Human-Computer Interaction
A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics
Lara Gauder, Martin Bernardo Meza, Javier Krick et al. (+4 more)
Human-
agent teams are collaborative systems where humans and
agents work interdependently to achieve shared goals. The success of such teams is associated with the human's perception of the
agent as a legitimate teammate. This perception is thought to depend not only on the
agent's capabilities and reliability but also on social factors. We explore whether simple changes in an
agent's communicative behavior can influence the human's perception of the
agent's teammate-likeness. To this end, we developed a protocol based on a collaborative game requiring spoken interaction, in which a human subject and a virtual
agent collaborate to identify a target object and place it on a board. Each subject interacted with two
agents which provided the same task-relevant information but differed in behavior. While one was a neutral tool-like
agent, the other was a team-building
agent that used simple social strategies such as empathy, politeness, and positivity. Our analysis shows that the team-building
agent was perceived as significantly more teammate-like, even in terms of its ability, despite both
agents having identical capabilities for game-solving. Further, subjects with a higher propensity to trust automated systems and lower neuroticism tended to have a more favorable perception of the
agent. Finally, we found that subjects tended to change their prosodic patterns when talking to the team-building
agent, as a classifier based on prosodic features was able to predict the type of
agent with better-than-random performance. A practically relevant conclusion is that minimal changes in spoken communication behavior of the
agent, easily implemented in a variety of scenarios, can have a significant positive impact on the human's perception of its teammate-likeness.
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All authors Lara Gauder, Martin Bernardo Meza, Javier Krick, Alejandro Masin, Luciana Benotti, Marcos Luis Pietto, Luciana Ferrer
Human-Computer Interaction
Assessing Acceptance and Privacy Preferences of Third-party Financial Data Sharing in Bipolar Disorder
Jeff Brozena, Johnna Blair, Dahlia Mukherjee et al. (+4 more)
Bipolar disorder is strongly associated with financial instability. We examine how different interventions motivate individuals with bipolar disorder to share financial data with others. This approach can inform the development of tools for digital monitoring and intervention designed to promote financial stability in this population. 500 individuals with BD completed a pre-registered factorial vignette survey to examine level of comfort with hypothetical scenarios involving third-party financial interventions during symptomatic and euthymic periods. Scenario components were systematically varied between third-party actors, mood states, and intervention types. Participants rated sharing comfort on a 0-10 point scale. Multilevel models tested differences alongside clinical and financial histories, relational trust, and personality. Participants were most comfortable involving care partners in financial planning. They were more comfortable with temporary spending restrictions during symptomatic states than euthymic periods, underscoring the importance of accurate mood detection for intervention delivery. Prior financial help-seeking behavior and higher relational trust predicted greater comfort. Bankruptcy experience — declared by 11.4% and considered by 31.7% — was associated with increased comfort with spending restrictions. Individuals with psychiatric advance directives (8%) were significantly more comfortable sharing spending behaviors than those without. Comfort with financial interventions was higher among those with prior financial challenges or help-seeking histories. Participants distinguished between symptomatic and euthymic periods, favoring targeted, time-limited restrictions over general monitoring. These findings extend prior work on financial data sharing for illness self-management.
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All authors Jeff Brozena, Johnna Blair, Dahlia Mukherjee, Erika F. H. Saunders, Thomas Richardson, Mark Matthews, Saeed Abdullah
Human-Computer Interaction
viaCross: A Pen-Based Technique for Selecting Objects and Attributes of Interest using Crossing-Based Gestures
Benedict Leung, Mariana Shimabukuro and Christopher Collins
Crossing-based selection is well-studied, yet past research has overlooked multi-object and attribute selection with crossing gestures. We present viaCross, a pen-based technique in which crossing strokes define attribute constraints, paired with attribute widgets that visualize and allow subsequent edits to these constraints. A controlled study compared viaCross with traditional WIMP filter panels. Results show that viaCross was more efficient for complex object-level selections, requiring fewer strokes and maintaining efficiency as task difficulty increased. WIMP panels were faster and more intuitive for simple attribute-based selections, but participants encountered repeated deselections and occasional misclicks. viaCross matched panel performance for creating attribute selections but was slower during refinement, as widgets had to be recreated, indicating the need for persistent widgets or hybrid support with a WIMP-style interface. Overall, viaCross demonstrates that crossing-based gestures can provide an efficient and scalable approach to multi-object and attribute selection.
