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2012 Report Unknown

The genus invariant for Artin groups
Moroni D., Salvetti M., Villa A.
Let (W; S) be a Coxeter system, S finite, and let G_W be the associated Artin group. One has conguration spaces Y; Y_W; where G_W = PI_1(Y_W); and a natural W-covering f_W : Y --> Y_W. We consider the Schwarz genus g(f_W) of this covering, which is a natural topological in- variant of the Artin group. Let K = K(W; S) be the simplicial scheme of all subsets J subset of S such that the parabolic group W_J is finite. We introduce the class of Artin groups, which includes affine-type Artin groups, for which dim(K) equals the homological dimension of K; and we show that g(f_W) is always the maximum possible for this class of groups. Such maximum is given by dim(X_W) + 1; where X_W (subset of Y_W) is a CW-complex which has the same homotopy type. This result extends a previous result in [Deconcini Salvetti 2000] obtained for all finite-type Artin groups, with the exception of case A_n (for which see [Deconcini Procesi Salvetti 2004]).

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2012 Article Unknown

La svolta Newtoniana nello studio dell'attività mentale
Beltrame R.
Critical review of a past paper on a visual perception approach in the framework of the Italian Operational School approach to human mental activity.Source: Methodologia online WP 259 (2012): 1–16.

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2012 Report Unknown

Color restoration in old photographs using dark channel processing
Kuruoglu E. E.
We propose a direct and effective method for color restoration and contrast enhancement in old images. The main idea is based on the dark channel assumption. Using this assumption with the faded old image model, we can directly estimate the degraded transmission coefficient of an old image and directly recover it to a high quality degradation-free image. Moreover, a novel average processing and dark channel processing chain method (A-D chain method) is proposed to partly avoid the side effects introduced by dark channel processing.Source: pp.1–13, 2012

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2012 Report Unknown

Estimation of motion blurring direction by rotated accumulation method
Liu Y., Gao C., Kuruoglu E. E.
Motion blur is usually caused by the motion between the camera and the scene during integration time of image. And the estimation of motion blurring direction is vital to restoration. There are existing methods such as Radon and cepstrum based approach. In this paper, a Rotated Accumulation Method by selecting the minimum value of rows in frequency domain is introduced to find the motion direction. Also, it is shown that this method is more advisable than spatial based method when the motion length is relatively small or the image is blurred by noise, and more accurate comparing to Radon method.

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2012 Report Unknown

Handwritten chinese character recognition using eigenspace decomposition
Long H., Zhang X., Kuruoglu E. E.
In this paper, we mainly describe a new approach of Handwritten Chinese Character Recognition (HCCR), which is based on eigen-character extraction. The procedure of the eigen-character extraction method is explained including initialization, eigen character extraction (or eigen spaces generation) and character recognition. Two different methods are presented to do eigen character recognition respectively. Besides, k Nearest Neighbor (kNN) is implemented to improve the recognition rate of the new approach. In the end, a comparison is made between the eigen-character extraction approach and other existing approaches through simulation based experiments. The results show that our approach has a satisfying rate and could be further improved if combined with some other methods such as elastic matching and wavelet methods.Source: pp.1–8, 2012

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2012 Report Unknown

Scale invariant feature detection for object recognition in colour images
Yang S., Li Y., Kuruoglu E. E.
This paper presents a method for extracting features from color images that can be used to perform reliable matching between different views of an object or scene. We extend the concept of the feature vector given in Lowe's method to color images by defining new gradient operator or giving new feature descriptor, which result in different algorithms with different optimality in the sense of information utilization of the original color image. Generally, better algorithm optimality is coupled with higher computation complexity, which is the case in this paper. Thus, the 4 methods proposed in this paper lead to a multi-optimality and multi-complexity scheme. In practice, people can choose which method to use according to their needs and computation abilities.Source: pp.1–14, 2012

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2012 Part of book or chapter of book Unknown

Some topological problems on the configuration spaces of Artin and Coxeter groups.
Moroni D., Salvetti M., Villa A.
In the first part we review some topological and algebraic aspects in the theory of Artin and Coxeter groups, both in the finite and infinite case (but still finitely generated). In the following parts, among other things, we compute the Schwartz genus of the covering associated to the orbit space for all affine Artin groups.Source: Configuration Spaces, edited by Björner A., Cohen F., De Concini C., Procesi C., Salvetti M., pp. 403–431. London: Springer, 2012

