PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML

Strategies to Use Nanoparticles to Generate CD4 and CD8 Regulatory T Cells for the Treatment of SLE and Other Autoimmune Diseases

Autoimmune diseases are disorders of immune regulation where the mechanisms responsible for self-tolerance break down and pathologic T cells overcome the protective effects of T regulatory cells (Tregs) that normally control them. The result can be the initiation of chronic inflammatory diseases. Systemic lupus erythematosus (SLE) and other autoimmune diseases are generally treated with pharmacologic or biological agents that have broad suppressive effects. These agents can halt disease progression, yet rarely cure while carrying serious adverse side effects. Recently, nanoparticles have been engineered to correct homeostatic regulatory defects and regenerate therapeutic antigen-specific Tregs. Some approaches have used nanoparticles targeted to antigen presenting cells to switch their support from pathogenic T cells to protective Tregs. Others have used nanoparticles targeted directly to T cells for the induction and expansion of CD4+ and CD8+ Tregs. Some of these T cell targeted nanoparticles have been formulated to act as tolerogenic artificial antigen presenting cells. This article discusses the properties of these various nanoparticle formulations and the strategies to use them in the treatment of autoimmune diseases. The restoration and maintenance of Treg predominance over effector cells should promote long-term autoimmune disease remission and ultimately prevent them in susceptible individuals

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

Analysis of Score-Level Fusion Rules for Deepfake Detection

Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach

REAÇÕES DE CRIANÇAS E ADOLESCENTES SUBMETIDOS À ANALGESIA TÓPICA LOCAL NA PUNÇÃO VENOSA PERIFÉRICA.

El objeto del estudio es la reacción del cliente sometido a analgesia tópica local en el período anterior, durante y después de la punción venosa periférica. El objetivo es conocer estas reacciones y evaluar la eficacia de la medicina. Fue utilizado el método cualitativo y los instrumentos para la producción de los datos fueron la observación participante y la entrevista semiestructurada. El lugar fue el Hospital de Día Pediátrico en el Hospital dos Servidores do Estado. Los sujetos son 12 clientes de 5 a 18 años que con sus cuidadores habían sido informados de las etapas del procedimiento. Antes de la punción, el 84% de los sujetos dijeron que tenían miedo del procedimiento, el 16% había demostrado tranquilidad y había dicho no sentir miedo. Durante la punción el 58% había cooperado, mientras que el 42% había necesitado la contención física por la madre. Después del procedimiento, 100% hablaron no haber sentido dolor. Se concluye que tiene eficacia la analgesia, pero que los períodos pre y trans-punción habían sido traumáticos. El razonamiento inductivo nos ha hecho reflexionar sobre la necesidad de evaluar los factores causales que habían llevado a niños a la sensación de miedo y a necesitar de la contención física por la madre, incluso después de haber recibido las explicaciones de la enfermera sobre el procedimiento.O objeto de estudo é a reação do cliente submetido à analgesia tópica local, nos períodos pré, trans e pós-punção venosa periférica. O objetivo é conhecer estas reações e avaliar a eficácia do medicamento. Foi utilizada a abordagem qualitativa e os instrumentos para produção dos dados foram a observação participante e entrevista semi-estruturada. O cenário foi o Hospital Dia Pediátrico do Hospital dos Servidores do Estado. Os sujeitos são 12 clientes de 5 à 18 anos que com seus cuidadores foram informados sobre as etapas do procedimento. Antes da punção 84% dos sujeitos verbalizaram medo do procedimento, 16% demonstraram tranqüilidade e disseram não sentir medo. Durante a punção 58% cooperaram, enquanto 42% necessitaram de contenção física pela mãe. Após o procedimento 100% verbalizaram não terem sentido dor. Conclui-se que há eficácia na analgesia, mas que os períodos pré e trans-punção foram traumáticos. O raciocínio indutivo nos fez refletir que há necessidade de avaliarmos os fatores causais que levaram as crianças a sentir medo e necessitarem de contenção física pela mãe, mesmo após receberem as explicações da enfermeira sobre o procedimento

Serafino Zappacosta: An Enlightened Mentor and Educator

With this article, the authors aim to honor the memory of Serafino Zappacosta, who had been their mentor during the early years of their career in science. The authors discuss how the combination of Serafino Zappacosta's extraordinary commitment to teaching and passion for science created a fostering educational environment that led to the creation of the “Ruggero Ceppellini Advanced School of Immunology.” The review also illustrates how the research on the MHC and the inspirational scientific context in the Zappacosta's laboratory influenced the authors' early scientific interests, and subsequent professional work as immunologists