Two is better than one: A diploid genotype for neural networks

In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.     

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.     

Analysis and implementation of the multiprocessor bandwidth inheritance protocol

The Multiprocessor Bandwidth Inheritance (M-BWI) protocol is an extension of the Bandwidth Inheritance (BWI) protocol for symmetric multiprocessor systems. Similar to Priority Inheritance, M-BWI lets a task that has locked a resource execute in the resource reservations of the blocked tasks, thus reducing their blocking time. The protocol is particularly suitable for open systems where different kinds of tasks dynamically arrive and leave, because it guarantees temporal isolation among independent subsets of tasks without requiring any information on their temporal parameters. Additionally, if the temporal parameters of the interacting tasks are known, it is possible to compute an upper bound to the interference suffered by a task due to other interacting tasks. Thus, it is possible to provide timing guarantees for a subset of interacting hard real-time tasks. Finally, the M-BWI protocol is neutral to the underlying scheduling policy: it can be implemented in global, clustered and semi-partitioned scheduling. After introducing the M-BWI protocol, in this paper we formally prove its isolation properties, and propose an algorithm to compute an upper bound to the interference suffered by a task. Then, we describe our implementation of the protocol for the?LITMUS ^RT real-time testbed, and measure its overhead. Finally, we compare M-BWI against FMLP and OMLP, two other protocols for resource sharing in multiprocessor systems.     

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.