Local Learnings: An Essay on Designing to Facilitate Effective Use of ICT s

In this essay, we explore some of the details of what it takes to own, use and derive benefit from information and communication technologies, with a focus on regions where ICT adoption and use is especially low. We begin with a fairly meticulous description from our ethnographic work to which we'll refer throughout the paper. Though we consider this particular instance, we note that it represents of a wide range of instances from our ethnographic work in homes and businesses over several years in Brazil, Costa Rica, Chile, Ecuador, Bolivia, Peru, Korea and India. Our goal in this paper, however, is to change the conversation from discussions of infrastructure and capacity building to considerations of local, lived conditions in actual homes and actual businesses to suggest design alternatives that make effective use of ICTs more amenable to various locales. We offer two design directions especially for high tech corporations: Designing for Locus of Control and Designing for Local Participation. Along the way, we'll argue to re-frame of the current conception of "digital divide", putting the burden not on those with limited access, but on limited understanding within the high tech industry

Correlates of Co-production: Evidence From a Five-Nation Survey of Citizens

We employ data from an original survey of citizens in the UK, France, Germany, Denmark, and the Czech Republic to examine correlates of citizen co-production of public services in three key policy areas: public safety, the environment, and health. The correlates of co-production we consider include demographic factors (age, gender, education, and employment status), community characteristics (urban, non-urban), performance perceptions (how good a job government is doing), government outreach (providing information and seeking consultation), and self-efficacy (how much of a difference citizens believe they can make). We also report on results from a series of focus groups on the topic of co-production held in each country. Our results suggest that women and elderly citizens generally engage more often in co-production and that self-efficacy—the belief that citizens can make a difference—is an especially important determinant across sectors. Interestingly, good outcome performance (in the sense of a safe neighborhood, a clean environment, and good health) seems to discourage co-production somewhat. Thus citizens' co-production appears to depend in part on awareness of a shortfall in public performance on outcomes. Our results also provide some evidence that co-production is enhanced when governments provide information or engage citizens in consultation. The specific determinants vary, however, not only by sector but across national contexts

Una introducción a las ideas fundamentales de la lógica borrosa a través del arte

In 2015, to celebrate the 50th anniversary of the publication of the article that led to the birth of fuzzy sets theory, the “CosmoCaixa” Science Museum of Barcelona, in collaboration with several Catalan universities and institutions, organized an exhibition to present a sample of the research conducted in the first half century of fuzzy sets theory. In order to introduce visitors to the principles of non - bivalent logics, the exhibition displayed a collection of works of art related to the logical principles of fuzzy thinking under the name “FuzzyArt”.  The aim of this article is to present those references to fuzzy logic theory that inspired the creation o f said works. In parallel, in order to further consolidate the theory it also presents different ideas for using the works for teaching purposes.Durante el año 2015, con motivo de la celebración de los cincuenta años de la publicación del artículo que dio lugar al nacimiento de la teoría de los subconjuntos borrosos, el museo de la Ciencia de Barcelona CosmoCaixa, en colaboración con varias universidades e instituciones catalanas, organizó una muestra expositiva sobre el desarrollo de la investigación en el campo de la teoría de la lógica borrosa durante su primer medio siglo de vida. Con la finalidad de introducir a los visitantes en los principios de las lógicas no bivalentes se presentó en la exposición una colección de obras pictóricas bajo el nombre “FuzzyArt”, relacionada con los principios lógicos del pensamiento borroso. El presente trabajo está centrado en exponer las referencias a la teoría de la lógica borrosa que han sido fuente de inspiración para la creación de dichas obras. Paralelamente, con la finalidad de avanzar en el afianzamiento de esta teoría, se muestran diferentes posibilidades didácticas relacionadas con las obras

Exploring the future of data-driven product design

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Community-driven ELIXIR activities in single-cell omics

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research. Keywor

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements