Breakdown of local convertibility through Majorana modes in a quantum quench

The local convertibility of quantum states, measured by the R\'enyi entropy, is concerned with whether or not a state can be transformed into another state, using only local operations and classical communications. We found that in the one-dimensional Kitaev chain with quenched chemical potential $\mu$, the convertibility between the state for $\mu$ and that for $\mu+\delta\mu$, depends on the quantum phases of the system ($\delta\mu$ is a perturbation). This is similar to the adiabatic case where the ground state is considered. Specifically, when the quenched system has edge modes and the subsystem size for the partition is much larger than the correlation length of the Majorana fermions which forms the edge modes, the quenched state is locally inconvertible. We give a physical interpretation for the result, based on analyzing the interactions between the two subsystems for various partitions. Our work should help to better understand the many-body phenomena in topological systems and also the entanglement properties in the Majorana fermionic quantum computation.Comment: 8 pages, 5 figures, accepted by Physical Review

Extracting entangled qubits from Majorana fermions in quantum dot chains through the measurement of parity

We propose a scheme for extracting entangled charge qubits from quantum-dot chains that support zero-energy edge modes. The edge mode is composed of Majorana fermions localized at the ends of each chain. The qubit, logically encoded in double quantum dots, can be manipulated through tunneling and pairing interactions between them. The detailed form of the entangled state depends on both the parity measurement (an even or odd number) of the boundary-site electrons in each chain and the teleportation between the chains. The parity measurement is realized through the dispersive coupling of coherent-state microwave photons to the boundary sites, while the teleportation is performed via Bell measurements. Our scheme illustrates \emph{localizable entanglement} in a fermionic system, which serves feasibly as a quantum repeater under realistic experimental conditions, as it allows for finite temperature effect and is robust against disorders, decoherence and quasi-particle poisoning.Comment: Accepted by Scientific Report

Microwave and hard X-ray emissions during the impulsive phase of solar flares: Nonthermal electron spectrum and time delay

On the basis of the summing-up and analysis of the observations and theories about the impulsive microwave and hard X-ray bursts, the correlations between these two kinds of emissions were investigated. It is shown that it is only possible to explain the optically-thin microwave spectrum and its relations with the hard X-ray spectrum by means of the nonthermal source model. A simple nonthermal trap model in the mildly-relativistic case can consistently explain the main characteristics of the spectrum and the relative time delays

Local Cyber-physical Attack with Leveraging Detection in Smart Grid

A well-designed attack in the power system can cause an initial failure and then results in large-scale cascade failure. Several works have discussed power system attack through false data injection, line-maintaining attack, and line-removing attack. However, the existing methods need to continuously attack the system for a long time, and, unfortunately, the performance cannot be guaranteed if the system states vary. To overcome this issue, we consider a new type of attack strategy called combinational attack which masks a line-outage at one position but misleads the control center on line outage at another position. Therefore, the topology information in the control center is interfered by our attack. We also offer a procedure of selecting the vulnerable lines of its kind. The proposed method can effectively and continuously deceive the control center in identifying the actual position of line-outage. The system under attack will be exposed to increasing risks as the attack continuously. Simulation results validate the efficiency of the proposed attack strategy.Comment: Accepted by IEEE SmartGridComm 201

Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid

Malicious attacks in the power system can eventually result in a large-scale cascade failure if not attended on time. These attacks, which are traditionally classified into \emph{physical} and \emph{cyber attacks}, can be avoided by using the latest and advanced detection mechanisms. However, a new threat called \emph{cyber-physical attacks} which jointly target both the physical and cyber layers of the system to interfere the operations of the power grid is more malicious as compared with the traditional attacks. In this paper, we propose a new cyber-physical attack strategy where the transmission line is first physically disconnected, and then the line-outage event is masked, such that the control center is misled into detecting as an obvious line outage at a different position in the local area of the power system. Therefore, the topology information in the control center is interfered by our attack. We also propose a novel procedure for selecting vulnerable lines, and analyze the observability of our proposed framework. Our proposed method can effectively and continuously deceive the control center into detecting fake line-outage positions, and thereby increase the chance of cascade failure because the attention is given to the fake outage. The simulation results validate the efficiency of our proposed attack strategy.Comment: accepted by IEEE Transactions on Smart Grid. arXiv admin note: text overlap with arXiv:1708.0320

