A Two-Stage Approach for Network Monitoring

A goal of network tomography is to infer the status (e.g. delay) of congested links internal to a network, through end-to-end measurements at boundary nodes (end-hosts) via insertion of probe signals. Because (a) probing constitutes traffic overhead, and (b) in any typical scenario, the number of congested links is a small fraction of the total number in the network, a desirable design objective is to identify those (few) congested links using a minimum number of probes. In this paper, we make a contribution to solving this problem, by proposing a new two-stage approach for this problem. First, we develop a binary observation model linking end-to-end observations with individual link statuses and derive necessary and sufficient conditions for whether at least one link in the network is congested. Stage I of the proposed method shows that achieving 1-identifiability with a minimum number of probes is equivalent to the familiar minimum set covering problem that can be efficiently solved via a greedy heuristic. A sequential algorithm is described, leading to a significantly lowered computational complexity vis-a-vis a batch algorithm. Next, a binary splitting algorithm originally developed in group testing is used to identify the location of the congested links. The proposed scheme is evaluated by simulations in OPNET and experiments on the PlanetLab testbed to validate the advantages of our 2-stage approach vis-a-vis a conventional (batch) algorithm.     

Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems.     

On preconditioning for a parallel solution of the Richards equation

In this paper, we present a class of preconditioning methods for a parallel solution of the three-dimensional Richards equation. The preconditioning methods Jacobi scaling, block-Jacobi, incomplete lower–upper, incomplete Cholesky and algebraic multigrid were applied in combination with a parallel conjugate gradient solver and tested for robustness and convergence using two model scenarios. The first scenario was an infiltration into initially dry, sandy soil discretised in 500,000 nodes. The second scenario comprised spatially distributed soil properties using 275,706 numerical nodes and atmospheric boundary conditions. Computational results showed a high efficiency of the nonlinear parallel solution procedure for both scenarios using up to 64 processors. Using 32 processors for the first scenario reduced the wall clock time to slightly more than 1% of the single processor run. For scenario 2 the use of 64 processors reduces the wall clock time to slightly more than 20% of the 8 processors wall clock time. The difference in the efficiency of the various preconditioning methods is moderate but not negligible. The use of the multigrid preconditioning algorithm is recommended, since on average it performed best for both scenarios.     

Automatic Construction and Verification of Isotopy Invariants

We extend our previous study of the automatic construction of isomorphic classification theorems for algebraic domains by considering the isotopy equivalence relation. Isotopism is an important generalisation of isomorphism, and is studied by mathematicians in domains such as loop theory. This extension was not straightforward, and we had to solve two major technical problems, namely, generating and verifying isotopy invariants. Concentrating on the domain of loop theory, we have developed three novel techniques for generating isotopic invariants, by using the notion of universal identities and by using constructions based on subblocks. In addition, given the complexity of the theorems that verify that a conjunction of the invariants form an isotopy class, we have developed ways of simplifying the problem of proving these theorems. Our techniques employ an interplay of computer algebra, model generation, theorem proving, and satisfiability-solving methods. To demonstrate the power of the approach, we generate isotopic classification theorems for loops of size 6 and 7, which extend the previously known enumeration results. This work was previously beyond the capabilities of automated reasoning techniques. The author’s work was supported by EPSRC MathFIT grant GR/S31099.     

Language independent unsupervised learning of short message service dialect

Noise in textual data such as those introduced by multilinguality, misspellings, abbreviations, deletions, phonetic spellings, non-standard transliteration, etc. pose considerable problems for text-mining. Such corruptions are very common in instant messenger and short message service data and they adversely affect off-the-shelf text mining methods. Most techniques address this problem by supervised methods by making use of hand labeled corrections. But they require human generated labels and corrections that are very expensive and time consuming to obtain because of multilinguality and complexity of the corruptions. While we do not champion unsupervised methods over supervised when quality of results is the singular concern, we demonstrate that unsupervised methods can provide cost effective results without the need for expensive human intervention that is necessary to generate a parallel labeled corpora. A generative model based unsupervised technique is presented that maps non-standard words to their corresponding conventional frequent form. A hidden Markov model (HMM) over a “subsequencized” representation of words is used, where a word is represented as a bag of weighted subsequences. The approximate maximum likelihood inference algorithm used is such that the training phase involves clustering over vectors and not the customary and expensive dynamic programming (Baum–Welch algorithm) over sequences that is necessary for HMMs. A principled transformation of maximum likelihood based “central clustering” cost function of Baum–Welch into a “pairwise similarity” based clustering is proposed. This transformation makes it possible to apply “subsequence kernel” based methods that model delete and insert corruptions well. The novelty of this approach lies in that the expensive (Baum–Welch) iterations required for HMM, can be avoided through an approximation of the loglikelihood function and by establishing a connection between the loglikelihood and a pairwise distance. Anecdotal evidence of efficacy is provided on public and proprietary data.     

