Zhicheng Liu

Zhicheng Liu, Liuquan Yao, Shuai Yuan et al.

In this paper, we analyze the effect of perturbation power in delaying the first error position, i.e., the first information bit incorrectly decoded by the successive cancellation (SC) decoding. It is conducted over the finite-length perturbation-enhanced SC (PE-SC) decoding paradigm. We show that the FEP delaying probability exhibits a non-monotonic dependence on the perturbation power \(σ_{p}^{2}\). Based on this property, an efficient perturbation power selection algorithm that maximizes the delay probability is proposed to enhance the perturbation efficiency. It results in a more efficient perturbation power selection in finite-length PE-SC decoding.

Zhicheng Liu

Scene-centric visualization systems expose semantic components, such as marks, encodings, layouts, and axes, as first-class objects that can be directly manipulated. Existing interaction abstractions, however, are largely based on event streams, signals, and data selections rather than semantic scene components. This mismatch makes interactions involving scene components less natural to specify and limits the expressive power of scene-centric visualization systems. We present Interactive Mascot, a scene-centric interaction grammar for data visualizations. Interactive Mascot extends scene-centric representations for static visualizations by modeling interactive behavior as information flow among four interaction components (trigger, responder, evaluator, and updater) and two forms of context (event context and state context). To realize these semantics, we introduce a dependency-graph execution model that systematically transforms interaction specifications into executable dependency graphs using reusable graph patterns associated with semantic visualization components. We implement Interactive Mascot in the JavaScript library Mascot.js and evaluate its expressiveness, performance, and usability. Interactive Mascot naturally covers Vega-Lite's interaction design space while additionally supporting stateful interactions, direct manipulation of scene components, and freeform selection. It achieves runtime performance comparable to Vega-Lite, and a qualitative user study shows that the grammar is learnable and usable for interaction authoring.