IBM (Italy)

It is generally argued that the energy dissipation of three-dimensional turbulent flow is concentrated on a set with non-integer Hausdorff dimension. Recently, in order to explain experimental data, it has been proposed that this set does not possess a global dilatation invariance: it can be considered to be a multifractal set. The authors review the concept of multifractal sets in both turbulent flows and dynamical systems using a generalisation of the beta -model.

This book teaches newcomers all they need to know to profit from today's powerful data mining technologies.Through extensive case studies and examples, you'll learn how companies are using data mining right now to achieve powerful results -- and how you can do it, too. Learn how data mining can help you build intimate relationships with customers that haven't existed for generations. Discover which areas of your business can benefit from data mining; the business, social and technical issues involved; how to prevent fraud and abuse; and how to make the most of outside consultants. The book also demonstrates IBM's powerful new Intelligent Miner tool and shows how it can be applied.Business and systems analysts; strategic planners; marketing executives; and other managers who can benefit from data mining.

An algorithm applicable to the problem of locating supply points optimally with respect to transport costs is given.Although the algorithm may fail to converge to an optimal solution, repeated application with judicious selections of alternative starting values will assure a good, if not optimal, solution.The algorithm has been tested and some sample results are included.

Reflection seismology seeks to determine the structure of the earth from seismic records obtained at the surface. The processing of these data by digital computers is aimed at rendering them more comprehensible geologically. Seismic migration is one of these processes. Its purpose is to "migrate" the recorded events to their correct spatial positions by backward projection or depropagation based on wave theoretical considerations. During the last 15 years several methods have appeared on the scene. The purpose of this paper is to provide an overview of the major advances in this field. Migration methods examined here fall in three major categories: 1) integral solutions, 2) depth extrapolation methods, and 3) time extrapolation methods. Within these categories, the pertinent equations and numerical techniques are discussed in some detail. The topic of migration before stacking is treated separately with an outline of two different approaches to this important problem.

The authors discuss the characterisation of intermittency in chaotic dynamical systems by means of the time fluctuations of the response to a slight perturbation on the trajectory. A set of exponents is introduced which generalise the maximum Lyapunov characteristic exponent. The link with the statistical mechanics formalism is emphasised and they show that the exponents are connected to a free energy formally defined for chaotic systems. They perform some analytical computations in simple cases and give a few numerical examples.

Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe derivative-based prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99 percent and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.

It is shown that simulated annealing, a statistical mechanics method recently proposed as a tool in solving complex optimization problems, can be used in problems arising in image processing. The problems examined are the estimation of the parameters necessary to describe a geometrical pattern corrupted by noise, the smoothing of bi-level images, and the process of halftoning a continuous-level image. The analogy between the system to be optimized and an equivalent physical system, whose ground state is sought, is put forward by showing that some of these problems are formally equivalent to ground state problems for two-dimensional Ising spin systems. In the case of low signal-to-noise ratios (particularly in image smoothing), the methods proposed here give better results than those obtained with standard techniques.

Although service-oriented architectures go a long way toward providing interoperability in distributed, heterogeneous environments, managing semantic differences in such environments remains a challenge. We give an overview of the issue of semantic interoperability (integration), provide a semantic characterization of services, and discuss the role of ontologies. Then we analyze four basic models of semantic interoperability that differ in respect to their mapping between service descriptions and ontologies and in respect to where the evaluation of the integration logic is performed. We also provide some guidelines for selecting one of the possible interoperability models.

The mechanism of stochastic resonance is studied in the case of the Landau-Ginzburg equation stochastically and periodically perturbed, by taking advantage of recent developments on the stochastic partial differential equations. Analytical expressions are given for computing the exit times of the system and to estimate the range of the noise for which the stochastic resonance is possible.

