ppxscal/embedding_network huggingface dataset:
Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler
Authors: Marco Grzegorczyk, Dirk Husmeier, Kieron D. Edwards, Peter Ghazal, Andrew J. Millar
Venue: Bioinformatics (2008)

Method: The objective of the present article is to propose and evaluate a probabilistic approach based on Bayesian networks for modelling non-homogeneous and non-linear gene regulatory processes. The method is based on a mixture model, using latent variables to assign individual measurements to different classes. The practical inference follows the Bayesian paradigm and samples the network structure, the number of classes and the assignment of latent variables from the posterior distribution with Markov Chain Monte Carlo (MCMC), using the recently proposed allocation sampler as an alternative to RJMCMC. Results: We have evaluated the method using three criteria: network reconstruction, statistical significance and biological plausibility. In terms of network reconstruction, we found improved results both for a synthetic network of known structure and for a small real regulatory network derived from the literature. We have assessed the statistical significance of the improvement on gene expression time series for two different systems (viral challenge of macrophages, and circadian rhythms in plants), where the proposed new scheme tends to outperform the classical BGe score. Regarding biological plausibility, we found that the inference results obtained with the proposed method were in excellent agreement with biological findings, predicting dichotomies that one would expect to find in the studied systems. Availability: Two supplementary papers on theoretical (T) and experi-mental (E) aspects and the datasets used in our study are available from http://www.bioss.ac.uk/associates/marco/supplement/ Contact:marco@bioss.ac.uk, dirk@bioss.ac.uk
Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index
Authors: C. Song, M. B. Dickinson
Venue: International Journal of Remote Sensing (2008)

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer-and hardwood-dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer-and hardwood-dominated stands are separated, image spatial information estimates tree crown size much better for conifer-dominated stands than for the hardwood-dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information.
Exploiting Additional Context for Graph-Based Tag Recommendations in Folksonomy Systems
Authors: Fabian Abel, Nicola Henze, Daniel Krause
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

Folksonomy systems enable users to participate in the Webcontent creation process by annotating (tagging) resourceswith freely chosen keywords. Still, it is an open issue howto exploit this user-created content, and how to processand use these emergent semantics effectively. We investigatehow the context of Web resources can be utilizedto improve recommender strategies in social tagging systems.We focus on the GroupMe! system, which enablesusers to create groups in order to bundle Web resources.GroupMe! groups form valuable context information forthe resources contained in such groups. In this paper we exploitgraph-based tag recommendation strategies, evaluatethem in the GroupMe! dataset, and benchmark our resultsagainst other approaches. In our evaluations we show thatgraph-based strategies outperform other approaches, andshow the immanent benefit of graph-based recommendationstrategies which exploit the group context for recommendingtags to untagged resources.
Relaxed proximal point algorithms for variational inequalities with multi-valued operators
Authors: E. Huebner, R. Tichatschke
Venue: Optimization Methods Software (2008)

In this paper two versions of the relaxed proximal point schemes for solving variational inequalities with maximal monotone and multi-valued operators are investigated. The first one describes an algorithm with an adaptive choice of the relaxation parameter and is combined with the use of ε-enlargements of multi-valued operators. The second one makes use of Bregman functions in order to construct relaxed proximal point algorithms with an interior point effect. For both algorithms convergence is proved under a numerically tractable error summability criterion, based on the ideas in [R. T. Rockafellar, Augmented Lagrangians and applications of the proximal point algorithm in convex programming, Math. Oper. Res. 1(2) (1976), pp. 97-116.] [R. T. Rockafellar, Monotone operators and the proximal point algorithm, SIAM J. Control Optim. 14(5) (1976), pp. 877-898.] Finally, some numerical aspects are discussed and test examples show the performance of the first algorithm when applying to non-smooth optimization problems.
Nonlinear Behaviour in the MPI-Parallelised Model of the Rat Somatosensory Cortex
Authors: Grzegorz M. Wojcik, Wieslaw A. Kaminski
Venue: Informatica (2008)

