Real Topologies Research Articles

Real Topological Phonons in 3D Carbon Allotropes

AbstractThere has been a significant focus on real topological systems that enjoy space‐time inversion symmetry and lack spin‐orbit coupling. While the theoretical classification of the real topology has been established, more progress has yet to be made in the materials realization of real topological phononic states in 3D. To address this crucial issue, high‐throughput computing is performed to inspect the real topology in the phonon spectrums of the 3D carbon allotropes. Among 1661 carbon allotropes listed in the Samara Carbon Allotrope Database (SACADA), 79 candidates host a phononic real Chern insulating (PRCI) state, 2 candidates host a phononic real nodal line (PRNL) state, 12 candidates host a phononic real Dirac point (PRDP) state, and 10 candidates host a phononic real triple‐point pair (PRTPP) state. The PRCI, PRNL, PRTPP, and PRDP states of 27‐SG. 166‐pcu‐h, 1081‐SG. 194‐42T13‐CA, 52‐SG. 141‐gis, and 132‐SG. 191‐3,4T157 are exhibited as illustrative examples, and the second‐order phononic hinge modes are explored. This study broadens the understanding of 3D topological phonons and expands the material candidates with phononic hinge modes and phononic real topology.

Polarization textures in crystal supercells with topological bands

Two-dimensional materials are a highly tunable platform for studying the momentum space topology of the electronic wavefunctions and real space topology in terms of skyrmions, merons, and vortices of an order parameter. Such textures for electronic polarization can exist in moiré heterostructures. A quantum-mechanical definition of local polarization textures in insulating supercells was recently proposed. Here, we propose a definition for local polarization that is also valid for systems with topologically nontrivial bands. We introduce semilocal hybrid polarizations, which are valid even when the Wannier functions in a system cannot be made exponentially localized in all dimensions. We use this definition to explicitly show that nontrivial real-space polarization textures can exist in topologically nontrivial systems with nonzero Chern number under (1) an external superlattice potential, and (2) under a stacking-induced moiré potential. In the latter, we find that while the magnitude of the local polarization decreases discontinuously across a topological phase transition from trivial to topologically nontrivial, the polarization does not completely vanish. Our findings suggest that band topology and real-space polar topology may coexist in real materials. Published by the American Physical Society 2024

Body-centered tetragonal C4: A carbon allotrope with real topology and second-order bulk-boundary correspondence

Carbon, being the most common element on Earth, exhibits a diverse range of allotropic phases, hence contributing to its intricate physical characteristics. In recent times, a number of carbon allotropes near the Fermi surfaces have been predicted from first principles with rich topological phases. In this study, we present body-centered tetragonal C4 (bct C4), a new form of crystalline sp3 carbon, is a potential candidate for both an obstructed atomic insulator (OAI) and a real Chern insulator (RCI). It is noteworthy that bct C4 demonstrates an unconventional bulk-boundary correspondence due to its manifestation of hinges boundary states. Our current work reveals that bct C4 is a viable carbon phase platform for investigating the real topology and second-order bulk-boundary correspondence. It is hoped that our work can serve as a good starting point for future studies on three-dimensional (3D) real Chern insulators.

Z-ACA allotrope: a topological carbon material with obstructed Wannier charge center, real topology, and hinge states

Z-ACA allotrope: a topological carbon material with obstructed Wannier charge center, real topology, and hinge states

Obstructed atomic and real Chern insulating states in bcc C8

Carbon, an element of fundamental importance in our universe, has a diverse range of allotropic phases, the majority of which exhibit the potential for topological fermions. In this work, by means of first-principle calculations, we report a carbon allotrope with a body-centered cubic form, denoted as bcc C8, which is a candidate for obstructed atomic insulator and real Chern insulator. Interestingly, bcc C8 hosts an unconventional bulk-boundary correspondence because it exhibits boundary states (hinge states) in two dimensions lower than the 3D bulk. Importantly, bcc C8 has already been synthesized in experiments, making it a real carbon phase platform for studying the real topology and hinge states, which can be leveraged to create efficient topological electronic devices.

An effective deep-Q learning scheme for QoS improvement in physical layer of software-defined networks

The demands of diverse applications necessitate specific communication requirements, such as jitter, packet loss ratio (PLR), and latency, at the end-to-end (E2E) level within the physical layer of software-defined networking (SDN). In heterogeneous network environments, E2E routes traverse multiple domains with varying quality-of-service (QoS) classes of traffic, posing challenges in meeting unique E2E QoS criteria. Hence the performance on the E2E path is significant to meet the needs of applications. Hence, the issue of providing services on the E2E route is challenging due to several QoS classes in a domain on the source-to-destination route. This paper presents an SDN architecture leveraging Deep Q-learning (DQL) to ascertain the appropriate QoS class for E2E routes in software-defined heterogeneous networks, thereby reducing overall E2E delay. The proposed model with DQL is compared with benchmark reinforcement learning. The proposed framework is validated using various real Internet topologies such as Abilene, USNet, and OS3E, assessing not only E2E delay but also jitter, PLR, and throughput, showcasing its efficacy in optimizing network performance regarding these performance metrics.

