Explore the vast range of titles available.

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying objects or particular particle types. We explore approaches that use the entire calorimeter, combined with track information, for directly conducting physics analyses: i.e. classifying events as known-physics background or new-physics signals. We use an existing RPV-Supersymmetry analysis as a case study and explore CNNs on multi-channel, high-resolution sparse images: applied on GPU and multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes) on the Cori supercomputer at NERSC. We compare statistical performance of our approaches with selections on high-level physics variables from the current physics analyses, and shallow classifiers trained on those variables. We also compare time-to-solution performance of CPU (scaling to multiple KNL nodes) and GPU implementations.

Films on safety stock were housed in a vault adjoining the garage building, where his 1924 and 1925 Rolls Royce automobiles were kept. 2) (SGF): How were the films labeled? Harold's collection of original master elements and projection prints included about 70 of the \"Glass Character\" one-reelers, 9 two-reelers, 4 three-reelers, 1 four-reeler, and 10 silent features of five reels in length or more. [...]the later Harold Lloyd was a comic actor of great subtlety and skill, presenting situations with which anyone in the audience might identify and sympathize. 8) (SGF): Harold Lloyd archives have appeared all over the world!

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength while accounting for systematic uncertainties affecting collider experiments. Unlike traditional classification tasks, participants must construct confidence intervals that properly cover systematic distortions. The HiggsML Uncertainty Challenge establishes a benchmark for uncertainty-aware AI, with applications in high-energy physics and beyond. The competition is hosted on Codabench, an open AI benchmarking platform, and uses highperformance computing resources at NERSC Perlmutter for scalable and reproducible model evaluation. The dataset and evaluation framework will remain publicly available for continued research.

Unfortunately, older patients are at risk of developing nutritional compromise during a period of acute illness, with 35% to 65% presenting with nutritional deficiencies.1 Early and sustained evaluation and management of nutritional status of elderly patients are critical, as malnutrition is directly related to a wide range of poor outcomes, including wound complications, infection rates, and mortality.2 3 In this clinical consensus document, the American Association for the Surgery of Trauma (AAST) Critical Care and Geriatric Trauma Committees aim to provide practical guidance to the surgeon and the surgical intensivist on the best practices for evaluation and management of nutrition in the geriatric surgical patient. Bedside swallow evaluations are appropriate for most patients, but formal swallow evaluations should be used for anyone who fails the bedside evaluation. A combination of body composition, indirect calorimetry, nitrogen balance, functional tests, and anthropometric parameters may be useful, depending on the severity of illness and institutional availability. The ideal screening tools incorporate anthropometry, diet, severity of illness, and physical and psychological components.4 The NRS-2002 and the NUTRIC scores are two validated tools that are supported by the Society of Critical Care Medicine (SCCM), the American Society for Parenteral and Enteral Nutrition (ASPEN), and the European Society for Clinical Nutrition and Metabolism (ESPEN).5 Both tools factor in nutritional status and illness severity to predict length of stay (LOS), morbidity, mortality, and complications (table 2).6–12 The NRS-2002 was designed for general hospital use and the NUTRIC score was designed to focus on critical illness.9 10 The SCCM and ASPEN recommend considering patients with either an NRS-2002 score of ≥5 or a NUTRIC score of ≥6 to be at high risk for malnutrition.5 Additionally, as interleukin 6 is not routinely obtained, a modified NUTRIC score that does not include this cytokine has also been validated, with a score of ≥5 being associated with high nutritional risk.13 Table 2 Components of nutritional assessment tools Nutrition Risk Screening 2002 Nutrition Risk in Critically Ill score BMI <20.5 kg/m2: yes/no Age (years): <50, 50–74, ≥75 Weight loss within 3 months: yes/no APACHE II: <15, 25–19, 20–27, ≥28 Reduced dietary intake in the last week: yes/no SOFA score: <6, 6–9, ≥10 ICU patient (critically ill): yes/no Number of comorbidities: 0–1, ≥2 Days in hospital to ICU admit: 0, ≥1 IL-6 (pg/mL) (optional): 0–399, ≥400 APACHE II, Acute Physiology and Chronic Health Evaluation II; BMI, body mass index; ICU, intensive care unit; IL-6, interleukin 6; SOFA, Sequential Organ Failure Assessment.

BackgroundThe prevalence of diverticulitis has steadily increased during the past century. One possible complication of large bowel diverticulitis (LBD) is the concurrent development of a small bowel obstruction (SBO). The literature regarding these joint diagnoses is primarily limited to small case series from the 1950s. Consequently, no official recommendations or recent literature exists to guide decision making.MethodsThis is a retrospective case–control study with 5:1 matching by demographics, comorbidities, and Hinchey classification of patients presenting with concomitant LBD and SBO and patients with LBD alone. The primary outcome assessed was the need for same admission surgical intervention.ResultsPatients with concurrent LBD and SBO were more likely to require surgical intervention (OR 4.2, p<0.001) and more likely to receive an open operation than patients with only LBD (p<0.001). The length of stay (LOS) was longer for LBD with SBO (mean LOS +3.2 days, p=0.003).DiscussionPatients with concurrent LBD and SBO are more likely to fail non-operative management. Given this, along with their longer LOS and higher rate of open surgery, earlier surgical intervention may improve outcomes and reduce hospital LOS.Level of evidence4.

Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues.We apply GANs to to generate full particle physics events (not individual physics objects), explore conditioning of generated events based on physics theory parameters and evaluate the precision and generalization of the produced datasets. We apply this to SUSY mass parameter interpolation and pileup generation. We also discuss recent developments in convergence and representations that match the structure of the detector better than images.In addition we describe on-going work making use of large-scale distributed resources on the Cori supercomputer at NERSC, and developments to control distributed training via interactive jupyter notebook sessions. This will allow tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment.

The evaluation and workup of fever and the use of antibiotics to treat infections is part of daily practice in the surgical intensive care unit (ICU). Fever can be infectious or non-infectious; it is important to distinguish between the two entities wherever possible. The evidence is growing for shortening the duration of antibiotic treatment of common infections. The purpose of this clinical consensus document, created by the American Association for the Surgery of Trauma Critical Care Committee, is to synthesize the available evidence, and to provide practical recommendations. We discuss the evaluation of fever, the indications to obtain cultures including urine, blood, and respiratory specimens for diagnosis of infections, the use of procalcitonin, and the decision to initiate empiric antibiotics. We then describe the treatment of common infections, specifically ventilator-associated pneumonia, catheter-associated urinary infection, catheter-related bloodstream infection, bacteremia, surgical site infection, intra-abdominal infection, ventriculitis, and necrotizing soft tissue infection.

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.