This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
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Super-Resolution Reconstruction and Detector Geometric Error Correction for Parallel-Beam Low-Resolution Multi-Detector SPECT: A Proof of Concept. [PDF]
Cheng Z, Zhang J, Chen P, Wen J.
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Security of Quantum Key Distribution with One-Time-Pad-Protected Error Correction and Its Performance Benefits. [PDF]
Novak R.
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Proximity-based activation of AURORA A by MPS1 potentiates error correction. [PDF]
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Causal Concentration-Response Modeling with Continuous Curves and Exposure Error Correction: PM2.5 and Mortality in the Medicare Cohort. [PDF]
Schwartz J, Feng Y, Castro E, Wei Y.
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DNA StairLoop: enabling high-fidelity data recovery and robust error correction in DNA-based data storage. [PDF]
Yan Z, Qu G, Chen X, Zheng G, Wu H.
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Two-dimensional trial-by-trial error correction for accurate baseball pitching. [PDF]
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Error Correction Capability of Random Network Error Correction Codes
2007 IEEE International Symposium on Information Theory, 2007In this paper, we study the error correction capability of random linear network error correction codes (Z. Zhang, 2006). We derive bounds on the probability mass function of the minimum distance of a random network error correction code and the field size required for the existence of a network error correction code with a given degradation, which is ...
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