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2012 Part of book or chapter of book Unknown

Real time image analysis for infomobility.
Magrini M., Moroni D., Pieri G., Salvetti O.
In our society, the increasing number of information sources is still to be fully exploited for a global improvement in urban living. Among these, a big role is played by images and multimedia data (i.e. coming from CCTV and surveillance videos, traffic cameras, etc.). This along with the wide availability of embedded sensor platforms and low-cost cameras makes it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the characteristics of image processing algorithms coupled to visual sensor networks. In particular the aim is to define strategies to accomplish the tasks of image processing and analysis over these systems which have rather strong constraints in computational power and data transmission. Thus, such embedded platform cannot use advanced computer vision and pattern recognition methods, which are power consuming, on the other hand, the platform may be able to exploit a multi-node strategy that allows to perform a hierarchical processing, in order to decompose a complex task into simpler problems. In order to apply and test the described methods, a solution to a visual sensor network for infomobility is proposed. The experimental setting considered is two-fold: acquisition and integration of different views of parking lots, and acquisition and processing of traffic-flow images, in order to provide a complete description of a parking scenario and its surrounding area.Source: Computational Intelligence for Multimedia Understanding International Workshop - MUSCLE 2011 (Pisa, Italy, December 13-15, 2011). Revised Selected Papers, edited by Emanuele Salerno , A. Enis Çetin, Ovidio Salvetti, pp. 207–218. Berlin: Springer, 2012
DOI: 10.1007/978-3-642-32436-9_18

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2012 Article Unknown

Branched covers of the sphere and the prime-degree conjecture
Pascali M. A., Petronio C.
To a branched cover ${widetilde{Sigma} to Sigma}$ between closed, connected, and orientable surfaces, one associates a branch datum, which consists of ? and ${widetilde{Sigma}}$ , the total degree d, and the partitions of d given by the collections of local degrees over the branching points. This datum must satisfy the Riemann-Hurwitz formula. A candidate surface cover is an abstract branch datum, a priori not coming from a branched cover, but satisfying the Riemann- Hurwitz formula. The old Hurwitz problem asks which candidate surface covers are realizable by branched covers. It is now known that all candidate covers are realizable when ? has positive genus, but not all are when ? is the 2-sphere. However, a long-standing conjecture asserts that candidate covers with prime degree are realizable. To a candidate surface cover, one can associate one ${widetilde {X} dashrightarrow X}$ between 2-orbifolds, and in Pascali and Petronio (Trans Am Math Soc 361:5885-5920, 2009), we have completely analyzed the candidate surface covers such that either X is bad, spherical, or Euclidean, or both X and ${widetilde{X}}$ are rigid hyperbolic orbifolds, thus also providing strong supporting evidence for the prime-degree conjecture. In this paper, using a variety of different techniques, we continue this analysis, carrying it out completely for the case where X is hyperbolic and rigid and ${widetilde{X}}$ has a 2-dimensional Teichmüller space. We find many more realizable and non-realizable candidate covers, providing more support for the prime-degree conjecture.Source: Annali di matematica pura ed applicata 191 (2012): 563–594. doi:10.1007/s10231-011-0197-y
DOI: 10.1007/s10231-011-0197-y

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2012 Article Unknown

A multitemporal analysis of tsunami impact on coastal vegetation using remote sensing: a case study on Koh Phra Thong Island, Thailand
Villa P., Boschetti M., Morse J. L., Politte N.
The Indian Ocean tsunami event of 26 December 2004 not only left massive casualties and economic damages, but also raised concerns about the destruction and recovery of coastal ecosystems. This work aimed to analyze the spatial patterns and temporal trajectories of vegetation damage and recovery using a multisensor multitemporal remote sensing, dataset. Using the study area of Koh Phra Thong, Thailand as a case study, we demonstrate the capabilities of remote sensing analysis in assessing the consequences of an extreme flooding event on the dynamics of coastal vegetation. Field surveys and satellite mid-resolution multispectral satellite data covering the period from February 2003 to December 2009 were used to map flooded areas and coastal vegetation loss and recovery following the tsunami. Normalized Difference Reflectance change detection was performed to map the extent of flooded areas. Vegetation Fraction Cover derived using spectral unmixing techniques was used to study the multitemporal changes in coastal vegetation after the event. Vegetation change detection techniques were applied to characterize the vegetation cover changes in two different timeframes: short term changes (from 4 days to 1 year after the event), and long term dynamics (up to 5 years after). Estimates of vegetation change (decline, recovery, and gain) were quantified and mapped, with extreme vegetation losses found directly after the tsunami (up to 79% in flooded areas). After one year, different trends had developed, indicating that recovering vegetation had reached up to 55% of pre-tsunami land cover, but with different trajectories for each vegetation type.Source: Natural hazards (Dordr., Online) 64 (2012): 667–689. doi:10.1007/s11069-012-0261-y
DOI: 10.1007/s11069-012-0261-y

