Chiral Stoner magnetism in Dirac bands

We argue that Stoner magnetism in bands endowed with Berry curvature is profoundly influenced by the chiral interaction between Berry's orbital magnetization and spin chirality density. In graphene multilayers, moir\'e or non-moir\'e, carriers moving in the presence of a spin texture see it as a source of a pseudo-magnetic field coupled to their orbital motion through a chiral Aharonov-Bohm effect. Pseudo-magnetic fields take different values in different valleys and can be present even without an externally applied $B$ field. This interaction favors chiral spin textures such as skyrmions -- the topologically protected objects with particle-like properties, stabilized in the ground state. The chiral interaction softens the threshold for Stoner instability, rendering chiral spin-ordered phases readily accessible under realistic conditions

Spin-triplet superconductivity at the onset of isospin order in biased bilayer graphene

The quest for unconventional superconductivity governed by Coulomb repulsion between electrons rather than phonon attraction received new momentum with the advent of moir\'e graphene. Initially, delineating the phonon and Coulomb-repulsion-based pairing mechanisms has proven to be a challenging task, however the situation has changed after recent discovery of superconductivity in non-twisted graphene bilayers and trilayers. Superconductivity occurring at the phase boundaries of spin and valley polarized orders calls for non-phonon scenarios, yet the specific pairing mechanisms remain to be understood. Here we analyze a striking example -- superconductivity in graphene bilayers occurring at the onset of valley-polarized order induced by a magnetic field. We describe an attraction-from-repulsion mechanism for pairing mediated by a quantum-critical mode, which fully explains the observed phenomenology. While it is usually notoriously difficult to infer the pairing mechanism from the observed superconducting phases, this case presents a rare exception, allowing for a fairly unambiguous identification of the origin of the pairing glue. A combination of factors such as the location of superconducting phase at the onset of isospin-polarized phase, a threshold in a magnetic field, above which superconductivity occurs, and its resilience at high magnetic fields paints a clear picture of a triplet superconductivity driven by quantum-critical fluctuations

Signatures of Cooper pair dynamics and quantum-critical superconductivity in tunable carrier bands

Different superconducting pairing mechanisms are markedly distinct in the underlying Cooper pair kinematics. Pairing interactions mediated by quantum-critical soft modes are dominated by highly collinear processes, falling into two classes: forward scattering and backscattering. In contrast, phonon mechanisms have a generic non-collinear character. We show that the type of kinematics can be identified by examining the evolution of superconductivity when tuning the Fermi surface geometry. We illustrate our approach using recently measured phase diagrams of various graphene systems. Our analysis unambiguously connects the emergence of superconductivity at ``ghost crossings'' of Fermi surfaces in distinct valleys to the pair kinematics of a backscattering type. Together with the observed non-monotonic behavior of superconductivity near its onset (sharp rise followed by a drop), it provides strong support for a particular quantum-critical superconductivity scenario. These findings conclusively settle the long-standing debate on the origin of superconductivity in this system and demonstrate the essential role of quantum-critical modes in superconducting pairing. Moreover, our work highlights the potential of tuning bands via ghost crossings as a promising means of boosting superconductivity.Comment: 13pgs, 3fg

Study on preparation and characteristics of CMC/ZrCit/GDL fire-fighting gel foam

In order to prevent coal spontaneous combustion, a new kind of gel foam was prepared with sodium carboxymethyl cellulose (CMC), zirconium citrate (ZrCit), gluconate-δ-lactone (GDL) and compound foaming agent in a certain proportion. The basic performance tests of thermal stability, permeability and adhesion of the prepared gel foam were carried out respectively. TG experiment was used to study the influence of the coal sample treated with gel foam on the mass and heat of the coal sample compared with the raw coal sample during the heating process. Through the change of the concentration of the indicator gas CO of coal spontaneous combustion in the programmed temperature experiment, The retarding property of gel foam was investigated and the flame retardant mechanism of gel foam was analyzed based on the experimental results. The experimental study shows that the gel foam with high price Zr4+ as metal crosslinking agent has stable structure, can significantly enhance the thermal stability and water retention of the gel foam, make the coal oxidation heat loss significantly reduced in TG analysis, the maximum heat release power decreased by 19.9%, make the coal oxygen composite oxygen consumption rate and CO production rate decreased in the temperature program experiment, indicating that the gel foam can Coal spontaneous combustion can be effectively inhibited by physical and chemical reactions

Performance enhancement of permeable asphalt mixtures with recycled aggregate for concrete pavement application

The incorporation of recycled concrete aggregate (RCA) in permeable asphalt mixtures (PAMs) is an efficient method of utilizing construction demolished waste. It not only conforms to the trend of building sponge cities, but also alleviates the problem of overexploitation of natural aggregate resources. As the performance of PAM containing recycled aggregate is not comparable to natural aggregate, modification treatments and the addition of hybrid fibers are adopted as two enhancement methods to improve the performance of PAM with RAC in this study. It is found that replacing natural aggregate with recycled aggregate increases the optimum asphalt content (OAC) but decreases the residual stability. The OAC is increased by 45% when the RCA ratio is 100%, whereas applying silicone resin can give a 16.2% decrease in the OAC. Enhancing RCA with silicone resin can increase the water stability to be comparable with natural aggregate. Moreover, with modification treatment using calcium hydroxide solution, the mechanical strength of PAM is enhanced to even higher than that of natural coarse aggregate mixture alone. Improvements in both mechanical strength and water stability are also achieved by strengthening recycled aggregate with cement slurry, although the performance is less effective than using silicone resin. With the increase in the content of RCA, the permeability coefficients of PAM first decrease and then exhibit an increasing trend. The results indicate that the PAM with RCA and modification treatments can perform satisfactorily as a pavement material in practice. Applying probable modification, PAM incorporating RCA meets the criteria for use in concrete pavement applications

Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry

The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insurance industry datasets without requiring the datasets themselves to be shared from one company to another. The application of FL addresses two of the most pressing concerns: limited data volume and data variety, which are caused by privacy concerns, the rarity of claim events, the lack of informative rating factors, etc.. During each round of FL, collaborators compute improvements on the model using their local private data, and these insights are combined to update a global model. Such aggregation of insights allows for an increase to the effectiveness in forecasting claims losses compared to models individually trained at each collaborator. Critically, this approach enables machine learning collaboration without the need for raw data to leave the compute infrastructure of each respective data owner. Additionally, the open-source framework, OpenFL, that is used in our experiments is designed so that it can be run using confidential computing as well as with additional algorithmic protections against leakage of information via the shared model updates. In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions. This paper's application of FL can also be expanded to other areas including fraud detection, catastrophe modeling, etc., that have a similar need to incorporate data privacy into machine learning collaborations. Our framework and empirical results provide a foundation for future collaborations among insurers, regulators, academic researchers, and InsurTech experts