Hidden Real Topology and Unusual Magnetoelectric Responses in Two-Dimensional Antiferromagnets.

Recently, the real topology has been attracting widespread interest in two dimensions (2D). Here, based on first-principles calculations and theoretical analysis, the monolayer Cr2Se2O (ML-CrSeO) is revealed as the first material example of a 2D antiferromagnetic (AFM) real Chern insulator (RCI) with topologically protected corner states. Unlike previous RCIs, it is found that the real topology of the ML-CrSeO is rooted in one certain mirror subsystem of the two spin channels, and cannot be directly obtained from all the valence bands in each spin channel as commonly believed. In particular, due to antiferromagnetism, the corner modes in ML-CrSeO exhibit strong corner-contrasted spin polarization, leading to spin-corner coupling (SCC). This SCC enables a direct connection between spin space and real space. Consequently, large and switchable net magnetization can be induced in the ML-CrSeO nanodisk by electrostatic means, such as potential step and in-plane electric field, and the corresponding magnetoelectric responses behave like a sign function, distinguished from that of the conventional multiferroic materials. This work considerably broadens the candidate range of RCI materials, and opens up a new direction for topo-spintronics and 2D AFM materialsresearch.

3D Carbon Allotropes: Topological Quantum Materials with Obstructed Atomic Insulating Phases, Multiple Bulk‐Boundary Correspondences, and Real Topology

AbstractThe study of topological phases with unconventional bulk‐boundary correspondences and nontrivial real Chern number has garnered significant attention in the topological states of matter. Using the first‐principle calculations and theoretical analysis, a high‐throughput material screening of the 3D obstructed atomic insulators (OAIs) and 3D real Chern insulators (RCIs) based on the Samara Carbon Allotrope Database (SACADA) are performed. Results show that 422 out of 703 3D carbon allotropes are 3D OAIs with multiple bulk‐boundary correspondences, including 2D obstructed surface states (OSSs) and 1D hinge states, which are in 1D and 2Ds lower than the 3D bulk, respectively. The 2D OSSs in these OAIs can be modified when subjected to appropriate boundaries, which benefits the investigation of surface engineering and the development of efficient topological catalysts. These 422 OAIs, which have 2D and 1D boundary states, are excellent platforms for multi‐dimensional topological boundaries research. Remarkably, 138 of 422 OAIs are also 3D RCIs, which show a nontrivial real topology in the protection of spacetime inversion symmetry. This work not only provides a comprehensive list of 3D carbon‐based OAIs and RCIs, but also guides their application in various aspects based on multiple bulk‐boundary correspondences and real topological phases.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

A distributed energy resources control method to improve the loading profile of radial distribution feeders using solid-state transformer

The multi-purpose use of distributed energy resources (DER) can bring a lot of income to its owners. For example, the load imbalance of the radial feeders in the distribution network is an old problem that occurs due to the simple structure of the radial topology and the unidirectional power flow in traditional networks. With emerging DERs and the development of power electronic technology, previous concepts are changing continuously. This paper aims to balance the load between two feeders whilst, it yields better voltage profiles and more appropriate utilization of distribution feeders. This paper proposes a new control method for DERs (including PV panels plus battery storage) that makes the radial system pretends a ring topology properties. It is done with the help of a solid-state transformer (SST). The relationships of the DER current controller are formulated based on current passes through the ring connection. Case studies are carried out on improved IEEE 33-node and 13-node systems with DERs and SSTs. The results show that the suggested control method can simulate the real ring topology and operating objective namely, load balancing is reached.

Foundation of Revolutionary Topologies: An Overview, Examples, Trend Analysis, Research Issues, Challenges, and Future Directions


 We now found nine new topologies, such as: NonStandard Topology, Largest Extended NonStandard Real Topology, Neutrosophic Triplet Weak/Strong Topologies, Neutrosophic Extended Triplet Weak/Strong Topologies, Neutrosophic Duplet Topology, Neutrosophic Extended Duplet Topology, Neutrosophic MultiSet Topology, and recall and improve the seven previously founded topologies in the years (2019-2023), namely: NonStandard Neutrosophic Topology, NeutroTopology, AntiTopology, Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, SuperHyperTopology, and Neutrosophic SuperHyperTopology. They are called avantgarde topologies because of their innovative forms.