A robust test of uncertain linear systems

In this paper, it is shown that for low-order uncertain systems, there is no need to calculate all the minimum and maximum values of the coefficients for a perturbed system which is expressed in terms of polynomials and hence no need to formulate and test all the four Kharitonov＇s polynomials. Furthermore, for higher-order systems such as n ≥ 5, the usual four Kharitonov＇s polynomials need not be tested initially for sufficient condition of perturbed systems; rather, the necessary condition can be checked before going for sufficient condition. In order to show the effectiveness of the proposed method, numerical examples are shown and computational efficiency is highlighted.     

Transport of the dicyanogold(i) anion

We have shown that dicyanogold(I), [Au(CN)(2)](-) is a common metabolite found in blood and urine samples of patients treated with different gold based drugs. Some patients have high levels of gold within their red blood cells (RBCs). Size exclusion and C18 reversed phase chromatography show that the majority of the gold in RBC lysates is bound to protein, but small molecules such as dicyanogold(I) and gold-glutathione complexes are also present. Dicyanogold incubation with red blood cells in vitro leads to a rapid and complete uptake of gold. This uptake is unaffected by DIDS, an inhibitor of the anion channel, but is blocked by the addition of external cyanide. Dicyanogold is also readily taken up by H9 cells, a continuous CD(4+) cell line. This uptake is significantly inhibited by N-ethylmaleimide, suggesting a requirement for sulfhydryl groups. Dicyanogold inhibits the replication of the AIDS virus, HIV, in a cell culture model.     

Dispersed spatial input demand functions

Most recent attempts to improve the empirical performance of classical trade models by formulation of probabilistic models of the DSPE (Dispersed Spatial Price Equilibrium) type have concentrated on determination of flows of a single commodity. However, a further challenge is to develop consistent probabilistic multi-sector spatial input demand functions and the corresponding spatial supply functions as part of an eventual probabilistic spatial CGE (Computable General Equilibrium) framework. In this paper, we derive short-run aggregate spatial input demand functions as the generators of the spatial supply functions, introducing via entropy some distributional assumptions about the average behaviour of the representative firm. In order to remain compatible with entropy principles, all demand functions are derived in a form guaranteeing compliance with a probabilistic accounting framework, calibrated on observed quantities, costs and prices. Firstly, the paper develops dispersed spatial input demand functions using various classical production functions, such as Linear and CES, accounting for the transport costs and tariffs incurred by the supply of intermediate inputs. It then formulates dispersed interregional input demand functions based on multiregional input-output production functions, transcending the normal relations connecting input and output via constant coefficients, with solution via a price-sensitive iterative adjustment technique. Finally, guidelines for an extension to a long-run analysis are sketched.     

Response of mouse plantaris muscle to functional overload: comparison with rat and cat

Functional overload (FO) of a muscle by removing its synergists results in a compensatory hypertrophy of the muscle. However, the extent of the response appears to be dependent, at least in part, on the activity and/or loading levels of the muscle following surgery. Thus, differences in the inherent physical activity levels across species may be an important factor to consider. In the present study, the effects of 8 weeks of FO on the isometric mechanical properties of the plantaris of mice (highly active) were determined and the findings compared with the results from previous studies performed on the plantaris of rats (highly active) and cats (less active). FO resulted in approximately a doubling of the mass, the physiological cross-sectional area and the maximum tetanic tension per unit cross-sectional area, was similar in the plantaris of control and FO mice. Isometric twitch speed properties were unaffected, but the tension enhancement in response to an increase in the rate of stimulation showed the pattern of a "faster" muscle following FO. The fatigue resistance of the plantaris in FO mice was significantly higher than in control mice. Although the degree of hypertrophy that occurred in the mouse plantaris was similar to that observed after FO in rats and in cats that are exercised intermittently at high intensities, there were differences in the mechanical properties that may be related to the adaptability of species and/or the behavioral responses to the overload.