Mammalian brains consisting of up to 10 11 neurons belong to group of the most complex systems in the Universe. For years they have been one of the hardest objects of simulation. There are many different approaches to modelling of neurons, but one of the most biologically correct is Hodgkin-Huxley (HH) model. Simulations that require solving a large number of nonlinear differential equations (fundamental in HH model) are always time and power consuming. The structures discussed in this article simulate a part of the rat somatosensory cortex. We use a modular architecture of the network divided into layers and sub-regions. Because of a high degree of complexity effective parallelisation of algorithms is required. We propose method of parallelisation for the network and the results of simulations using GENESIS parallelised for MPI environment are presented. An occurrence of nonlinear behaviour is demonstrated. Most notably, in large biological neural networks consisting of the HH neurons, nonlinearity is shown to manifest itself in the Poincar&eacute; sections generated for the varying value of neural membrane's potential.
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Authors: Hui Li, Jie Sun, Bo-Liang Sun
Venue: Expert Systems with Applications: An International Journal (2009)

Financial distress prediction including bankruptcy prediction has called broad attention since 1960s. Various techniques have been employed in this area, ranging from statistical ones such as multiple discriminate analysis (MDA), Logit, etc. to machine learning ones like neural networks (NN), support vector machine (SVM), etc. Case-based reasoning (CBR), which is one of the key methodologies for problem-solving, has not won enough focus in financial distress prediction since 1996. In this study, outranking relations (OR), including strict difference, weak difference, and indifference, between cases on each feature are introduced to build up a new feature-based similarity measure mechanism in the principle of k-nearest neighbors. It is different from traditional distance-based similarity mechanisms and those based on NN, fuzzy set theory, decision tree (DT), etc. Accuracy of the CBR prediction method based on OR, which is called as OR-CBR, is determined directly by such four types of parameters as difference parameter, indifference parameter, veto parameter, and neighbor parameter. It is described in detail that what the model of OR-CBR is from various aspects such as its developed background, formalization of the specific model, and implementation of corresponding algorithm. With three year's real-world data from Chinese listed companies, experimental results indicate that OR-CBR outperforms MDA, Logit, NN, SVM, DT, Basic CBR, and Grey CBR in financial distress prediction, under the assessment of leave-one-out cross-validation and the process of Max normalization. It means that OR-CBR may be a preferred model dealing with financial distress prediction in China.
Semigroups and Their Applications, 1 edition
Authors: Simon M. Goberstein, Peter M. Higgins
Venue:  (2007)

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Minimum-energy broadcast in random-grid ad-hoc networks: approximation and distributed algorithms
Authors: Tiziana Calamoneri, Andrea E.F. Clementi, Angelo Monti, Gianluca Rossi, Riccardo Silvestri
Venue: Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (2008)

The Min Energy Broadcast problem consists in assigning transmission ranges to the nodes of an ad-hoc network in order to guarantee a directed spanning tree from a given source node and, at the same time, to minimize the energy consumption (i.e. the energy cost) yielded by the range assignment. Min Energy Broadcast is known to be NP-hard. We consider random-grid networks where nodes are chosen independently at random from the n points of a √n x √n square grid in the plane. The probability of the existence of a node at a given point of the grid does depend on that point, that is, the probability distribution can be non-uniform. By using information-theoretic arguments, we prove a lower bound (1 -- µ) n/À on the energy cost of any feasible solution for this problem. Then, we provide an efficient solution of energy cost not larger than 1.1204n/À. Finally, we present a fully-distributed protocol that constructs a broadcast range assignment of energy cost not larger than 8n, thus still yielding constant approximation. The energy load is well balanced and, at the same time, the work complexity (i.e. the energy due to all message transmissions of the protocol) is asymptotically optimal. The completion time of the protocol is only a O(log n) factor slower than the optimum. The approximation quality of our distributed solution is also experimentally evaluated. All bounds hold with probability at least 1 -- 1/n¸(1).
RAID0.5: design and implementation of a low cost disk array data protection method
Authors: John A. Chandy
Venue: The Journal of Supercomputing (2008)