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Human-Computer Interaction · Cross-listed from cs.CY
Govern, Map, Measure, Absorb: The Legibility Trap in Public Sector Participatory AI Governance
Srijan Pandey and Devansh Saxena
Public sector agencies have adopted participatory design as a prominent procedural safeguard proposed against algorithmic harm. Across methods, institutional contexts, and well-intentioned implementations, the participation gap persists. Communities engage, input is documented, and systems are deployed largely unchanged. This paper locates the persistence of that gap in the mechanism institutions use to document participation itself. We develop the concept of the legibility trap, in which institutional mechanisms for documenting participation systematically select for forms of community input that fit institutional categories and filter out the forms that would most constrain state power, including foundational dissent, demands for non-deployment, and what Kelly Oliver calls witnessing to structural conditions of harm. Drawing on James Scott's theory of state simplification, we demonstrate the legibility trap through critical discourse analysis of twenty-two official, community-produced, and public-record documents across three public sector AI deployments: the Allegheny Family Screening Tool, Detroit Project Green Light, and the Community Control Over Police Surveillance (CCOPS) ordinances in Oakland. We identify the structural conditions under which participation partially escapes the trap, and we theorize that repeated non-response constitutes a harm to community subjectivity that AI ethics frameworks have not yet named.
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Human-Computer Interaction
Interactive Machine Learning Interfaces for Disease Risk Prediction: Effects on Risk Perception and Behaviour
Tiffany Ngai, Max Homm, Matthew Bradbury et al. (+1 more)
Machine learning risk models are increasingly being used in patient-facing health tools, but it remains unclear how well users understand the information these systems present. In this work, we study how people interpret an interactive Type 2 Diabetes (T2D) risk interface and whether interacting with it influences their attitudes toward behavioural change. Through an exploratory mixed-methods study with 15 participants, we compare participants' perceived understanding with their actual understanding and identify key themes from qualitative interviews. We find that participants often understood the interface better than they initially believed, but still faced important barriers related to unclear terminology, ambiguous risk framing, and limited explanations of model inputs. Finally, we propose relevant design guidelines and discuss broader issues surrounding trust and
fairness. Our findings highlight the importance of intuitive visual design, familiar presentation, and clear explanations in patient-facing ML interfaces.
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All authors Tiffany Ngai, Max Homm, Matthew Bradbury, Anamaria Crisan
Human-Computer Interaction · Cross-listed from cs.SD
Harmonic Eigenspace: A Web-based Application for Navigating and Composing Microtonal Harmony
David Dalmazzo and Ken Déguernel
This paper presents a web-based application for navigating and composing microtonal harmony, built on the Harmonic Eigenspace, a four-dimensional psychoacoustically grounded space in which tetrad chord types are located by their spectral dissonance profiles, computed with Sethares's roughness/dissonance model. The coordinate system is transposition-invariant: a coordinate triple ({\alpha}, e̱ta}, {\gamma}) locates the three upper notes in relation to the root, so a chord quality corresponds to a direction in the space, the invariant ray along which transposition acts, while the root frequency sets the scale. The dissonance field over these coordinates can be computed at any register; the locations of its local minima are register-invariant, as they arise from partial-coincidence ratio conditions. The dissonance volume contains 100 local minima that align with just-intonation intervals and act as landmarks, organising the space into basins around the most consonant tetrads. We embed tetrads from three tonal equal temperaments as discrete
lattices within this continuous volume. The application presents this space through two components: the Harmonic Eigenspace as a navigable 3D visualisation of the dissonance volume in which all nodes are playable, and a Modal Studio that extends modal interchange logic to the ten-gradation interval vocabulary of 53-TET. The application also functions as a MIDI controller with MIDI Polyphonic Expression support, usable in any digital audio workstation that supports this format. A listening study with 31 participants used both scenes of the application: listeners first rated isolated 53-TET chords alongside chords familiar from Western practice, such as the maj7 and the m7; they then rated chord progressions composed in the Modal Studio, measuring their acceptance or rejection of microtonal progressions heard for the first time.
