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Xueping Xu

Publications and source records attributed to Xueping Xu.

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Odd and even cycle lengths, minimum degree and chromatic number in graphs

We present the relations between clique number and chromatic number with given the number of odd or even or all cycle lengths. Let $L_o(G)$ be the set of odd cycle lengths of $G$ and $\ell_o(G)$ be the longest odd cycle length. Gy\'arf\'as (DM, 1992) proved a classic result: $\chi(G)\leq 2|L_o(G)|+2$, and if $w(G)\leq 2|L_o(G)|+1$, then $\chi(G)\leq 2|L_o(G)|+1$. Later, Wang (SIAM DM, 2008), Ma and Ning (SIAM DM, 2018) together determined the exact chromatic number when $|L_o(G)|=2$. We further prove that if $w(G)\leq 2|L_o(G)|$ for any $|L_o(G)|\geq 2$, then $\chi(G)\leq 2|L_o(G)|$. We also construct a class of graphs with $w(G)=2|L_o(G)|-1$ but $\chi(G)=2|L_o(G)|$ for every $|L_o(G)|\geq 2$. Using our result, we give a short proof of the relation between $w(G)$ and $\chi(G)$ with given $\ell_o(G)$ proved by Kenkre and Vishwanathan (JGT, 2006). Let $L_e(G)$ be the set of even cycle lengths of $G$ and $\ell_e(G)$ be the longest even cycle length. Mih\'ok and Schiermeyer (DM, 2004) proved that $\chi(G)\leq 2|L_e(G)|+3$, and if $w(G)\leq 2|L_e(G)|+2$, then $\chi(G)\leq 2|L_e(G)|+2$. We further prove that if $w(G)\leq 2|L_e(G)|+1$ and $|L_e(G)|\geq 3$, then $\chi(G)\leq 2|L_e(G)|+1$. We also construct a class of graphs with $w(G)=2|L_e(G)|$ but $\chi(G)=2|L_e(G)|+1$ for every $|L_e(G)|\geq 2$. Our result can deduce the relation between $w(G)$ and $\chi(G)$ with given $\ell_e(G)$. Combining all the above results, we deduce the similar relations between $w(G)$ and $\chi(G)$ with given the number of cycle lengths or the longest cycle length.

math.CO

The IQIYI System for Voice Conversion Challenge 2020

This paper presents the IQIYI voice conversion system (T24) for Voice Conversion 2020. In the competition, each target speaker has 70 sentences. We have built an end-to-end voice conversion system based on PPG. First, the ASR acoustic model calculates the BN feature, which represents the content-related information in the speech. Then the Mel feature is calculated through an improved prosody tacotron model. Finally, the Mel spectrum is converted to wav through an improved LPCNet. The evaluation results show that this system can achieve better voice conversion effects. In the case of using 16k rather than 24k sampling rate audio, the conversion result is relatively good in naturalness and similarity. Among them, our best results are in the similarity evaluation of the Task 2, the 2nd in the ASV-based objective evaluation and the 5th in the subjective evaluation.

cs.SD