Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Human health risk evaluation of selected VOC, SVOC and particulate emissions from scented candles

a b s t r a c t Airborne compounds in the indoor environment arise from a wide variety of sources such as environmental tobacco smoke, heating and cooking, construction materials as well as outdoor sources. To understand the contribution of scented candles to the indoor load of airborne substances and particulate matter, candle emission testing was undertaken in environmentally controlled small and large emission chambers. Candle emission rates, calculated on the basis of measured chamber concentrations of volatile and semivolatile organic compounds (VOC, SVOC) and particulate matter (PM), were used to predict their respective indoor air concentrations in a standard EU-based dwelling using 2 models: the widely accepted ConsExpo 1-box inhalation model and the recently developed RIFM 2-box indoor air dispersion model. The output from both models has been used to estimate more realistic consumer exposure concentrations of specific chemicals and PM in candle emissions. Potential consumer health risks associated with the candle emissions were characterized by comparing the exposure concentrations with existing indoor or ambient air quality guidelines or, where not existent, to established toxicity thresholds. On the basis of this investigation it was concluded that under normal conditions of use scented candles do not pose known health risks to the consumer

A high-coverage draft genome of the mycalesine butterfly <i>Bicyclus anynana</i>

The mycalesine butterfly Bicyclus anynana, the "Squinting bush brown," is a model organism in the study of lepidopteran ecology, development, and evolution. Here, we present a draft genome sequence for B. anynana to serve as a genomics resource for current and future studies of this important model species. Seven libraries with insert sizes ranging from 350 bp to 20 kb were constructed using DNA from an inbred female and sequenced using both Illumina and PacBio technology; 128 Gb of raw Illumina data was filtered to 124 Gb and assembled to a final size of 475 Mb (∼×260 assembly coverage). Contigs were scaffolded using mate-pair, transcriptome, and PacBio data into 10 800 sequences with an N50 of 638 kb (longest scaffold 5 Mb). The genome is comprised of 26% repetitive elements and encodes a total of 22 642 predicted protein-coding genes. Recovery of a BUSCO set of core metazoan genes was almost complete (98%). Overall, these metrics compare well with other recently published lepidopteran genomes. We report a high-quality draft genome sequence for Bicyclus anynana. The genome assembly and annotated gene models are available at LepBase (http://ensembl.lepbase.org/index.html)

Isometric Exercise Training and Arterial Hypertension: An Updated Review

Hypertension is recognised as a leading attributable risk factor for cardiovascular disease and premature mortality. Global initiatives towards the prevention and treatment of arterial hypertension are centred around non-pharmacological lifestyle modification. Exercise recommendations differ between professional and scientific organisations, but are generally unanimous on the primary role of traditional aerobic and dynamic resistance exercise. In recent years, isometric exercise training (IET) has emerged as an effective novel exercise intervention with consistent evidence of reductions in blood pressure (BP) superior to that reported from traditional guideline-recommended exercise modes. Despite a wealth of emerging new data and endorsement by select governing bodies, IET remains underutilised and is not widely prescribed in clinical practice. This expert-informed review critically examines the role of IET as a potential adjuvant tool in the future clinical management of BP. We explore the efficacy, prescription protocols, evidence quality and certainty, acute cardiovascular stimulus, and physiological mechanisms underpinning its anti-hypertensive effects. We end the review with take-home suggestions regarding the direction of future IET research

Negative Reciprocity and its Relation to Anger-Like Emotions in Homogeneous and Heterogeneous Groups

Several studies have shown that social identity fosters the provision of public goods and enhances the willingness to reciprocate cooperative behavior of group members dependent on the social environment. Yet, the question of how social identity affects negative reciprocity in identityhomogeneous and -heterogeneous groups has received only little attention. Consequently, we seek to fill this gap by examining whether social identity affects individuals' willingness to sanction deviating group members in a public good context. Moreover, we devote particular attention to the role of anger-like emotions in negative reciprocity. To test our hypotheses we employ one-shot public good games in strategy method with induced social identity. Our results indicate that members of identity homogeneous groups punish much less often and in smaller amounts than of identity heterogeneous groups when they face contributions smaller than their own. We also find that anger-like emotions influence punishment behavior much stronger when individuals are matched with members of different identities than in identity homogenous groups. These findings contribute to the better understanding of the nature of social identity and its impact on reciprocity, improving economists ability to predict behavior taking emotions also into consideration

Dispositional free riders do not free ride on punishment

Strong reciprocity explains prosocial cooperation by the presence of individuals who incur costs to help those who helped them (‘strong positive reciprocity’) and to punish those who wronged them (‘strong negative reciprocity’). Theories of social preferences predict that in contrast to ‘strong reciprocators’, self-regarding people cooperate and punish only if there are sufficient future benefits. Here, we test this prediction in a two-stage design. First, participants are classified according to their disposition towards strong positive reciprocity as either dispositional conditional cooperators (DCC) or dispositional free riders (DFR). Participants then play a one-shot public goods game, either with or without punishment. As expected, DFR cooperate only when punishment is possible, whereas DCC cooperate without punishment. Surprisingly, dispositions towards strong positive reciprocity are unrelated to strong negative reciprocity: punishment by DCC and DFR is practically identical. The ‘burden of cooperation’ is thus carried by a larger set of individuals than previously assumed