RAID has long been established as an effective way to provide highly reliable as well as high-performance disk subsystems. However, reliability in RAID systems comes at the cost of extra disks. In this paper, we describe a mechanism that we have termed RAID0.5 that enables striped disks with very high data reliability but low disk cost. We take advantage of the fact that most disk systems use offline backup systems for disaster recovery. With the use of these offline backup systems, the disk system needs to only replicate data since the last backup, thus drastically reducing the storage space requirement. Though RAID0.5 has the same data loss characteristics of traditional mirroring, the lower storage space comes at the cost of lower availability. Thus, RAID0.5 is a tradeoff between lower disk cost and lower availability while still preserving very high data reliability. We present analytical reliability models and experimental results that demonstrate the enhanced reliability and performance of the proposed RAID0.5 system.
Generalized Zernike or disc polynomials: An application in quantum optics
Authors: A. Torre
Venue: Journal of Computational and Applied Mathematics (2008)

An application of the ''generalized Zernike or disc polynomials'', recently introduced in the literature, is shown, resorting to the Lie algebra based investigation of the dynamics of quantum systems driven by two-mode interaction Hamiltonians. Further properties of the associated ''disc functions'' are deduced. Also, a generalization of the disc polynomials towards the Hahn polynomials is suggested.
From strawman to Ada 2005: a socio-technical retrospective
Authors: Ben Brosgol
Venue: ACM SIGAda Ada Letters (2008)

Dr. Brosgol will look at Ada's evolution, including the requirements formulation phase that preceded the language design, and highlight key developments.
A modified Hopfield neural network for perfect calculation of magnetic resonance spectroscopy
Authors: Hazem M. El-Bakry, Nikos Mastorakis
Venue: WSEAS Transactions on Information Science and Applications (2008)

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called "modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.
Adaptive Duplicated Filters and Interference Canceller for DS-CDMA Systems
Authors: François Nougarou, Daniel Massicotte, Messaoud Ahmed-Ouameur
Venue: Journal of Signal Processing Systems (2008)

A low complexity multiuser detection (MUD) technique, the Adaptive Duplicated Filters and Interference Canceller (ADIC) (patent pending), is proposed in the DS-CDMA context. Of particular interest is the use of adaptive filters block (AFB) dedicated to each user with its respective input signals independent from other users' contributions. The AFBs are mixed with interference canceller block in a cascade arrangement. As shown in this paper, this MUD can outperform the Decision Feedback Soft MultiStage Interference Canceller (DF-Soft-MPIC) MUD with complexity reduction by a factor of 4 to 8 for the data payload throughput from 64 kbps to 384 kbps, respectively. In addition to performance and algorithmic description of the proposed MUD method, a VLSI implementation strategy and hardware resources evaluation are investigated; permitting to estimate the maximum number of users in FPGA devices with respect to WCDMA constraints. The present work proposes a low complexity MUD wherein an interesting trade-off between performance and implementation complexity is described.
Failure modeling of ACA-glued flip-chip on flex assemblies
Authors: B. Wunderle, C. Kallmayer, H. Walter, T. Braun, A. Gollhardt, B. Michel, H. Reichl
Venue: Microsystem Technologies (2008)

