Accurate audio quality estimation is essential for developing and evaluating audio generation, retrieval, and enhancement systems. Existing non-intrusive assessment models predict a single Mean Opinion Score (MOS) for speech, merging diverse perceptual factors and failing to generalize beyond speech. We propose MMMOS, a no-reference, multidomain audio quality assessment system that estimates four orthogonal axes: Production Quality, Production Complexity, Content Enjoyment, and Content Usefulness across speech, music, and environmental sounds. MMMOS fuses frame-level embeddings from three pretrained encoders (WavLM, MuQ, and M2D) and evaluates three aggregation strategies with four loss functions. By ensembling the top eight models, MMMOS shows a $\mathbf{2 0 - 3 0 \%}$ reduction in mean squared error and a 4-5% increase in Kendall’s $\tau$ versus baseline, gains first place in six of eight Production Complexity metrics, and ranks among the top three on 17 of 32 challenge metrics.

In this paper, we propose a K-disjointness paradigm that can effectively search all pairs of minterms with Hamming distance K between two Boolean functions. By this paradigm, we correlate it with symmetry detection problem and propose an efficient symmetry detection algorithm for Boolean functions. Our algorithm can not only handle completely specified functions but also incompletely specified functions. Experimental results on a set of MCNC and ISCAS benchmarking circuits show that our algorithm is indeed very effective and efficient for detecting symmetries of large Boolean functions.

In this paper, we formulate symmetry detection for incompletely specified functions as an equation without using cofactor computation and equivalence checking. Based on this equation, a symmetry detection algorithm is proposed. This algorithm can simultaneously find non-equivalence and equivalence symmetries. Experimental results on a set of benchmarks show that our algorithm is indeed very effective in solving symmetry detection problem for incompletely specified functions.