Energy storage system based on hybrid wind and photovoltaic technologies

Clean energy sources like wind and solar have a huge potential to lessen reliance on fossil fuels. Due to the stochastic nature of various energy sources, dependable hybrid systems have recently been developed. This paper's major goal is to use the existing wind and solar resources to provide electricity. A 6 kWp solar-wind hybrid system installed on the roof of an educational building is studied and optimized using HOMER (Hybrid Optimization of Multiple Energy Resources) software at different levels of reliability. At an average annual Cost of Energy (COE) of $1.156 per kWh, the system generates 1996 kWh of power overall. Investigations are made on the techno-economic characteristics of real and ideal hybrid system topologies with maximum capacity shortfalls of 0 %, 5 %, 10 %, and 20 %. The hybrid system's sensitivity analysis looks at how a capacity gap affects overall net present costs and excess power generation. A 2 kWp PV system with one string of ten 12V batteries is shown to be more cost-effective than the existing system with a COE of $0.575/kWh. The most effective configuration for utilizing the site's solar and wind resources is demonstrated to be a 5 kWp wind turbine, a 2 kWp PV system, and battery storage. A wind-solar hybrid system is more expensive than the current system. Despite this, an additional 1 kWp solar PV system may be added to the current system due to the reduction in the limit deficit from 22.3 % to 3.1 %. The findings show that solar-wind hybrid energy systems may efficiently use renewable energy sources for dispersed applications. Additionally, new research directions are noted.

OPlaceRAN—A Placement Orchestrator for Virtualized Next-Generation of Radio Access Network

The fifth-generation mobile evolution enables Next-Generation Radio Access Networks (NG-RAN) transformations. The RAN protocol stack is split into eight disaggregated options combined in three network units, i.e., Central, Distributed, and Radio. Further advances allow the RAN functions to be virtualized on top of general-purpose hardware using the virtualized RAN (vRAN). The combination of NG-RAN and vRAN results in vNG-RAN, enabling the management of the disaggregated units and protocols as a set of radio functions. However, the orchestration-based placement of these radio functions is challenging since the best decision can be determined by multiple constraints involving RAN disaggregation, crosshaul network requirements, availability of computational resources, etc. This article proposes OPlaceRAN, a vNG-RAN deployment orchestrator framed within the NFV reference architecture and aligned with the Open RAN initiative. OPlaceRAN supports the dynamic placement of radio functions focusing on vNG-RAN planning and is designed to be agnostic to the placement optimization solution. We developed a prototype based on cloud-native tools to deploy RAN using containerized virtualization and the OpenAirInterface emulator. The evaluation is analyzed considering two different approaches as a proof-of-concept. First, we applied two placement solutions in a controlled real computing infrastructure with a crosshaul network. Second, we investigated the orchestrator’s scalability with a real and larger-scale topology. Our results show that OPlaceRAN is an effective cloud-native solution for containerized network function placement and agnostic to the placement solution, handling scale-out well. OPlaceRAN is up-to-date with the most advanced vNG-RAN design and development approaches, contributing to the evolution of fifth-generation networks.

Cost-effective network capacity upgrade by heterogeneous wavelength division multiplexing density with bandwidth-variable virtual direct links

A cost-effective and fast network capacity upgrade with what we believe to be novel bandwidth-variable virtual direct links (BV-VDLs) is proposed. BV-VDLs are designed to pass through the physical links expected to be heavily congested and realize very dense path accommodation. We adopt cut-set analysis to identify the links likely to be heavily congested; however, high computation loads will be incurred in finding all the cut-sets, and justifying these loads is essential. Thus a simple-path-search based graph splitting method is proposed that substantially shortens the calculation time. This strategy allows us to efficiently upgrade network capacity while keeping existing facilities unchanged and can be installed in networks in operation. The proposal is also valid for enhancing the capacity of new networks. The capacity enhancement can reach 13%–33% even in dynamic path operation scenarios on the real topologies of pan-European, North American, and Japanese networks.

GENIND: An industrial network topology generator

The availability of industrial network topologies is limited, which presents a significant challenge for researchers in this field. Network generators are essential tools for generating realistic simulation outcomes in network research. Thus, it is crucial to use realistic topologies that can accurately represent the characteristics of the target network. The primary objective of this study is to address the existing research gap by developing an industrial network topology generator for novel topology datasets specific to industrial networks and the industrial Internet of Things (IIoT). In this paper, we describe the development of the graph theoretic industrial topology generator (GENIND), a topology generator that can produce realistic IIoT and industrial network topologies. The proposed GENIND network topology generator is validated through comparisons with real-world industrial network topologies. The evaluation framework assesses the performance of the proposed GENIND system using three main measures: the overall network structure, path-related factors, and resilience topological structure features. The results of the evaluation demonstrate a high degree of similarity between the graphs generated by the GENIND system and the real-world industrial networks used for comparison. This finding suggests that the generated graphs can be utilized as reliable tools to predict the behavior of industrial networks under various conditions.

