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.
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.
In machine learning, model multiplicity is the existence of multiple models that perform equally well for a given prediction task (also known as the "Rashomon effect" ). The set of near-optimal ...models is referred to as the "Rashomon set." Predictive multiplicity examines how predictions change over this set of near-optimal models. If model outputs vary significantly across similar models, this information can offer insight into predictive arbitrariness. In this thesis, I introduce frameworks for evaluating and leveraging predictive multiplicity in different settings.First, I present methods to measure predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome) and develop optimization-based methods to compute these measures efficiently and reliably for convex empirical risk minimization problems. Empirical results show that real-world probabilistic classification tasks can in fact admit competing models that assign substantially different risk estimates. Additionally, I provide insight into how predictive multiplicity arises by analyzing dataset characteristics.Second, I formulate predictive multiplicity analysis in a resource constrained setting recognizing that predictive allocation tasks are governed by a resource budget. I also extend the multiplicity framing, outlining the concept of multi-target multiplicity for quantifying the impact of choices made in regard to target specification for a given predictive allocation task. With this framework, I demonstrate how to fit separate models that are useful for predicting the three outcomes of interest independently and arriving at a way of ranking patients that results in a more equitable allocation.Third, I investigate the connections between predictive multiplicity and predictive churn which is the change in predictions pre- and post- model update in response to a change in training data. I present empirical and theoretical results on characterizing churn in terms of the Rashomon set. Results show that churn unstable points overlap by more than 50 percent with ambiguity points. This points to similarities in the two concepts. Theoretical results to characterize predictive churn between two Rashomon sets as well as churn between models within one Rashomon set hinges on the type of Rashomon set.I focus on predictive multiplicity to advocate for transparency in the prediction model training procedure. These methods to evaluate predictive multiplicity, as well as connections with predictive churn, contribute to a larger effort for machine learning researchers to be accountable to the individuals affected by model predictions. Similar to a person deciding between roads to take while travelling, insight into alternative options (i.e., roads not taken) may provide insight into the significance of the decisions made.
•Intra-nasal magnetic delivery was performed to guide magnetic rods to the brain.•Multi-segmented micro magnetic rods were used for magnetic delivery.•Pulling and spinning magnetic fields were ...applied simultaneously.•Particles were transported by translational and rotational motions.•Pull and drill rods can greatly improve the transport of drugs to the brain.
Getting drugs deep into the brain to treat cancers, neurological disease, and behavioral disorders is challenging. In this work, we tried to improve the efficiency of intra-nasal transport into the brain via the cribriform plate using magnetic particles. We and others have used magnetic particles for delivering heat, drugs, and genes. We performed experiments with mouse cadavers that received 250-nm-wide intra-nasal magnetic rods intra-nasally under different combinations of magnetic fields. We found that the application of helical dynamic gradients to the particles (i.e., both rotational and linear) improved transport from the nose into the brain, as compared to linear magnetic gradients alone. On histological examination, no tracks were observed to suggest significant damage to the brain during the transport process. We are currently building a system for testing with live animals, with eventual proposed application to humans.
Our focus lies at the intersection between two broader research perspectives: (1) the scientific study of algorithms and (2) the scholarship on race and racism. Many streams of research related to ...algorithmic fairness have been born out of interest at this intersection. We think about this intersection as the product of work derived from both sides. From (1) algorithms to (2) racism, the starting place might be an algorithmic question or method connected to a conceptualization of racism. On the other hand, from (2) racism to (1) algorithms, the starting place could be recognizing a setting where a legacy of racism is known to persist and drawing connections between that legacy and the introduction of algorithms into this setting. In either direction, meaningful disconnection can occur when conducting research at the intersection of racism and algorithms. The present paper urges collective reflection on research directions at this intersection. Despite being primarily motivated by instances of racial bias, research in algorithmic fairness remains mostly disconnected from scholarship on racism. In particular, there has not been an examination connecting algorithmic fairness discussions directly to the ideology of color-blind racism; we aim to fill this gap. We begin with a review of an essential account of color-blind racism then we review racial discourse within algorithmic fairness research and underline significant patterns, shifts and disconnects. Ultimately, we argue that researchers can improve the navigation of the landscape at the intersection by recognizing ideological shifts as such and iteratively re-orienting towards maintaining meaningful connections across interdisciplinary lines.
Prediction models have been widely adopted as the basis for decision-making 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 is not 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.
Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a ...person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, this is referred to as predictive multiplicity i.e. the prevalence of conflicting predictions assigned by near-optimal competing models. In this paper, we present a framework for measuring predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome). We introduce measures that capture the variation in risk estimates over the set of competing models, and develop optimization-based methods to compute these measures efficiently and reliably for convex empirical risk minimization problems. We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks. Further, we provide insight into how predictive multiplicity arises by analyzing the relationship between predictive multiplicity and data set characteristics (outliers, separability, and majority-minority structure). Our results emphasize the need to report predictive multiplicity more widely.
Multimodal large language models (MLLMs) like LLaVA and GPT-4(V) enable general-purpose conversations about images with the language modality. As off-the-shelf MLLMs may have limited capabilities on ...images from domains like dermatology and agriculture, they must be fine-tuned to unlock domain-specific applications. The prevalent architecture of current open-source MLLMs comprises two major modules: an image-language (cross-modal) projection network and a large language model. It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models. To this end, via experiments on 4 datasets and under 2 fine-tuning settings, we find that as the MLLM is fine-tuned, it indeed gains domain-specific visual capabilities, but the updates do not lead to the projection extracting relevant domain-specific visual attributes. Our results indicate that the domain-specific visual attributes are modeled by the LLM, even when only the projection is fine-tuned. Through this study, we offer a potential reinterpretation of the role of cross-modal projections in MLLM architectures. Project webpage: https://claws-lab.github.io/projection-in-MLLMs/
Machine learning models in modern mass-market applications are often updated over time. One of the foremost challenges faced is that, despite increasing overall performance, these updates may flip ...specific model predictions in unpredictable ways. In practice, researchers quantify the number of unstable predictions between models pre and post update -- i.e., predictive churn. In this paper, we study this effect through the lens of predictive multiplicity -- i.e., the prevalence of conflicting predictions over the set of near-optimal models (the Rashomon set). We show how traditional measures of predictive multiplicity can be used to examine expected churn over this set of prospective models -- i.e., the set of models that may be used to replace a baseline model in deployment. We present theoretical results on the expected churn between models within the Rashomon set from different perspectives. And we characterize expected churn over model updates via the Rashomon set, pairing our analysis with empirical results on real-world datasets -- showing how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications. Further, we show that our approach is useful even for models enhanced with uncertainty awareness.
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