Search Results (6)

The mathematical universe of the quantum topos, which is formulated on the basis of classical Boolean snapshots, delivers a neo-realist description of quantum mechanics that preserves realism. The main contribution of this article is developing formal objectivity in physical theories beyond quantum mechanics in the topos-theory approach. It will be shown that neo-realist responses to non-perturbative structures of quantum field theory do not preserve realism. In this regard, the method of Feynman graphons is applied to reframe the task of describing objectivity in quantum field theory in terms of replacing the standard Hilbert-space/operator-algebra ontology with a new context category built from a certain family of topological Hopf subalgebras of the topological Hopf algebra of renormalization as algebraic/combinatorial data tied to non-perturbative structures. This topological-Hopf-algebra ontology, which is independent of instrumentalist probabilities, enables us to reconstruct gauge field theories on the basis of the mathematical universe of the non-perturbative topos. The non-Boolean logic of the non-perturbative topos cannot be recovered by classical Boolean snapshots, which is in contrast to the quantum-topos reformulation of quantum mechanics. The article formulates a universal version of the non-perturbative topos to show that quantum field theory is a globally and locally neo-realist theory which can be reconstructed independent of the standard Hilbert-space/operator-algebra ontology. Formal objectivity of the universal non-perturbative topos offers a new route to build objective semantics for non-perturbative structures. Full article

Recent advances in prompt learning have opened new avenues for enhancing natural language understanding in domain-specific tasks, including code vulnerability detection. Motivated by the limitations of conventional binary classification methods in capturing complex code semantics, we propose a novel framework that integrates a two-stage prompt optimization mechanism with hierarchical representation learning. Our approach leverages graphon theory to generate task-adaptive, structurally enriched prompts by encoding both contextual and graphical information into trainable vector representations. To further enhance representational capacity, we incorporate the pretrained model CodeBERTScore, a syntax-aware encoder, and Graph Neural Networks, enabling comprehensive modeling of both local syntactic features and global structural dependencies. Experimental results on three public datasets—FFmpeg+Qemu, SVulD and Reveal—demonstrate that our method performs competitively across all benchmarks, achieving accuracy rates of 64.40%, 83.44% and 90.69%, respectively. These results underscore the effectiveness of combining prompt-based learning with graph-based structural modeling, offering a more accurate and robust solution for automated vulnerability detection. Full article

The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard tradeoff among read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics, and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists (ALs) to achieve the best of both worlds. We compare CSR++ to CSR, ALs from the Boost Graph Library (BGL), and the following state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular GP algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average) while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed those of several state-of-the-art graph structures while maintaining low memory consumption when the workload includes updates. Full article

We report the geometry, kinetic energy, and some optical properties of the 6,6,12-graphyne-based systems. We obtained the values of their binding energies and structural characteristics such as bond lengths and valence angles. Moreover, using nonorthogonal tight-binding molecular dynamics, we carried out a comparative analysis of the thermal stability of 6,6,12-graphyne-based isolated fragments (oligomer) and two-dimensional crystals constructed on its basis in a wide temperature range from 2500 to 4000 K. We found the temperature dependence of the lifetime for the finite graphyne-based oligomer as well as for the 6,6,12-graphyne crystal using a numerical experiment. From these temperature dependencies, we obtained the activation energies and frequency factors in the Arrhenius equation that determine the thermal stability of the considered systems. The calculated activation energies are fairly high: 1.64 eV for the 6,6,12-graphyne-based oligomer and 2.79 eV for the crystal. It was confirmed that the thermal stability of the 6,6,12-graphyne crystal concedes only to traditional graphene. At the same time, it is more stable than graphene derivatives such as graphane and graphone. In addition, we present data on the Raman and IR spectra of the 6,6,12-graphyne, which will help distinguish it from the other carbon low-dimensional allotropes in the experiment. Full article

With the incredible discovery of graphene (Gr), all of the properties studied to date suggest that it has promising applications in the development of semiconductor, spintronic, insulating, and polymer materials. However, efforts are still underway to fully understand the nature of metal–graphone(GrH) interaction in order to offer better scope for tuning the electronic and magnetic properties, which can be performed by intercalation of atoms via metal support on graphene. We chose metal atoms belonging to the s and p blocks, namely Na and Al, respectively, as the intercalating atoms. Herein, the maximum coverage of a monolayer of Na and Al was comparatively studied on a Ni(111) surface. Significant changes in the magnetic and electronic properties at the Ni(111)/graphone interface were observed upon intercalation. Of the two intercalating metal atoms, Na proved to be more effective, such that the magnetic properties of the surface Ni were only slightly decreased, and the graphone also showed better magnetic properties than in the absence of Na. Full article

Graphene, a two-dimensional carbon in honeycomb crystal with single-atom thickness, possesses extraordinary properties and fascinating applications. Graphene mechanics is very important, as it relates to the integrity and various nanomechanical behaviors including flexing, moving, rotating, vibrating, and even twisting of graphene. The relationship between the strain and stress plays an essential role in graphene mechanics. Strain can dramatically influence the electronic and optical properties, and could be utilized to engineering those properties. Furthermore, graphene with specific kinds of defects exhibit mechanical enhancements and thus the electronic enhancements. In this short review, we focus on the current development of graphene mechanics, including tension and compression, fracture, shearing, bending, friction, and dynamics properties of graphene from both experiments and numerical simulations. We also touch graphene derivatives, including graphane, graphone, graphyne, fluorographene, and graphene oxide, which carve some fancy mechanical properties out from graphene. Our review summarizes the current achievements of graphene mechanics, and then shows the future prospects. Full article