Effect of state-dependent time delay on dynamics of trimming of thin walled structures

Acknowledgments This work was supported by the National Key R&D Program of China (2020YFA0714900), National Natural Science Foundation of China (52075205, 92160207, 52090054, 52188102).Peer reviewedPostprin

FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools

Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Previous research efforts have explored learning-based methods to validate the reported warnings. Nevertheless, their coarse granularity, focusing on either long-term warnings or function-level alerts, which are insensitive to individual bugs. Also, they rely on manually crafted features or solely on source code semantics, which is inadequate for effective learning. In this paper, we propose FineWAVE, a learning-based approach that verifies bug-sensitive warnings at a fine-grained granularity. Specifically, we design a novel LSTM-based model that captures multi-modal semantics of source code and warnings from ASATs and highlights their correlations with cross-attention. To tackle the data scarcity of training and evaluation, we collected a large-scale dataset of 280,273 warnings. We conducted extensive experiments on the dataset to evaluate FineWAVE. The experimental results demonstrate the effectiveness of our approach, with an F1-score of 97.79\% for reducing false alarms and 67.06% for confirming actual warnings, significantly outperforming all baselines. Moreover, we have applied our FineWAVE to filter out about 92% warnings in four popular real-world projects, and found 25 new bugs with minimal manual effort

Self-consistent Validation for Machine Learning Electronic Structure

Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies.Comment: 6 pages, 4 figure

Clinicopathologic features and outcomes following surgery for pancreatic adenosquamous carcinoma

<p>Abstract</p> <p>Background</p> <p>Pancreatic adenosquamous carcinoma (ASC) is a rare pancreatic malignancy subtype. We investigated the clinicopathological features and outcome of pancreatic ASC patients after surgery.</p> <p>Methods</p> <p>The medical records of 12 patients with pancreatic ASC undergoing surgical treatment (1993 to 2006) were retrospectively reviewed. Survival data of patients with stage IIB pancreatic adenocarcinoma and ASC undergoing surgical resection were compared.</p> <p>Results</p> <p>Symptoms included abdominal pain (91.7%), body weight loss (83.3%), anorexia (41.7%) and jaundice (25.0%). Tumors were located at pancreatic head in 5 (41.7%) patients, tail in 5 (41.7%), and body in 4 (33.3%). Median tumor size was 6.3 cm. Surgical resection was performed on 7 patients, bypass surgery on 3, and exploratory laparotomy with biopsy on 2. No surgical mortality was identified. Seven (58.3%) and 11 (91.7%) patients died within 6 and 12 months of operation, respectively. Median survival of 12 patients was 4.41 months. Seven patients receiving surgical resection had median survival of 6.51 months. Patients with stage IIB pancreatic ASC had shorter median survival compared to those with adenocarcinoma.</p> <p>Conclusion</p> <p>Aggressive surgical management does not appear effective in treating pancreatic ASC patients. Strategies involving non-surgical treatment such as chemotherapy, radiotherapy or target agents should be tested.</p

Draft Genome Sequence of Frankia sp. Strain BMG5.12, a Nitrogen-Fixing Actinobacterium Isolated from Tunisian Soils

Members of the actinomycete genus Frankia form a nitrogen-fixing symbiosis with 8 different families of actinorhizal plants. We report a draft genome sequence for Frankia sp. strain BMG5.12, a nitrogen-fixing actinobacterium isolated from Tunisian soils with the ability to infect Elaeagnus angustifolia and Myrica gale

Draft Genome Sequence of Frankia sp. Strain BCU110501, a Nitrogen-Fixing Actinobacterium Isolated from Nodules of Discaria trinevis

Frankia forms a nitrogen-fixing symbiosis with actinorhizal plants. We report a draft genome sequence for Frankia sp. strain BCU110501, a nitrogen-fixing actinobacterium isolated from nodules of Discaria trinevis grown in the Patagonia region of Argentina