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Discussing data related to news is one form of participatory journalism. \textit{Data talk} can promote data literacy, foster community around grounded understanding, and encourage civic engagement. What are the challenges and potential of scaffolding data talk on news websites? To understand current practices and perceptions around future visions for audience engagement in news, we interviewed 12 diverse stakeholders including journalists, data scientists, readers, and moderators. To provoke future thinking, we asked participants to react to our interactive design probe with three scenarios: questioning data on a chart, adding data views, and telling personal data stories. We identified several challenges stakeholders face with data talk, including the fast news cycle hindering thorough data discussions and the difficulty of creating accessible insights and visuals. Despite significant data wrangling, discussions around the data remain rare. Participants reacted positively to the interactive scaffolding features in the three design scenarios, noting that these features can make data an effective entry point for discussions, scaffold audience participation in the data pipeline, and lower the barrier for engagement. 

Recent advances in Large Language Models (LLMs) show the potential to significantly augment or even replace complex human writing activities. However, for complex tasks where people need to make decisions as well as write a justification, the trade offs between making work efficient and hindering decisions remain unclear. In this paper, we explore this question in the context of designing intelligent scaffolding for writing meta-reviews for an academic peer review process. We prototyped a system called MetaWriter'' trained on five years of open peer review data to support meta-reviewing. The system highlights common topics in the original peer reviews, extracts key points by each reviewer, and on request, provides a preliminary draft of a meta-review that can be further edited. To understand how novice and experienced meta-reviewers use MetaWriter, we conducted a within-subject study with 32 participants. Each participant wrote meta-reviews for two papers: one with and one without MetaWriter. We found that MetaWriter significantly expedited the authoring process and improved the coverage of meta-reviews, as rated by experts, compared to the baseline. While participants recognized the efficiency benefits, they raised concerns around trust, over-reliance, and agency. We also interviewed six paper authors to understand their opinions of using machine intelligence to support the peer review process and reported critical reflections. We discuss implications for future interactive AI writing tools to support complex synthesis work. 

The current design of email authentication mechanisms has made it challenging for email providers to establish the authenticity of email messages with complicated provenance, such as in the case of forwarding or third-party sending services, where the purported sender of an email is different from the actual originator. Email service providers such as Gmail have tried to address this issue by deploying sender identity indicators (SIIs), which seek to raise users' awareness about where a message originated and encourage safe behavior from users. However, the success of such indicators depends heavily on user interpretation and behavior, and there exists no work that empirically investigates these aspects. In this work, we conducted an interactive survey (n=180) that examined user comprehension of and behavior changes prompted by Gmail's passive SII, the 'via' indicator. Our quantitative analysis shows that although most participants (89%) noticed the indicator, it did not have a significant impact on whether users would adopt safe behaviors. Additionally, our qualitative analysis suggests that once prompted to consider why 'via' is presented, the domain name displayed after 'via' heavily influenced participants' interpretation of the message 'via' is communicating. Our work highlights the limitations of using passive indicators to assist users in making decisions about email messages with complicated provenance. 

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable network. The homogenously integrated cells are implemented in a 2×2 network array to demonstrate the enabled addressability and programmability. The potential for assembling scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems. 

ABSTRACT Chromatin is more than a simple genome packaging system, and instead locally distinguished by histone post-translational modifications (PTMs) that can directly change nucleosome structure and / or be “read” by chromatin-associated proteins to mediate downstream events. An accurate understanding of histone PTM binding preference is vital to explain normal function and pathogenesis, and has revealed multiple therapeutic opportunities. Such studies most often use histone peptides, even though these cannot represent the full regulatory potential of nucleosome context. Here we apply a range of complementary and easily adoptable biochemical and genomic approaches to interrogate fully defined peptide and nucleosome targets with a diversity of mono or multivalent chromatin readers. In the resulting data, nucleosome context consistently refined reader binding, and multivalent engagement was more often regulatory than simply additive. This included abrogating the binding of the Polycomb group L3MBTL1 MBT to histone tails with lower methyl states (me1 or me2 at H3K4, H3K9, H3K27, H3K36 or H4K20); and confirmation that the CBX7 chromodomain and AT-hook-like motif (CD-ATL) tandem act as a functional unit to confer specificity for H3K27me3. Further,in vitronucleosome preferences were confirmed byin vivoreader-CUT&RUN genomic mapping. Such data confirms that more representative chromatin substrates provide greater insight to biological mechanism and its disorder in human disease. 

Many AI system designers grapple with how best to collect human input for different types of training data. Online crowds provide a cheap on-demand source of intelligence, but they often lack the expertise required in many domains. Experts offer tacit knowledge and more nuanced input, but they are harder to recruit. To explore this trade off, we compared novices and experts in terms of performance and perceptions on human intelligence tasks in the context of designing a text-based conversational agent. We developed a preliminary chatbot that simulates conversations with someone seeking mental health advice to help educate volunteer listeners at 7cups.com. We then recruited experienced listeners (domain experts) and MTurk novice workers (crowd workers) to conduct tasks to improve the chatbot with different levels of complexity. Novice crowds perform comparably to experts on tasks that only require natural language understanding, such as correcting how the system classifies a user statement. For more generative tasks, like creating new lines of chatbot dialogue, the experts demonstrated higher quality, novelty, and emotion. We also uncovered a motivational gap: crowd workers enjoyed the interactive tasks, while experts found the work to be tedious and repetitive. We offer design considerations for allocating crowd workers and experts on input tasks for AI systems, and for better motivating experts to participate in low-level data work for AI.