Robust named entity detection from optical character recognition output

In this paper, we focus on information extraction from optical character recognition (OCR) output. Since the content from OCR inherently has many errors, we present robust algorithms for information extraction from OCR lattices instead of merely looking them up in the top-choice (1-best) OCR output. Specifically, we address the challenge of named entity detection in noisy OCR output and show that searching for named entities in the recognition lattice significantly improves detection accuracy over 1-best search. While lattice-based named entity (NE) detection improves NE recall from OCR output, there are two problems with this approach: (1) the number of false alarms can be prohibitive for certain applications and (2) lattice-based search is computationally more expensive than 1-best NE lookup. To mitigate the above challenges, we present techniques for reducing false alarms using confidence measures and for reducing the amount of computation involved in performing the NE search. Furthermore, to demonstrate that our techniques are applicable across multiple domains and languages, we experiment with optical character recognition systems for videotext in English and scanned handwritten text in Arabic.     

Worst-case optimal algorithm for XPath evaluation over XML streams

We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D; i.e., determine the set Q(D) of document elements selected by Q. We mainly consider Conjunctive XPath queries that involve only the child and descendant axes. Previously known in-memory algorithms for this problem use O(|D|) space and O(|Q||D|) time. Several previously known algorithms for the streaming version use Ω(dn) space and Ω(dn|D|) time in the worst case; d denotes the depth of D, and n denotes the number of location steps in Q. Their exponential space requirement could well exceed the O(|D|) space used by the in-memory algorithms. We present an efficient algorithm that uses O(d|Q|+nc) space and O((|Q|+dn)|D|) time in the worst case; c denotes the maximum number of elements of D that can be candidates for output, at any one instant. For some worst case Q and D, the memory space used by our algorithm matches our lower bound proved in a different paper; so, our algorithm uses optimal memory space in the worst case.     

QoS constrained resource allocation to secondary users in cognitive radio networks

In this paper, we consider a multi-channel cognitive radio network (CRN) where multiple secondary users share a single channel and multiple channels are simultaneously used by a single secondary user (SU) to satisfy their rate requirements. In such an environment, we attempt to evaluate the optimal power and rate distribution choices that each secondary user has to make in order to maintain their quality of service (QoS). Our measures for QoS include signal to interference plus noise ratio (SINR)/bit error rate (BER) and minimum rate requirement. We propose two centralized optimization frameworks in order to solve for the optimal resource management strategies. In the first framework, we determine the minimum transmit power that SUs should employ in order to maintain a certain SINR and use that result to calculate the optimal rate allocation strategy across channels. In the second framework, both transmit power and rate per channel are simultaneously optimized with the help of a bi-objective problem formulation. Unlike prior efforts, we transform the BER constraint into a convex constraint in order to guarantee optimality of the resulting solutions. Simulation results demonstrate that in both frameworks, optimal transmit power follows “reverse water filling” process and rate allocation follows SINR. We also observe that, due to the ability to adapt both power and rate simultaneously to attain a certain BER, the joint optimization framework results in a lower total transmit power than the two-stage approach.     

A combined scheme of edge-based and node-based smoothed finite element methods for Reissner–Mindlin flat shells

In this paper, a combined scheme of edge-based smoothed finite element method (ES-FEM) and node-based smoothed finite element method (NS-FEM) for triangular Reissner–Mindlin flat shells is developed to improve the accuracy of numerical results. The present method, named edge/node-based S-FEM (ENS-FEM), uses a gradient smoothing technique over smoothing domains based on a combination of ES-FEM and NS-FEM. A discrete shear gap technique is incorporated into ENS-FEM to avoid shear-locking phenomenon in Reissner–Mindlin flat shell elements. For all practical purpose, we propose an average combination (aENS-FEM) of ES-FEM and NS-FEM for shell structural problems. We compare numerical results obtained using aENS-FEM with other existing methods in the literature to show the effectiveness of the present method.     

