Tunable Fano-Kondo resonance in side-coupled double quantum dot system

We study the interference between the Fano and Kondo effects in a side-coupled double-quantum- dot system where one of the quantum dots couples to conduction electron bath while the other dot only side-couples to the first dot via antiferromagnetic (AF) spin exchange coupling. We apply both the perturbative renormalization group (RG) and numerical renormalization group (NRG) approaches to study the effect of AF coupling on the Fano lineshape in the conduction leads. With particle-hole symmetry, the AF exchange coupling competes with the Kondo effect and leads to a local spin-singlet ground state for arbitrary small coupling, so called "two-stage Kondo effect". As a result, via NRG we find the spectral properties of the Fano lineshape in the tunneling density of states (TDOS) of conduction electron leads shows double dip-peak features at the energy scale around the Kondo temperature and the one much below it, corresponding to the two-stage Kondo effect; it also shows an universal scaling behavior at very low energies. We find the qualitative agreement between the NRG and the perturbative RG approach. Relevance of our work to the experiments is discussed.Comment: 7 pages, 7 figure

Critical role of electronic correlations in determining crystal structure of transition metal compounds

The choice that a solid system "makes" when adopting a crystal structure (stable or metastable) is ultimately governed by the interactions between electrons forming chemical bonds. By analyzing 6 prototypical binary transition-metal compounds we demonstrate here that the orbitally-selective strong $d$-electron correlations influence dramatically the behavior of the energy as a function of the spatial arrangements of the atoms. Remarkably, we find that the main qualitative features of this complex behavior can be traced back to simple electrostatics, i.e., to the fact that the strong $d$-electron correlations influence substantially the charge transfer mechanism, which, in turn, controls the electrostatic interactions. This result advances our understanding of the influence of strong correlations on the crystal structure, opens a new avenue for extending structure prediction methodologies to strongly correlated materials, and paves the way for predicting and studying metastability and polymorphism in these systems.Comment: Main text: 8 pages, 4 figures, 1 table; Supplemental material: 2 pages, 1 figure, 2 table

Coordinated Multicasting with Opportunistic User Selection in Multicell Wireless Systems

Physical layer multicasting with opportunistic user selection (OUS) is examined for multicell multi-antenna wireless systems. By adopting a two-layer encoding scheme, a rate-adaptive channel code is applied in each fading block to enable successful decoding by a chosen subset of users (which varies over different blocks) and an application layer erasure code is employed across multiple blocks to ensure that every user is able to recover the message after decoding successfully in a sufficient number of blocks. The transmit signal and code-rate in each block determine opportunistically the subset of users that are able to successfully decode and can be chosen to maximize the long-term multicast efficiency. The employment of OUS not only helps avoid rate-limitations caused by the user with the worst channel, but also helps coordinate interference among different cells and multicast groups. In this work, efficient algorithms are proposed for the design of the transmit covariance matrices, the physical layer code-rates, and the target user subsets in each block. In the single group scenario, the system parameters are determined by maximizing the group-rate, defined as the physical layer code-rate times the fraction of users that can successfully decode in each block. In the multi-group scenario, the system parameters are determined by considering a group-rate balancing optimization problem, which is solved by a successive convex approximation (SCA) approach. To further reduce the feedback overhead, we also consider the case where only part of the users feed back their channel vectors in each block and propose a design based on the balancing of the expected group-rates. In addition to SCA, a sample average approximation technique is also introduced to handle the probabilistic terms arising in this problem. The effectiveness of the proposed schemes is demonstrated by computer simulations.Comment: Accepted by IEEE Transactions on Signal Processin

Mott domain walls: a (strongly) non-Fermi liquid state of matter

Most Mott systems display a low-temperature phase coexistence region around the metal-insulator transition. The domain walls separating the respective phases have very recently been observed both in simulations and in experiments, displaying unusual properties. First, they often cover a significant volume fraction, thus cannot be neglected. Second, they neither resemble a typical metal nor a standard insulator, displaying unfamiliar temperature dependence of (local) transport properties. Here we take a closer look at such domain wall matter by examining an appropriate unstable solution of the Hubbard model. We show that transport in this regime is dominated by the emergence of "resilient quasiparticles" displaying strong non-Fermi liquid features, reflecting the quantum-critical fluctuations in the vicinity of the Mott point

Efficient slave-boson approach for multiorbital two-particle response functions and superconductivity

We develop an efficient approach for computing two-particle response functions and interaction vertices for multiorbital strongly correlated systems based on fluctuation around rotationally-invariant slave-boson saddle-point. The method is applied to the degenerate three-orbital Hubbard-Kanamori model for investigating the origin of the s-wave orbital antisymmetric spin-triplet superconductivity in the Hund's metal regime, previously found in the dynamical mean-field theory studies. By computing the pairing interaction considering the particle-particle and the particle-hole scattering channels, we identify the mechanism leading to the pairing instability around Hund's metal crossover arises from the particle-particle channel, containing the local electron pair fluctuation between different particle-number sectors of the atomic Hilbert space. On the other hand, the particle-hole spin fluctuations induce the s-wave pairing instability before entering the Hund's regime. Our approach paves the way for investigating the pairing mechanism in realistic correlated materials

Emergent Bloch excitations in Mott matter

We develop a unified theoretical picture for excitations in Mott systems, portraying both the heavy quasiparticle excitations and the Hubbard bands as features of an emergent Fermi liquid state formed in an extended Hilbert space, which is nonperturbatively connected to the physical system. This observation sheds light on the fact that even the incoherent excitations in strongly correlated matter often display a well-defined Bloch character, with pronounced momentum dispersion. Furthermore, it indicates that the Mott point can be viewed as a topological transition, where the number of distinct dispersing bands displays a sudden change at the critical point. Our results, obtained from an appropriate variational principle, display also remarkable quantitative accuracy. This opens an exciting avenue for fast realistic modeling of strongly correlated materials

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models