Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

Preliminary notions of arguments from commonsense knowledge

The field of Computational Argumentation is well-tailored to approach commonsense reasoning, due to its ability to model contradictory information. In this paper, we present preliminary work on how an argumentation framework can explicitly model commonsense knowledge, both at a logically structured and at an abstract level. We discuss the correlation with current research and present interesting future directions

Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

A Multi Attack Argumentation Framework

This paper presents a novel abstract argumentation framework, called Multi-Attack Argumentation Framework (MAAF), which supports different types of attacks. The introduction of types gives rise to a new family of non-standard semantics which can support applications that classical approaches cannot, while also allowing classical semantics as a special case. The main novelty of the proposed semantics is the discrimination among two different roles that attacks play, namely an attack as a generator of conflicts, and an attack as a means to defend an argument. These two roles have traditionally been considered together in the argumentation literature. Allowing some attack types to serve one of those roles only, gives rise to the different semantics presented here

Abstract Argumentation Frameworks with Domain Assignments

Argumentative discourse rarely consists of opinions whose claims apply universally. As with logical statements, an argument applies to specific objects in the universe or relations among them, and may have exceptions. In this paper, we propose an argumentation formalism that allows associating arguments with a domain of application. Appropriate semantics are given, which formalise the notion of partial argument acceptance, i.e., the set of objects or relations that an argument can be applied to. We show that our proposal is in fact equivalent to the standard Argumentation Frameworks of Dung, but allows a more intuitive and compact expression of some core concepts of commonsense and non-monotonic reasoning, such as the scope of an argument, exceptions, relevance and others

Enhanced Piezoresponse and Nonlinear Optical Properties of Fluorinated Self-Assembled Peptide Nanotubes

Self-assembled L,L-diphenylalanine (FF) nanostructures offer an attractive platform for photonics and nonlinear optics. The nonlinear optical (NLO) coefficients of FF nanotubes depend on the diameter of the tube [S. Khanra et al. Phys. Chem. Chem. Phys. 19(4), 3084-3093 (2017)]. To further enhance the NLO properties of FF, we search for structural modifications. Here, we report on the synthesis of fluorinated FF dipeptides by replacing one ortho-hydrogen atom in each of the phenyl groups of FF by a fluorine atom. Density-functional theoretical calculations yield insights into minimum energy conformers of fluorinated FF (Fl-FF). Fl-FF self-assembles akin to FF into micron-length tubes. The effects of fluorination are evaluated on the piezoelectric response and nonlinear optical properties. The piezoelectric d15 coefficient of Fl-FF is found to be more than 10 times higher than that of FF nanotubes, and the intensity of second harmonic generation (SHG) polarimetry from individual Fl-FF nanotubes is more than 20 times that of individual FF nanotubes. Furthermore, we obtain SHG images to compare the intensities of FF and Fl-FF tubes. This work demonstrates the potential of fluorine substitution in other self-assembled biomimetic peptides for enhancing nonlinear optical response and piezoelectricity

PLK1 inhibitors as a new targeted treatment for adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is an aggressive malignancy with limited treatment options. Polo-like kinase 1 (PLK1) is a promising drug target; PLK1 inhibitors (PLK1i) have been investigated in solid cancers and are more effective in TP53-mutated cases. We evaluated PLK1 expression in ACC samples and the efficacy of two PLK1i in ACC cell lines with different genetic backgrounds. PLK1 protein expression was investigated by immunohistochemistry in tissue samples and correlated with clinical data. The efficacy of rigosertib (RGS), targeting RAS/PI3K, CDKs and PLKs, and poloxin (Pol), specifically targeting the PLK1 polo-box domain, was tested in TP53-mutated NCI-H295R, MUC-1, and CU-ACC2 cells and in TP53 wild-type CU-ACC1. Effects on proliferation, apoptosis, and viability were determined. PLK1 immunostaining was stronger in TP53-mutated ACC samples vs wild-type (P = 0.0017). High PLK1 expression together with TP53 mutations correlated with shorter progression-free survival (P= 0.041). NCI-H295R showed a time- and dose-dependent reduction in proliferation with both PLK1i (P< 0.05at 100 nM RGS and 30 µM Pol). In MUC-1, a less pronounced decrease was observed (P< 0.05at 1000 nM RGS and 100 µM Pol). 100 nM RGS increased apoptosis in NCI-H295R (P< 0.001), with no effect on MUC-1. CU-ACC2 apoptosis was induced only at high concentrations (P < 0.05 at 3000 nM RGS and 100 µM Pol), while proliferation decreased at 1000 nM RGS and 30 µM Pol. CU-ACC1 proliferation reduced, and apoptosis increased, only at 100 µM Pol. TP53-mutated ACC cell lines demonstrated better response to PLK1i than wild-type CU-ACC1. These data suggest PLK1i may be a promising targeted treatment of a subset of ACC patients, pre-selected according to tumour genetic signature