Faster Mutation Analysis via Equivalence Modulo States

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.Comment: Submitted to conferenc

M3-AUDIODEC: Multi-channel multi-speaker multi-spatial audio codec

We introduce M3-AUDIODEC, an innovative neural spatial audio codec designed for efficient compression of multi-channel (binaural) speech in both single and multi-speaker scenarios, while retaining the spatial location information of each speaker. This model boasts versatility, allowing configuration and training tailored to a predetermined set of multi-channel, multi-speaker, and multi-spatial overlapping speech conditions. Key contributions are as follows: 1) Previous neural codecs are extended from single to multi-channel audios. 2) The ability of our proposed model to compress and decode for overlapping speech. 3) A groundbreaking architecture that compresses speech content and spatial cues separately, ensuring the preservation of each speaker's spatial context after decoding. 4) M3-AUDIODEC's proficiency in reducing the bandwidth for compressing two-channel speech by 48% when compared to individual binaural channel compression. Impressively, at a 12.6 kbps operation, it outperforms Opus at 24 kbps and AUDIODEC at 24 kbps by 37% and 52%, respectively. In our assessment, we employed speech enhancement and room acoustic metrics to ascertain the accuracy of clean speech and spatial cue estimates from M3-AUDIODEC. Audio demonstrations and source code are available online at https://github.com/anton-jeran/MULTI-AUDIODEC .Comment: More results and source code are available at https://anton-jeran.github.io/MAD

Clinical relevance of miR-423-5p levels in chronic obstructive pulmonary disease patients

Objective: This study aimed to examine changes in miRNAs expression profile of COPD patients. Methods: Thirty-six COPD patients as well as thirty-three healthy volunteers were recruited. Total RNAs were collected from the plasma of each participant. The differentially expressed miRNAs in COPD were screened from the GEO database. RT-qPCR was carried out to detect miRNA expression. Results: In total, 9 out of 55 miRNAs were expressed differentially in COPD patients. Confirmed by RT-qPCR validation, 6 miRNAs increased while 3 miRNAs decreased. Further analysis of miR-423-5p, which has not been reported in COPD, showed that AUC for the diagnosis of COPD was 0.9651, and miR-423-5p levels was inversely correlated with the duration of smoking. Conclusion: The present study demonstrates that miR-423-5p is a potential marker for identifying COPD patients