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2012 Article Unknown

Mapping urban growth using Soil and Vegetation Index and Landsat Data: the Milan (Italy) city area case study
Villa P.
Remote sensing based on mid-resolution multi-spectral data has proven a powerful tool in urban areas study. This work introduces a novel methodology based on spectral indices ratios for mapping urban changes in terms of impervious surface expansion. At the methodological core, the Soil and Vegetation Index (SVI), a spectral index aimed at discriminating urban from non-urban land cover, has been utilized over Landsat TM-ETM+ satellite data. As a case study, the approach was applied to a multi-temporal dataset, with the aim of mapping the urban growth of Milan, Italy, during 20 years (1984-2003). The multistep processing framework is composed of: SVI values derivation and normalization, multi-temporal SVI ratios thresholding for identifying urban growth area, and multiscale segmentation of urban change maps produced. Results analysis showed the feasibility of the approach and reliability of urban change maps derived, which reached a value of Overall Accuracy up to 80%, while multi-scale assessment of results revealed the 25 pixels segmentation scale as the optimal one for urban change detection using Landsat data over the study area.Source: Landscape and urban planning 107 (2012): 245–254. doi:10.1016/j.landurbplan.2012.06.014
DOI: 10.1016/j.landurbplan.2012.06.014

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2012 Conference object Unknown

Modeling enzymatic reactions via chemical Langevin-Levy equation.
Altinkaya M., Kuruoglu E. E.
Chemical Langevin Equation (CLE) describes a useful approximation in stochastic modeling of chemical reactions. CLE-based ?-leaping algoritm updates the quantities of every molecule in a reaction system with a period of ?, firing every reaction in the system so many times that the concentration of each molecule can be assumed to remain in the current concentration state. Substituting the Brownian motion in the CLE with a Levy flight, one might expect the CLE to converge more rapidly. This work shows that alpha (Levy)-stable increments can be used in ?-leaping, demonstrating it with the example of a detailed kinetic model describing the enzymatic transgalactosylation reaction during lactulose hydrolysis.Source: Signal Processing and Applications Conference, pp. 1–4, Fethiye, Turchia, 18-20 Aprile 2012
DOI: 10.1109/SIU.2012.6204746

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2012 Article Unknown

Robust data clustering by learning multi-metric Lq-norm distances
Zhang J., Peng L., Zhao X., Kuruoglu E. E.
Unsupervised clustering for datasets with severe outliers inside is a difficult task. In this approach, we propose a cluster-dependent multi-metric clustering approach which is robust to severe outliers. A dataset is modeled as clusters each contaminated by noises of cluster-dependent unknown noise level in formulating outliers of the cluster. With such a model, a multi-metric Lp-norm transformation is proposed and learnt which maps each cluster to the most Gaussian distribution by minimizing some non-Gaussianity measure. The approach is composed of two consecutive phases: multi-metric location estimation (MMLE) and multi-metric iterative chi-square cutoff (ICSC). Algorithms for MMLE and ICSC are proposed. It is proved that the MMLE algorithm searches for the solution of a multi-objective optimization problem and in fact learns a cluster-dependent multi-metric Lq-norm distance and/or a cluster-dependent multikernel defined in data space for each cluster. Experiments on heavy-tailed alpha-stable mixture datasets, Gaussian mixture datasets with radial and diffuse outliers added respectively, and the real Wisconsin breast cancer dataset and lung cancer dataset show that the proposed method is superior to many existent robust clustering and outlier detection methods in both clustering and outlier detection performances.Source: Expert systems with applications 39 (2012): 335–349. doi:10.1016/j.eswa.2011.07.023
DOI: 10.1016/j.eswa.2011.07.023

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2012 Article Unknown

MUSCLE Working Group International Workshop on Computational Intelligence for Multimedia Understanding
Salerno E.
The National Research Council of Italy in Pisa hosted the International Workshop on Computational Intelligence for Multimedia Understanding, organized by the ERCIM Working Group on Multimedia Understanding through Semantics, Computation and Learning (Muscle), 11-13 December 2011. The workshop was co-sponsored by CNR and Inria. Proceedings to come in LNCS 7252.Source: ERCIM news 89 (2012): 7–7.

