Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Linshuichonetes gen. nov., a new rugosochonetid (Brachiopoda) genus from the Liangshan Formation (Early Permian) in Sichuan, China, and its ecology

A new genus and species, Linshuichonetes elfinis, belonging to the Rugosochonetidae, is described from the Early Permian (Late Artinskian or early Kungurian) Liangshan Formation of the Yangtze block. The new genus is defined externally by the presence of fine, but delayed, capillation and a weak or absent median sulcus and fold and by the presence of a distinct posteromedian sinus on the ventral umbo; and internally by a lack of median, lateral and accessory septa in the dorsal interior; absence of vascular trunks in the ventral interior and the presence of distinct radiating rows of papillae in the interior of both valves, particularly an unusual clustered arrangement of papillae on the posteromedian portion of the dorsal interior. The local environment during the deposition of the Liangshan Formation appears to have been a restricted tidal flat or lagoon which experienced frequent sealevel fluctuations associated with the onset of the Yanghsingian transgression. The new species, L. elfinis, appears to have several morphological adaptations enabling successful exploitation of this environment. It was typically a very small and thin-valved species with a high surface area to volume ratio, an advantage in an oxygen restricted environment. The small size and numerous body spinules would have aided individuals to remain suspended at the top of the fine, soft substrate. It also dominated the brachiopod assemblage in the Liangshan Formation, comprising up to 94%of specimens within a bed. These factors indicate that the new species appears to be an opportunistic species.

Security Issues in a SOA-based Provenance System

Recent work has begun exploring the characterization and utilization of provenance in systems based on the Service Oriented Architecture (such as Web Services and Grid based environments). One of the salient issues related to provenance use within any given system is its security. In a broad sense, security requirements arise within any data archival and retrieval system, however provenance presents unique requirements of its own. These requirements are additionally dependent on the architectural and environmental context that a provenance system operates in. We seek to analyze the security considerations pertaining to a Service Oriented Architecture based provenance system. Towards this end, we describe the components of such a system and illustrate the security considerations that arise within it. Concurrently, we outline possible approaches to address them.

Extracting Causal Graphs from an Open Provenance Data Model

The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, emphprocess documentation, and the emphprovenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and emphstyling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.

The First Provenance Challenge

The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions.

Soft-sediment deformation structures in the middle permian Wandrawandian siltstone, southern Sydney basin: implications for depositional environment and basin tectonics

The Middle Permian Wandrawandian Siltstone at Warden Head near Ulladulla in the southern Sydney Basin is dominated by fossiliferous siltstone and mudstone, with a large amount of dropstones (lonestones) and some pebbly sandstone beds. Two general types of deposits are recognised from the cliff succession in view of the timing and mechanism of their formation. One is represented by the background (or primary) deposits of offshore to slope environments with abundant dropstones of glacial marine origin. This facies occurs throughout the cliff sections at Warden Head. The second type is distinguished by secondary, soft-sediment deformational deposits and structures of the primary (background) deposits, and comprises three successive layers of sandy mudstone dikes. In the second type of deposit, metre scale, laterally extensive syn-depositional slump deformation structures occur extensively in the middle part of the Wandrawandian Siltstone. The deformation structures vary in morphology and pattern, including large-scale complex-type folds, flexural stratification, concave-up structures, small-magnitude -faults accompanied by folding and brecciation. The slumps and associated syn-depositional structures are herein attributed to penecontemporaneous deformations of soft sediments (mostly mud and silty mud), formed as a result of mass movement of unconsolidated and/or semi-consolidated substrate following earthquake events. The occurrence of the earthquake event deposits (or seismites) at Warden Head supports the current view that the Sydney Basin was located in a back-arc setting near the New England magmatic arc on an active continental margin during the Middle Permian, and the timing of the earthquake events is here interpreted to indicate the onset of the Hunter Bowen Orogeny in the southern Sydney Basin.

Email categorization using multi-stage classification technique

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

Monitoring employees emails without violating their privacy right

The capability of an employee to violate the policy of an organization is a concern for an employer. Monitoring is a measure taken by an employer to discourage an employee from acting inappropriately. However, current monitoring techniques tend to raise privacy issues because they violate the privacy rights of employees. Applying a monitoring technique without violating the privacy of employees is the aim of this paper. We propose a design and a protocol which give an employer the opportunity to monitor employee email in order to detect company policy violations. This can be achieved without violating the privacy of honest employees, while at the same time revealing evidence about the illegal actions of dishonest employees.

A resource-search and routing algorithm within PCDN autonomy area

This paper studied a new type of network model; it is formed by the dynamic autonomy area, the structured source servers and the proxy servers. The new network model satisfies the dynamics within the autonomy area, where each node undertakes different tasks according to their different abilities, to ensure that each node has the load ability fit its own; it does not need to exchange information via the central servers, so it can carry out the efficient data transmission and routing search. According to the highly dynamics of the autonomy area, we established dynamic tree structure-proliferation system routing and resource-search algorithms and simulated these algorithms. Test results show the performance of the proposed network model and the algorithms are very stable.

An image retrieval system based on fractal dimension

This paper presents a new kind of image retrieval system which obtains the feature vectors of images by estimating their fractal dimension; and at the same time establishes a tree-structure image database. After preprocessing and feature extracting, a given image is matched with the standard images in the image database using a hierarchical method of image indexing.