RESUMO
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.
RESUMO
A key component of the phase diagram of many iron-based superconductors and electron-doped cuprates is believed to be a quantum critical point (QCP), delineating the onset of antiferromagnetic spin-density wave order in a quasi-two-dimensional metal. The universality class of this QCP is believed to play a fundamental role in the description of the proximate non-Fermi liquid behavior and superconducting phase. A minimal model for this transition is the O(3) spin-fermion model. Despite many efforts, a definitive characterization of its universal properties is still lacking. Here, we numerically study the O(3) spin-fermion model and extract the scaling exponents and functional form of the static and zero-momentum dynamical spin susceptibility. We do this using a Hybrid Monte Carlo (HMC) algorithm with a novel auto-tuning procedure, which allows us to study unprecedentedly large systems of 80 × 80 sites. We find a strong violation of the Hertz-Millis form, contrary to all previous numerical results. Furthermore, the form that we do observe provides good evidence that the universal scaling is actually governed by the analytically tractable fixed point discovered near perfect "hot-spot'" nesting, even for a larger nesting window. Our predictions can be directly tested with neutron scattering. Additionally, the HMC method we introduce is generic and can be used to study other fermionic models of quantum criticality, where there is a strong need to simulate large systems.
RESUMO
BACKGROUND: Triage is the act of stratifying the need for medical attention. Effective triage must account for injury patterns and severity. Personnel making triage decisions must also consider the patients' physiologic states. Vital signs can possibly be used to assess for the presence of physiological derangements such as coagulopathy, acidosis, or a significant base deficit. Providers could use this knowledge to assist with triage at casualty collection points where laboratory studies or point of care testing may not be available. METHODS: With institutional approval, data were extracted from the Joint Theater Trauma Registry for all patients with thoracic trauma between 2002 and 2012. Patients were identified by International Statistical Classification of Diseases and Related Health Problems, 9th Revision (ICD-9) codes. Heart rate (HR), systolic blood pressure (SBP), and pulse pressure were correlated with coagulopathy (international normalization ratio ≥ 1.5), acidosis (pH < 7.2) or an elevated base deficit (>6) on admission. Sensitivity, specificity, positive predictive values, negative predictive values, and odds ratios were calculated. FINDINGS: HR > 100, SBP < 90, or pulse pressure <30 were associated with an increased risk for acidosis (odds ratio 3.06 [95% confidence interval 2.48-3.78], 4.72 [3.85-5.78], and 2.73 [2.15-3.48], respectively), coagulopathy (2.21 [1.72-2.83], 4.55 [3.57-5.80], and 2.73 [2.15-3.48], respectively), and base deficit >6 (2.17 [1.88-2.50], 3.48 [2.87-4.22], and 2.22 [1.78-2.77], respectively). HR was a moderately sensitive marker (0.74), whereas SBP was a specific marker (0.93). DISCUSSION: SBP < 90 is an effective marker for ruling in physiologic derangement after thoracic trauma. HR > 100 was associated with over twice the odds for physiologic derangement. Vital signs can be used to assess for physiologic derangement in the population studied and may help in triage.