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2012 Article Unknown

MUSCLE working group co-organised mass data analysis conference
Perner P., Salerno E.
The seventh international conference on mass data analysis of images and signals, MDA 2012, was held in Berlin from 13 to 20 July 2012. ERCIM was one of the sponsors and the ERCIM MUSCLE Working Group (Multimedia Understanding through Semantics, Computation, and Learning), participated in the organization.Source: ERCIM news 91 (2012): 6.

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2012 Conference object Unknown

Mapping techniques of underwater environments by optical-acoustic data integration
Moroni D., Pascali M. A., Reggiannini M., Salvetti O.
A new method is proposed to integrate 3D optical and acoustic images relative to the same underwater environment. The combination of optical and acoustic sensors in terms of uniform reference system, georeferencing and time allows: (i) integration cascade (operational level), (ii) safety data acquisition in various domains (distance from ground, turbid water, vegetation, etc.), (iii) replanning of missions in progress. Furthermore, data fusion can be faced according to different approaches: (a) stratification of referenced data layers, (b) correlation of quantities of different nature, (c) comparison of extracted features: 2D geometries (segments, elementary curves) and 3D (planes, simple surfaces), repetitive patterns, (d) integration of semantic information, (e) template matching for recognizing known structures, (f) creation and refinement of probability maps as a measure of optical (geometry, texture) and acoustic (elevation or reflectivity maps) properties. Unlike most of the existing approaches that perform recognition and matching of different interesting relevant points appearing in two images, we look at the correspondence of salient features present both in optical and acoustic images representing the same scene. A set of geometrical and textural feature extraction algorithms is applied to the multi-sensor images and compared with the output results. We aim thus at emphasizing the geometric structure alignment of features (e.g., lines or different kind of curves), instead of descriptor-based individual feature matching. This is due to the fact that optical and acoustic image properties and patch statistics of corresponding features are generally quite different. Besides, the spatial layout of features is preserved in both maps.Source: 11th European Conference on Underwater Acoustics, pp. 1057–1063, Edimburgh, Scotland, 2-6 July 2012

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2012 Conference object Unknown

The Hitchhiker's guide to the galaxy of mathematical tools for shape analysis
S. Biasotti, B. Falcidieno, D. Giorgi, M. Spagnuolo
A practical guide for researchers who are exploring the new frontiers of 3D shape analysis and managing the complex mathematical tools that most methods rely on. Many research solutions come from advances in pure and applied mathematics, as well as from re-reading classical mathematical theories. Managing these math tools is critical to understanding and solving current problems in 3D shape analysis. This course is designed to help mathematicians and scientists communicate in a world where boundaries between disciplines are (fortunately) blurred, so they can quickly find the right mathematical tools for a bright intuitive idea and strike a balance between theoretical rigor and computationally feasible solutions. The course presents basic concepts in differential geometry and proceeds to advanced topics in algebraic topology, always keeping an eye on their computational counterparts. It includes examples of applications to shape correspondence, symmetry detection, and shape retrieval that show how these mathematical concepts can be translated into practical solutions.Source: SIGGRAPH'12 - ACM SIGGRAPH 2012 Courses, Los Angeles, CA, USA, 5-9 July 2012
DOI: 10.1145/2343483.2343499

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2012 Article Open Access OPEN

Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area
Pizzigalli C., Palatella L., Zampieri M., Lionello P., Miglietta M., Paradisi P.
In this paper we present and discuss a comparison between statistical and regional climate modeling techniques for downscaling GCM prediction. The comparison is carried out over the Capitanata region, an area of agricultural interest in south-eastern Italy, for current (1961-1990) and future (2071-2100) climate. The statistical model is based on Canonical Correlation Analysis (CCA), associated with a data pre-filtering obtained by a Principal Component Analysis (PCA), whereas the Regional Climate Model REGCM3 was used for dynamical downscaling. Downscaling techniques were applied to estimate rainfall, maximum and minimum temperatures and average number of consecutive wet and dry days. Both methods have comparable skills in estimating stations data. They show good results for spring, the most important season for agriculture. Both statistical and dynamical models well reproduce the statistical properties of precipitation, the crucial variable for the growth of crops.Source: Italian journal of agronomy (Online) 7 (2012): 3–12. doi:10.4081/ija.2012.e2
DOI: 10.4081/ija.2012.e2

