Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ) over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI) index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches

On the Throughput Cost of Physical Layer Security in Decentralized Wireless Networks

This paper studies the throughput of large-scale decentralized wireless networks with physical layer security constraints. In particular, we are interested in the question of how much throughput needs to be sacrificed for achieving a certain level of security. We consider random networks where the legitimate nodes and the eavesdroppers are distributed according to independent two-dimensional Poisson point processes. The transmission capacity framework is used to characterize the area spectral efficiency of secure transmissions with constraints on both the quality of service (QoS) and the level of security. This framework illustrates the dependence of the network throughput on key system parameters, such as the densities of legitimate nodes and eavesdroppers, as well as the QoS and security constraints. One important finding is that the throughput cost of achieving a moderate level of security is quite low, while throughput must be significantly sacrificed to realize a highly secure network. We also study the use of a secrecy guard zone, which is shown to give a significant improvement on the throughput of networks with high security requirements.Comment: Accepted for publication in IEEE Transactions on Wireless Communication

Efficient Truss Maintenance in Evolving Networks

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work

Dualities of Dynamic Stochastic Higher Spin Vertex Models through Drinfeld Twister

We introduce a new, algebraic method to construct duality functions for integrable dynamic models. This method will be implemented on dynamic stochastic higher spin vertex models, where we prove the duality functions are the $_3\varphi_2$ functions. The method involves using the universal twister of $\mathcal{U}_q(\mathfrak{sl}_2)$, viewed as a quasi--triangular, quasi--$^*$--Hopf algebra. The algebraic method is presented very generally and is expected to produce duality functions for other dynamic integrable models

Transfer Learning for Low-Resource Part-of-Speech Tagging

Neural network approaches to Part-of-Speech tagging, like other supervised neural network tasks, benefit from larger quantities of labeled data. However, in the case of low-resource languages, additional methods are necessary to improve the performances of POS taggers. In this paper, we explore transfer learning approaches to improve POS tagging in Afrikaans using a neural network. We investigate the effect of transferring network weights that were originally trained for POS tagging in Dutch. We also test the use of pretrained word embeddings in our POS tagger, both independently and in conjunction with the transferred weights from a Dutch POS tagger. We find a marginal increase in performance due to transfer learning with the Dutch POS tagger, and a significant increase due to the use of either unaligned or aligned pretrained embeddings. Notably, there is little difference in performance when using either unaligned or aligned embeddings, even when utilizing cross-lingual transfer learning

Real-time, in situ monitoring of surface reactions during plasma passivation of GaAs

Real-time, in situ observations of surface chemistry during the remote plasma passivation of GaAs is reported herein. Using attenuated total reflection Fourier transform infrared spectroscopy, the relative concentrations of -As-O, -As-H, -H2O, and -CH2 bonds are measured as a function of exposure to the effluent from a microwave discharge through NH3, ND3, H2, and D2. The photoluminescence intensity (PL) from the GaAs substrate is monitored simultaneously and used qualitatively to estimate the extent of surface state reduction. It was found that, while the -CHx(x = 2,3) and -As-O concentrations are reduced rapidly, the rates at which the -As-H concentration and the PL intensity increase are relatively slow. The concentration of -H2O on the GaAs surface increases throughout the process as surface arsenic oxides and the silica reactor walls are reduced by atomic hydrogen. These observations suggest that removal of elemental As by reaction with H at the GaAs–oxide interface limits the passivation rate

Further evidence for the likely completeness of the library of solved single domain protein structures

Recent studies questioned whether the Protein Data Bank (PDB) contains all compact, single domain protein structures. Here, we show that all quasi-spherical, QS, random protein structures devoid of secondary structure are in the PDB and are excellent templates for all native PDB proteins up to 250 residues. Because QS templates have a similar global contour as native, TASSER can refine 98% (90%) of those whose TM-score is 0.4 (0.35) to structures greater than or equal to the 0.5 TM-score threshold (0.74 (0.64) mean TM-score) for CATH/SCOP assignment. On the basis of this and the fact that, at a TM-score of 0.4, 83% (90%) of all (internal) core secondary structure elements are recovered, a 0.40 TM-score is an appropriate fold similarity assignment threshold. Despite the claims of Taylor, Trovato, and Zhou that many of their structures lack a PDB counterpart, using fr-TM-align, at a 0.45 (0.5) TM-score threshold, essentially all (most) are found in the PDB. Thus, the conclusion that the PDB is likely complete is further supported. © 2012 American Chemical Society

Equivalent Consumption Minimization Strategy With Consideration of Battery Aging for Parallel Hybrid Electric Vehicles

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. This paper introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Simulation results show that the proposed aging ECMS algorithm significantly improves battery aging with little or no fuel economy penalty compared to ordinary ECMS