T Xiao

Jingguo Qu, Xinyang Han, Xiang Wang et al.

Medical ultrasound (US) image segmentation faces significant challenges due to speckle noise, low-contrast boundaries, acoustic shadowing, and acquisition variation across operators and clinical centers. Although encoder-decoder and transformer-based networks have achieved strong performance, many methods recover boundary details through dense decoders or larger backbones, which may still produce over-smoothed contours or unstable predictions under external distribution shifts. In this article, we propose Risk-routed Implicit Boundary Refinement (RIBR), a compact segmentation framework that uses implicit neural representation as a risk-routed residual correction rather than an unconstrained full-mask predictor. RIBR combines boundary-refinement implicit residuals, risk-routed residual control, and geometry- and speckle-aware boundary regularization to refine uncertain contours while suppressing non-boundary oscillations. Evaluation on nine US datasets covering lymph nodes, breast lesions, thyroid nodules, and prostate shows that RIBR achieves the best overall macro-average and consistently reduces boundary error across grouped and organ-specific comparisons under a compact parameter budget. These findings suggest that controlled implicit residual learning is a practical strategy for resource-constrained and boundary-sensitive US segmentation. Source code is available at https://github.com/jinggqu/ribr.

Lingfeng Chen, Tao Xiao, Masanari Kondo et al.

Symbolic execution effectively discovers security violations but suffers from path explosion. Engines like KLEE therefore use path prioritization heuristics to order state exploration, typically optimizing code coverage. However, path prioritization can become trapped in cyclic control-flow regions, where repeated branching consumes the exploration budget before exploration reaches vulnerable code beyond these cyclic regions. We propose KLEECopilot, a Large Language Model (LLM)-guided directed symbolic execution approach built on KLEE. KLEECopilot uses LLMs to mark potentially vulnerable code and guide path prioritization. It also integrates loop-exit prioritization to escape potentially non-vulnerable cycles and progress toward deeper vulnerabilities. Compared with baselines such as Empc, KLEECopilot improves basic block coverage by 42.24% and line coverage by 125.82%. It discovers 1,335 total violations and 87 unique violations, outperforming the second-best baseline by 32.2% in total violations and Empc by 24.3% in unique violations. Although KLEECopilot is sensitive to model family, it exhibits only marginal sensitivity to model scale, supporting the efficacy of integrating security semantics and loop-exit prioritization. Ablation studies further show that individual components contribute to effectiveness: alternative configurations involving searchers, internal components, marking sources, and prompt variants yield only 54--61 unique violations, while KLEECopilot maintains competitive code coverage.