We prove a dimension-invariant imbedding estimate for Sobolev spaces of first order into a small Lebesgue space, and we establish the optimality of its fundamental function. Namely, for any 1 < p < ?, the inequality with a constant c_p, related to the imbedding of W_0^{1,p}(B_n) into Y_p(0,1), where Yp(0,1) is a rearrangement-invariant Banach function space independent of the dimension n, B_n is the ball in R^n of measure 1 and c_p is a constant independent of n, is satisfied by the small Lebesgue space L(p,p? /2 (0, 1). Moreover, we show that the smallest space Yp (0, 1) (in the sense of the continuous imbedding) such that (*) is true has the fundamental function equivalent to that of L(p,p?/2(0,1). As a byproduct of our results, we get that the space Lp (log L)p/2 is optimal in the framework of the Orlicz spaces satisfying the imbedding inequality.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. However, available datasets consist of a small number of samples (n patients) and a large number of gene expression data (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches have been proposed. In our analysis, we compare four network penalized Cox models for high-dimensional survival data aimed to determine pathway structures and biomarkers involved in cancer progression. Using these network-based models, it is possible to obtain a deeper understanding of the gene-regulatory networks and investigate the gene signatures related to the cancer survival time. We evaluate cancer survival prediction to illustrate the benefits and drawbacks of the network techniques and to understand how patient features (i.e. age, gender and coexisting diseases-comorbidity) can influence cancer treatment, detection and outcome. In particular, we show results obtained in simulation and real cancer datasets using the Functional Linkage network, as network prior information.

A hydro-kinetic scheme for water-like fluids, based on a lattice version of the Boltzmann equation, is presented and applied to the popular TIP3P model of liquid water. By proceeding in much larger steps than molecular dynamics, the scheme proves to be very effective in attaining global minima of classical pair potentials with directional and radial interactions, as confirmed by further simulations using the three-dimensional Ben-Naim water potential. The scheme is shown to reproduce the propensity of water to form nearly four hydrogen bonds per molecule, as well as their statistical distribution in the presence of thermal fluctuations, at a linear cost of computational time with the system size. This journal is © the Partner Organisations 2014.

We review recent advances on the mesoscopic modeling of water-like fluids, based on the lattice Boltzmann (LB) methodology. The main idea is to enrich the basic LB (hydro)-dynamics with angular degrees of freedom responding to suitable directional potentials between water-like molecules. The model is shown to reproduce some microscopic features of liquid water, such as an average number of hydrogen bonds per molecules (HBs) between 3 and 4, as well as a qualitatively correct statistics of the hydrogen bond angle as a function of the temperature. Future developments, based on the coupling the present water-like LB model with the dynamics of suspended bodies, such as biopolymers, may open new angles of attack to the simulation of complex biofluidic problems, such as protein folding and aggregation, and the motion of large biomolecules in complex cellular environments.

We discuss the steps needed to introduce the horizontal gradients in a MTR ORM

Cloud computing offers unprecedented ways to split and offload the workload of parallel algorithms to remote computing nodes. However, such remote parties can potentially misbehave, for instance by providing fake computation results in order to save resources. In turn, these erroneous partial results can affect the timeliness and correctness of the overall outcome of the algorithm. The widely successful cloud approach increases the economic feasibility of leveraging computational redundancy to enforce some degree of assurance about the results. However, naïve solutions that dumbly replicate the same computation over several sets of nodes are not cost-efficient. In this paper, we provide several contributions as for the distribution of workload over (heterogeneous) cloud nodes. In particular, we first formalize the problem of computing a parallel function over a set of nodes; later, we introduce CheR (for Cheating Resilience), a novel approach based upon modelling the assignment of input elements to cloud nodes as a linear integer programming problem aimed at minimizing cost while being resilient against misbehaving nodes. Further, we describe the CheR approach in different scenarios and highlight the novelty with respect to other state-of-the-art solutions. Finally, we present and discuss some experimental results showing the viability and quality of our proposal. © 2014 Springer International Publishing Switzerland.

