Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges
本文探讨了CAD中透明度渲染的问题，通过比较不同方法的性能与质量平衡，并提出针对特定应用场景的技术和未来研究方向。
0 citationsRead paper
Selected work
Representative Papers
Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector
This study addresses the need for accurate multivariate forecasting of key meteorological variables—reference evapotranspiration, vapor pressure deficit, wind speed, and wind direction—for agricultural irrigation and farming decisions. Leveraging ERA5 reanalysis data, the authors systematically evaluate GRU, LSTM, and their hybrid architectures combined with 1D-CNN for both short-term (24-hour) and medium-term (168-hour) predictions. Results demonstrate that hybrid CNN-recurrent models consistently outperform standalone recurrent networks: CNN-GRU achieves weighted quantile scores (WQS) of 0.8275 and 0.7829 at the two forecast horizons, representing improvements of 0.44%–1.63% over pure recurrent models. Moreover, CNN-LSTM attains comparable accuracy with fewer parameters, confirming the efficacy and computational efficiency of hybrid architectures in agrometeorological forecasting.
0 citationsRead paper
Performance evaluation of scheduling tasks in many-core systems utilizing processes and threads
This study investigates the scalability and performance of process and thread schedulers under memory-intensive workloads in multi-core shared-memory systems, focusing on a 3D tensor row-sorting task. The authors design and evaluate several scheduling strategies: on the thread side, an AIMD-based adaptive chunking mechanism inspired by TCP congestion control is introduced, coupled with exponential weighted moving average to dynamically adjust concurrency; on the process side, a bounded prolific/collective model is employed alongside one-to-one, one-to-many, and many-to-many pipelined communication patterns to enable flexible task distribution. Experimental results on a 24-core x86-64 platform demonstrate that thread-level scheduling consistently outperforms process-level scheduling, with dynamic and guided strategies achieving the best performance, while the many-to-many pipeline exhibits superior scalability for large-scale tasks.
0 citationsRead paper
ANN Search: Recall What Matters
This work addresses the limitations of Recall@k as the dominant evaluation metric in approximate nearest neighbor (ANN) search, which often overestimates retrieval quality and incurs redundant computation. The authors propose 1/Ratio@k—an inverse approximation ratio that is hyperparameter-free and directly computable from benchmark data—as a more principled alternative. Through extensive evaluation of state-of-the-art ANN algorithms across diverse high-dimensional datasets, coupled with efficiency analysis and validation on downstream tasks such as classification and retrieval-augmented generation, the study demonstrates that optimizing 1/Ratio@k significantly reduces computational overhead while preserving practical utility. Moreover, 1/Ratio@k exhibits substantially stronger correlation with real-world effectiveness—measured by label accuracy and semantic similarity—than Recall@k.
0 citationsRead paper
Recent publications
Latest Papers
Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges
本文探讨了CAD中透明度渲染的问题，通过比较不同方法的性能与质量平衡，并提出针对特定应用场景的技术和未来研究方向。
0 citationsRead paper
Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector
This study addresses the need for accurate multivariate forecasting of key meteorological variables—reference evapotranspiration, vapor pressure deficit, wind speed, and wind direction—for agricultural irrigation and farming decisions. Leveraging ERA5 reanalysis data, the authors systematically evaluate GRU, LSTM, and their hybrid architectures combined with 1D-CNN for both short-term (24-hour) and medium-term (168-hour) predictions. Results demonstrate that hybrid CNN-recurrent models consistently outperform standalone recurrent networks: CNN-GRU achieves weighted quantile scores (WQS) of 0.8275 and 0.7829 at the two forecast horizons, representing improvements of 0.44%–1.63% over pure recurrent models. Moreover, CNN-LSTM attains comparable accuracy with fewer parameters, confirming the efficacy and computational efficiency of hybrid architectures in agrometeorological forecasting.
0 citationsRead paper
Performance evaluation of scheduling tasks in many-core systems utilizing processes and threads
This study investigates the scalability and performance of process and thread schedulers under memory-intensive workloads in multi-core shared-memory systems, focusing on a 3D tensor row-sorting task. The authors design and evaluate several scheduling strategies: on the thread side, an AIMD-based adaptive chunking mechanism inspired by TCP congestion control is introduced, coupled with exponential weighted moving average to dynamically adjust concurrency; on the process side, a bounded prolific/collective model is employed alongside one-to-one, one-to-many, and many-to-many pipelined communication patterns to enable flexible task distribution. Experimental results on a 24-core x86-64 platform demonstrate that thread-level scheduling consistently outperforms process-level scheduling, with dynamic and guided strategies achieving the best performance, while the many-to-many pipeline exhibits superior scalability for large-scale tasks.
0 citationsRead paper
ANN Search: Recall What Matters
This work addresses the limitations of Recall@k as the dominant evaluation metric in approximate nearest neighbor (ANN) search, which often overestimates retrieval quality and incurs redundant computation. The authors propose 1/Ratio@k—an inverse approximation ratio that is hyperparameter-free and directly computable from benchmark data—as a more principled alternative. Through extensive evaluation of state-of-the-art ANN algorithms across diverse high-dimensional datasets, coupled with efficiency analysis and validation on downstream tasks such as classification and retrieval-augmented generation, the study demonstrates that optimizing 1/Ratio@k significantly reduces computational overhead while preserving practical utility. Moreover, 1/Ratio@k exhibits substantially stronger correlation with real-world effectiveness—measured by label accuracy and semantic similarity—than Recall@k.
0 citationsRead paper