Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems

In this study, we proposed an adaptive fuzzy multi-surface sliding control (AFMSSC) for trajectory tracking of 6 degrees of freedom inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). It is shown that an adaptive fuzzy logic-based function approximator can be used to estimate the system uncertainties and an iterative multi-surface sliding control design can be carried out to control flight. Using AFMSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. It is proved that the AFMSSC system guarantees asymptotic output tracking and ultimate uniform boundedness of the tracking error. Simulation results are presented to validate the analysis.

Is there a Kuznets' process in Southeast Asia?

Relatively little is known about the determinants of inequality in Southeast Asia. This paper fills this void by comprehensively testing Kuznets’ hypothesis for Southeast Asia. We estimate both unconditional and conditional Kuznets’ curves using panel data for 8 countries. The analysis suggests the existence of a Kuznets’ curve with respect to per capita income; the path of inequality is nonlinear with respect to economic development. There is no evidence of a Kuznets curve with respect to non-agricultural employment. There is some evidence in terms of urbanization, though this is not robust. There is robust evidence on the role of national governments and education in shaping the path of inequality in the region. Government involvement reduces inequality. Education appears to have a non-linear effect on inequality.

Pooled panel unit root tests and the effect of past initialization

This paper analyzes the role of initialization when testing for a unit root in panel data, an issue that has received surprisingly little attention in the literature. In fact, most studies assume that the initial value is either zero or bounded. As a response to this, the current paper considers a model in which the initialization is in the past, which is shown to have several distinctive features that makes it attractive, even in comparison to the common time series practice of making the initial value a draw from its unconditional distribution under the stationary alternative. The results have implications not only for theory, but also for applied work. In particular, and in contrast to the time series case, in panels the effect of the initialization need not be negative but can actually lead to improved test performance.

Ownership structure and collateral requirements: Evidence from China’s listed firms

This paper examines the effect of ownership structure on collateral requirements using a sample of China's listed firms from 2007 to 2009. We find that compared to privately controlled companies, state-controlled companies are less likely to be required to pledge collateral, and such a difference is more pronounced for firms in troubled industries. The empirical results also show that the effect of state control on collateral requirements is weaker in companies with more foreign ownership. Moreover, the effect of state control on collateral requirements is weaker in companies with more third party guarantees. Finally, we find that the effect of state control on collateral requirements is more pronounced for firms operating in regions with more government intervention.

Ex situ electrochemical sodiation/desodiation observation of Co₃O₄ anchored carbon nanotubes: a high performance sodium-ion battery anode produced by pulsed plasma in a liquid

Liquid plasma, produced by nanosecond pulses, provides an efficient and simple way to fabricate a nanocomposite architecture of Co3O4/CNTs from carbon nanotubes (CNTs) and clusters of Co3O4 nanoparticles in deionized water. The crucial feature of the composite's structure is that Co3O4 nanoparticle clusters are uniformly dispersed and anchored to CNT networks in which Co3O4 guarantees high electrochemical reactivity towards sodium, and CNTs provide conductivity and stabilize the anode structure. We demonstrated that the Co3O4/CNT nanocomposite is capable of delivering a stable and high capacity of 403 mA h g(-1) at 50 mA g(-1) after 100 cycles where the sodium uptake/extract is confirmed in the way of reversible conversion reaction by adopting ex situ techniques. The rate capability of the composite is significantly improved and its reversible capacity is measured to be 212 mA h g(-1) at 1.6 A g(-1) and 190 mA h g(-1) at 3.2 A g(-1), respectively. Due to the simple synthesis technique with high electrochemical performance, Co3O4/CNT nanocomposites have great potential as anode materials for sodium-ion batteries.

The economic significance of CDS price discovery

© 2015 Springer Science+Business Media New York Between 2005 and 2009, we document evident time-varying credit risk price discovery between the equity and credit default swap (CDS) markets for 174 US non-financial investment-grade firms. We test the economic significance of a simple portfolio strategy that utilizes fluctuation in CDS spreads as a trading signal to set stock positions, conditional on the CDS price discovery status of the reference entities. We show that a conditional portfolio strategy which updates the list of CDS-influenced firms over time, yields a substantively larger realized return net of transaction cost over the unconditional strategy. Furthermore, the conditional strategy’s Sharpe ratio outperforms a series of benchmark portfolios over the same trading period, including buy-and-hold, momentum and dividend yield strategies.

Robust finite-time tracking control of nonholonomic mobile robots without velocity measurements

The problem of robust finite-time trajectory tracking of nonholonomic mobile robots with unmeasurable velocities is studied. The contributions of the paper are that: first, in the case that the angular velocity of the mobile robot is unmeasurable, a composite controller including the observer-based partial state feedback control and the disturbance feed-forward compensation is designed, which guarantees that the tracking errors converge to zero in finite time. Second, if the linear velocity as well as the angular velocity of mobile robot is unmeasurable, with a stronger constraint, the finite-time trajectory tracking control of nonholonomic mobile robot is also addressed. Finally, the effectiveness of the proposed control laws is demonstrated by simulation.