BACKGROUND/AIMS: Systemic corticosteroids are highly effective at inducing clinical remission in cases of acute exacerbation of Crohn's disease (CD) and ulcerative colitis (UC); however, their use is limited by their frequent and sometimes severe side effects. Thus, a second generation of corticosteroids with less systemic effects has been developed. This review analyzed all of the studies on the new formulations of steroids with limited absorption (budesonide, budesonide MMX®, beclomethasone dipropionate and erythrocyte-mediated delivery of dexamethasone) in patients with CD and UC. METHODS: All relevant articles published in English between September 1960 and April 2011 were reviewed. RESULTS: Budesonide is superior to placebo, and as effective as systemic corticosteroids in inducing clinical remission in patients with ileo-colonic CD, but evidence of mucosal healing is limited. When administered as an MMX formula, budesonide can also effectively induce clinical remission in patients with UC, but budesonide alone is not effective in maintaining clinical remission in CD or UC. Beclomethasone dipropionate seems to be effective in patients with mild-to-moderate left-sided and extensive UC, while data on erythrocyte-mediated delivery of dexamethasone are encouraging but still limited. The safety profile for all these products is good but more studies are needed. CONCLUSION: Steroids remain the mainstay for the induction of clinical remission in cases of acute relapse of both CD and UC. Second-generation corticosteroids are an interesting alternative, with the advantage of high topical activity, less systemic toxicity and limited side effects.

In complex emergency scenarios, teams from various emergency-response organizations must collaborate. These teams include both first responders, such as police and fire departments, and those operators who coordinate the effort from operational centers. The Workpad architecture consists of a front- and a back-end layer. The front-end layer is composed of several front-end teams of first responders, and the back-end layer is an integrated peer-to-peer network that lets front-end teams collaborate through information exchange and coordination. Team members at the front end carry PDAs, with team leaders' PDAs equipped with gateway communication technologies that let them communicate with the back-end centers.

BACKGROUND: Several factors influence patients' trust, and trust influences the doctor-patient relationship. Recent literature has investigated the quality of the personal relationship and its dynamics by considering the role of communication and the elements that influence trust giving in the frame of general practitioner (GP) consultations. OBJECTIVE: We analysed certain aspects of the interaction between patients and GPs to understand trust formation and maintenance by focusing on communication channels. The impact of socio-demographic variables in trust relationships was also evaluated. METHOD: A cross-sectional design using concurrent mixed qualitative and quantitative research methods was employed. One hundred adults were involved in a semi-structured interview composed of both qualitative and quantitative items for descriptive and exploratory purposes. The study was conducted in six community-based departments adjacent to primary care clinics in Trento, Italy. RESULTS: The findings revealed that patients trusted their GP to a high extent by relying on simple signals that were based on the quality of the one-to-one communication and on behavioural and relational patterns. Patients inferred the ability of their GP by adopting simple heuristics based mainly on the so-called social "honest signals" rather than on content-dependent features. Furthermore, socio-demographic variables affected trust: less literate and elderly people tended to trust more. CONCLUSIONS: This study is unique in attempting to explore the role of simple signals in trust relationships within medical consultation: people shape trust and give meaning to their relationships through a powerful channel of communication that orbits not around words but around social relations. The findings have implications for both clinicians and researchers. For doctors, these results suggest a way of thinking about encounters with patients. For researchers, the findings underline the importance of analysing some new key factors around trust for future investigations in medical practice and education.

The buoyancy range, which represents a transition from large-scale wave-dominated motions to small-scale turbulence in the oceans and the atmosphere, is investigated through large-eddy simulations. The model presented here uses a continual forcing based on large-scale standing internal waves and has a spectral truncation in the isotropic inertial range. Evidence is presented for a break in the energy spectra from the anisotropic k −3 buoyancy range to the small-scale k −5/3 isotropic inertial range. Density structures that form during wave breaking and periods of high strain rate are analysed. Elongated vertical structures produced during periods of strong straining motion are found to collapse in the subsequent vertically compressional phase of the strain resulting in a zone or patch of mixed fluid.

Collective communication operations (CCOs) are one of the most powerful tools for parallel processing on distributed memory architectures. From the theoretical viewpoint there has been a major effort in the design of optimal algorithms for these operations, especially for massive parallel processors (MPPs). However, in spite of the increasing availability of MPPs, there are just a few limited experimental checks of the different theories, so the assessment of their real value is not easy. The aim of the present paper is to address such issues for the most common CCOs, considering practical algorithms that can be included in a generic communication library. The main result is a new algorithm for building a quasi-optimal broadcast tree that is much simpler than, and as efficient as, previously available algorithms. To investigate the advantages and drawbacks of the proposed algorithms, a large set of experimental data has been collected on an IBM SP2 parallel system. The data demonstrate the efficiency of our approach in a number of interesting cases. Finally, all the experimental results have been related to the model used in designing the algorithms. © 1998 John Wiley & Sons, Ltd.