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Human-Computer Interaction
Seeing through the Eyes of AI: Situated Explainability in Augmented Reality
Ana Stanescu, Lucchas Ribeiro Skreinig, Tobias Langlotz et al. (+5 more)
Explainable Artificial Intelligence (AI) enables humans to understand and interpret decisions of AI models. Instead of having a black box, explainability supports humans in understanding AI models' behavior. Existing explainable AI approaches often present explanations on 2D displays using pre-recorded data, requiring users to relate the displayed information back to the physical objects and real world locations involved in a model's decision. Users are forced to decouple data exploration and capture from AI model interpretation. For AI systems that work within physical environments, this separation can make explanations difficult to interpret in context. We propose using Augmented Reality (AR) to enhance the understanding of AI models by enabling spatial explainability information directly in a user's workspace, in real time, as they explore the world. We show how known explainability methods can be applied in AR and provide insights into user experiences with such an application.
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All authors Ana Stanescu, Lucchas Ribeiro Skreinig, Tobias Langlotz, Stefanie Zollmann, Peter Mohr, Dieter Schmalstieg, Mark Billinghurst, Denis Kalkofen
Human-Computer Interaction
The Effects of Air-Conditioning and Road-Traffic Noise on Perceived, Cognitive, and EEG Responses in a University Classroom
Yuanzhi Su and Cynthia Hou
Air-conditioning and road-traffic noise are common in classrooms, and their effects are often judged by cognitive performance. However, a sound that leaves performance unchanged may still be perceived differently or alter
brain activity. In a within-participant design, this study tested whether perceived, cognitive, and
EEG responses are consistent, and whether perceived and
EEG responses distinguish conditions that performance does not. Sixteen students completed cognitive tasks in a university classroom under air-conditioning and road-traffic noise, each played back at 55, 60, and 65 dBA. Participants rated acoustic appraisal and perceived workload after each condition, and
EEG was recorded during the tasks. The results show that cognitive performance did not differ significantly between conditions in any task. By contrast, noise was rated louder, less pleasant, and more arousing than a no-noise control, and all three ratings varied with sound level; perceived workload was also higher under noise. Task-period
EEG power also showed significant differences between sources or levels in specific tasks and scalp regions. Within participants, relative
EEG band power covaried mainly with loudness and pleasantness; louder and less pleasant ratings coincided with higher relative theta and lower relative gamma power. Associations with arousal and performance were rare, and none were found for workload. The three response types were thus only partly consistent, and perceived responses and
EEG distinguished conditions that performance did not. Non-significant performance differences do not mean the conditions were equivalent for students. Assessments of air-conditioning and road-traffic noise in classrooms should therefore consider perceived responses alongside cognitive performance, with
EEG providing complementary information on neural activity during tasks.
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Human-Computer Interaction · Cross-listed from cs.DB
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
Imane Hocine, Asma Abboura, Soror Sahri et al. (+3 more)
Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference. Although recent imputation methods exploit temporal and spatial dependencies effectively, most either assume offline access to future observations or prioritize throughput without enforcing domain plausibility. We present TSGuard, a real-time demonstration system for monitoring, validating, and imputing missing values in streaming time series. TSGuard combines a lightweight graph-aware temporal imputation model with constraint-aware validation, fallback estimation, and operator-facing explanations. Rather than treating imputation as an isolated prediction task, TSGuard integrates it into a broader data-quality loop: detect problematic observations, impute missing values, validate estimated against physical and spatial constraints, and either retain the original value as a plausible anomaly or replace it when it violates domain constraints. Using environmental sensing as a motivating setting, the demo enables users to inspect delayed sensors, compare imputers, define constraints, and validate flagged values in real time. The combination of lightweight online spatiotemporal imputation, domain-aware validation, and explicit retain-or-replace decisions is our central contribution, while interactive explanations make these decisions inspectable and actionable. for operators.