A privacy-preserving location tracking system for smartphones based on cloud storage

With the widespread use of smartphones, the loss of a device is a critical problem, which results both in disrupting daily communications and losing valuable property. As a result, tracking systems have been developed to track mobile devices. Previous tracking systems focus on recovering the device's locations after it goes missing, with security methods implemented on the clients. However, users' locations are stored in untrusted third-party services, which may be attacked or eavesdropped. In this paper, we propose a system, named Android Cloud Tracker, to provide a privacy-preserving tracking client and safe storing of user's locations. We use cloud storage controlled by users themselves as storage facilities, and they do not need to worry about any untrusted third party. We implement Android Cloud Tracker prototype on Android phones, and the evaluation shows that it is both practical and lightweight: it generates a small amount of data flow and its distributed architecture provides strong guarantees of location privacy while preserving the ability to efficiently track missing devices.

Lingo: linearized grassmannian optimization for nuclear norm minimization

As a popular heuristic to the matrix rank minimization problem, nuclear norm minimization attracts intensive research attentions. Matrix factorization based algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for convergence caused by their non-unique factorizations. This paper proposes an efficient and accurate Linearized Grass-mannian Optimization (Lingo) algorithm, which adopts matrix factorization and Grassmann manifold structure to alternatively minimize the subproblems. More specially, linearization strategy makes the auxiliary variables unnecessary and guarantees the close-form solution for low periteration complexity. Lingo then converts linearized objective function into a nuclear norm minimization over Grass-mannian manifold, which could remedy the non-unique of solution for the low-rank matrix factorization. Extensive comparison experiments demonstrate the accuracy and efficiency of Lingo algorithm. The global convergence of Lingo is guaranteed with theoretical proof, which also verifies the effectiveness of Lingo.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

Query verification schemes for cloud-hosted databases: a brief survey

Database query verification schemes provide correctness guarantees for database queries. Typically such guarantees are required and advisable where queries are executed on untrusted servers. This need to verify query results, even though they may have been executed on one’s own database, is something new that has arisen with the advent of cloud services. The traditional model of hosting one’s own databases on one’s own servers did not require such verification because the hardware and software were both entirely within one’s control, and therefore fully trusted. However, with the economical and technological benefits of cloud services beckoning, many are now considering outsourcing both data and execution of database queries to the cloud, despite obvious risks. This survey paper provides an overview into the field of database query verification and explores the current state of the art in terms of query execution and correctness guarantees provided for query results. We also provide indications towards future work in the area.

String matching query verification on the cloud-hosted databases

Database query verification schemes attempt to provide authenticity, completeness, and freshness guarantees for queries executed on untrusted cloud servers. A number of such schemes currently exist in the literature, allowing query verification for queries that are based on matching whole values (such as numbers, dates, etc.) or for queries based on keyword matching. However, there is a notable gap in the research with regard to query verification schemes for pattern-matching queries. Our contribution here is to provide such a verification scheme that provides correctness guarantees for pattern-matching queries executed on the cloud. We describe a trivial scheme, ȃŸż and show how it does not provide completeness guarantees, and then proceed to describe our scheme based on efficient primitives such as cryptographic hashing and Merkle hash trees along with suffix arrays. We also provide experimental results based on a working prototype to show the practicality of our scheme.Ÿż

Privacy aware K-means clustering with high utility

Privacy-preserving data mining aims to keep data safe, yet useful. But algorithms providing strong guarantees often end up with low utility. We propose a novel privacy preserving framework that thwarts an adversary from inferring an unknown data point by ensuring that the estimation error is almost invariant to the inclusion/exclusion of the data point. By focusing directly on the estimation error of the data point, our framework is able to significantly lower the perturbation required. We use this framework to propose a new privacy aware K-means clustering algorithm. Using both synthetic and real datasets, we demonstrate that the utility of this algorithm is almost equal to that of the unperturbed K-means, and at strict privacy levels, almost twice as good as compared to the differential privacy counterpart.

Differentially private multi-task learning

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.

SCLPV: secure certificateless public verification for cloud-based cyber-physical-social systems against malicious auditors

Cyber-physical-social system (CPSS) allows individuals to share personal information collected from not only cyberspace but also physical space. This has resulted in generating numerous data at a user's local storage. However, it is very expensive for users to store large data sets, and it also causes problems in data management. Therefore, it is of critical importance to outsource the data to cloud servers, which provides users an easy, cost-effective, and flexible way to manage data, whereas users lose control on their data once outsourcing their data to cloud servers, which poses challenges on integrity of outsourced data. Many schemes have been proposed to allow a third-party auditor to verify data integrity using the public keys of users. Most of these schemes bear a strong assumption: the auditors are honest and reliable, and thereby are vulnerability in the case that auditors are malicious. Moreover, in most of these schemes, an auditor needs to manage users certificates to choose the correct public keys for verification. In this paper, we propose a secure certificateless public integrity verification scheme (SCLPV). The SCLPV is the first work that simultaneously supports certificateless public verification and resistance against malicious auditors to verify the integrity of outsourced data in CPSS. A formal security proof proves the correctness and security of our scheme. In addition, an elaborate performance analysis demonstrates that the SCLPV is efficient and practical. Compared with the only existing certificateless public verification scheme (CLPV), the SCLPV provides stronger security guarantees in terms of remedying the security vulnerability of the CLPV and resistance against malicious auditors. In comparison with the best of integrity verification scheme achieving resistance against malicious auditors, the communication cost between the auditor and the cloud server of the SCLPV is independent of the size of the processed data, meanwhile, the auditor in the SCLPV does not need to manage certificates.