I'll review the construction of topological string theory which has a lot of applications in usual string theory and mathematics. I'll start by summarizing the geometry of Calabi-Yau manifolds, where topological strings will be defined, introduce topological twist, define A and B models, and discuss a relation to F-terms in the effective action of type II strings. In the end, I'll explain the wave function property related to the issue of background independence.

BPS indices encoding entropy of supersymmetric black holes in compactifications of Type II string theory on compact Calabi-Yau threefolds coincide with generalized Donaldson-Thomas invariants whose computation represents an outstanding problem. I'll show how this problem can be solved for a set of one-parameter threefolds by combining a direct integration of topological string, modular properties of rank 0 DT invariants counting D4-D2-D0 BPS states, and wall-crossing relations between rank 1 and rank 0 DT invariants. As a result, one obtains explicit (mock) modular functions encoding infinite sets of D4-D2-D0 BPS indices, which in turn can be used to generate new boundary conditions for the holomorphic anomaly equation of topological string allowing to overcome the limitations of the direct integration method.

Importance : Pain is a common out-of-hospital symptom among patients, and opioids are often prescribed. Research suggests that overprescribing for acute traumatic pain is still prevalent, even when limits restricting opioid prescriptions have been implemented. Ketamine hydrochloride is an alternative to opioids in adults with out-of-hospital traumatic pain. Objective : To assess the noninferiority of intravenous ketamine compared with intravenous morphine sulfate to provide pain relief in adults with out-of-hospital traumatic pain. Design, Setting, and Participants : The Intravenous Subdissociative-Dose Ketamine Versus Morphine for Prehospital Analgesia (KETAMORPH) study was a multicenter, single-blind, noninferiority randomized clinical trial comparing ketamine hydrochloride (20 mg, followed by 10 mg every 5 minutes) with morphine sulfate (2 or 3 mg every 5 minutes) in adult patients with out-of-hospital trauma and a verbal pain score equal to or greater than 5. Enrollment occurred from November 23, 2017, to November 26, 2022, in 11 French out-of-hospital emergency medical units. Interventions : Patients were randomly assigned to ketamine (n = 128) or morphine (n = 123). Main Outcomes and Measures : The primary outcome was the between-group difference in mean change in verbal rating scale pain scores measured from the time before administration of the study drug to 30 minutes later. A noninferiority margin of 1.3 was chosen. Results : A total of 251 patients were randomized (median age, 51 [IQR, 34-69] years; 111 women [44.9%] and 140 men [55.1%] among the 247 with data available) and were included in the intention-to-treat population. The mean pain score change was −3.7 (95% CI, −4.2 to −3.2) in the ketamine group compared with −3.8 (95% CI, −4.2 to −3.4) in the morphine group. The difference in mean pain score change was 0.1 (95% CI, −0.7 to 0.9) points. There were no clinically meaningful differences for vital signs between the 2 groups. The intravenous morphine group had 19 of 113 (16.8% [95% CI, 10.4%-25.0%]) adverse effects reported (most commonly nausea [12 of 113 (10.6%)]) compared with 49 of 120 (40.8% [95% CI, 32.0%-49.6%]) in the ketamine group (most commonly emergence phenomenon [24 of 120 (20.0%)]). No adverse events required intervention. Conclusions and Relevance : In the KETAMORPH study of patients with out-of-hospital traumatic pain, the use of intravenous ketamine compared with morphine showed noninferiority for pain reduction. In the ongoing opioid crisis, ketamine administered alone is an alternative to opioids in adults with out-of-hospital traumatic pain. Trial Registration : ClinicalTrials.gov Identifier: NCT03236805

We develop a machine learning model, which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature Tg, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry, and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near Tg compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques.

I'll review the known results about BPS indices, which encode in particular the entropy of BPS black holes, appearing in string compactifications down to four dimensions with various number of supersymmetries. First, I'll recall the well-known results about BPS states in N=8 and N=4 compactifications, and then present what is known about them in the N=2 case. Depending on time, I hope to cover some recent advances where an important role was played by (mock) modular symmetry.

We numerically elucidate the microscopic mechanisms controlling the relaxation dynamics of a three-dimensional lattice glass model that has static properties compatible with the approach to a random first-order transition. At low temperatures, the relaxation is triggered by a small population of particles with low-energy barriers forming mobile clusters. These emerging quasiparticles act as facilitating defects responsible for the spatially heterogeneous dynamics of the system, whose characteristic lengthscales remain strongly coupled to thermodynamic fluctuations. We compare our findings both with existing theoretical models and atomistic simulations of glass-formers.