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All authors Imane Hocine, Asma Abboura, Soror Sahri, Abhijith Senthilkumar, Yacine Hakimi, Grégoire Danoy
Human-Computer Interaction · Cross-listed from cs.CV
A Benchmark for Spatially Grounded Gesture Generation
Anna Deichler, Rishabh Dabral, Fethiye Irmak Dogan et al. (+2 more)
Communication in shared space interweaves verbal and non-verbal signals, and pointing gestures anchor
language to the environment: "put the cup on that one" is uninterpretable without the gesture that fixes the referent. Yet no common framework exists for evaluating whether generated gestures indicate their intended referent; distributional metrics reward a gesture aimed at the wrong object as long as it looks natural. We introduce a
benchmark for spatially grounded gesture generation, comprising ~2K pointing-annotated clips from naturalistic VR dialogue with ground-truth 3D referents, a task in which systems must decide when, how and where to point within conversational
speech, and a protocol that separates temporal
alignment, spatial grounding and perceived naturalness. We also provide a flow-matching baseline, MM-Conv-Flow. Evaluating it alongside an independent
retrieval-based system and captured human motion, we find that
geometric grounding can exceed that of human pointing without any gain in perceived naturalness, showing that referential gesture quality must be measured along separate dimensions.
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All authors Anna Deichler, Rishabh Dabral, Fethiye Irmak Dogan, Anindita Ghosh, Jonas Beskow
Human-Computer Interaction · Cross-listed from cs.AI
Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations
David Nadrchal, Monorama Swain, Florian Schmid et al. (+2 more)
This work presents an automatic
speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria rendering their
speech unintelligible to untrained listeners. We release a public
dataset containing 33 annotated hours of the speaker's
speech, collected using a novel "artificial conversation" protocol designed for high engagement and dialogue realism. We propose a multi-stage training pipeline based on Whisper Base: fine-tuning on standard Czech
speech, acoustically simulated tracheostomic
speech, and the speaker's data. We evaluate the system across three near real-time scenarios: scripted conversations, question answering, and spontaneous dialogue, achieving a 50% relative reduction in Character Error Rate compared to Whisper Base baseline and surpassing the average recognition accuracy of their assistants in acoustic recognition of isolated utterances. We demonstrate that even for severely impeded
speech, a helpful ASR is achievable, as evidenced by the quantitative results and the feedback from the speaker.
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All authors David Nadrchal, Monorama Swain, Florian Schmid, Gerhard Widmer, Paul Primus
Human-Computer Interaction
Tracking Human Daily Cognitive Activity from EEG and Biometric Data
Alina Gutoreva and Zhaniya Omar
Understanding human cognitive activity in everyday life remains challenging due to the dynamic, context-dependent, and
multimodal nature of cognition. Laboratory-based studies often fail to capture real-world cognitive processes, while single-modality approaches provide only partial insight into cognitive states. Advances in wearable sensing now enable the collection of heterogeneous data streams for a more comprehensive view of daily cognition. This paper presents a
multimodal framework for tracking human cognitive activity using electroencephalography (
EEG), wearable physiological signals, behavioral context, and self-reported measures. A preliminary pilot study was conducted with observational data from three participants (N = 3) over 280 annotated 10-minute intervals spanning nine activity domains across two weeks. Results reveal consistent temporal patterns, including a discernible mid-day decrease in motivation and energy at 13:00, followed by afternoon recovery. Work and IADLs yielded the highest flow state rates (51% and 50%), while ADLs produced the lowest (15%). Motivation correlated strongly with arousal (r = 0.78) and
attention (r = 0.74), whereas perceived stress showed a weaker negative relationship (r = -0.33). A linear regression model predicting motivation from arousal,
attention, energy, and stress achieved R2 = 0.76 (MAE = 9.84, RMSE = 12.85). Lag-based analysis indicates that prior energy levels positively predict subsequent motivation, confirming temporal dependencies in cognitive dynamics. These findings demonstrate the feasibility of
multimodal cognitive activity analysis in real-world environments and highlight the importance of integrating physiological and behavioral indicators. The proposed framework provides a foundation for future large-scale
multimodal systems and applied intelligent solutions.
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Human-Computer Interaction
Co-Designing AI For Mental Health Support With Young Adults of Color (YOC): Needs, Expectations, and Implications for AI Literacy
Elaine Dabin Jeon, John Bosco S. Bunyi, Hannah Kim et al. (+7 more)
Young adults of color (YOC) face heightened mental health challenges and barriers to care while navigating developmental and life transitions. Situated between youth-oriented safeguards and adult-oriented AI systems, little is known about how they use AI chatbots for mental health and well-being support or how sociocultural contexts shape their expectations, concerns, and design preferences. We conducted a two-day co-design workshop with 13 Asian, Black, and Hispanic/Latino/a young adults aged 18–24. Participants found generic chatbot advice to flatten their lived experiences; rather than making incorrect assumptions, they wanted more opportunities for identity-informed disclosure. Preferences for YOC-centered personalization also revealed gaps in
privacy and AI literacy. Participants negotiated different therapeutic roles for chatbots and sought greater AI accountability and user agency, highlighting blurred boundaries between clinical and non-clinical AI-mediated support. Findings suggest directions for integrating AI literacy with mental health literacy and centering YOC's experiences in the
privacy calculus.