The use of population dynamics models is essential to provide assessment of the fish abundance and advice on management and strategies for the fisheries. The stock-recruitment curve define the relationship between the spawning stock and the subsequent recruitment, describing nature's regulation of population size, whether or not the populations are being exploited. The two classical relations, established by Ricker and Shepherd, are: R (S) = b1S e-b2S , b1, b2 > 0 (4) R (S) = S b1 + b2Sb3 , b1, b2, b3 > 0 where S is the spawning stock and R is the recruitment, i.e. the amount of offspring coming in the fishing exploitable phase. We present a mathematical model for studying the evolution of an age-structured population living in a bounded domain Ohm ? R. Under suitable assumptions, which are biologically consistent, we prove the well posedness of the system, using a semigroup approach. Numerical methods for the approximation of the solution are under development. This is joint work with Gabriela Marinoschi from Institute of Mathematical Statistics and Applied Mathematics, Romanian Academy, Buchare

Performance of N-ary classification testing (expanding binary false positive/negative, true positive/negative)

This paper aims at building a variational approach to the dynamics of discrete topological singularities in two dimensions, based on I"-convergence. We consider discrete systems, described by scalar functions defined on a square lattice and governed by periodic interaction potentials. Our main motivation comes from XY spin systems, described by the phase parameter, and screw dislocations, described by the displacement function. For these systems, we introduce a discrete notion of vorticity. As the lattice spacing tends to zero we derive the first order I"-limit of the free energy which is referred to as renormalized energy and describes the interaction of vortices. As a byproduct of this analysis, we show that such systems exhibit increasingly many metastable configurations of singularities. Therefore, we propose a variational approach to the depinning and dynamics of discrete vortices, based on minimizing movements. We show that, letting first the lattice spacing and then the time step of the minimizing movements tend to zero, the vortices move according with the gradient flow of the renormalized energy, as in the continuous Ginzburg-Landau framework.

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Methods: Twenty-five infants were recruited and classified as good and non-/low-responders according to serological test results. Whole genome DNA methylation states were profiled by Illumina HumanMethylation 450 K beadchips. Data were processed through quality and dispersion filtering and with differential methylation analysis based on a combination of average methylation differences and non-parametric statistical tests. Results were finally associated to already published transcriptomics and post-transcriptomics to gain further insight. Background: Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data. We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).

Social Web Services (SWSs) constitute a novel paradigm of service-oriented computing, where Web services, just like humans, sign up in social networks that guarantee, e.g., better service discovery for users and faster replacement in case of service failures. In past work, composition of SWSs was mainly supported by specialised social networks of competitor services and cooperating ones. In this work, we continue this line of research, by proposing a novel SWSs composition procedure driven by the SWSs reputation. Making use of a well-known formal language and associated tools, we specify the composition steps and we prove that such reputation-driven approach assures better results in terms of the overall quality of service of the compositions, with respect to randomly selecting SWSs.

The popularity of the cloud computing paradigm is opening new opportunities for collaborative computing. In this paper we tackle a fundamental problem in open-ended cloud-based distributed computing platforms, i.e., the quest for potential collaborators. We assume that cloud participants are willing to share their computational resources for shared distributed computing problems, but they are not willing to disclose the details of their resources. Lacking such information, we advocate to rely on reputation scores obtained by evaluating the interactions among participants. More specifically, we propose a methodology to assess, at design time, the impact of different (reputation-based) collaborator selection strategies on the system performance. The evaluation is performed through statistical analysis on a volunteer cloud simulator.

Two conflicting high-level goals govern the enforcement of security policies, abridged in the phrase ``high security at a low cost''. While these drivers seem irreconcilable, formal modelling languages and automated verification techniques can facilitate the task of finding the right balance. We propose a modelling language and a framework in which security checks can be relaxed or strengthened to save resources or increase protection, on the basis of trust relationships among communicating parties. Such relationships are automatically derived through a reputation system, hence adapt dynamically to the observed behaviour of the parties and are not fixed a priori. In order to evaluate the impact of the approach, we encode our modelling language in StoKlaim, which enables verification via the dedicated statistical model checker SAM. The overall approach is applied to a fragment of a Wireless Sensor Network, where there is a clear tension between devices with limited resources and the cost for securing the communication.

In recent years we are witnessing a continuous growth in the amount of data that both public and private organizations collect and profit by. Search engines are the most common tools used to retrieve information, and more recently, clustering techniques showed to be an effective tool in helping users to skim query results. The majority of the systems proposed to manage information, provide textual interfaces to explore search results that are not specifically designed to provide an interactive experience to the users. Trying to find a solution to this problem, we focus on how to extract conveniently data from sources of interest, and how to enhance their analysis and consultation through visualization techniques. In this work we present a customizable framework able to acquire, search and interactively visualize data. This framework is built upon a modular architectural schema and its effectiveness will be illustrated by a prototype implemented for a specific application domain.

In this paper we introduce new variables that can be used to retrieve the atmospheric continuum emission in the inversion of remote sensing measurements. This modification tackles the so-called sloppy model problem. We test this approach on an extensive set of real measurements from the Michelson Interferometer for Passive Atmospheric Sounding. The newly introduced variables permit to achieve a more stable inversion and a smaller value of the minimum of the cost function.

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called "the curse of dimensionality". As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.