The application of ideas and methods of statistical mechanics to problems of biological relevance is one of the most promising frontiers of theoretical and computational mathematical physics. 1,2 Among others, the computer simulation of the immune system dynamics stands out as one of the prominent candidates for this type of investigations. In the recent years immunological research has been drawing increasing benefits from the resort to advanced mathematical modeling on modern computers. 3,4 Among others, Cellular Automata (CA), i.e., fully discrete dynamical systems evolving according to boolean laws, appear to be extremely well suited to computer simulation of biological systems. 5 A prominent example of immunological CA is represented by the Celada–Seiden automaton, that has proven capable of providing several new insights into the dynamics of the immune system response. To date, the Celada–Seiden automaton was not in a position to exploit the impressive advances of computer technology, and notably parallel processing, simply because no parallel version of this automaton had been developed yet. In this paper we fill this gap and describe a parallel version of the Celada–Seiden cellular automaton aimed at simulating the dynamic response of the immune system. Details on the parallel implementation as well as performance data on the IBM SP2 parallel platform are presented and commented on.

BACKGROUND: A standardized and cost-effective molecular identification system is now an urgent need for Fungi owing to their wide involvement in human life quality. In particular the potential use of mitochondrial DNA species markers has been taken in account. Unfortunately, a serious difficulty in the PCR and bioinformatic surveys is due to the presence of mobile introns in almost all the fungal mitochondrial genes. The aim of this work is to verify the incidence of this phenomenon in Ascomycota, testing, at the same time, a new bioinformatic tool for extracting and managing sequence databases annotations, in order to identify the mitochondrial gene regions where introns are missing so as to propose them as species markers. METHODS: The general trend towards a large occurrence of introns in the mitochondrial genome of Fungi has been confirmed in Ascomycota by an extensive bioinformatic analysis, performed on all the entries concerning 11 mitochondrial protein coding genes and 2 mitochondrial rRNA (ribosomal RNA) specifying genes, belonging to this phylum, available in public nucleotide sequence databases. A new query approach has been developed to retrieve effectively introns information included in these entries. RESULTS: After comparing the new query-based approach with a blast-based procedure, with the aim of designing a faithful Ascomycota mitochondrial intron map, the first method appeared clearly the most accurate. Within this map, despite the large pervasiveness of introns, it is possible to distinguish specific regions comprised in several genes, including the full NADH dehydrogenase subunit 6 (ND6) gene, which could be considered as barcode candidates for Ascomycota due to their paucity of introns and to their length, above 400 bp, comparable to the lower end size of the length range of barcodes successfully used in animals. CONCLUSION: The development of the new query system described here would answer the pressing requirement to improve drastically the bioinformatics support to the DNA Barcode Initiative. The large scale investigation of Ascomycota mitochondrial introns performed through this tool, allowing to exclude the introns-rich sequences from the barcode candidates exploration, could be the first step towards a mitochondrial barcoding strategy for these organisms, similar to the standard approach employed in metazoans.

Abstract A modification (Irons; 6 Concus et al. 1 ) to the conjugate gradient (CG) method by Hestenes and Stiefel 5 has recently renewed the interest in this elegant technique which appears to be extraordinarily promising for large sparse systems Ax = b , where A is a symmetric positive definite matrix. A good approximation K −1 for the inverse of A is needed in the modified algorithm. For finite difference sets of equations, Meijerink and van der Vorst 9 and Kershaw 7 have experienced a very fast convergence with a matrix K −1 determined by the incomplete Cholesky decomposition of A . A further acceleration of the iteration may be achieved by preliminarily processing the initial guessed solution by the Newton iterative scheme before using the modified conjugate gradient (MCG) method. The initial Newton iterations (NI) have the useful property of significantly reducing the components of the residual r 0 along the eigenvectors of AK −1 associated with the eigenvalues lying in the vicinity of 1. The latter are expected to include the vast majority of the eigenvalues of AK −1 . As a result the MCG method is left with a smaller number of r 0 components to set to zero in a reduced dimensional eigenvector space and hence the solution is arrived at in fewer iterations. Depending on the desired final accuracy, up to 50 per cent of the MCG iterations may be equivalently replaced by an equal number of NI which are computationally faster. This approach has been applied to the solution of finite element sets of linear equations arising from the arbitrarily irregular triangle discretization of groundwater flow domains in both steady and unsteady conditions. For diagonally dominant matrices the results emphasize the excellent performance of the MCG method which proved much faster than the first‐degree Chebyshev iteration (CHI) and required a number of iterations an order of magnitude smaller than the successive over‐relaxation technique (SOR) with optimum over‐relaxation factor.

