A Reformulation of Matrix Graph Grammars with Boolean Complexes

Prior publication in the Electronic Journal of Combinatorics.Graph transformation is concerned with the manipulation of graphs by means of rules. Graph grammars have been traditionally studied using techniques from category theory. In previous works, we introduced Matrix Graph Grammars (MGG) as a purely algebraic approach for the study of graph dynamics, based on the representation of simple graphs by means of their adjacency matrices. The observation that, in addition to positive information, a rule implicitly defines negative conditions for its application (edges cannot become dangling, and cannot be added twice as we work with simple digraphs) has led to a representation of graphs as two matrices encoding positive and negative information. Using this representation, we have reformulated the main concepts in MGGs, while we have introduced other new ideas. In particular, we present (i) a new formulation of productions together with an abstraction of them (so called swaps), (ii) the notion of coherence, which checks whether a production sequence can be potentially applied, (iii) the minimal graph enabling the applicability of a sequence, and (iv) the conditions for compatibility of sequences (lack of dangling edges) and G-congruence (whether two sequences have the same minimal initial graph).This work has been partially sponsored by the Spanish Ministry of Science and Innovation, project METEORIC (TIN2008-02081/TIN)

College Access and Completion among Boys and Young Men of Color: Literature Review of Promising Practices

This literature review examines challenges and promising practices for increasing college access and completion among boys and young men of color. It moves beyond issues of academic preparation to other factors that appear to mediate college access and success for boys and young men of color

Effects of carbon nanotubes/graphene nanoplatelets hybrid systems on the structure and properties of polyetherimide-based foams

Foams based on polyetherimide (PEI) with carbon nanotubes (CNT) and PEI with graphene nanoplatelets (GnP) combined with CNT were prepared by water vapor induced phase separation. Prior to foaming, variable amounts of only CNT(0.1–2.0wt%) or a combination of GnP(0.0–2.0 wt %) and CNT (0.0–2.0 wt %) for a total amount of CNT-GnP of 2.0 wt %, were dispersed in a solvent using high power sonication, added to the PEI solution, and intensively mixed. While the addition of increasingly higher amounts of only CNT led to foams with more heterogeneous cellular structures, the incorporation of GnP resulted in foams with ¿ner and more homogeneous cellular structures. GnP in combination with CNT effectively enhanced the thermal stability of foams by delaying thermal decomposition and mechanically-reinforced PEI. The addition of 1.0 wt % GnP in combination with 1.0 wt % CNT resulted in foams with extremely high electrical conductivity, which was related to the formation of an optimum conductive network by physical contact between GnP layers and CNT, enabling their use in electrostatic discharge (ESD) and electromagnetic interference (EMI) shielding applications. The experimental electrical conductivity values of foams containing only CNT ¿tted well to a percolative conduction model, with a percolation threshold of 0.06 vol % (0.1 wt %) CNTPostprint (published version

Effects of graphene nanoplatelets and cellular structure on the thermal conductivity of polysulfone nanocomposite foams

Polysulfone (PSU) foams containing 0–10 wt% graphene nanoplatelets (GnP) were prepared using two foaming methods. Alongside the analysis of the cellular structure, their thermal conductivity was measured and analyzed. The results showed that the presence of GnP can a ect the cellular structure of the foams prepared by both water vapor induced phase separation (WVIPS) and supercritical CO2 (scCO2) dissolution; however, the impact is greater in the case of foams prepared by WVIPS. In terms of thermal conductivity, the analysis showed an increasing trend by incrementing the amount of GnP and increasing relative density, with the tortuosity of the cellular structure, dependent on the used foaming method, relative density, and amount of GnP, playing a key role in the final value of thermal conductivity. The combination of all these factors showed the possibility of preparing PSU-GnP foams with enhanced thermal conductivity at lower GnP amount by carefully controlling the cellular structure and relative density, opening up their use in lightweight heat dissipatorsPostprint (published version

Polyetherimide foams filled with low content of graphene nanoplatelets prepared by scCO2 dissolution

Polyetherimide (PEI) foams with graphene nanoplatelets (GnP) were prepared by supercritical carbon dioxide (scCO2) dissolution. Foam precursors were prepared by melt-mixing PEI with variable amounts of ultrasonicated GnP (0.1–2.0 wt %) and foamed by one-step scCO2 foaming. While the addition of GnP did not significantly modify the cellular structure of the foams, melt-mixing and foaming induced a better dispersion of GnP throughout the foams. There were minor changes in the degradation behaviour of the foams with adding GnP. Although the residue resulting from burning increased with augmenting the amount of GnP, foams showed a slight acceleration in their primary stages of degradation with increasing GnP content. A clear increasing trend was observed for the normalized storage modulus of the foams with incrementing density. The electrical conductivity of the foams significantly improved by approximately six orders of magnitude with only adding 1.5 wt % of GnP, related to an improved dispersion of GnP through a combination of ultrasonication, melt-mixing and one-step foaming, leading to the formation of a more effective GnP conductive network. As a result of their final combined properties, PEI-GnP foams could find use in applications such as electrostatic discharge (ESD) or electromagnetic interference (EMI) shieldingPostprint (published version

A Differentiable Generative Adversarial Network for Open Domain Dialogue

Paper presented at the IWSDS 2019: International Workshop on Spoken Dialogue Systems Technology, Siracusa, Italy, April 24-26, 2019This work presents a novel methodology to train open domain neural dialogue systems within the framework of Generative Adversarial Networks with gradient-based optimization methods. We avoid the non-differentiability related to text-generating networks approximating the word vector corresponding to each generated token via a top-k softmax. We show that a weighted average of the word vectors of the most probable tokens computed from the probabilities resulting of the top-k softmax leads to a good approximation of the word vector of the generated token. Finally we demonstrate through a human evaluation process that training a neural dialogue system via adversarial learning with this method successfully discourages it from producing generic responses. Instead it tends to produce more informative and variate ones.This work has been partially funded by the Basque Government under grant PRE_2017_1_0357, by the University of the Basque Country UPV/EHU under grant PIF17/310, and by the H2020 RIA EMPATHIC (Grant N: 769872)