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All authors Elaine Dabin Jeon, John Bosco S. Bunyi, Hannah Kim, Renkai Ma, Yaman Yu, Michal Luria, Jason Yip, Alexis Hiniker, Katie Davis, Angel Hsing-Chi Hwang
Human-Computer Interaction
Characterizing the Performance Gap in Human Activity Recognition for Older Adults
Hossein Khayami, Sungjin Hwang, Eshed Ohn-Bar et al. (+4 more)
Human activity recognition (HAR) from wrist-worn accelerometers is increasingly used for health and behavioral tracking. Yet, most wearable HAR models are developed and evaluated on
datasets dominated by younger adults, leaving it unclear whether
benchmark progress generalizes across age groups. In this work, we leverage MyMove, our carefully annotated, free-living older-adult HAR
dataset (mean age 71), to evaluate deep-learning architectures and training regimes under both leave-one-subject-out and cross-
dataset transfer. We find that improvements on younger-adult
benchmarks fail to transfer equally to data collected from older adults, resulting in a persistent and often widening performance gap. However, richer representations, particularly frozen self-supervised features pretrained on the age-diverse UK Biobank
dataset, substantially improve performance on data from older adults and consistently narrow the performance gap, at modest cost to younger-adult performance, though disparities remain. These findings suggest that
benchmark gains and architectural scaling alone provide an incomplete picture of progress in wearable HAR, and broader advances may require representations that better capture population diversity, alongside personalized adaptation to individual movement patterns and routines.
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All authors Hossein Khayami, Sungjin Hwang, Eshed Ohn-Bar, David E. Conroy, Amanda Lazar, Eun Kyoung Choe, Hernisa Kacorri
Human-Computer Interaction · Cross-listed from cs.CY
LearnAdapt Praxis: Controlled AI Assistance and Evidence Traces for Adult Workplace Learning
Nizam Kadir
AI can help adults produce plausible workplace artefacts while leaving their independent
reasoning difficult to inspect. LearnAdapt Praxis addresses this design problem through a five-stage learning workspace: Frame, Learn, Build, Validate and Transfer. The application stores an initial response, versioned specifications, coaching records, validation observations, a separate transfer response and facilitator feedback. Project-level server controls restrict coaching, access to earlier work and learner export during an active transfer attempt; they do not establish that a learner avoided assistance outside the application. This technical report describes the implemented architecture and evaluates selected workflow, authority, failure-recovery and provenance properties. A reproducible synthetic rehearsal exercised 120 serial project episodes across three fictional workplace contexts and four injected provider conditions. All 3,990 recorded checks met their specified expectations. The resulting records contained 240 specification versions, 1,200 activity events and 60 stored synthetic hints; 60 injected provider failures produced no fabricated hints. Separate authentication regressions exposed and resolved a session-expiry defect, and limited staging and production checks verified account access and live model connectivity. The evidence supports the tested engineering properties of the recorded release. It does not establish learning gains, model-output quality, unaided assessment validity, population usability or production capacity. The contribution is an implemented arrangement for separating assisted work, project-level assistance withdrawal and inspectable evidence, accompanied by executable tests and an explicit account of what remains to be validated with adult learners.
