Soft, untethered microrobots composed of biocompatible materials for completing micromanipulation and drug delivery tasks in lab-on-a-chip and medical scenarios are currently being developed. Alginate holds significant potential in medical microrobotics due to its biocompatibility, biodegradability, and drug encapsulation capabilities. Here, we describe the synthesis of MANiACs—Magnetically Aligned Nanorods in Alginate Capsules—for use as untethered microrobotic surface tumblers, demonstrating magnetically guided lateral tumbling via rotating magnetic fields. MANiAC translation is demonstrated on tissue surfaces as well as inclined slopes. These alginate microrobots are capable of manipulating objects over millimeter-scale distances. Finally, we demonstrate payload release capabilities of MANiACs during translational tumbling motion.

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Emotions are a central driving force of activism; they motivate participation in movements and encourage sustained involvement. We use natural language processing techniques to analyze emotions expressed or solicited in tweets about 2020 Black Lives Matter protests. Traditional off-the-shelf emotion analysis tools often fail to generalize to new datasets and are unable to adapt to how social movements can raise new ideas and perspectives in short time spans. Instead, we use a few-shot domain adaptation approach for measuring emotions perceived in this specific domain: tweets about protests in May 2020 following the death of George Floyd. While our analysis identifies high levels of expressed anger and disgust across overall posts, it additionally reveals the prominence of positive emotions (encompassing, e.g., pride, hope, and optimism), which are more prevalent in tweets with explicit pro-BlackLivesMatter hashtags and correlated with on the ground protests. The prevalence of positivity contradicts stereotypical portrayals of protesters as primarily perpetuating anger and outrage. Our work offers data, analyses, and methods to support investigations of online activism and the role of emotions in social movements.

For a prediction task, there may exist multiple models that perform almost equally well. This multiplicity complicates how we typically develop and deploy machine learning models. We study how multiplicity affects predictions -i.e., predictive multiplicity -in probabilistic classification. We introduce new measures for this setting and present optimization-based methods to compute these measures for convex empirical risk minimization problems like logistic regression. We apply our methodology to gain insight into why predictive multiplicity arises. We study the incidence and prevalence of predictive multiplicity in real-world risk assessment tasks. Our results emphasize the need to report multiplicity more widely.

Prediction models have been widely adopted as the basis for decisionmaking in domains as diverse as employment, education, lending, and health. Yet, few real world problems readily present themselves as precisely formulated prediction tasks. In particular, there are often many reasonable target variable options. Prior work has argued that this is an important and sometimes underappreciated choice, and has also shown that target choice can have a significant impact on the fairness of the resulting model. However, the existing literature does not offer a formal framework for characterizing the extent to which target choice matters in a particular task. Our work fills this gap by drawing connections between the problem of target choice and recent work on predictive multiplicity. Specifically, we introduce a conceptual and computational framework for assessing how the choice of target affects individuals' outcomes and selection rate disparities across groups. We call this multi-target multiplicity. Along the way, we refine the study of single-target multiplicity by introducing notions of multiplicity that respect resource constraints-a feature of many real-world tasks that isn't captured by existing notions of predictive multiplicity. We apply our methods on a healthcare dataset, and show that the level of multiplicity that stems from target variable choice can be greater than that stemming from nearly-optimal models of a single target.