Fair Visual Recognition via Intervention with Proxy Features

Deep learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societal applications, e.g., hiring, banking, and criminal justice. Existing work tackles this issue by minimizing information about social attributes in models for debiasing. However, the high correlation between target task and social attributes makes bias mitigation incompatible with target task accuracy. Recalling that model bias arises because the learning of features in regard to bias attributes (i.e., bias features) helps target task optimization, we explore the following research question: \emph{Can we leverage proxy features to replace the role of bias feature in target task optimization for debiasing?} To this end, we propose \emph{Proxy Debiasing}, to first transfer the target task's learning of bias information from bias features to artificial proxy features, and then employ causal intervention to eliminate proxy features in inference. The key idea of \emph{Proxy Debiasing} is to design controllable proxy features to on one hand replace bias features in contributing to target task during the training stage, and on the other hand easily to be removed by intervention during the inference stage. This guarantees the elimination of bias features without affecting the target information, thus addressing the fairness-accuracy paradox in previous debiasing solutions. We apply \emph{Proxy Debiasing} to several benchmark datasets, and achieve significant improvements over the state-of-the-art debiasing methods in both of accuracy and fairness

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production

Synthetic biology advanced natural product discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions

Singaporean Women's Perceptions and Barriers to Breast and Cervical Cancer Screening

<p>Objectives: To understand the key factors guiding women's decision of whether or not to use breast and cervical cancer screening services (in order to determine how to cost effectively increase screening uptake in following conjoint).</p><p>Methods: We conducted eight focus groups, with Singaporean women aged between 40 and 64 for breast cancer screening, and between 25 and 64 years for cervical cancer screening, to identify the key factors that drive cancer screening. Using the Health Belief Model to guide our focus group questions, we analyzed the responses and compared similarities and differences among screeners and non-screeners. </p><p>Results: Singaporean women understand the severity of both breast and cervical cancer and fear the associated lifestyle challenges that come with a cancer diagnosis. With the exception of several non-screeners in the breast cancer group, all women reported they believed they were at risk of developing cancer. All women reported the benefits of early detection and accuracy of preventative screening. Both screeners and non-screeners feared cancer detection during screening and saw the screening clinic as a place of possible cancer diagnosis. </p><p>Conclusion: How women perceive their cancer diagnosis, accepting the cancer reality or succumbing to fatalist beliefs, greatly impacts their decision to screen. Screeners were more likely to report that they had recommendations from friends, referrals from doctors, and influences from promotion campaigns. Non-screeners were more likely to have perceive fatalistic views (lack of control over a diagnosis (fatalism) was a unique barrier reported by non-screeners.</p>Thesi