893 resultados para data analysis: algorithms and implementation
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This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.
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Background: Sleeping sickness is a major cause of death in Africa. Since no secure treatment is available, the development of novel therapeutic agents is urgent. In this context, the enzyme trypanothione reductase (TR) is a prominent molecular target that has been investigated in drug design for sleeping sickness. Results: In this study, comparative molecular field analysis models were generated for a series of Trypanosoma brucei TR inhibitors. Statistically significant results were obtained and the models were applied to predict the activity of external test sets, with good correlation between predicted and experimental results. We have also investigated the structural requirements for the selective inhibition of the parasite's enzyme over the human glutathione reductase. Conclusion: The quantitative structure-activity relationship models provided valuable information regarding the essential molecular requirements for the inhibitory activity upon the target protein, providing important insights into the design of more potent and selective TR inhibitors.
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This thesis proposes a new document model, according to which any document can be segmented in some independent components and transformed in a pattern-based projection, that only uses a very small set of objects and composition rules. The point is that such a normalized document expresses the same fundamental information of the original one, in a simple, clear and unambiguous way. The central part of my work consists of discussing that model, investigating how a digital document can be segmented, and how a segmented version can be used to implement advanced tools of conversion. I present seven patterns which are versatile enough to capture the most relevant documents’ structures, and whose minimality and rigour make that implementation possible. The abstract model is then instantiated into an actual markup language, called IML. IML is a general and extensible language, which basically adopts an XHTML syntax, able to capture a posteriori the only content of a digital document. It is compared with other languages and proposals, in order to clarify its role and objectives. Finally, I present some systems built upon these ideas. These applications are evaluated in terms of users’ advantages, workflow improvements and impact over the overall quality of the output. In particular, they cover heterogeneous content management processes: from web editing to collaboration (IsaWiki and WikiFactory), from e-learning (IsaLearning) to professional printing (IsaPress).
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[EN]This works aims at assessing the acoustic efficiency of differente this noise barrier models. These designs frequently feature complex profiles and their implementarion in shape optimization processes may not always be easy in terms of determining their topological feasibility. A methodology to conduct both overall shape and top edge optimisations of thin cross section acoustic barriers by idealizing them as profiles with null boundary thickness is proposed.
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In this thesis we present some combinatorial optimization problems, suggest models and algorithms for their effective solution. For each problem,we give its description, followed by a short literature review, provide methods to solve it and, finally, present computational results and comparisons with previous works to show the effectiveness of the proposed approaches. The considered problems are: the Generalized Traveling Salesman Problem (GTSP), the Bin Packing Problem with Conflicts(BPPC) and the Fair Layout Problem (FLOP).
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In this PhD thesis the crashworthiness topic is studied with the perspective of the development of a small-scale experimental test able to characterize a material in terms of energy absorption. The material properties obtained are then used to validate a nu- merical model of the experimental test itself. Consequently, the numerical model, calibrated on the specific ma- terial, can be extended to more complex structures and used to simulate their energy absorption behavior. The experimental activity started at University of Washington in Seattle, WA (USA) and continued at Second Faculty of Engi- neering, University of Bologna, Forl`ı (Italy), where the numerical model for the simulation of the experimental test was implemented and optimized.
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Interactive theorem provers are tools designed for the certification of formal proofs developed by means of man-machine collaboration. Formal proofs obtained in this way cover a large variety of logical theories, ranging from the branches of mainstream mathematics, to the field of software verification. The border between these two worlds is marked by results in theoretical computer science and proofs related to the metatheory of programming languages. This last field, which is an obvious application of interactive theorem proving, poses nonetheless a serious challenge to the users of such tools, due both to the particularly structured way in which these proofs are constructed, and to difficulties related to the management of notions typical of programming languages like variable binding. This thesis is composed of two parts, discussing our experience in the development of the Matita interactive theorem prover and its use in the mechanization of the metatheory of programming languages. More specifically, part I covers: - the results of our effort in providing a better framework for the development of tactics for Matita, in order to make their implementation and debugging easier, also resulting in a much clearer code; - a discussion of the implementation of two tactics, providing infrastructure for the unification of constructor forms and the inversion of inductive predicates; we point out interactions between induction and inversion and provide an advancement over the state of the art. In the second part of the thesis, we focus on aspects related to the formalization of programming languages. We describe two works of ours: - a discussion of basic issues we encountered in our formalizations of part 1A of the Poplmark challenge, where we apply the extended inversion principles we implemented for Matita; - a formalization of an algebraic logical framework, posing more complex challenges, including multiple binding and a form of hereditary substitution; this work adopts, for the encoding of binding, an extension of Masahiko Sato's canonical locally named representation we designed during our visit to the Laboratory for Foundations of Computer Science at the University of Edinburgh, under the supervision of Randy Pollack.
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In this thesis we made the first steps towards the systematic application of a methodology for automatically building formal models of complex biological systems. Such a methodology could be useful also to design artificial systems possessing desirable properties such as robustness and evolvability. The approach we follow in this thesis is to manipulate formal models by means of adaptive search methods called metaheuristics. In the first part of the thesis we develop state-of-the-art hybrid metaheuristic algorithms to tackle two important problems in genomics, namely, the Haplotype Inference by parsimony and the Founder Sequence Reconstruction Problem. We compare our algorithms with other effective techniques in the literature, we show strength and limitations of our approaches to various problem formulations and, finally, we propose further enhancements that could possibly improve the performance of our algorithms and widen their applicability. In the second part, we concentrate on Boolean network (BN) models of gene regulatory networks (GRNs). We detail our automatic design methodology and apply it to four use cases which correspond to different design criteria and address some limitations of GRN modeling by BNs. Finally, we tackle the Density Classification Problem with the aim of showing the learning capabilities of BNs. Experimental evaluation of this methodology shows its efficacy in producing network that meet our design criteria. Our results, coherently to what has been found in other works, also suggest that networks manipulated by a search process exhibit a mixture of characteristics typical of different dynamical regimes.
