Immersive creation tools enhance spatial understanding, yet they push animators to abandon mature 2D desktop practices; Pure desktop setups, however, lack embodied spatial manipulation, making tool switching cognitively costly. To address this issue, We present AniXDim, a cross-dimensional interaction system for 3D character animation that unifies precise planar editing and embodied spatial control without explicit mode toggles. A commodity mouse is augmented for on-demand 6DoF manipulation, and a VR controller is endowed with high-precision planar input through adaptive projection, forming bidirectional dimensional augmentation. Users simply raise or rest the device to enable near-frictionless transitions, while a single autostereoscopic desktop display adapts depth to the inferred interaction state, supporting keyframe, pose, and reference authoring within one consistent workspace. A controlled user study comparing AniXDim with pure desktop and pure VR baselines indicates significant performance gains over the desktop baseline and comparable efficiency to the VR baseline, and higher usability ratings while maintaining comparable workload. These findings suggest that near-frictionless dimensional switching can reconcile 2D precision and 3D embodiment, offering a low-learning-overhead pathway for future hybrid desktop animation pipelines and broader 2D–3D authoring domains.

Data-driven decision making has become a popular practice in science, industry, and public policy. Yet data alone, as an imperfect and partial representation of reality, is often insufficient to make good analysis decisions. Knowledge about the context of a dataset, its strengths and weaknesses, and its applicability for certain tasks is essential. Analysts are often not only familiar with the data itself, but also have data hunches about their analysis subject. In this work, we present an interview study with analysts from a wide range of domains and with varied expertise and experience, inquiring about the role of contextual knowledge. We provide insights into how data is insufficient in analysts' workflows and how they incorporate other sources of knowledge into their analysis. We analyzed how knowledge of data shaped their analysis outcome. Based on the results, we suggest design opportunities to better and more robustly consider both knowledge and data in analysis processes.

Semantic change detection (SCD) in high-resolution (HR) remote sensing images faces two issues: (1) isolated network branch for binary change detection (BCD) within multi-task architecture result in suboptimal SCD performance; (2) false alarms or missed detections caused by illumination differences or seasonal transform. To address these issues, this study proposes a bi-temporal binary change enhancement network (Bi-BCENet). Specifically, we introduce a binary change enhancement (BCE) strategy based on multi-network joint learning to achieve superior SCD via improving change areas prediction. Within the network’s reasoning process, we develop a cross-attention fusion module (CAFM) to enhance the global similarity modeling via cross-network prompt fusion, and we employ a cosine similarity-based auxiliary loss to optimize non-change’s semantic consistency. The experiments on SECOND and CINA-FX datasets demonstrate that Bi-BCENet outperforms representative SCD networks, achieving 62.08%, 84.95% in FSCD and 66.88%, 83.10% in mIoUsCD, respectively. And the ablation analysis of network validates Bi-BCENet’s effectiveness in reducing false alarms and missed detections in SCD results. Moreover, for specific SCD of cropland, Bi-BCENet shows its strong potential in single-to-multi SCD.

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches. We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.