Accurate anomaly detection in industrial inspection and medical imaging is essential for product quality, clinical screening, and system reliability. Although recent methods have minimized their reliance on abnormal annotations, they still require target-domain training data, clean normal samples, or parameter updates. However, in real-world environments, such as new production lines or medical screening, verified normal samples are often unavailable, and the only data accessible at inference are unlabeled test datasets with unreliable normality. Therefore, we propose QueCo, a zero-shot anomaly detection framework that requires no training and infers reliable normal evidence directly from an unlabeled test dataset. QueCo constructs an unverified evidence bank (UEB) from unlabeled test samples, without assuming any clean subset. Rather than uniformly matching all test patches, it performs query-conditioned reference consensus (QRC) for each query. QRC validates candidate references through transport-guided cross-image correspondence, mutual- k -nearest-neighbor maximum similarity (Mutual- k NN MaxSim) filtering, and minimum-support aggregation, such that only structurally consistent and repeatedly observed evidence contributes to anomaly scoring. Feature-conditioned semantic prompts further complement structural consensus via query-adaptive text alignment using vision-language semantics without additional learning or learnable prompts. Without training, QueCo achieves 97.5% image-level AUROC and 96.8% pixel-level AUROC on MVTec AD while demonstrating robust performance across industrial benchmarks among both training-based and training-free methods.

Summary Distributed deep learning is an effective way to reduce the training time for large datasets as well as complex models. However, the limited scalability caused by network‐overheads makes it difficult to synchronize the parameters of all workers and gossip‐based methods that demonstrate stable scalability regardless of the number of workers have been proposed. However, to use gossip‐based methods in general cases, the validation accuracy for a large mini‐batch needs to be verified. For this, we first empirically study the characteristics of gossip methods in a large mini‐batch problem and observe that gossip methods preserve higher validation accuracy than AllReduce‐SGD (stochastic gradient descent) when the number of batch sizes is increased, and the number of workers is fixed. However, the delayed parameter propagation of the gossip‐based models decreases validation accuracy in large node scales. To cope with this problem, we propose Crossover‐SGD that alleviates the delay propagation of weight parameters via segment‐wise communication and random network topology with fair peer selection. We also adapt hierarchical communication to limit the number of workers in gossip‐based communication methods. To validate the effectiveness of our method, we conduct empirical experiments and observe that our Crossover‐SGD shows higher node scalability than stochastic gradient push.

First fit decreasing (FFD) is the most popular heuristic for virtual machine (VM) placement problems. However, FFD does not perform as much in a heterogeneous cluster environment. Moreover, FFD and other heuristics, such as best fit decreasing (BFD), are limited to handle the VM placement problem effectively when multiple resources are considered together. In this study, we analyze the reason why the ant colony system performs better than FFD for VM placement in a heterogeneous cluster. We verified our logical observations through experimental comparisons with other heuristics.