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Human-Computer Interaction · Cross-listed from cs.CY
Lessons from Trauma-Informed Training on Technology-Facilitated Abuse for Gender-Based Violence Advocates
Naman Gupta, Connie W. Chau, Sophie Stephenson et al. (+2 more)
Technology-facilitated abuse (TFA) is an emerging gendered public health crisis affecting millions of people across the globe. TFA coincides with other forms of gender-based violence (GBV), such as emotional, psychological, and physical abuse by an intimate partner. Survivors often seek support from GBV advocates who specialize in helping survivors navigate abusive situations and potential pathways for healing and remediation. With the rise in TFA, however, survivors and GBV advocates experience barriers and gaps in knowledge in identifying and mitigating this newer, ever-evolving form of abuse. To address this, we developed trauma-informed training as a critical capacity-building intervention to improve GBV advocates' knowledge and skills for responding to survivors and to encourage multi-stakeholder collaboration toward a structural response. We facilitated 8 training workshops at local, state, and national US-based GBV organizations. Our study demonstrates that the trainings increased advocates' perceived understanding of TFA abuse vectors and their impacts on survivors, along with advocates' confidence in providing support, safety planning, and referrals through collaboration with community stakeholders. We then conducted retrospective reflections through autoethnography to surface key lessons, contestations, and tensions that emerged from our experiences in designing and coordinating trainings with host GBV organizations. Our analysis of autoethnographic notes revealed three key design decisions: coordinating logistics flexibly, reducing reflexive distance from host organizations and attendees, and using our field experience to tailor content for accessibility. We contribute a concrete checklist to guide future HCI scholars in conducting training interventions for stakeholders in high-stakes contexts.
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All authors Naman Gupta, Connie W. Chau, Sophie Stephenson, Kate Walsh, Rahul Chatterjee
Human-Computer Interaction · Cross-listed from cs.AI
DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis
Raquib Bin Yousuf, Harith Laxman, Vitaliy Shkremetko et al. (+10 more)
Data journalism, the practice of using data analysis to surface newsworthy stories, depends increasingly on the ability of reporters and investigative journalists to uncover trends, disparities, and accountability narratives. In practice, exploring large structured
datasets remains slow and brittle: journalists must navigate hundreds of variables across many
datasets over years, understand data coding conventions, and write non-trivial analysis code while hypotheses evolve. Although LLMs are often touted as "ask in English, get SQL/answers," real newsroom workflows expose recurring failures, e.g., schema mismatches and drift, misread domain semantics and units, and silent assumptions. We present DataWeave, a system that addresses these needs by combining conversational interaction, schema grounding, analytical planning, and executable query generation to support exploratory analysis over structured data. Rather than treating LLMs as autonomous answer engines, DataWeave frames them as interactive partners whose outputs can be inspected, corrected, and steered as hypotheses shift. We present a case study with professional journalists using our system to analyze the U.S. Department of Education's Integrated Postsecondary Education Data System (IPEDS), a high-stakes public
dataset with substantial domain semantics and frequent schema updates. We also report how deployment experience and iterative refinement shaped the current DataWeave architecture and its analytical workflow. Our findings distill design principles and deployment lessons for trustworthy human-LLM collaboration in structured data analysis.
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All authors Raquib Bin Yousuf, Harith Laxman, Vitaliy Shkremetko, Eunice Son, Shambhavi Verma, Brian O'Leary, Venketesh Subramony, Sylvain Nazef, Jacquelyn Elias, Ron Coddington, Chris Contakes, Michael Riley, Naren Ramakrishnan
Human-Computer Interaction
Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories
Joseph Chee Chang, Michael D'Arcy, Amy X. Zhang et al. (+15 more)
A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observations scattered across many papers, each describing related concepts but often in different terms. Which concepts matter most also depends on their preferences and research questions. Recent approaches scale theory synthesis with LLMs, but automate away choices and intuitions from researchers. We present Asterism, which extracts observations from hundreds of papers as concept-relation triples, with concepts unified in a hierarchical ontology. Researchers curate an evidence graph using the ontology and aggregate observations at different levels of granularity to focus theory formation on specific phenomena of interest. In a field deployment (n=10), researchers worked from observations to theories, and kept concepts and hypotheses fitting their preferences. In two case studies, teams of immunology and agriculture researchers discovered mechanisms outside their standard analyses and constructed hypotheses worth follow-up experiments.
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All authors Joseph Chee Chang, Michael D'Arcy, Amy X. Zhang, Pao Siangliulue, Sangho Suh, Aakanksha Naik, Jena D. Hwang, Javier Ramos Benitez, Stella Wroblewski, Matt Latzke, Michael Cuoco, Ruben Lozano-Aguilera, Kris Ganjam, Joel Chan, Doug Downey, Peter Jansen, Kyle J. Travaglini, Daniel S. Weld