Esophageal cancer remains a highly aggressive malignancy with low survival rates, requiring advanced surgical interventions like esophagectomy. Traditional manual techniques, including circular staplers, face challenges such as limited precision, prolonged recovery times, and complications like leaks and tissue misalignment. This paper presents a novel robotic circular stapler designed to enhance the dexterity in confined spaces, improve tissue alignment, and reduce post-operative risks. Integrated with a cognitive robot that serves as a surgeon's assistant, the surgical stapler uses three actuators to perform anvil motion, cutter/stapler motion and allows a 75-degree bending of the cartridge (distal tip). Kinematic analysis is used to compute the stapler tip's position, ensuring synchronization with a robotic system.

Supervised learning-based software vulnerability detectors often fall short due to the inadequate availability of labelled training data. In contrast, Large Language Models (LLMs) such as GPT-4, are not trained on labelled data, but when prompted to detect vulnerabilities, LLM prediction accuracy is only marginally better than random guessing. In this paper, we explore a different approach by reframing vulnerability detection as one of anomaly detection. Since the vast majority of code does not contain vulnerabilities and LLMs are trained on massive amounts of such code, vulnerable code can be viewed as an anomaly from the LLM's predicted code distribution, freeing the model from the need for labelled data to provide a learnable representation of vulnerable code. Leveraging this perspective, we demonstrate that LLMs trained for code generation exhibit a significant gap in prediction accuracy when prompted to reconstruct vulnerable versus non-vulnerable code. Using this insight, we implement ANVIL, a detector that identifies software vulnerabilities at line-level granularity. Our experiments explore the discriminating power of different anomaly scoring methods, as well as the sensitivity of ANVIL to context size. We also study the effectiveness of ANVIL on various LLM families, and conduct leakage experiments on vulnerabilities that were discovered after the knowledge cutoff of our evaluated LLMs. On a collection of vulnerabilities from the Magma benchmark, ANVIL outperforms state-of-the-art line-level vulnerability detectors, LineVul and LineVD, which have been trained with labelled data, despite ANVIL having never been trained with labelled vulnerabilities. Specifically, our approach achieves $1.62\times$ to $2.18\times$ better Top-5 accuracies and $1.02\times$ to $1.29\times$ times better ROC scores on line-level vulnerability detection tasks.

In this work, we introduce an open-source integrated CAD-CFD tool, Anvil, which combines FreeCAD for CAD modeling and OpenFOAM for CFD analysis, along with an AI-based optimization method (Bayesian optimization) and other sampling algorithms. Anvil serves as a scientific machine learning tool for shape optimization in three modes: data generation, CFD evaluation, and shape optimization. In data generation mode, it automatically runs CFD evaluations and generates data for training a surrogate model. In optimization mode, it searches for the optimal design under given requirements and optimization metrics. In CFD mode, a single CAD file can be evaluated with a single OpenFOAM run. To use Anvil, experimenters provide a JSON configuration file and a parametric CAD seed design. Anvil can be used to study solid-fluid dynamics for any subsonic flow conditions and has been demonstrated in various simulation and optimization use cases. The open-source code for the tool, installation process, artifacts (such as CAD seed designs and example STL models), experimentation results, and detailed documentation can be found at \url{https://github.com/symbench/Anvil}.

I've been a Masochist user for more than a decade now, but I'm afraid that my time on this platform has come to an end. The Masochist app has seen me go from a young, single, childless person to an older, less healthy person at the tail end of my first divorce, with two, maybe three, kids. I've really enjoyed sharing my posts ("cries for help," as Masochist calls them) with other "masochists" whom I've come to know through this platform. And I've loved seeing all of your cries for help as well. Whether I was simply making sure that you were also miserable or berating you for your content, at Masochist I always felt like I was part of a community--a deeply unhealthy family, if you will--that I will keep with me as I venture forth in my offline life and try to figure out exactly how many kids I have and what their full names are.

This summer, five new National Science Foundation (NSF)-funded advanced computing systems have been awarded to partner institutions across the country, all with their own unique specialties and applications. Once deployed, all of these systems will be primarily allocated through XSEDE to help connect researchers, regardless of physical location in the United States, to the system that best suits their research needs. "The recent announcements of awards by the NSF is a clear indicator of the NSF's re-commitment to providing critical advanced research computing capabilities to enable research in the United States and beyond," said John Towns, principal investigator and project director of XSEDE. "These resources will enable research across all fields of scholarly pursuit and XSEDE stands ready to engage researchers in harnessing these resources.". These new machines continue XSEDE's robust demonstrated tradition of world-class, cutting-edge research, including a commitment to emerging domains like Artificial Intelligence, Big Data, Machine Learning, and more.