Systematic Search and A New Family of Skyrmion Materials

Magnetic skyrmions have recently attracted great attentions. However they are harbored in very limited numbers of magnets up to now. The search of new helimagnetic materials is thus an urgent topic in the field of skyrmion physics. In this letter, we provide a guideline on this issue, and discuss the possibility of realizing skyrmions in a new family of molybdenum nitrides $A_2$Mo$_3$N ($A$=Fe, Co, and Rh). By means of the first-principles calculations, the electronic and magnetic structures are calculated and the existence of strong Dzyaloshinskii-Moriya interaction is demonstrated.Comment: 5 pages, 2 figures, 3 table

Size Effects on Transport Properties in Topological Anderson Insulators

We study the size effects on the transport properties in topological Anderson insulators by means of the Landauer-B\"uttiker formalism combined with the nonequilibrium Green function method. Conductances calculated for serval different widths of the nanoribbons reveal that there is no longer quantized plateaus for narrow nanoribbons. The local spin-resolved current distribution demonstrates that the edge states on the two sides can be coupled, leading to enhancement of backscattering as the width of the nanoribbon decreases, thus destroying the perfect quantization phenomena in the topological Anderson insulator. We also show that the main contribution to the nonquantized conductance also comes from edge states. Experiment proposals on topological Anderson insulator are discussed finally.Comment: 4 pages, 4 figure

Learning classifier systems with memory condition to solve non-Markov problems

In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments, compared with some well-known classifier systems proposed for these environments.Comment: 34 pages, 15 figures, 1 tabl

n-type Markov Branching Processes with Immigration

In this paper, we consider $n$-type Markov branching processes with immigration and resurrection. The uniqueness criteria are first established. Then, a new method is found and the explicit expression of extinction probability is successfully obtained in the absorption case, the mean extinction time is also given. The recurrence and ergodicity criteria are given if the state ${\bf 0}$ is not absorptive. Finally, if the resurrection rates are same as the immigration rates, the branching property and decay property are discussed in detail, it is shown that the process is a superimposition of a $n$-type branching process and an immigration. The exact value of the decay parameter $\lambda_Z$ is given for the irreducible class ${\bf Z}_+^n$. Moreover, the corresponding $\lambda_Z$-invariant measures/vectors and quasi-distributions are presented.Comment: 31page

Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables

Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network. In this paper,via dropping the task-irrelevant input variables the performance of auto-encoders can be obviously improved .Specifically, an importance-based variable selection method is proposed to aim at finding the task-irrelevant input variables and dropping them.It firstly estimates importance of each variable,and then drops the variables with importance value lower than a threshold. In order to obtain better performance, the method can be employed for each layer of stacked auto-encoders. Experimental results show that when combined with our method the stacked denoising auto-encoders achieves significantly improved performance on three challenging datasets

Over-the-Air Computation Systems: Optimal Design with Sum-Power Constraint

Over-the-air computation (AirComp), which leverages the superposition property of wireless multiple-access channel (MAC) and the mathematical tool of function representation, has been considered as a promising technique for effective collection and computation of massive sensor data in wireless Big Data applications. In most of the existing work on AirComp, optimal system-parameter design is commonly considered under the peak-power constraint of each sensor. In this paper, we propose an optimal transmitter-receiver (Tx-Rx) parameter design problem to minimize the computation mean-squared error (MSE) of an AirComp system under the sum-power constraint of the sensors. We solve the non-convex problem and obtain a closed-form solution. Also, we investigate another problem that minimizes the sum power of the sensors under the constraint of computation MSE. Our results show that in both of the problems, the sensors with poor and good channel conditions should use less power than the ones with moderate channel conditions.Comment: Paper accepted by IEEE Wireless Communications Letters. Copyright may be transferred without notice, after which this version may no longer be accessibl

Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws

For future Internet of Things (IoT)-based Big Data applications (e.g., smart cities/transportation), wireless data collection from ubiquitous massive smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for edge fusion centers running computing tasks over large data sets with limited computation capacity. To tackle these challenges, by exploiting the superposition property of a multiple-access channel and the functional decomposition properties, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of $K$ sensors and one receiver (i.e., the fusion center). We consider an optimization problem to minimize the computation mean-squared error (MSE) of the $K$ sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of AirComp systems in terms of the average computation MSE and the average power consumption under Rayleigh fading channels with different Tx-Rx policies. For the computation-optimal policy, we prove that its average computation MSE has a decay rate of $O(1/\sqrt{K})$, and our numerical results illustrate that the policy also has a vanishing average power consumption with the increasing $K$, which jointly show the computation effectiveness and the energy efficiency of the policy with a large number of sensors.Comment: Paper accepted by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessibl

Effects of Attractive correlation on Topological Flat-bands Model

In this paper, we study the effects of attractive correlation on the topological insulator ($TI$) with topological flat-bands using an extended attractive Kane-Mele-Hubbard model (KMHM). In the KMHM, we found a quantum phase transition from $TI$ to the superconductor ($SC$) state upon the increasing of the attractive Hubbard interaction $U$ at the mean field level. This type of $SC$ phase transition is different from the traditional $SC$ phase transition which develops from the gapless Fermi Liquid. Cooperon-type gapped excitations exist in the $TI$ side near this type of $SC$ phase transition

Preparation of NOON State Induced by Macroscopic Quantum Tunneling in an Ising Chain

In this brief report, we propose a possible way, theoretically and experimentally, to generate a NOON state of the two degenerate ferromagnetic ground states of the Transverse Ising Model. In our scheme we employ the macroscopic quantum tunneling (MQT) effect between the two degenerate ferromagnetic ground states to realize the NOON state. Our calculation about the MQT process is based on a higher-order degenerate perturbation method. After doing a transformation, the MQT process could also be treated as the hopping of individual virtual fermions in the spin chain, which will leads to an analytical description of tunneling process. The experimental feasibility for generating the NOON state is discussed in the setup of linear ion trap.Comment: 4.5 pages, 3 figure

Valley Anisotropy in Elastic Metamaterials

Valley, as a new degree of freedom, raises the valleytronics in fundamental and applied science. The elastic analogs of valley states have been proposed by mimicking the symmetrical structure of either two-dimensional materials or photonic valley crystals. However, the asymmetrical valley construction remains unfulfilled. Here, we present the valley anisotropy by introducing asymmetrical design into elastic metamaterials. The elastic valley metamaterials are composed of bio-inspired hard spirals and soft materials. The anisotropic topological nature of valley is verified by asymmetrical distribution of the Berry curvature. We show the high tunability of the Berry curvature both in magnitude and sign enabled by our anisotropic valley metamaterials. Finally, we demonstrate the creation of valley topological insulators and show topologically protected propagation of transverse elastic waves relying on operating frequency. The proposed topological properties of elastic valley metamaterials pave the way to better understanding the valley topology and to creating a new type of topological insulators enabled by an additional valley degree of freedom.Comment: 22 pages, 7 figure