Virtual agent multimodal mimicry of humans

This work is about multimodal and expressive synthesis on virtual agents, based on the analysis of actions performed by human users. As input we consider the image sequence of the recorded human behavior. Computer vision and image processing techniques are incorporated in order to detect cues needed for expressivity features extraction. The multimodality of the approach lies in the fact that both facial and gestural aspects of the user’s behavior are analyzed and processed. The mimicry consists of perception, interpretation, planning and animation of the expressions shown by the human, resulting not in an exact duplicate rather than an expressive model of the user’s original behavior.     

Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster

This study is meant to characterize the serum proteome in a small geographical cluster of sporadic ALS subjects originating from a restricted geographical area and sharing the same environmental exposure, in a broader context of evaluating the relevance of environmental factors to disease onset, status, and progression. An Artificial Neural Network based software is used to compare the relative abundance of proteins identified as different (by means of bi‐dimensional electrophoresis and mass spectrometry) in the serum proteome of patients and age‐matched healthy controls. The patient's group is characterized by altered levels of acute phase reactants and of proteins involved in lipid homeostasis, along with over‐representation of the APOE*4 allele. Characterization of the serum proteome in a small cluster of sporadic ALS patients, originating from a geographically restricted area with a high prevalence of the disease and evaluation of the results with software based on artificial neural networks, highlights the association of the relative abundance of some proteins (most notably, acute phase reactants and lipid homeostasis proteins) with the disease presence and status.     

Effect of surface modification on NO2 sensing properties of SnO2 varistor-type sensors

NO₂ sensing properties of SnO₂-based varistor-type sensors have been investigated in the temperature range of 400-650°C and in the NO₂ concentration range of 15–30 ppm. Pure SnO₂ exhibited a weak nonlinear I–V characteristic in air, but clear nonlinearity in NO₂ at 450°C. The breakdown voltage of SnO₂ shifted to a high electric field upon exposure to NO₂ and the magnitude of the shift was well correlated with NO₂ concentration. Thus, SnO₂ exhibited some sensitivity to NO₂ as a varistor-type sensor. When SnO₂ particles coated with a SiO₂ thin film were used as a raw material for fabricating a varistor, the breakdown voltage in air was approximately the double that of pure SnO₂ and the sensitivity to 15 ppm NO₂ was enhanced slightly. However, the sensitivity to 30 ppm NO₂ decreased. The Cr₂O₃-loading on SnO₂ also led to an increase in the breakdown voltage in air, but the Cr₂O₃ addition was not effective for promoting the NO₂ sensitivity under the present experimental conditions.     

Econometric Effects of Utility Order-Preserving Transformations in Discrete Choice Models

In the random utility modelling context, choice probabilities are unaffected by increasing linear transformations of the systematic utility; hence its empirical specification is derived on the basis that only differences in utility matters and that the scale of utility is arbitrary. We argue that choice probabilities remain unchanged if these linear transformations are made under the deterministic perspective of a single individual choosing several times. But, in the random utility setting, parameter estimates might be significantly affected by these transformations. In particular we focus on the effect of two order-preserving transformations usually applied in the derivation of the representative utility from the conditional indirect utility function: adding a constant to the utility of all alternatives and multiplying each alternative utility by a constant. We concentrate on the two most popular specifications in transport mode choice: the “wage rate” (Train and McFadden Transport Res 12:349–353, 1978) and the “expenditure rate” (Jara-Díaz and Farah Transport Res 22B:159–171, 1987) specifications. Using a collection of synthetic datasets generated in a new fashion directly from the conditional indirect utility function, i.e. before applying any expansion or transformation, we demonstrate how taking this class of order-preserving transformations could lead to misinterpretation of the econometric results, such as detecting randomly distributed and correlated parameters and/or income and time effects which are in fact not present.     

Usage of SoS methodologies in production system design

Production systems design is a multifaceted task, due to a variety of aspects such as the mutual interdependency between the sub-systems, the variety of configurations and alternative system control strategies, the multiple managerial and “soft” aspects that cast an influence on the behavior of the system. A reasonable number of modeling tools can be applied to production system design, but they tend to divide the problem into unconnected sub-problems whose individual solutions may result in a poor global one. This is despite the fact that production design encompasses all aspects of manufacturing operations, and needs a systemic approach, as clearly shown in practitioner-oriented literature. This paper proposes to apply the “System of Systems” approach to production system design in order to represent their main aspects and support the rational definition of the path leading from corporate strategy to system (re)design.     

A comparison of graph-based word sense induction clustering algorithms in a pseudoword evaluation framework

This article presents a comparison of different Word Sense Induction (wsi) clustering algorithms on two novel pseudoword data sets of semantic-similarity and co-occurrence-based word graphs, with a special focus on the detection of homonymic polysemy. We follow the original definition of a pseudoword as the combination of two monosemous terms and their contexts to simulate a polysemous word. The evaluation is performed comparing the algorithm’s output on a pseudoword’s ego word graph (i.e., a graph that represents the pseudoword’s context in the corpus) with the known subdivision given by the components corresponding to the monosemous source words forming the pseudoword. The main contribution of this article is to present a self-sufficient pseudoword-based evaluation framework for wsi graph-based clustering algorithms, thereby defining a new evaluation measure (top2) and a secondary clustering process (hyperclustering). To our knowledge, we are the first to conduct and discuss a large-scale systematic pseudoword evaluation targeting the induction of coarse-grained homonymous word senses across a large number of graph clustering algorithms.     

On the use of formal methods to model and verify neuronal archetypes

Having a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetypes.