Young wall realization of crystal graphs for U_q(C_n^{(1)})

We give a realization of crystal graphs for basic representations of the quantum affine algebra U_q(C_n^{(1)}) using combinatorics of Young walls. The notion of splitting blocks plays a crucial role in the construction of crystal graphs

Vertex Models with Alternating Spins

The diagonalisation of the transfer matrices of solvable vertex models with alternating spins is given. The crystal structure of (semi-)infinite tensor products of finite-dimensional $U_q(\hat{sl}_2)$ crystals with alternating dimensions is determined. Upon this basis the vertex models are formulated and then solved by means of $U_q(\hat{sl}_2)$ intertwiners.Comment: 54 pages, uses epic.sty and texdraw.sty (references added

Active Learning of Discriminative Subgraph Patterns for API Misuse Detection

A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs). API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives. One limitation of prior work is the inability to reliably identify correct patterns of usage. Many approaches confuse a usage pattern's frequency for correctness. Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses. We address these challenges and propose ALP (Actively Learned Patterns), reformulating API misuse detection as a classification problem. After representing programs as graphs, ALP mines discriminative subgraphs. While still incorporating frequency information, through limited human supervision, we reduce the reliance on the assumption relating frequency and correctness. The principles of active learning are incorporated to shift human attention away from the most frequent patterns. Instead, ALP samples informative and representative examples while minimizing labeling effort. In our empirical evaluation, ALP substantially outperforms prior approaches on both MUBench, an API Misuse benchmark, and a new dataset that we constructed from real-world software projects

CC2Vec: Distributed representations of code changes

National Research Foundation (NRF) Singapore; ANR ITrans projec

Towards generating transformation rules without examples for android API replacement

National Research Foundation (NRF) Singaporeauthors' own version</p