A Crucial Role of Flagellin in the Induction of Airway Mucus Production by Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen involved in nosocomial infections. Flagellin is a P. aeruginosa virulence factor involved in host response to this pathogen. We examined the role of flagellin in P. aeruginosa-induced mucus secretion. Using a mouse model of pulmonary infection we showed that PAK, a wild type strain of P. aeruginosa, induced airway mucus secretion and mucin muc5ac expression at higher levels than its flagellin-deficient mutant (ΔFliC). PAK induced expression of MUC5AC and MUC2 in both human airway epithelial NCI-H292 cell line and in primary epithelial cells. In contrast, ΔFliC infection had lower to no effect on MUC5AC and MUC2 expressions. A purified P. aeruginosa flagellin induced MUC5AC expression in parallel to IL-8 secretion in NCI-H292 cells. Accordingly, ΔFliC mutant stimulated IL-8 secretion at significantly lower levels compared to PAK. Incubation of NCI-H292 cells with exogenous IL-8 induced MUC5AC expression and pre-incubation of these cells with an anti-IL-8 antibody abrogated flagellin-mediated MUC5AC expression. Silencing of TLR5 and Naip, siRNA inhibited both flagellin-induced MUC5AC expression and IL-8 secretion. Finally, inhibition of ERK abolished the expression of both PAK- and flagellin-induced MUC5AC. We conclude that: (i) flagellin is crucial in P. aeruginosa-induced mucus hyper-secretion through TLR5 and Naip pathways; (ii) this process is mediated by ERK and amplified by IL-8. Our findings help understand the mechanisms involved in mucus secretion during pulmonary infectious disease induced by P. aeruginosa, such as in cystic fibrosis

Diversity and carbon storage across the tropical forest biome

Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.Additional co-authors: Kofi Affum-Baffoe, Shin-ichiro Aiba, Everton Cristo de Almeida, Edmar Almeida de Oliveira, Patricia Alvarez-Loayza, Esteban Álvarez Dávila, Ana Andrade, Luiz E. O. C. Aragão, Peter Ashton, Gerardo A. Aymard C., Timothy R. Baker, Michael Balinga, Lindsay F. Banin, Christopher Baraloto, Jean-Francois Bastin, Nicholas Berry, Jan Bogaert, Damien Bonal, Frans Bongers, Roel Brienen, José Luís C. Camargo, Carlos Cerón, Victor Chama Moscoso, Eric Chezeaux, Connie J. Clark, Álvaro Cogollo Pacheco, James A. Comiskey, Fernando Cornejo Valverde, Eurídice N. Honorio Coronado, Greta Dargie, Stuart J. Davies, Charles De Canniere, Marie Noel Djuikouo K., Jean-Louis Doucet, Terry L. Erwin, Javier Silva Espejo, Corneille E. N. Ewango, Sophie Fauset, Ted R. Feldpausch, Rafael Herrera, Martin Gilpin, Emanuel Gloor, Jefferson S. Hall, David J. Harris, Terese B. Hart, Kuswata Kartawinata, Lip Khoon Kho, Kanehiro Kitayama, Susan G. W. Laurance, William F. Laurance, Miguel E. Leal, Thomas Lovejoy, Jon C. Lovett, Faustin Mpanya Lukasu, Jean-Remy Makana, Yadvinder Malhi, Leandro Maracahipes, Beatriz S. Marimon, Ben Hur Marimon Junior, Andrew R. Marshall, Paulo S. Morandi, John Tshibamba Mukendi, Jaques Mukinzi, Reuben Nilus, Percy Núñez Vargas, Nadir C. Pallqui Camacho, Guido Pardo, Marielos Peña-Claros, Pascal Pétronelli, Georgia C. Pickavance, Axel Dalberg Poulsen, John R. Poulsen, Richard B. Primack, Hari Priyadi, Carlos A. Quesada, Jan Reitsma, Maxime Réjou-Méchain, Zorayda Restrepo, Ervan Rutishauser, Kamariah Abu Salim, Rafael P. Salomão, Ismayadi Samsoedin, Douglas Sheil, Rodrigo Sierra, Marcos Silveira, J. W. Ferry Slik, Lisa Steel, Hermann Taedoumg, Sylvester Tan, John W. Terborgh, Sean C. Thomas, Marisol Toledo, Peter M. Umunay, Luis Valenzuela Gamarra, Ima Célia Guimarães Vieira, Vincent A. Vos, Ophelia Wang, Simon Willcock & Lise Zemagh

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page