See at: Italian Journal of Agronomy Open Access | Italian Journal of Agronomy Open Access | PUblication MAnagement Open Access | Italian Journal of Agronomy Open Access | DOI Resolver | www.agronomy.it | CNR People


2012 Article Unknown

Diffusion Scaling in event-driven random walks: an application to turbulence
Paradisi P., Cesari R., Donateo A., Contini D., Allegrini P.
Scaling laws for the diffusion generated by three different random walk models are reviewed. The random walks, defined on a one-dimensional lattice, are driven by renewal intermittent events with non-Poisson statistics and inverse power-law tail in the distribution of the inter-event or waiting times, so that the event sequences are characterized by self-similarity. Intermittency is a ubiquitous phenomenon in many complex systems and the power exponent of the waiting time distribution, denoted as complexity index, is a crucial parameter characterizing the system's complexity. It is shown that different scaling exponents emerge from the different random walks, even if the self-similarity, i.e. the complexity index, of the underlying event sequence remains the same. The direct evaluation of the complexity index from the time distribution is affected by the presence of added noise and secondary or spurious events. It is possible to minimize the effect of spurious events by exploiting the scaling relationships of the random walk models. This allows to get a reliable estimation of the complexity index and, at the same time, a confirmation of the renewal assumption. An application to turbulence data is shown to explain the basic ideas of this approach.Source: Reports on mathematical physics 70 (2012): 205–220. doi:10.1016/S0034-4877(12)60040-8
DOI: 10.1016/S0034-4877(12)60040-8

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2012 Article Unknown

Sleep unconsciousness and the fragmentation of the global workspace
Allegrini P., Paradisi P., Laurino M., Menicucci D., Piarulli A., Gemignani A.
While several mental functions, from visual perception up to working memory, are characterized by parallel computation performed by integrated moduli in the cortex, consciousness is sustained by a serial process of global integration, insofar as a single scene at a time takes place. Rigorous studies on the theoretical physics of second order phase transition (i.e. critical phenomena) show that the so called order parameters, defined as the macroscopic variables (i.e. thermodynamically measurable quantities that integrate the activity of many components) that respond to external fields (i.e. interactions), display, in the absence of interactions, coordinated fluctuations with an intermittent serial structure when the system is at the transition, termed critical state. This "criticality" state is operationally defined by the presence of avalanches with inverse-power-law (scale-free) distribution densities of sizes and inter-event times (duration of metastable states, i.e. states without events). Criticality is a state of maximal complexity, with maximal redundancies in the dynamical patterns, and complex topologies supporting the structure of cross correlations between different areas. The state of criticality has been established by our group in human brain dynamics in basal conditions, by studying abrupt transitions (RTPs, or rapid transition processes) to and from stationary states, via multichannel EEGs (electroencephalograms). RTPs are the events that delimit quasi-stationary (sometimes called metastable) epochs with constant frequencies and amplitude. It remained unsolved whether this complex behavior correlates with consciousness or, alternatively, with a non-task-driven default mode activity of the brain, also present in non-conscious states, such as NREM sleep. This does not mean that default mode and consciousness are in conceptual contrast, but that so far we had not assessed whether our finding of critical neural dynamics was a correlate of either the former or the latter. Here we show that in NREM sleep this serial dynamical behavior breaks down, insofar as the inverse-power-law distributions of the inter-event times are replaced, in the long-time regime, by exponential cutoffs whose time scales correlate with the average time between episodes of neural bistability,marked in EEGs by Sleep SlowOscillations (SSOs). During REM sleep the dynamics turns back to the scale-free behavior observed during the pre-sleep wakefulness. We demonstrate that the dynamics of passage between quasi stationary states in unconscious NREM sleep has a spatial connectivity not significantly different from the spatial connectivity of wakefulness and REM sleep. However, NREM RTPs are not dynamically compatible with a serial (single time) scenario, with a strong discrepancy with respect to the serial behavior of wakefulness and REM sleep, where consciousness takes place. This is ultimately due to the fact that the global workspace, the most used model to describe consciousness, cannot emerge due to NREM neural bistability.Source: International journal of psychophysiology 85 (2012): 332–333. doi:10.1016/j.ijpsycho.2012.06.117
DOI: 10.1016/j.ijpsycho.2012.06.117

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