A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language

Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS. We have also provided a comparison of our model with a state of the art method used for the same purpos

On Representing Concepts in High-dimensional Linear Spaces

Producing a mathematical model of concepts is a very important issue in artificial intelligence, because if such a model were found this, besides being a very interesting result in its own right, would also contribute to the emergence of what we could call the \u2018mathematics of thought.\u2019 One of the most interesting attempts made in this direction is P. Gardenfors\u2019 theory of conceptual spaces, a \ua8 theory which is mostly presented by its author in an informal way. The main aim of the present article is contributing to Gardenfors\u2019 theory of conceptual spaces \ua8 by discussing some of the advantages which derive from the possibility of representing concepts in high-dimensional linear spaces

Polyamide Nanocomposites for Selective Laser Sintering

Current polyamide 11 and 12 are lacking in fire retardancy and high strength/high heat resistance characteristics for a plethora of finished parts that are desired and required for performance driven applications. It is anticipated that nanomodification of polyamide 11 and 12 will result in enhanced polymer performance, i.e., fire retardancy, high strength and high heat resistance for polyamide 11 and 12. It is expected that these findings will expand the market opportunities for polyamide 11 and 12 resin manufacturers. The objective of this research is to develop improved polyamide 11 and 12 polymers with enhanced flame retardancy, thermal, and mechanical properties for selective laser sintering (SLS) rapid manufacturing (RM). A nanophase was introduced into the polyamide 11 and 12 via twin screw extrusion to provide improved material properties of the polymer blends. Arkema RILSAN® polyamide 11 molding polymer pellets and Degussa VESTAMID® L1670 polyamide 12 were examined with three types of nanoparticles: chemically modified montmorillonite (MMT) organoclays, surface modified nanosilica, and carbon nanofibers (CNFs) to create polyamide 11 and 12 nanocomposites. Wide angle X-ray diffraction (WAXD) and transmission electron microscopy (TEM) were used to determine the degree of dispersion. Injection molded test specimens were fabricated for physical, thermal, mechanical properties, and flammability tests. Thermal stability of these polyamide 11 and 12 nanocomposites was examined by TGA. Mechanical properties such as tensile, flexural, and elongation at break were measured. Flammability properties were also obtained using the Cone Calorimeter at an external heat flux of 50 kW/m2. TEM micrographs, physical, mechanical, and flammability properties are included in the paper. Polyamide 11 and 12 nanocomposites properties are compared with polyamide 11 and 12 baseline polymers. Based on flammability and mechanical material performance, selective polymers including polyamide 11 nanocomposites and control polyamide 11 were cryogenically ground into fine powders and fabricated into SLS parts.Mechanical Engineerin

Innovative Selective Laser Sintering Rapid Manufacturing using Nanotechnology

The objective of this research is to develop an improved nylon 11 (polyamide 11) polymer with enhanced flame retardancy, thermal, and mechanical properties for selective laser sintering (SLS) rapid manufacturing (RM). A nanophase was introduced into nylon 11 via twin screw extrusion to provide improved material properties of the polymer blends. Atofina (now known as Arkema) RILSAN® nylon 11 injection molding polymer pellets was used with three types of nanoparticles: chemically modified montmorillonite (MMT) organoclays, nanosilica, and carbon nanofibers (CNF) to create nylon 11 nanocomposites. Wide angle X-ray diffraction (WAXD) and transmission electron microscopy (TEM) were used to determine the degree of dispersion. Fifteen nylon 11 nanocomposites and control nylon 11 were fabricated by injection molding. Flammability properties (using a cone calorimeter with a radiant flux of 50 kW/m2 ) and mechanical properties such as tensile strength and modulus, flexural modulus, elongation at break were determined for the nylon 11 nanocomposites and compared with the baseline nylon 11. Based on flammability and mechanical material performance, five polymers including four nylon 11 nanocomposites and a control nylon 11 were cryogenically ground into fine powders for SLS RM. SLS specimens were fabricated for flammability, mechanical, and thermal properties characterization. Nylon 11-CNF nanocomposites exhibited the best overall properties for this study.Mechanical Engineerin

Towards a deep-learning-based methodology for supporting satire detection

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers

Creativity in Conceptual Spaces

The main aim of this paper is contributing to what in the last few years has been known as computational creativity. This will be done by showing the relevance of a particular mathematical representation of G"ardenfors's conceptual spaces to the problem of modelling a phenomenon which plays a central role in producing novel and fruitful representations of perceptual patterns: analogy

A Quantum Planner for Robot Motion

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot's behavior. According to the production rules, the planning of the robot activities is processed in a recognize-act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up

Intumescent Flame Retardant Polyamide 11 Nanocomposites

Current polyamide 11 and 12 are lacking in fire retardancy and high strength/high heat resistance characteristics for a plethora of fabricated parts that are desired and required for performance driven applications. The introduction of selected nanoparticles such as surface modified montmorillonite (MMT) clay or carbon nanofibers (CNFs), combined with a conventional intumescent flame retardant (FR) additive into the polyamide 11/polyamide 12 (PA11/PA12) by melt processing conditions has resulted in the preparation of a family of intumescent polyamide nanocomposites. These intumescent polyamide 11 and 12 nanocomposites exhibit enhanced polymer performance characteristics, i.e., fire retardancy, high strength and high heat resistance and are expected to expand the market opportunities for polyamide 11 and polyamide 12 polymer manufacturers. The objective of this research is to develop improved polyamide 11 and 12 polymers with enhanced flame retardancy, thermal, and mechanical properties for selective laser sintering (SLS) rapid manufacturing (RM). In the present study, a nanophase was introduced into the polyamide 11 and combining it with a conventional intumescent FR additive via twin screw extrusion. Arkema RILSAN® polyamide 11 molding polymer pellets were examined with two types of nanoparticles: chemically modified montmorillonite (MMT) organoclays, and carbon nanofibers (CNFs); and Clairant’s Exolit® OP 1230 intumescent FR additive were used to create a family of FR intumescent polyamide 11 nanocomposites. Transmission electron microscopy (TEM) was used to determine the degree of nanoparticles dispersion. Injection molded specimens were fabricated for physical, thermal, and flammability measurements. Thermal stability of these intumescent polyamide 11 nanocomposites was examined by TGA. Flammability properties were obtained using the Cone Calorimeter at an external heat flux of 35 kW/m 2 and UL 94 Test Method. Heat deflection temperatures (HDT) were also measured. TEM micrographs, physical, thermal, and flammability properties are presented. FR intumescent polyamide 11 nanocomposites properties are compared with polyamide 11 baseline polymer. Based on flammability and mechanical material performance, selective polymers including polyamide 11 nanocomposites and control polyamide 11 will be cryogenically ground into fine powders for SLS RM processing. SLS specimens will be fabricated for thermal, flammability, and mechanical properties characterization.Mechanical Engineerin