miRNAs targeting. Furthermore we found a high linkage disequilibrium rate between rs11030100, rs11030099 and the non-synonymous coding variant rs6265 (Val66Met), which modulates BDNF mRNA localization and protein intracellular trafficking. Such observation led to hypothesize that miR-26s mediated regulation could extend to rs6265 leading to an allelic imbalance with potentially functional effects, such as peptide's localization and activity-dependent secretion. Since rs6265 has been previously implicated in various neuropsychiatric disorders, we evaluated the distribution of rs11030100, rs11030099 and rs6265 both in a control and schizophrenic group, but no significant difference in allele frequencies emerged. In conclusion, in the present study we identified two novel miRNAs regulating BDNF expression and the first BDNF 3′UTR functional variants altering miRNAs-BDNF binding

A genetic algorithm for color image segmentation

Abstract. A genetic algorithm for color image segmentation is proposed. The method represents an image as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels is computed by taking into account not only brightness, but also color and texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that the method is able to partition natural and human scenes in a number of regions consistent with human visual perception. A quantitative evaluation of the method compared with other approaches shows that the genetic algorithm can be very competitive in partitioning color images

Pattern extraction from data with application to image processing

Dottorato di Ricerca in Ingegneria dei Sistemi e Informatica, Ciclo XXV, a.a. 2012The term Information Extraction refers to the automatic extraction of structured information from data. In such a context, the task of pattern extraction plays a key role, as it allows to identify particular trends and recurring structures of interest to a given user. For this reason, pattern extraction techniques are available in a wide range of applications, such as enterprise applications, personal information management, web oriented and scientific applications. In this thesis, analysis is focused on pattern extraction techniques from images and from political data. Patterns in image processing are defined as features derived from the subdivision of the image in regions or objects and several techniques have been introduced in the literature for extracting these kinds of features. Specifically, image segmentation approaches divide an image in ”uniform” region patterns and both boundary detection and region-clustering based algorithms have been adopted to solve this problem. A drawback of these methods is that the number of clusters must be predetermined. Furthermore, evolutionary techniques have been successfully applied to the problem of image segmentation. However, one of the main problems of such approaches is the determination of the number of regions, that cannot be changed during execution. Consequently, we formalize a new genetic graph-based image segmentation algorithm that, thanks to the new fitness function, a new concept of neighborhood of pixels and the genetic representation, is able to partition images without the need to set a priori the number of segments. On the other hand, some image compression algorithms, recently proposed in literature, extract image patterns for performing compression, such as extensions to 2D of the classical Lempel-Ziv parses, where repeated occurrences of a pattern are substituted by a pointer to that pattern. However, they require a preliminary linearization of the image and a consequent extraction of linear patterns. This could miss some 2D recurrent structures which are present inside the image. We propose here a new technique of image compression which extracts 2D motif patterns from the image in which also some pixels are omitted in order to increase the gain in compression and which uses these patterns to perform compression. About pattern extraction in political science, it consists in detecting voter profiles, ideological positions and political interactions from political data. Some proposed pattern extraction techniques analyze the Finnish Parliament and the United States Senate in order to discover political trends. Specifically, hierarchical clustering has been employed to discover meaningful groups of senators inside the United States Senate. Furthermore, different methods of community detection, based on the concept of modularity, have been used to detect the hierarchical and modular design of the networks of U.S. parliamentarians. In addition, SVD has been applied to analyze the votes of the U.S. House of Representatives. In this thesis, we analyze the Italian Parliament by using different tools coming from Data Mining and Network Analysis with the aim of characterizing the changes occurred inside the Parliament, without any prior knowledge about the ideology or political affiliation of its representatives, but considering only the votes cast by each parliamentarian.Università della Calabri

Pacing of human locomotion on land and in water: 1500 m swimming vs.5000 m running

International audienc

Proposal of a system for diagnosing with inefficient occupant behaviour and systems malfunctioning in the household sector

One of the key factors in curbing energy consumption in the household sector, together with energy efficiency and renewable energies, is widely recognized to be the amendment of occupant erroneous behaviour and systems malfunctioning, mainly explained by the lack of awareness of the final user. The aim of this work is to propose a diagnostic system in the household sector which can improve the users’ awareness with respect to the energy consumption in final uses. In particular, this paper presents an energy, environmental and economic analysis of different diagnostics systems, corresponding to different degrees of complexity and cost. Given a reference occupancy and thermal user profile, for each diagnostic system the relevant energy consumption is assessed by simulation and the subsequent economic savings are calculated and compared to the diagnostic system cost in order to evaluate the payback period of the architecture proposed

Low order grey-box models for short-term thermal behavior prediction in buildings

Low order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1°C for a prediction horizon of 3-hours.status: publishe