Quantum impurity in the bulk of topological insulator

We investigate physical properties of an Anderson impurity embedded in the bulk of a topological insulator. The slave-boson mean-field approximation is used to account for the strong electron correlation at the impurity. Different from the results of a quantum impurity on the surface of a topological insulator, we find for the band-inverted case, a Kondo resonant peak and in-gap bound states can be produced simultaneously. However, only one type of them appears for the normal case. It is shown that the mixed-valence regime is much broader in the band-inverted case, while it shrinks to a very narrow regime in the normal case. Furthermore, a self-screening of the Kondo effect may appear when the interaction between the bound-state spin and impurity spin is taken into account.Comment: 11 pages, 8 figure

Plexin-mediated neuronal development and neuroinflammatory responses in the nervous system

Plexins are a large family of single-pass transmembrane proteins that mediate semaphorin signaling in multiple systems. Plexins were originally characterized for their role modulating cytoskeletal activity to regulate axon guidance during nervous system development. Thereafter, different semaphorin-plexin complexes were identified in the nervous system that have diverse functions in neurons, astrocytes, glia, oligodendrocytes, and brain derived-tumor cells, providing unexpected but meaningful insights into the biological activities of this protein family. Here, we review the overall structure and relevant downstream signaling cascades of plexins. We consider the current knowledge regarding the function of semaphorin-plexin cascades in the nervous system, including the most recent data regarding their roles in neuronal development, neuroinflammation, and glioma

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, the existing studies that rely on spatial information of CSI are susceptible to environmental changes, such as object movement, atmospheric factors, and machine rebooting, which degrade prediction accuracy. Moreover, SL-based methods require time-consuming labeling for retraining models. Therefore, it is imperative to design a continuously monitored model life-cycle using a semi-supervised learning (SSL) based scheme. In this paper, we conceive a bifold teacher-student (BTS) learning approach for presence detection systems that combines SSL by utilizing partially labeled and unlabeled datasets. The proposed primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. The experimental results demonstrate that the proposed BTS system sustains asymptotic accuracy after retraining the model with unlabeled data. Furthermore, the label-free BTS outperforms existing SSL-based models in terms of the highest detection accuracy while achieving the asymptotic performance of SL-based methods

CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks

With proliferation of fifth generation (5G) new radio (NR) technology, it is expected to meet the requirement of diverse traffic demands. We have designed a coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for 5G-NR networks to improve the network performance in terms of throughput and latency. We have proposed a CoMP enhanced joint subcarrier and power allocation (CESP) scheme which aims at maximizing sum rate under the considerations of transmit power limitation and guaranteed quality-of-service (QoS) including throughput and latency restrictions. By employing difference of two concave functions (D.C.) approximation and abstract Lagrangian duality method, we theoretically transform the original non-convex nonlinear problem into a solvable maximization problem. Moreover, the convergence of our proposed CESP algorithm with D.C. approximation is analytically derived with proofs, and is further validated via numerical results. Simulation results demonstrated that our proposed CESP algorithm outperforms the conventional non-CoMP and single numerology mechanisms along with other existing benchmarks in terms of lower latency and higher throughput under the scenarios of uniform and edge users