A system for analyzing and generating Italian texts is under development at the IBM Rome Scientific Center. Detailed semantic knowledge on word-sense patterns is used to relate the linguistic structure of a sentence to a conceptual representation (a conceptual graph). Conceptual graphs are stored in a database and accessed by a natural-language query/answering module. The system analyzes a text supplied by a press-agency-release database. It consists of three modules: a morphological, a syntactic, and a semantic processor. The semantic analyzer uses a conceptual lexicon of word-sense descriptions, currently including about 850 entries. A description is an extended case frame providing the surface semantic patterns (SSP) of a word-sense w. SSPs express both semantic constraints and word-usage information, such as commonly found word patterns, idioms, and metaphoric expressions. SSPs are used by the semantic interpreter to build a conceptual graph of the sentence, which is then accessed by the query-answering and language-generation modules. This paper makes the claim that the SSP approach is viable and necessary to cope with language phenomena in unrestricted domains. Surface patterns are easily acquired inductively from the natural-language corpus rather than deductively from predefined conceptual structures. SSPs map quite complex sentences into surface semantic representations that can be generalized at a subsequent stage. In contrast, the current state of the art does not provide viable theory or methodology to go from superficial to deep structures. This issue is more extensively addressed in the body of the paper.

The degradation of spatial resolution in star-forming regions observed at large distances ($d\gtrsim1$ kpc) with Herschel,can lead to estimates of the physical parameters of the detected compact sources (clumps) which do not necessarily mirror the properties of the original population of cores. This paper aims at quantifying the bias introduced in the estimation of these parameters by the distance effect. To do so, we consider Herschel maps of nearby star-forming regions taken from the Herschel-Gould-Belt survey, and simulate the effect of increased distance to understand what amount of information is lost when a distant star-forming region is observed with Herschel resolution. In the maps displaced to different distances we extract compact sources, and we derive their physical parameters as if they were original Hi-GAL maps of the extracted source samples. In this way, we are able to discuss how the main physical properties change with distance. In particular, we discuss the ability of clumps to form massive stars: we estimate the fraction of distant sources that are classified as high-mass stars-forming objects due to their position in the mass vs radius diagram, that are only "false positives". We give also a threshold for high-mass star-formation $M>1282 \ \left(\frac{r}{[\mathrm{pc}]}\right)^{1.42} M_{\odot}$. In conclusion, this paper provides the astronomer dealing with Herschel maps of distant star-forming regions with a set of prescriptions to partially recover the character of the core population in unresolved clumps.

Loading data efficiently from classical memories to quantum computers is a key challenge of noisy intermediate-scale quantum computers. Such a problem can be addressed through quantum generative adversarial networks (qGANs), which are noise tolerant and agnostic with respect to data. Tuning a qGAN to balance accuracy and training time is a hard task that becomes paramount when target distributions are multivariate. Thanks to our tuning of the hyper-parameters and of the optimizer, the training of qGAN reduces, on average, the Kolmogorov–Smirnov statistic of 43–64% with respect to the state of the art. The ability to reach optima is non-trivially affected by the starting point of the search algorithm. A gap arises between the optimal and sub-optimal training accuracy. We also point out that the simultaneous perturbation stochastic approximation (SPSA) optimizer does not achieve the same accuracy as the Adam optimizer in our conditions, thus calling for new advancements to support the scaling capability of qGANs.