A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms

In recent years, the increasing interest in fuzzy rough set theory has allowed the definition of novel accurate methods for feature selection. Although their stand-alone application can lead to the construction of high quality classifiers, they can be improved even more if other preprocessing techniques, such as instance selection, are considered. With the aim of enhancing the nearest neighbor classifier, we present a hybrid algorithm for instance and feature selection, where evolutionary search in the instances’ space is combined with a fuzzy rough set based feature selection procedure. The preliminary results, contrasted through nonparametric statistical tests, suggest that our proposal can improve greatly the performance of the preprocessing techniques in isolation.Project TIN2008-06681-C06-01Spanish Ministry of EducationResearch Foundation - Flander

5 semanas, 25 días, 175 horas (2016), de Maria Eichhorn: Subjetividad y tiempo en el trabajo posfordista

In this essay, Maria Eichhorn’s 2016 intervention at Chisenhale Gallery 5 weeks, 25 days, 175 hours serves as a foundation to examine the figure of the artistic worker in the Post-Fordist context. Departing from an art-historical analysis of Eichhorn’s gesture of closing the gallery and giving the staff free time, I explore the new subjectivity of the worker from a bio-political perspective, dwelling on the notion of self-precarization. An analysis of Eichhorn’s work shows how the neoliberal worker has been revealed as a subject who takes responsibility for her job insecurity and allows work to penetrate her private life. I argue that Eichhorn’s gesture acts as a reminder of how, as a consequence of the development of the new model of labor, every aspect of life is occupied by the imperative of productivity, complicating traditional ways of resistance.En el presente ensayo, la intervención de Maria Eichhorn de 2016 en la Galería Chisenhale 5 semanas, 25 días, 175 horas sirve de base para examinar la figura del trabajador artístico en el contexto posfordista. Partiendo de un análisis histórico-artístico del gesto de Eichhorn de cerrar la galería y dar tiempo libre a su personal, exploro la nueva subjetividad del trabajador desde una perspectiva biopolítica, deteniéndome en la noción de autoprecarización. Así, un análisis de la obra de Eichhorn evidencia cómo el trabajador neoliberal se ha revelado como un sujeto que asume la responsabilidad de su inseguridad laboral y permite que el trabajo penetre en su vida privada. Sostengo que la acción de Eichhorn nos recuerda que, como efecto del desarrollo del nuevo modelo laboral, todos los aspectos de la vida están ocupados por el imperativo de la productividad, complicando las formas tradicionales de resistencia al trabajo

Transcendiendo la crítica institucional: Scene of the Crime (Whose Crime?) (1993) de Pepón Osorio

As part of the controversial 1993 Whitney Biennial, Puerto Rican artist Pepón Osorio recreated the interior of a Nuyorican home in his work Scene of the Crime (Whose Crime?). This paper aims to unpack the installation’s critical dimension, to argue that it goes beyond the mere institutional critique. To that end, I will first examine the particular disruption of the aseptic museum’s space prompted by Osorio’s installation. Secondly, I will analyze the artist’s intentional use of Kitsch as a gesture of cultural resistance: through the exaggerated accumulation of objects and imageries, Osorio deftly called into question the conventional systems of visibility that operate within the institutional realm.Como parte de la polémica Bienal del Whitney Museum de 1993, el artista puertorriqueño Pepón Osorio recreó el interior de una casa neoyorquina en su obra Scene of the Crime (Whose Chrime?). El presente artículo se propone analizar la dimensión crítica de la instalación para argumentar que esta va más allá de la mera crítica institucional. Para ello, me centraré en primer lugar en la particular disrupción en el espacio del museo llevada a cabo por el artista. Por otro lado, analizaré su intencionado uso del kitsch como gesto de resistencia cultural: a través de la exacerbada acumulación de objetos e imágenes, la obra de Osorio pone en tela de juicio los sistemas convencionales de visibilidad que operan en el ámbito institucional

Negative regulation of the JAK/STAT signalling pathway in inflammatory arthritis

Background ¾ Rheumatoid arthritis (RA) is one of the most common forms of autoimmune disease, affecting about 1% of the population and causing chronic inflammation that primarily affects the joints. Cytokines that signal via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signalling pathway are major drivers of synovitis in patients with RA and contribute to the rapid progression of the disease. Biological agents targeting the cytokine interleukin (IL)-6, its receptor system or downstream pathway have revolutionised the treatment of immune-mediated diseases and can induce drug-free remission. However, RA is a highly heterogeneous and complex disease and consequently, around 40% of patients do not respond to current frontline biologics. This has raised the need for alternative therapeutic strategies or precision medicine approaches that improve treatment decisions for RA patients. My thesis aimed to investigate the action of novel therapeutic modalities that target the IL-6 receptor system or downstream signalling cassette to improve understanding of the underlying inflammatory processes that drive synovitis. Results ¾ Exploiting three novel classes of IL-6/STAT3 inhibitors, I have interrogated their mode of action in in vitro model systems and animal models of synovitis. (1) Using histopathology and RNA-sequencing of the inflamed synovium, I demonstrated that an anti-cancer therapy (CpG-Stat3siRNA) improved arthritis outcome, altered the balance of STAT1/STAT3 signalling and reduced the incidence of ectopic lymphoid-like structures in synovitis. (2) I also used a virus-derived SOCS3 modulator peptide that suppresses STAT3 activity through the induction of SOCS3. This agent was shown to block pathogenic Thelper (Th)17 cell differentiation in vitro and reduced disease pathology in mice with antigen-induced arthritis. (3) Blocking of IL-6 trans-signalling pathway with engineered inhibitors (cs-130Fc) based on regulatory domains of the sgp130 receptor proved efficacy over other related therapy variants (e.g., olamkicept) in inhibiting STAT3-driven Th17 cell expansion in vitro. In a final approach, I also examined the biology of genes that are suppressed by IL-6 and IL-27 in CD4+ T-cells. These factors might have immune-protective function in synovitis, and initial studies are presented on the identification of CRTAM as one such factor and its inflammatory regulation in mouse synovitis. Conclusions ¾ These studies showcase how JAK/STAT signalling through the IL-6 receptor cassette may be controlled at multiple levels and further demonstrate how investigations into their mode of action help to unearth new understanding of IL-6 biology in RA

Characterising semantic relatedness using interpretable directions in conceptual spaces

Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a conceptual space. In particular, we use multi-dimensional scaling to induce a conceptual space from a relevant text corpus and then identify directions that correspond to relative properties such as “more violent than” in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning

MRPR: a MapReduce solution for prototype reduction in big data classification

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data

An Interval Valued K-Nearest Neighbors Classifier

The K-Nearest Neighbors (k-NN) classifier has become a well-known, successful method for pattern classification tasks. In recent years, many enhancements to the original algorithm have been proposed. Fuzzy sets theory has been the basis of several proposed models towards the enhancement of the nearest neighbors rule, being the Fuzzy K-Nearest Neighbors (FuzzyKNN) classifier the most notable procedure in the field. In this work we present a new approach to the nearest neighbor classifier based on the use of interval valued fuzzy sets. The use and implementation of interval values facilitates the membership of the instances and the computation of the votes in a more flexible way than the original FuzzyKNN method, thus improving its adaptability to different supervised learning problems. An experimental study, contrasted by the application of nonparametric statistical procedures, is carried out to ascertain whether the Interval Valued K-Nearest Neighbor (IV-KNN) classifier proposed here is significantly more accurate than k-NN, FuzzyKNN and other fuzzy nearest neighbor classifiers. We conclude that the IV-KNN is indeed significantly more accurate than the rest of classifiers analyzed