This paper addresses the reliability of flip-chip on flex (FCOF) assemblies glued with an Ag-particle filled anisotropic conductive adhesive (ACA). As the description of FCOF failure gives still much scope for speculation, a physics of failure based approach is developed here, taking into account the changing thermo-mechanical properties of the ACA under temperature and moisture. A failure hypothesis is formulated based on the loss of contact pressure. Material analysis, material characterisation, finite element (FE) modeling and lifetime tests have been employed to establish correlations to support this failure hypothesis. It was found, that moisture plays the most important role for interconnect failure. The model is able to predict quantitative changes of force as function of loading parameters and correlate them qualitatively to the experimental mean time to failure. New insights are provided about the stress fields at the ACA bump. The model is discussed with respect to a direct prediction of failure versus time.
Self-stabilizing Numerical Iterative Computation
Authors: Ezra N. Hoch, Danny Bickson, Danny Dolev
Venue: Proceedings of the 10th International Symposium on Stabilization, Safety, and Security of Distributed Systems (2008)

Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, etc., can be formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods.In this work, we extend the settings of the above approaches, by adding another dimension to the problem. Specifically, we are interested in self-stabilizing algorithms, that continuously run and converge to a solution from any initial state. This aspect of the problem is highly important due to the dynamic nature of the network and the frequent changes in the measured environment.In this paper, we link together algorithms from two different domains. On the one hand, we use the rich linear algebra literature of linear iterative methods for solving systems of linear equations, which are naturally distributed with rapid convergence properties. On the other hand, we are interested in self-stabilizing algorithms, where the input to the computation is constantly changing, and we would like the algorithms to converge from any initial state. We propose a simple novel method called SS-Iterative as a self-stabilizing variant of the linear iterative methods. We prove that under mild conditions the self-stabilizing algorithm converges to a desired result. We further extend these results to handle the asynchronous case.As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach.
Pedagogical Effectiveness of Engagement Levels -- A Survey of Successful Experiences
Authors: Jaime Urquiza-Fuentes, J. Ángel Velázquez-Iturbide
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

In this paper we survey experiments with program and algorithm visualizations (PAVs) where learning improvements have been detected. We analyze these experiments based on the student's level of engagement with the visualizations. There are some features present in most of these, successful, experiments. Therefore they should be taken into account as important factors affecting pedagogical effectiveness of PAVs, these features are: narrative and textual contents, feedback to students' answers and a student centered approach when designing PAV construction kits.
Recording the Context of Action for Process Documentation
Authors: Ian Wootten, Omer Rana
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

In reviewing evidence about real world processes, being aware of the context in which activities within such processes are performed enables us to make more informed judgements. It is necessary to distinguish between the environment in which a process occurs, and the sequence of activities which form part of the description of that process. Each of these types of information is complementary to understanding the other and therefore making associations between them is also important. Our work has been exploring the use of context whilst documenting a process and working toward a solution which incorporates the two. We present an approach to automatically relating properties of workflow actors to the documentation of the process within which these actors are involved.
Intelligent Service System: An Agent-Based Approach for integrating Artifical Intelligence Components in SOA Landscapes
Authors: Michel Alessandrini, Wolfram-M. Lippe, Wilhelm Nuesser
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02 (2008)

The new paradigm of service-oriented architecture leads to new requirements for software systems. These requirements must be met when integrating AI systems into a SOA landscape, too. However, most AI systems today are monolithic. In this paper we develop a general approach to provide artificial intelligence based on the concept of intelligent agents as services in a SOA landscape. Here the AI system itself and each intelligent agent will act as services.
Determination of the weights for the ultimate cross efficiency using Shapley value in cooperative game
Authors: Jie Wu, Liang Liang, Feng Yang
Venue: Expert Systems with Applications: An International Journal (2009)

This paper firstly revisits the cross efficiency evaluation method which is an extension tool of data envelopment analysis (DEA), then analyzes the potential flaws which happens when the ultimate average cross efficiency scores are used. In this paper, we consider the DMUs as the players in a cooperative game, where the characteristic function values of coalitions are defined to compute the Shapley value of each DMU, and the common weights associate with the imputation of the Shapley values are used to determine the ultimate cross efficiency scores. In the end, an empirical example is illustrated to examine the validity of the proposed method, and we also point out some further research directions in future.