CrossData: Leveraging Text-Data Connections for Authoring Data Documents

Data documents play a central role in recording, presenting, and disseminating data. Despite the proliferation of applications and systems designed to support the analysis, visualization, and communication of data, writing data documents remains a laborious process, requiring a constant back-and-forth between data processing and writing tools. Interviews with eight professionals revealed that their workflows contained numerous tedious, repetitive, and error-prone operations. The key issue that we identified is the lack of persistent connection between text and data. Thus, we developed CrossData, a prototype that treats text-data connections as persistent, interactive, first-class objects. By automatically identifying, establishing, and leveraging text-data connections, CrossData enables rich interactions to assist in the authoring of data documents. An expert evaluation with eight users demonstrated the usefulness of CrossData, showing that it not only reduced the manual effort in writing data documents but also opened new possibilities to bridge the gap between data exploration and writing

CrossCode: Multi-level Visualization of Program Execution

Program visualizations help to form useful mental models of how programs work, and to reason and debug code. But these visualizations exist at a fixed level of abstraction, e.g., line-by-line. In contrast, programmers switch between many levels of abstraction when inspecting program behavior. Based on results from a formative study of hand-designed program visualizations, we designed CrossCode, a web-based program visualization system for JavaScript that leverages structural cues in syntax, control flow, and data flow to aggregate and navigate program execution across multiple levels of abstraction. In an exploratory qualitative study with experts, we found that CrossCode enabled participants to maintain a strong sense of place in program execution, was conducive to explaining program behavior, and helped track changes and updates to the program state.Comment: 13 pages, 6 figures Submitted to CHI 2023: Conference on Human Factors in Computing System

Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models

People are increasingly turning to large language models (LLMs) for complex information tasks like academic research or planning a move to another city. However, while they often require working in a nonlinear manner - e.g., to arrange information spatially to organize and make sense of it, current interfaces for interacting with LLMs are generally linear to support conversational interaction. To address this limitation and explore how we can support LLM-powered exploration and sensemaking, we developed Sensecape, an interactive system designed to support complex information tasks with an LLM by enabling users to (1) manage the complexity of information through multilevel abstraction and (2) seamlessly switch between foraging and sensemaking. Our within-subject user study reveals that Sensecape empowers users to explore more topics and structure their knowledge hierarchically. We contribute implications for LLM-based workflows and interfaces for information tasks

PGC-1 α

Aim. To investigate the effect of Tongxinluo (Txl), a Chinese herbal compound, on diabetic peripheral neuropathy (DPN). Methods and Results. Diabetic rat model was established by peritoneal injection of streptozotocin (STZ). Txl ultrafine powder treatment for 16 weeks from the baseline significantly reversed the impairment of motor nerve conductive velocity (MNCV), mechanical hyperalgesia, and nerve structure. We further proved that Tongxinluo upregulates PGC-1α and its downstream factors including COX IV and SOD, which were involved in mitochondrial biogenesis. Conclusion. Our study indicates that the protective effect of Txl in diabetic neuropathy may be attributed to the induction of PGC-1α and its downstream targets. This finding may further illustrate the pleiotropic effect of the medicine

1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture

Existing text selection techniques on touchscreen focus on improving the control for moving the carets. Coarse-grained text selection on word and phrase levels has not received much support beyond word-snapping and entity recognition. We introduce 1D-Touch, a novel text selection method that complements the carets-based sub-word selection by facilitating the selection of semantic units of words and above. This method employs a simple vertical slide gesture to expand and contract a selection area from a word. The expansion can be by words or by semantic chunks ranging from sub-phrases to sentences. This technique shifts the concept of text selection, from defining a range by locating the first and last words, towards a dynamic process of expanding and contracting a textual semantic entity. To understand the effects of our approach, we prototyped and tested two variants: WordTouch, which offers a straightforward word-by-word expansion, and ChunkTouch, which leverages NLP to chunk text into syntactic units, allowing the selection to grow by semantically meaningful units in response to the sliding gesture. Our evaluation, focused on the coarse-grained selection tasks handled by 1D-Touch, shows a 20% improvement over the default word-snapping selection method on Android

Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 8 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs

Chronic treatment with anesthetic propofol attenuates β-amyloid protein levels in brain tissues of aged mice

Alzheimer’s disease (AD) is the most common form of dementia. At the present time, however, AD still lacks effective treatments. Our recent studies showed that chronic treatment with anesthetic propofol attenuated brain caspase-3 activation and improved cognitive function in aged mice. Accumulation of β-amyloid protein (Aβ) is a major component of the neuropathogenesis of AD dementia and cognitive impairment. We therefore set out to determine the effects of chronic treatment with propofol on Aβ levels in brain tissues of aged mice. Propofol (50 mg/kg) was administrated to aged (18 month-old) wild-type mice once a week for 8 weeks. The brain tissues of mice were harvested one day after the final propofol treatment. The harvested brain tissues were then subjected to enzyme-linked immunosorbent assay (ELISA) and Western blot analysis. Here we report that the propofol treatment reduced Aβ (Aβ40 and Aβ42) levels in the brain tissues of the aged mice. Moreover, the propofol treatment decreased the levels of β-site amyloid precursor protein cleaving enzyme (the enzyme for Aβ generation), and increased the levels of neprilysin (the enzyme for Aβ degradation) in the brain tissues of the aged mice. These results suggested that the chronic treatment with propofol might reduce brain Aβ levels potentially via decreasing brain levels of β-site amyloid precursor protein cleaving enzyme, thus decreasing Aβ generation; and via increasing brain neprilysin levels, thus increasing Aβ degradation. These preliminary findings from our pilot studies have established a system and postulated a new hypothesis for future research

Augmenting Sports Videos with VisCommentator

Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by selecting the data to visualize instead of manually drawing the graphical marks. Our system can be generalized to other racket sports (e.g., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities

Sporthesia: Augmenting Sports Videos Using Natural Language

Augmented sports videos, which combine visualizations and video effects to present data in actual scenes, can communicate insights engagingly and thus have been increasingly popular for sports enthusiasts around the world. Yet, creating augmented sports videos remains a challenging task, requiring considerable time and video editing skills. On the other hand, sports insights are often communicated using natural language, such as in commentaries, oral presentations, and articles, but usually lack visual cues. Thus, this work aims to facilitate the creation of augmented sports videos by enabling analysts to directly create visualizations embedded in videos using insights expressed in natural language. To achieve this goal, we propose a three-step approach - 1) detecting visualizable entities in the text, 2) mapping these entities into visualizations, and 3) scheduling these visualizations to play with the video - and analyzed 155 sports video clips and the accompanying commentaries for accomplishing these steps. Informed by our analysis, we have designed and implemented Sporthesia, a proof-of-concept system that takes racket-based sports videos and textual commentaries as the input and outputs augmented videos. We demonstrate Sporthesia's applicability in two exemplar scenarios, i.e., authoring augmented sports videos using text and augmenting historical sports videos based on auditory comments. A technical evaluation shows that Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable entities in the text. An expert evaluation with eight sports analysts suggests high utility, effectiveness, and satisfaction with our language-driven authoring method and provides insights for future improvement and opportunities.Comment: 10 pages, IEEE VIS conferenc

Zirconium-Catalyzed Atom-Economical Synthesis of 1,1-Diborylalkanes from Terminal and Internal Alkenes

A general and atom-economical synthesis of 1,1-diborylalkanes from alkenes and a borane without the need for an additional H2 acceptor is reported for the first time. The key to our success is the use of an earth-abundant zirconium-based catalyst, which allows a balance of self-contradictory reactivities (dehydrogenative boration and hydroboration) to be achieved. Our method avoids using an excess amount of another alkene as an H2 acceptor, which was required in other reported systems. Furthermore, substrates such as simple long-chain aliphatic alkenes that did not react before also underwent 1,1-diboration in our system. Significantly, the unprecedented 1,1-diboration of internal alkenes enabled the preparation of 1,1-diborylalkanes. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA