Rate-dependent inhomogeneous-to-homogeneous transition of plastic flows during nanoindentation of bulk metallic glasses: Fact or artifact?

There has been considerable controversy over the "apparent" rate-dependent transition from inhomogeneous-to-homogeneous flow during nanoindentation of bulk metallic glasses (BMGs) at room temperature: whether it arises from the existence of homogeneous-flow regime in BMG deformation map or is an artifact due to the instrumental blurring at high rates. To provide a clue to address this dispute, the authors performed nanoindentation experiments on a Zr-based BMG with two geometrically self-similar indenters. The results are discussed in terms of the discrete plasticity ratio, which is a useful parameter in analyzing the contribution of inhomogeneous plasticity to the total plastic deformation.open232

Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy

Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning. Though large-scale data collections invariably contain annotation noise and numerous robust learning methods have been developed, data pruning for the noise-robust learning scenario has received little attention. With state-of-the-art Re-labeling methods that self-correct erroneous labels while training, it is challenging to identify which subset induces the most accurate re-labeling of erroneous labels in the entire training set. In this paper, we formalize the problem of data pruning with re-labeling. We first show that the likelihood of a training example being correctly re-labeled is proportional to the prediction confidence of its neighborhood in the subset. Therefore, we propose a novel data pruning algorithm, Prune4Rel, that finds a subset maximizing the total neighborhood confidence of all training examples, thereby maximizing the re-labeling accuracy and generalization performance. Extensive experiments on four real and one synthetic noisy datasets show that \algname{} outperforms the baselines with Re-labeling models by up to 9.1% as well as those with a standard model by up to 21.6%

Spleen-Preserving Distal Pancreatectomy for Blunt Pancreatic Trauma in a Pediatric Patient

Pancreatic injury is rare in pediatric cases of blunt abdominal trauma and nonoperative management is preferred in pediatric patients. There are more concerns about operative treatment observed in pediatric patients compared with adult patients. However, some pediatric cases require surgical treatment. If distal pancreatectomy is performed, the necessity of splenectomy should be considered, especially in pediatric patients. This study reports the case of a 17-month-old patient with a Grade 3 pancreatic injury following blunt abdominal trauma. The patient was successfully managed by spleen-preserving distal pancreatectomy. In conclusion, this surgical technique can be performed safely, and complications caused by splenectomy can be prevented using this technique