Automatic learning of fuzzy naming relations over finite languages

A method for learning the membership of strings belonging to finite languages is proposed. The learning is based on sets of strings of fuzzy linguistic variables. These strings belong to languages, each one of which describes a class of phenomena. The learning algorithm attempts to maximize the number of times a string reaches the highest possibility value for the language representing the class of phenomena containing the sample described by the string. Application to automatic speech recognition is described, and experimental results are presented showing the benefits of the proposed method.     

An adaptive pattern based nonlinear PID controller

This paper presents a nonlinear proportional-integral-derivative (PID) controller, combining a pattern based adaptive algorithm to cope with the problem of tuning the controller, and an associative memory to store the parameters, according to different operating conditions. The simplicity of the algorithm enables its implementation in current programmable logic controller technology. Several real-time experiments, carried out in a pressurized tank, illustrate the performance of the proposed controller.     

Gut Microbiota and Bipolar Disorder: An Overview on a Novel Biomarker for Diagnosis and Treatment

The gut microbiota is the set of microorganisms that colonize the gastrointestinal tract of living creatures, establishing a bidirectional symbiotic relationship that is essential for maintaining homeostasis, for their growth and digestive processes. Growing evidence supports its involvement in the intercommunication system between the gut and the brain, so that it is called the gut–brain–microbiota axis. It is involved in the regulation of the functions of the Central Nervous System (CNS), behavior, mood and anxiety and, therefore, its implication in the pathogenesis of neuropsychiatric disorders. In this paper, we focused on the possible correlations between the gut microbiota and Bipolar Disorder (BD), in order to determine its role in the pathogenesis and in the clinical management of BD. Current literature supports a possible relationship between the compositional alterations of the intestinal microbiota and BD. Moreover, due to its impact on psychopharmacological treatment absorption, by acting on the composition of the microbiota beneficial effects can be obtained on BD symptoms. Finally, we discussed the potential of correcting gut microbiota alteration as a novel augmentation strategy in BD. Future studies are necessary to better clarify the relevance of gut microbiota alterations as state and disease biomarkers of BD.     

A model of abstraction in visual perception

In artificial intelligence, abstraction has been mainly studied as a mapping between languages in relation to problem-solving, with the aim of reducing the complexity of the task. However, abstraction has a much larger scope in reasoning; we are investigating, in this article, how abstraction can be used in concept representation. To this aim, we propose a novel, perception-based model of abstraction, which originates from the observation that conceptualization of a domain, even though involving entities belonging to several epistemological levels, is nevertheless primarily based on perception. This view has been recently advocated by Goldstone and Barsalou in cognitive science. A model of representation/abstraction is then proposed and its application to a real-world problem of robot visual perception and categorization is presented.     

Energy-based control of a distributed parameter bi-zone model with moving interface

In this work, a model-based control of the melt temperature of a distributed parameter bi-zone model with moving interface of an extrusion process is studied. An energy-based controller is derived using internal energy as a storage function and controlled variable with the screw speed as control input. The stability of the controller is proved through the use of a Lyapunov-like candidate function. Finally, the practical usefulness of the method is illustrated by some closed-loop simulations of the experimentally verified model of the extrusion process. The interconnection of the moving interface is performed under the assumption of variable viscosity along the extruder and a finite-volume scheme is used for the discretisation of the dynamic model.     

A formal definition and a sound implementation of analogical reasoning in logic programming

A formal model of analogy is introduced in the logic programming setting, and an analogical reasoning program (called DIANA, i.e. Declarative Inference by ANAlogy) is developed in accordance with precise procedural and declarative semantics. Given the source and target domains of analogy as two logic programsP _s andP _t , together with a specificationS of the analogical correspondence between predicate symbols, atoms involving these symbols are analogically derived fromP=P _s P _t givenS, which are not derivable fromP _s orP _t orP _s P _t alone. In this paper, the requirements of the analogical process are first stated. The declarative semantics of analogy is then given, by defining the least analogical model ofP as an extension of the classical semantics of Horn clauses. A procedural semantics is also described, in terms of an extension of SLD resolution. Both semantics rely on implicit analogical axioms defining the kind of analogical reasoning envisaged. The implementation of DIANA has been done in Reflective Prolog, a metalogic programming language previously developed by the first two authors. It is shown that analogical axioms can be viewed as an instance of reflection axioms used in Reflective Prolog. By exploiting this feature, the implementation of DIANA is argued to be sound w.r.t. the defined semantics. Examples of analogical reasoning in DIANA are also described. By comparison with the AI literature on analogy, it is claimed that this is the first approach which gives a declarative semantics to analogical reasoning, thanks to the possibility of carrying over in this field the basic logic programming concepts.     

