Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Multi-q pattern analysis: A case study in image classification

This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann-Gibbs-Shannon entropy for general pattern recognition, and proposes a multi-q approach to improve pattern analysis using entropy. A series of experiments were carried out for the problem of classifying image patterns. Given a dataset of 40 pattern classes, the goal of our image case study is to assess how well the different entropies can be used to determine the class of a newly given image sample. Our experiments show that the Tsallis entropy using the proposed multi-q approach has great advantages over the Boltzmann-Gibbs-Shannon entropy for pattern classification, boosting image recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy and the multi-q approach. (C) 2012 Elsevier B.V. All rights reserved.

Multi-planet extrasolar systems - detection and dynamics

20 years after the discovery of the first planets outside our solar system, the current exoplanetary population includes more than 700 confirmed planets around main sequence stars. Approximately 50% belong to multiple-planet systems in very diverse dynamical configurations, from two-planet hierarchical systems to multiple resonances that could only have been attained as the consequence of a smooth large-scale orbital migration. The first part of this paper reviews the main detection techniques employed for the detection and orbital characterization of multiple-planet systems, from the (now) classical radial velocity (RV) method to the use of transit time variations (TTV) for the identification of additional planetary bodies orbiting the same star. In the second part we discuss the dynamical evolution of multi-planet systems due to their mutual gravitational interactions. We analyze possible modes of motion for hierarchical, secular or resonant configurations, and what stability criteria can be defined in each case. In some cases, the dynamics can be well approximated by simple analytical expressions for the Hamiltonian function, while other configurations can only be studied with semi-analytical or numerical tools. In particular, we show how mean-motion resonances can generate complex structures in the phase space where different libration islands and circulation domains are separated by chaotic layers. In all cases we use real exoplanetary systems as working examples.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

Horn-schunck optical flow with a multi-scale strategy

[EN] The seminal work of Horn and Schunck [8] is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one that computes the motion increment in the iterations, like in ; or the one we follow, that computes the full flow during the iterations, like in. The solutions are incrementally refined ower the scales. This pyramidal structure is a standard tool in many optical flow methods.

McMaster Model of Family Functioning: adattamento alla realtà italiana, implicazioni cliniche e di ricerca

Il presente lavoro comincia con una descrizione dettagliata del “McMaster Model of Family Functionig” (MMFF), modello che al suo interno integra una teoria multidimensionale sul funzionamento familiare, diversi strumenti di auto ed etero valutazione e chiare indicazioni terapeutiche racchiuse all’interno della “Problem Centered System Therapy of the Family” (PCSTF). Grazie alla sua completezza il Modello fornisce ai clinici metodi coerenti, pratici ed empiricamente validi per valutare e trattare le famiglie, essi inoltre, sono stati formulati in modo da essere adattabili a differenti setting clinici e di ricerca, applicabili ad un’ampia gamma di problematiche e verificabili empiricamente. Obiettivo finale della presente ricerca è stato quello di porre le basi per l’esportazione del MMFF in Italia e poter quindi procedere alla sua applicazione in ambito clinico. La ricerca è cominciata alla Brown University con la traduzione dall’inglese all’italiano del Family Assessment Device (FAD), uno degli strumenti di autovalutazione compresi nel MMFF, ed è in seguito continuata con la validazione del suddetto strumento in un campione di 317 soggetti appartenenti alla popolazione generale italiana. Il FAD si è dimostrato uno strumento valido ed affidabile, in grado quindi di fornire valutazioni stabili e coerenti anche nella sua versione italiana. Il passo successivo è stato caratterizzato dalla somministrazione di FAD, Symptom Questionnaire (SQ) e delle Psychological Well-Being scales (PWB) a 289 soggetti reclutati nella popolazione generale. In accordo con il modello bipsicosociale che vede l’ambiente familiare come il più immediato gruppo di influenza psicosociale dello stato di benessere o malessere dell’individuo, i nostri dati confermano una stretta relazione tra scarso funzionamento familiare, spesso espresso attraverso difficoltà di comunicazione, di problem solving e scarso coinvolgimento affettivo e distress psicologico esperito con sintomi depressivi, ansiogeni ed ostilità. I nostri dati sottoliano inoltre come un funzionamento familiare positivo sia altamente correlato ad elevati livelli di benessere psicologico. Obiettivo della parte finale del lavoro ed anche il più importante, è stato quello di esplorare l’efficacia della Problem Centered Systems Therapy of the Family nella gestione della perdita di efficacia degli antidepressivi nel trattamento della depressione ricorrente. 20 soggetti con diagnosi di depressione maggiore ricorrente secondo il DSM-IV sono stati randomizzati a due diverse condizioni di trattamento: 1) aumento del dosaggio dell’antidepressivo e clinical management, oppure 2) mantenimento dello stesso dosaggio di antidepressivo e PCSTF. I dati di questo studio mettono in evidenza come, nel breve termine, PCSTF e farmacoterapia sono ugualmente efficaci nel ridurre la sintomatologia depressiva. Diversamente, ad un follow-up di 12 mesi, la PCSTF si è dimostrata altamente superiore all’aumento del farmaco ner prevenire le ricadute. Nel gruppo sottoposto all’aumento del farmaco infatti ben 6 soggetti su 7 ricadono entro l’anno. Nel gruppo assegnato a terapia familiare invece solo 1 soggetto su 7 ricade. Questi risultati sono in linea con i dati della letteratura che sottolineano l’elevata probabilità di una seconda ricaduta dopo l’aumento dell’antidepressivo all’interno di una farmacoterapia di mantenimento e suggeriscono l’efficacia dell’utilizzo di strategie psicoterapiche nella prevenzione della ricaduta in pazienti con depressione ricorrente.