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This research work presents the design and implementation of a FFT pruning block, which is an extension to the FFT core for OFDM demodulation, enabling run-time 8 pruning of the FFT algorithm, without any restrictions on the distribution pattern of the active/inactive sub-carriers. The design and implementation of FFT processor core is not the part of this work. The whole design was prototyped on an ALTERA STRATIX V FPGA to evaluate the performance of the pruning engine. Synthesis and simulation results showed that the logic overhead introduced by the pruning block is limited to a 10% of the total resources utilization. Moreover, in presence of a medium-high scattering of the sub-carriers, power and energy consumption of the FFT core were reduced by a 30% factor.
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Among the scientific objectives addressed by the Radio Science Experiment hosted on board the ESA mission BepiColombo is the retrieval of the rotational state of planet Mercury. In fact, the estimation of the obliquity and the librations amplitude were proven to be fundamental for constraining the interior composition of Mercury. This is accomplished by the Mercury Orbiter Radio science Experiment (MORE) via a strict interaction among different payloads thus making the experiment particularly challenging. The underlying idea consists in capturing images of the same landmark on the surface of the planet in different epochs in order to observe a displacement of the identified features with respect to a nominal rotation which allows to estimate the rotational parameters. Observations must be planned accurately in order to obtain image pairs carrying the highest information content for the following estimation process. This is not a trivial task especially in light of the several dynamical constraints involved. Another delicate issue is represented by the pattern matching process between image pairs for which the lowest correlation errors are desired. The research activity was conducted in the frame of the MORE rotation experiment and addressed the design and implementation of an end-to-end simulator of the experiment with the final objective of establishing an optimal science planning of the observations. In the thesis, the implementation of the singular modules forming the simulator is illustrated along with the simulations performed. The results obtained from the preliminary release of the optimization algorithm are finally presented although the software implemented is only at a preliminary release and will be improved and refined in the future also taking into account the developments of the mission.
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This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.
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Data deduplication describes a class of approaches that reduce the storage capacity needed to store data or the amount of data that has to be transferred over a network. These approaches detect coarse-grained redundancies within a data set, e.g. a file system, and remove them.rnrnOne of the most important applications of data deduplication are backup storage systems where these approaches are able to reduce the storage requirements to a small fraction of the logical backup data size.rnThis thesis introduces multiple new extensions of so-called fingerprinting-based data deduplication. It starts with the presentation of a novel system design, which allows using a cluster of servers to perform exact data deduplication with small chunks in a scalable way.rnrnAfterwards, a combination of compression approaches for an important, but often over- looked, data structure in data deduplication systems, so called block and file recipes, is introduced. Using these compression approaches that exploit unique properties of data deduplication systems, the size of these recipes can be reduced by more than 92% in all investigated data sets. As file recipes can occupy a significant fraction of the overall storage capacity of data deduplication systems, the compression enables significant savings.rnrnA technique to increase the write throughput of data deduplication systems, based on the aforementioned block and file recipes, is introduced next. The novel Block Locality Caching (BLC) uses properties of block and file recipes to overcome the chunk lookup disk bottleneck of data deduplication systems. This chunk lookup disk bottleneck either limits the scalability or the throughput of data deduplication systems. The presented BLC overcomes the disk bottleneck more efficiently than existing approaches. Furthermore, it is shown that it is less prone to aging effects.rnrnFinally, it is investigated if large HPC storage systems inhibit redundancies that can be found by fingerprinting-based data deduplication. Over 3 PB of HPC storage data from different data sets have been analyzed. In most data sets, between 20 and 30% of the data can be classified as redundant. According to these results, future work in HPC storage systems should further investigate how data deduplication can be integrated into future HPC storage systems.rnrnThis thesis presents important novel work in different area of data deduplication re- search.
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Nella fisica delle particelle, onde poter effettuare analisi dati, è necessario disporre di una grande capacità di calcolo e di storage. LHC Computing Grid è una infrastruttura di calcolo su scala globale e al tempo stesso un insieme di servizi, sviluppati da una grande comunità di fisici e informatici, distribuita in centri di calcolo sparsi in tutto il mondo. Questa infrastruttura ha dimostrato il suo valore per quanto riguarda l'analisi dei dati raccolti durante il Run-1 di LHC, svolgendo un ruolo fondamentale nella scoperta del bosone di Higgs. Oggi il Cloud computing sta emergendo come un nuovo paradigma di calcolo per accedere a grandi quantità di risorse condivise da numerose comunità scientifiche. Date le specifiche tecniche necessarie per il Run-2 (e successivi) di LHC, la comunità scientifica è interessata a contribuire allo sviluppo di tecnologie Cloud e verificare se queste possano fornire un approccio complementare, oppure anche costituire una valida alternativa, alle soluzioni tecnologiche esistenti. Lo scopo di questa tesi è di testare un'infrastruttura Cloud e confrontare le sue prestazioni alla LHC Computing Grid. Il Capitolo 1 contiene un resoconto generale del Modello Standard. Nel Capitolo 2 si descrive l'acceleratore LHC e gli esperimenti che operano a tale acceleratore, con particolare attenzione all’esperimento CMS. Nel Capitolo 3 viene trattato il Computing nella fisica delle alte energie e vengono esaminati i paradigmi Grid e Cloud. Il Capitolo 4, ultimo del presente elaborato, riporta i risultati del mio lavoro inerente l'analisi comparata delle prestazioni di Grid e Cloud.