The dynamically induced Fermi arcs and Fermi pockets in two dimensions: a model for underdoped cuprates

We investigate the effects of the dynamic bosonic fluctuations on the Fermi surface reconstruction in two dimensions as a model for the underdoped cuprates. At energies larger than the boson energy $\omega_b$, the dynamic nature of the fluctuations is not important and the quasi-particle dispersion exhibits the shadow feature like that induced by a static long range order. At lower energies, however, the shadow feature is pushed away by the finite $\omega_b$. The detailed low energy features are determined by the bare dispersion and the coupling of quasi-particles to the dynamic fluctuations. We present how these factors reconstruct the Fermi surface to produce the Fermi arcs or the Fermi pockets, or their coexistence. Our principal result is that the dynamic nature of the fluctuations, without invoking a yet-to-be-established translational symmetry breaking hidden order, can produce the Fermi pocket centered away from the $(\pi/2,\pi/2)$ towards the zone center which may coexist with the Fermi arcs. This is discussed in comparison with the experimental observations.Comment: Some comments and references were added and typos were corrected. The published version. 9 page

General, Strong Impurity-Strength Dependence of Quasiparticle Interference

Quasiparticle interference (QPI) patterns in momentum space are often assumed to be independent of the strength of the impurity potential when compared with other quantities, such as the joint density of states. Here, using the $T$-matrix theory, we show that this assumption breaks down completely even in the simplest case of a single-site impurity on the square lattice with an $s$ orbital per site. Then, we predict from first-principles, a very rich, impurity-strength-dependent structure in the QPI pattern of TaAs, an archetype Weyl semimetal. This study thus demonstrates that the consideration of the details of the scattering impurity including the impurity strength is essential for interpreting Fourier-transform scanning tunneling spectroscopy experiments in general.Comment: main manuscript: 8 pages, 6 figures, Supplementary Information: 3 pages, 6 figure

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weight bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless Communication

Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.Comment: accepted for publication on IEEE Wireless Communications Letter

Deep Learning Methods for Universal MISO Beamforming

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.Comment: to appear in IEEE Wireless Communications Letters (5 pages, 3 figures, 2 tables

The Raf/MEK/extracellular signal-regulated kinase 1/2 pathway can mediate growth inhibitory and differentiation signaling via androgen receptor downregulation in prostate cancer cells.

Upregulated ERK1/2 activity is correlated with androgen receptor (AR) downregulation in certain prostate cancer (PCa) that exhibits androgen deprivation-induced neuroendocrine differentiation, but its functional relevance requires elucidation. We found that sustained ERK1/2 activation using active Raf or MEK1/2 mutants is sufficient to induce AR downregulation at mRNA and protein levels in LNCaP. Downregulation of AR protein, but not mRNA, was blocked by proteasome inhibitors, MG132 and bortezomib, indicating that the pathway regulation is mediated at multiple points. Ectopic expression of a constitutively active AR inhibited Raf/MEK/ERK-mediated regulation of the differentiation markers, neuron-specific enolase and neutral endopeptidase, and the cyclin-dependent kinase inhibitors, p16(INK4A) and p21(CIP1), but not Rb phosphorylation and E2F1 expression, indicating that AR has a specific role in the pathway-mediated differentiation and growth inhibitory signaling. However, despite the sufficient role of Raf/MEK/ERK, its inhibition using U0126 or ERK1/2 knockdown could not block androgen deprivation-induced AR downregulation in an LNCaP neuroendocrine differentiation model, suggesting that additional signaling pathways are involved in the regulation. We additionally report that sustained Raf/MEK/ERK activity can downregulate full length as well as hormone binding domain-deficient AR isoforms in androgen-refractory C4-2 and CWR22Rv1, but not in LAPC4 and MDA-PCa-2b. Our study demonstrates a novel role of the Raf/MEK/ERK pathway in regulating AR expression in certain PCa types and provides an insight into PCa responses to its aberrant activation