As a representative of cultural heritage, Suzhou Ming-style furniture embodies a composite knowledge system integrating material properties, structure, and craftsmanship. While Generative AI shows potential in creative design, current models lack an intrinsic understanding of physical and manufacturing constraints, often producing unproducible visual illusions. To bridge the gap between pixelated images and actual production, this paper proposes and develops a production-aware AI co-creation system that translates implicit traditional craftsmanship into explicit digital rules. Utilizing a node-based workflow and a knowledge-embedded estimation module, the system connects creative exploration with production configuration. It generates multimodal outputs, including renderings, structural exploded views, and practical cost and timeline estimations. Preliminary evaluations indicate high consistency with actual workshop production experience, effectively lowering the creative threshold for non-professionals. This research demonstrates how AI transcends mere visual generation to become a new pathway for the dynamic preservation and revitalization of cultural heritage.

Visual analytics (VA) is typically applied to complex data, thus requiring complex tools. While visual analytics empowers analysts in data analysis, analysts may get lost in the complexity occasionally. This highlights the need for intelligent assistance mechanisms. However, even the latest LLM-assisted VA systems only provide help when explicitly requested by the user, making them insufficiently intelligent to offer suggestions when analysts need them the most. We propose a ProactiveVA framework in which LLM-powered UI agent monitors user interactions and delivers context-aware assistance proactively. To design effective proactive assistance, we first conducted a formative study analyzing help-seeking behaviors in user interaction logs, identifying when users need proactive help, what assistance they require, and how the agent should intervene. Based on this analysis, we distilled key design requirements in terms of intent recognition, solution generation, interpretability and controllability. Guided by these requirements, we develop a three-stage UI agent pipeline including perception, reasoning, and acting. The agent autonomously perceives users' needs from VA interaction logs, providing tailored suggestions and intuitive guidance through interactive exploration of the system. We implemented the framework in two representative types of VA systems, demonstrating its generalizability, and evaluated the effectiveness through an algorithm evaluation, case and expert study and a user study. We also discuss current design trade-offs of proactive VA and areas for further exploration.

This paper proposes an algorithm for the decoding of low-density parity check (LDPC) codes, aiming to retrieve the performance degradation of conventional normalized min-sum algorithm, especially for irregular LDPC code. In our proposed algorithm, normalization factors are adjusted dynamically and adaptively during iterative decoding process according to look-up tables obtained by training and simulation in our proposal. The look-up tables construct the relationship between normalization factor a and the failed parity-check equation proportion p, which could be calculated in each decoding process. Simulation results demonstrate that the proposed algorithm can achieve a performance closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for rate 2/3 irregular LDPC codes over the AWGN channel. Our proposal is independent from the channel characteristics and retains low complexity. The implementation facility and the excellent performance make the proposed algorithm expect a wide application.

This paper proposes an adaptive normalized min-sum algorithm for the decoding of low-density parity check (LDPC) codes, which utilizes an adaptive normalization factor to improve the accuracy of the soft information transferred during the iterative decoding process, and provides superior performance accordingly. This adaptive normalization factor can be adjusted dynamically and adaptively at each decoding iteration according to a look-up table obtained via training and simulation. Its implementation facility and independence from the channel characteristics make the proposed adaptive normalized min-sum algorithm expect a wide application. Simulation results show that the proposed algorithm can achieve performance much closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for DVB-S2's rate 2/5 and 3/5 LDPC codes over the additive white Gaussian noise (AWGN) channel.