Evaluation of machine learning-based information extraction algorithms: criticisms and recommendations

We survey the evaluation methodology adopted in information extraction (IE), as defined in a few different efforts applying machine learning (ML) to IE. We identify a number of critical issues that hamper comparison of the results obtained by different researchers. Some of these issues are common to other NLP-related tasks: e.g., the difficulty of exactly identifying the effects on performance of the data (sample selection and sample size), of the domain theory (features selected), and of algorithm parameter settings. Some issues are specific to IE: how leniently to assess inexact identification of filler boundaries, the possibility of multiple fillers for a slot, and how the counting is performed. We argue that, when specifying an IE task, these issues should be explicitly addressed, and a number of methodological characteristics should be clearly defined. To empirically verify the practical impact of the issues mentioned above, we perform a survey of the results of different algorithms when applied to a few standard datasets. The survey shows a serious lack of consensus on these issues, which makes it difficult to draw firm conclusions on a comparative evaluation of the algorithms. Our aim is to elaborate a clear and detailed experimental methodology and propose it to the IE community. Widespread agreement on this proposal should lead to future IE comparative evaluations that are fair and reliable. To demonstrate the way the methodology is to be applied we have organized and run a comparative evaluation of ML-based IE systems (the Pascal Challenge on ML-based IE) where the principles described in this article are put into practice. In this article we describe the proposed methodology and its motivations. The Pascal evaluation is then described and its results presented.

Anisotropic Thermal Expansion of <Emphasis Type="Italic">p</Emphasis>-Terphenyl: a Self-Assembled Supramolecular Array of Poly-<Emphasis Type="Italic">p</Emphasis>-phenyl Nanoribbons

The recent discovery of superconductivity in a metallic aromatic hydrocarbon, alkali-doped p-terphenyl, has attracted considerable interest. The critical temperature T _c ranges from few to 123 K, the record for organic superconductors, due to uncontrolled competition of multiple phases and dopants concentrations. In the proposed mechanism of Fano resonance in a superlattice of quantum wires with coexisting polarons and Fermi particles, the lattice properties play a key role. Here, we report a study of the temperature evolution of the parent compound p-terphenyl crystal structure proposed to be made of a self-assembled supramolecular network of nanoscale nanoribbons. Using temperature-dependent synchrotron X-ray diffraction, we report the anisotropic thermal expansion in the ab plane, which supports the presence of a nanoscale network of one-dimensional nanoribbons running in the b-axis direction in the P21/a structure. Below the enantiotropic phase transition at 193 K, the order parameter of the C-1 structure follows a power law behavior with the critical exponent α =?0.34 ± 0.02 and the thermal expansion of the a-axis and b-axis show major changes supporting the formation of a two-dimensional bonds network. The large temperature range of the orientation fluctuations in a double well potential of the central phenyl has been determined.     

Biocompatible glass–ceramic materials for bone substitution

A new bioactive glass composition (CEL2) in the SiO₂–P₂O₅–CaO–MgO–K₂O–Na₂O system was tailored to control pH variations due to ion leaching phenomena when the glass is in contact with physiological fluids. CEL2 was prepared by a traditional melting-quenching process obtaining slices that were heat-treated to obtain a glass-ceramic material (CEL2GC) that was characterized thorough SEM analysis. Pre-treatment of CEL2GC with SBF was found to enhance its biocompatibility, as assessed by in vitro tests. CEL2 powder was then used to synthesize macroporous glass–ceramic scaffolds. To this end, CEL2 powders were mixed with polyethylene particles within the 300–600 μm size-range and then pressed to obtain crack-free compacted powders (green). This was heat-treated to remove the organic phase and to sinter the inorganic phase, leaving a porous structure. The biomaterial thus obtained was characterized by X-ray diffraction, SEM equipped with EDS, density measurement, image analysis, mechanical testing and in vitro evaluation, and found to be a glass–ceramic macroporous scaffold with uniformly distributed and highly interconnected porosity. The extent and size-range of the porosity can be tailored by varying the amount and size of the polyethylene particles.