rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model quality

Statistical models should accurately reflect analysts' domain knowledge about variables and their relationships. While recent tools let analysts express these assumptions and use them to produce a resulting statistical model, it remains unclear what analysts want to express and how externalization impacts statistical model quality. This paper addresses these gaps. We first conduct an exploratory study of analysts using a domain-specific language (DSL) to express conceptual models. We observe a preference for detailing how variables relate and a desire to allow, and then later resolve, ambiguity in their conceptual models. We leverage these findings to develop rTisane, a DSL for expressing conceptual models augmented with an interactive disambiguation process. In a controlled evaluation, we find that rTisane's DSL helps analysts engage more deeply with and accurately externalize their assumptions. rTisane also leads to statistical models that match analysts' assumptions, maintain analysis intent, and better fit the data

Orion: A system for modeling, transformation and visualization of multidimensional heterogeneous networks

The study of complex activities such as scientific production and software development often require modeling connections among heterogeneous entities including people, institutions and artifacts. Despite numerous advances in algorithms and visualization techniques for understanding such social networks, the process of constructing network models and performing exploratory analysis remains difficult and time-consuming. In this paper we present Orion, a system for interactive modeling, transformation and visualization of network data. Orion’s interface enables the rapid manipulation of large graphs — including the specification of complex linking relationships — using simple drag-and-drop operations with desired node types. Orion maps these user interactions to statements in a declarative workflow language that incorporates both relational operators (e.g., selection, aggregation and joins) and network analytics (e.g., centrality measures). We demonstrate how these features enable analysts to flexibly construct and compare networks in domains such as online health communities, academic collaboration and distributed software development

EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data -- e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools

ScatterShot: Interactive In-context Example Curation for Text Transformation

The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an LLM to their specific tasks with a small number of examples. However, users tend to include only the most obvious patterns when crafting examples, resulting in underspecified in-context functions that fall short on unseen cases. Further, it is hard to know when "enough" examples have been included even for known patterns. In this work, we present ScatterShot, an interactive system for building high-quality demonstration sets for in-context learning. ScatterShot iteratively slices unlabeled data into task-specific patterns, samples informative inputs from underexplored or not-yet-saturated slices in an active learning manner, and helps users label more efficiently with the help of an LLM and the current example set. In simulation studies on two text perturbation scenarios, ScatterShot sampling improves the resulting few-shot functions by 4-5 percentage points over random sampling, with less variance as more examples are added. In a user study, ScatterShot greatly helps users in covering different patterns in the input space and labeling in-context examples more efficiently, resulting in better in-context learning and less user effort.Comment: IUI 2023: 28th International Conference on Intelligent User Interface

Interfacial kinetics in a model emulsion polymerisation system using microelectrochemical measurements at expanding droplets (MEMED) and time lapse microscopy

Physicochemical processes that take place at the oil-water interface of an epoxy-amine emulsion polymerisation system influence the properties and structural morphology of the polymeric microparticles formed. Investigating these processes, such as the transport of monomers across the liquid/liquid interface brings new understanding which can be used to tune polymeric morphology. Two different approaches are used to provide new insights on these processes. Microelectrochemical measurements at expanding droplets (MEMED) are used to measure the transfer of amine from an organic phase comprised of epoxide and amine into an aqueous receptor phase. The rate of amine transfer across the liquid/liquid interface is characterised using MEMED and finite element method modelling and kinetic values are reported. Time lapse microscopy of epoxide droplets held in deionised water or an aqueous amine solution heated to different temperatures is further used to characterise epoxide dissolution into the aqueous phase. Mass-transport of epoxide into the aqueous phase is shown to be temperature-dependent. Epoxide homopolymerisation at the droplet-water interface is found to influence the rate of epoxide droplet dissolution. The rate of the epoxy- amine cure reaction is shown to be faster than the rate of the epoxide homopolymerisation reaction