Joint Caching and Routing in Cache Networks With Arbitrary Topology

In-network caching and flexible routing are two of the most celebrated advantages of next generation network infrastructures. Yet few solutions are available for jointly optimizing caching and routing that provide performance guarantees for networks with arbitrary topology. We take a holistic approach towards this fundamental problem by analyzing its complexity in all the cases and developing polynomial-time algorithms with approximation guarantees in important special cases. We also reveal the fundamental challenge in achieving guaranteed approximation in the general case and propose an alternating optimization algorithm with good empirical performance and fast convergence. Our algorithms have demonstrated superior performance in both routing cost and congestion compared to the state-of-the-art solutions in evaluations based on real topology and request traces.

Bound states at disclinations: an additive rule of real and reciprocal space topology

Focusing on the two-dimensional (2D) Su-Schrieffer-Heeger (SSH) model, we propose an additive rule between the real-space topological invariant s of disclinations (related to the Burgers vector B) and the reciprocal-space topological invariant p of bulk wave functions (the vectored Zak phase). The disclination-induced bound states in the 2D SSH model appear only if (s + p/2π) is nonzero modulo the lattice constant. These disclination-bound states are robust against perturbations respecting C4 point group symmetry and other perturbations within an amplitude determined by p. Besides the disclination-bound states, the proposed additive rule also suggests that a half-bound state extends over only half of a sample and a hybrid-bound state, which always have a nonvanishing component of s + p/2π.

Topological states in the polymerized carbon nanotubes

In this work, we report by ab initio calculations a systematical study on the energetic, dynamical, and electronic properties of polymerized (2n+1,0) (n=2,3,4) sing wall carbon nanotubes, termed as CNT(5,0), CNT(7,0), and CNT(9,0) carbon. The total energy calculations show that the equilibrium energies of the three carbon allotropes are lower than or comparable to the previously proposed bct-C16, bco-C16, and ors-C16 carbon. Their dynamical stabilities have been confirmed with phonon band spectrum calculations and their thermal stabilities have been confirmed with ab initio molecular dynamics simulations. Despite their unified space group symmetries and similar bonding types, their electronic properties are distinct: CNT(5,0) and CNT(7,0) carbon are topological nodal line semimetals with nodal rings in kx=0 and kz=0 mirror planes respectively, while the CNT(9,0) carbon is a semiconductor. To understand their distinct electronic behaviors, we provide a systematical explanation from the real space crystalline structure perspective. Our work has enriched the family of carbon allotropes with exotic band topologies and provide insights for the relations between real space crystalline structures and momentum space band topologies.

Weyl singularities in polaritonic multiterminal Josephson junctions

We study theoretically analog multi-terminal Josephson junctions formed by gapped superfluids created upon resonant pumping of cavity exciton-polaritons. We study the $p$-like bands of a 5-terminal junction in the 4D parameter space created by the superfluid phases acting as quasi-momenta. We find 4/6 Weyl points in 3D subspaces with preserved/broken time-reversal symmetry. We link the real space topology (vortices) to the parameter space one (Weyl points). We derive an effective Hamiltonian encoding the creation, motion, and annihilation of Weyl nodes in 4D. Our work paves the way to the study of exotic topological phases in a platform allowing direct measurement of eigenstates and band topology.

Laplacian Matrix Sampling for Communication- Efficient Decentralized Learning

We consider the problem of training a given machine learning model by decentralized parallel stochastic gradient descent over training data distributed across multiple nodes, which arises in many application scenarios. Although extensive studies have been conducted on improving the communication efficiency by optimizing what to communicate between nodes (e.g., model compression) and how often to communicate, recent studies have shown that it is also important to customize the communication patterns between each pair of nodes, which is the focus of this work. To this end, we propose a framework and efficient algorithms to design the communication patterns through Laplacian matrix sampling (LMS), which governs not only which nodes should communicate with each other but also what weights the communicated parameters should carry during parameter aggregation. Our framework is designed to minimize the total cost incurred until convergence based on any given cost model that is additive over iterations, with focus on minimizing the communication cost. Besides achieving a theoretically guaranteed performance in the special case of additive homogeneous communication costs, our solution also achieves superior performance under a variety of network settings and cost models in experiments based on real datasets and topologies, saving 24–50% of the cost compared to the state-of-the-art design without compromising the quality of the trained model.