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Yu-Sheng Shih

Publications and source records attributed to Yu-Sheng Shih.

3 recordsLinked to original sources

Space Complexity Dichotomies for Subgraph Finding Problems in the Streaming Model

We study the space complexity of four variants of the standard subgraph finding problem in the streaming model. Specifically, given an $n$-vertex input graph and a fixed-size pattern graph, we consider two settings: undirected simple graphs, denoted by $G$ and $H$, and oriented graphs, denoted by $\vec{G}$ and $\vec{H}$. Depending on the setting, the task is to decide whether $G$ contains $H$ as a subgraph or as an induced subgraph, or whether $\vec{G}$ contains $\vec{H}$ as a subgraph or as an induced subgraph. Let Sub$(H)$, IndSub$(H)$, Sub$(\vec{H})$, and IndSub$(\vec{H})$ denote these four variants, respectively. An oriented graph is well-oriented if it admits a bipartition in which every arc is oriented from one part to the other, and a vertex is non-well-oriented if both its in-degree and out-degree are non-zero. For each variant, we obtain a complete dichotomy theorem, briefly summarized as follows. (1) Sub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H$ is bipartite. (2) IndSub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H \in \{P_3, P_4, co\mbox{-}P_3\}$. (3) Sub$(\vec{H})$ can be solved by a single-pass $n^{2-\Omega(1)}$-space algorithm if and only if every connected component of $\vec H$ is either a well-oriented bipartite graph or a tree containing at most one non-well-oriented vertex. (4) IndSub$(\vec{H})$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if the underlying undirected simple graph $H$ is a $co\mbox{-}P_3$.

cs.DS

Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erd\H{o}s Problems

We present a case study in semi-autonomous mathematics discovery, using Gemini to systematically evaluate 700 conjectures labeled 'Open' in Bloom's Erd\H{o}s Problems database. We employ a hybrid methodology: AI-driven natural language verification to narrow the search space, followed by human expert evaluation to gauge correctness and novelty. We address 13 problems that were marked 'Open' in the database: 5 through seemingly novel autonomous solutions, and 8 through identification of previous solutions in the existing literature. Our findings suggest that the 'Open' status of the problems was through obscurity rather than difficulty. We also identify and discuss issues arising in applying AI to math conjectures at scale, highlighting the difficulty of literature identification and the risk of ''subconscious plagiarism'' by AI. We reflect on the takeaways from AI-assisted efforts on the Erd\H{o}s Problems.

cs.AI

Combinatorial proofs of Petrie Pieri rule and Plethystic Pieri rule

Petrie symmetric functions $G(k,n)$, also known as truncated homogeneous symmetric functions or modular complete symmetric functions, form a class of symmetric functions interpolating between the elementary symmetric functions $e_n$ and the homogeneous symmetric functions $h_n$. Analogous to the Pieri rule for $s_\mu h_n$ and the dual Pieri rule for $s_\mu e_n$, Grinberg showed that the Schur coefficients for the ``Pieri rule'' of $s_\mu G(k,n)$ can be determined by the determinant $\mathbf{pet}_k(\lambda,\mu)$ of Petrie matrices. Cheng, Chou, Eu, Fu, and Yao provided a ribbon tiling interpretation for the coefficient $\mathbf{pet}_k(\lambda,\varnothing)$, which was later generalized by Jin, Jing, and Liu to $\mathbf{pet}_k(\lambda,\mu)$ in the case where $\lambda/\mu$ is connected. The goal of this paper is to offer a more transparent combinatorial perspective on the structure and behavior of Petrie symmetric functions. First, we provide a refined combinatorial formula for the determinant of a Petrie matrix in terms of certain orientations of the associated graph derived from the matrix. We then generalize the result of JJL to arbitrary skew shapes using purely combinatorial proofs. In addition, we investigate the generating function of these orientations with respect to certain statistics. As an application of our method, we present a combinatorial proof of the plethystic Pieri rule.

math.CO