Learning first-order probabilistic models with combining rules

Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relationships. To keep these models succinct, each such influence is considered independent of others, which is called the assumption of “independence of causal influences” (ICI). In this paper, we describe a language that consists of quantified conditional influence statements and captures most relational probabilistic models based on directed graphs. The influences due to different statements are combined using a set of combining rules such as Noisy-OR. We motivate and introduce multi-level combining rules, where the lower level rules combine the influences due to different ground instances of the same statement, and the upper level rules combine the influences due to different statements. We present algorithms and empirical results for parameter learning in the presence of such combining rules. Specifically, we derive and implement algorithms based on gradient descent and expectation maximization for different combining rules and evaluate them on synthetic data and on a real-world task. The results demonstrate that the algorithms are able to learn both the conditional probability distributions of the influence statements and the parameters of the combining rules.     

Efficient computation of Morse-Smale complexes for three-dimensional scalar functions

The Morse-Smale complex is an efficient representation of the gradient behavior of a scalar function, and critical points paired by the complex identify topological features and their importance. We present an algorithm that constructs the Morse-Smale complex in a series of sweeps through the data, identifying various components of the complex in a consistent manner. All components of the complex, both geometric and topological, are computed, providing a complete decomposition of the domain. Efficiency is maintained by representing the geometry of the complex in terms of point sets.     

A reference architecture for telecommunications operations applications

The emerging multi-layer functional model for network operations decomposes management functions into several layers:Service Management Layer, Network Management Layer, Element Management Layer, andElement Layer. To realize the full benefits of this functional distribution, a reference architecture for operations applications is needed. This architecture should enable flexible placement of functions in computing systems or nodes, enable hiding of the complexities of distribution from applications, and prescribe principles for the specification of application interfaces to facilitate application interoperability. This paper describes such a framework architecture. In this framework, applications are deployed in software units calledbuilding blocks, each of which is independently installed and administered. Building blocks interact via interfaces calledcontracts. A contract is an open interface that is specified according to some principles, such as adherence to standard communication paradigms, separation of engineering aspects from functional aspects, and support for previous interface versions. The infrastructure, called theDistributed Processing Environment (DPE), provides communication and security services that enable building blocks to interact via contracts. The DPE provides access and location transparency to applications. The paper presents details of this architecture framework and an example to illustrate specification of contracts for network management functions.     

Ulcerative systematized porokeratosis (Mibelli)

An unusual clinical variant of porokeratosis (Mibelli's disease) appeared in a patient soon after birth; the lesions were widely distributed mostly on the right half of the body. Spontaneous recurrent ulcerations occurred over certain sites. The right foot was grossly deformed as a result of atrophic scarring after ulcerations. Biopsy specimens of the classical lesion showed cornoid lamella. It is suggested that the term "ulcerative systematized porokeratosis" be used to describe this clinical variant of porokeratosis.     

Uncertainty visualization using HDR volume rendering

In this paper, we explore a novel idea of using high dynamic range (HDR) technology for uncertainty visualization. We focus on scalar volumetric data sets where every data point is associated with scalar uncertainty. We design a transfer function that maps each data point to a color in HDR space. The luminance component of the color is exploited to capture uncertainty. We modify existing tone mapping techniques and suitably integrate them with volume ray casting to obtain a low dynamic range (LDR) image. The resulting image is displayed on a conventional 8-bits-per-channel display device. The usage of HDR mapping reveals fine details in uncertainty distribution and enables the users to interactively study the data in the context of corresponding uncertainty information. We demonstrate the utility of our method and evaluate the results using data sets from ocean modeling.     

Mechanochemically assisted room temperature solid state metathesis reaction for the synthesis of MMoO4 (M = Ca, Sr and Ba)

This communication reports a novel mechanochemically assisted room temperature solid state metathesis reaction for the synthesis of submicron-size alkaline-earth molybdates crystallizing in a tetragonal Scheelite structure. The solids were characterized by powder XRD, FTIR, TGA, DTA, SEM, EDAX and TEM to ascertain their composition, phase homogeneity and morphology.