Structural analysis of combinatorial optimization problem characteristics and their resolution using hybrid approaches

Many combinatorial problems coming from the real world may not have a clear and well defined structure, typically being dirtied by side constraints, or being composed of two or more sub-problems, usually not disjoint. Such problems are not suitable to be solved with pure approaches based on a single programming paradigm, because a paradigm that can effectively face a problem characteristic may behave inefficiently when facing other characteristics. In these cases, modelling the problem using different programming techniques, trying to ”take the best” from each technique, can produce solvers that largely dominate pure approaches. We demonstrate the effectiveness of hybridization and we discuss about different hybridization techniques by analyzing two classes of problems with particular structures, exploiting Constraint Programming and Integer Linear Programming solving tools and Algorithm Portfolios and Logic Based Benders Decomposition as integration and hybridization frameworks.

Simulatore di interfaccia uomo-macchina per i controllo di UAV

Recent statistics have demonstrated that two of the most important causes of failures of the UAVs (Uninhabited Aerial Vehicle) missions are related to the low level of decisional autonomy of vehicles and to the man machine interface. Therefore, a relevant issue is to design a display/controls architecture which allows the efficient interaction between the operator and the remote vehicle and to develop a level of automation which allows the vehicle the decision about change in mission. The research presented in this paper focuses on a modular man-machine interface simulator for the UAV control, which simulates UAV missions, developed to experiment solution to this problem. The main components of the simulator are an advanced interface and a block defined automation, which comprehend an algorithm that implements the level of automation of the system. The simulator has been designed and developed following a user-centred design approach in order to take into account the operator’s needs in the communication with the vehicle. The level of automation has been developed following the supervisory control theory which says that the human became a supervisor who sends high level commands, such as part of mission, target, constraints, in then-rule, while the vehicle receives, comprehends and translates such commands into detailed action such as routes or action on the control system. In order to allow the vehicle to calculate and recalculate the safe and efficient route, in term of distance, time and fuel a 3D planning algorithm has been developed. It is based on considering UASs representative of real world systems as objects moving in a virtual environment (terrain, obstacles, and no fly zones) which replicates the airspace. Original obstacle avoidance strategies have been conceived in order to generate mission planes which are consistent with flight rules and with the vehicle performance constraints. The interface is based on a touch screen, used to send high level commands to the vehicle, and a 3D Virtual Display which provides a stereoscopic and augmented visualization of the complex scenario in which the vehicle operates. Furthermore, it is provided with an audio feedback message generator. Simulation tests have been conducted with pilot trainers to evaluate the reliability of the algorithm and the effectiveness and efficiency of the interface in supporting the operator in the supervision of an UAV mission. Results have revealed that the planning algorithm calculate very efficient routes in few seconds, an adequate level of workload is required to command the vehicle and that the 3D based interface provides the operator with a good sense of presence and enhances his awareness of the mission scenario and of the vehicle under his control.