A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further “authoritative” tags to enrich his query and proposes them to him. All “authoritative” tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.     

Expression of high-molecular-mass neurofilament protein in horse (Equus caballus) spinal ganglion neurons

Spinal ganglion (SG) neurons are subdivided, on the basis of their cytoplasmic aspect at light and electron microscopy, into dark (D) and light (L) neurons. Numerous efforts have been made to find specific markers able to identify D and L neuronal cytotypes. The isolectin B4 (IB4), utilized to identify nonpeptidergic D neurons in mice, unfortunately, has not proved as effective in other species. The 200-kDa neurofilament protein (NF200) is considered as a typical marker of L neurons in the rat, cat, and chick. The aim of this study was to analyze the histological, morphometric, and neurochemical characteristic of NF200-immunoreactive (IR) horse SG neurons, to better characterize them morphologically and functionally. NF200-IR neurons showed two levels (strong and weak) of staining intensity. Most (84%) strongly stained NF200-IR neurons corresponded to L neurons, and showed similar bimodality as in the size distribution study, which seems to indicate a third population of neurons, in addition to the two populations (small and large) previously identified. In triple-staining experiments where NF200 was colocalized with IB4, substance P (SP), and neuronal nitric oxide synthase (nNOS) neuronal markers, most NF200-IR neurons were single stained. On the contrary, most IB4-, SP-, and nNOS-stained neurons were triple labeled and almost equally subdivided between strong and weak NF200-IR with the latter being always smaller in size than strong NF200-IR neurons. In conclusion, horse SG neurons display significant morphometric and neurochemical differences compared with those of rodents.     

A control oriented model for manual-pick warehouses

Nowadays the low cost of wireless communications allows the real time monitoring of the state of manual-pick warehouse systems making possible the real time control of these systems. At this aim, an approach based on a two-level model is presented in this paper. At the lower level, Petri nets are used to build online a model representing the active missions state and to detect conflicts among resources. At the upper level, logical expressions are used to add constraints for a single mission or item. The model is proved to be effective for online monitoring, scheduling and rescheduling of warehouse activities.     

Integration of XML Schemas at various “severity” levels

This paper presents a novel approach for the integration of a set of eXtensible Markup Language (XML) Schemas. The proposed approach is specialized for XML, almost automatic, semantic and “light”. Further, an original peculiarity is that it is parametric w.r.t. a “severity” level against which the integration task is performed. The paper describes the approach in all details, illustrates various theoretical results, presents the experiments we have performed for testing it and, finally, compares it with various related approaches already proposed in the literature.     

Ecological Neural Networks for Object Recognition and Generalization

Generalization is a critical capacity for organisms. Modeling the behavior of organisms with neural networks, some type of generalizations appear to be accessible to neural networks but other types do not. In this paper we present two simulations. In the first simulation we show that while neural networks can recognize where an object is located in the retina even if they have never experienced that object in that position ('where' generalization subtask), they have difficulty in recognizing the identity of a familiar object in a new position ('what' generalization subtask). In the second simulation we explore the hypothesis that organisms find another solution to the problem of recognizing objects in different positions on their retina: they move their eyes so that objects are always seen in the same position in the retina. This strategy emerges spontaneously in ecological neural networks that are allowed to move their 'eye' in order to bring different portions of the visible world in the central portion of their retina.     

Optimal evaluation of generalized Euler angles with applications to control

Given two linearly independent matrices in so(3), Z₁ and Z₂, every rotation matrix, X_f∈SO(3), can be written as the product of alternate elements from the one-dimensional subgroups corresponding to Z₁ and Z₂, namely X_f=e^Z₁t₁e^Z₂t₂e^Z₁t₃?e^Z₁t_s. The parameters t_i, i=1,…,s are called Generalized Euler Angles. In this paper, the minimum number of factors required for the factorization of X_f∈SO(3), as a function of X_f, is evaluated. An algorithm is given to determine the generalized Euler angles, in the optimal factorization. The results can be applied to the bang-bang control, with minimum number of switches, of some classical and quantum systems.     

Adaptive local learning techniques for multiple-step-ahead wind speed forecasting

A massive deployment of wind energy in power systems is expected in the near future. However, a still open issue is how to integrate wind generators into existing electrical grids by limiting their side effects on network operations and control. In order to attain this objective, accurate short and medium-term wind speed forecasting is required.This paper discusses and compares a physical (white-box) model (namely a limited-area non hydrostatic model developed by the European consortium for small-scale modeling) with a family of local learning techniques (black-box) for short and medium term forecasting. Also, an original model integrating machine learning techniques with physical knowledge modeling (grey-box) is proposed.A set of experiments on real data collected from a set of meteorological sensors located in the south of Italy supports the methodological analysis and assesses the potential of the different forecasting approaches.     

University-owned and university-invented patents: a network analysis on two Italian universities

The paper presents results from social network analysis applied to data on patenting of academics inventors employed in two Italian universities (Trieste University and Udine university, both located in Friuli Venezia Giulia region). The aim is to compare the co-invention networks generated by the academic inventors, tenured by one of the two universities, in their patenting activity with several organisations—firms, public research organisations—and in their activity for patents owned by one of the two universities. Results show that, despite the structural similarity, non-marginal differences emerge in the interaction of the two forms of patenting across the two universities. Empirical evidence suggests new research questions related in particular to the role played by the differing university patenting strategies in shaping local networks.     

On the onset of fracture as a silicon-based polymer converts into the ceramic phase

Silicon-based polymers evolve into refractory ceramics when heated gradually up to ~1000°C. The conversion is accompanied by the loss of gaseous species, and by a two-fold increase in density. The shrinkage can produce microcracks if the heating rate is too high, or if the specimen is too thick. This communication builds on earlier work whereby the measurement of gas evolution, and its relationship with viscous flow, are related to the onset of fracture in disk-shaped green (polymer) samples. The onset is determined as a function of the thickness of the disks, and of the heating rate. The results are presented in the form of a processing map. The overlay with gas evolution, and strain-rate measurements, suggest that fracture initiates with the release of hydrogen and methane, starting at temperatures near 750°C.