Predictors of psychiatric readmissions in the short- and long-term: a population-based study in taiwan

OBJECTIVES: To explore the risks and rates of readmission and their predictors 14 days, one year, and five years after discharge for the psychiatric population in Taiwan. METHODS: This was a prospective study based on claims from 44,237 first-time hospitalized psychiatric patients discharged in 2000, who were followed for up to five years after discharge. The cumulative incidence and incidence density of readmission were calculated for various follow-up periods after discharge, and Cox proportional hazard models were generated to identify the significant predictors for psychiatric readmission. RESULTS: The less than 14-day, one-year, and five-year cumulative incidences were estimated at 6.1%, 22.3%, and 37.8%, respectively. The corresponding figures for incidence density were 4.58, 1.04, and 0.69 per 1,000 person-days, respectively. Certain factors were significantly associated with increased risk of readmission irrespective of the length of follow-up, including male gender, length of hospital stay >15 days, economic poverty, a leading discharge diagnosis of schizophrenia/affective disorders, and residence in less-urbanized regions. Compared to children/adolescents, young adults (20-39 years) were significantly associated with increased risks of <one-year and <five-year readmissions, but not <14-day readmission. Additionally, hospital characteristics were significantly associated with increased risk of <14-day and <one-year readmission, but not with risk of <five-year readmission. CONCLUSIONS: This study found that the significant predictors for psychiatric readmission 14 days to five years after discharge were essentially the same except for patient's age and hospital accreditation level. This study also highlighted the importance of socioeconomic factors in the prediction of readmission

Variational Metric Scaling for Metric-Based Meta-Learning

Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial role in the performance of metric-based meta-learning algorithms. However, there still lacks a principled method for learning the metric scaling parameter automatically. In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. Firstly, we propose a stochastic variational method to learn a single global scaling parameter. To better fit the embedding space to a given data distribution, we extend our method to learn a dimensional scaling vector to transform the embedding space. Furthermore, to learn task-specific embeddings, we generate task-dependent dimensional scaling vectors with amortized variational inference. Our method is end-to-end without any pre-training and can be used as a simple plug-and-play module for existing metric-based meta-algorithms. Experiments on mini-ImageNet show that our methods can be used to consistently improve the performance of existing metric-based meta-algorithms including prototypical networks and TADAM. The source code can be downloaded from https://github.com/jiaxinchen666/variational-scaling.Comment: AAAI202

The Association between Traditional Chinese Dietary and Herbal Therapies and Uterine Involution in Postpartum Women

Background. Traditional Chinese postpartum care is believed to help in the recovery of women after delivery. Objective. This study investigated the association of elements in dietary and herbal therapy with uterine involution. Methods. Indices of uterine involution were measured ultrasonographically in 127 postpartum women between 4-6 weeks after delivery. A self-reported retrospective questionnaire was used to query women about their frequencies of taking herbal medicines and consuming special diets during the first month after delivery. Correlation coefficients were calculated to identify the associations, then the regression models were used to identify the predictors. Result. Among the herbal medicines and diet, consumption of Eucommia ulmoides (E. ulmoides) negatively correlated with the AP diameter of the uterus and the cavity. E. ulmoides was also the only predictor of maximum AP diameter of the uterus, AP diameter of the uterus 5 cm from the fundus, and the maximum AP diameter of the cavity. Moreover, consumption of Sheng-hau-tang was significantly correlated with anteverted uterus and was a predictor of anteverted uterus. Conclusion. E. ulmoides and Sheng-hau-tang positively correlated with the degree of uterine involution after delivery, implying that both therapies might possess the pharmacological efficacy of uterine contraction in postpartum women