The Evolving Roles of Nuclear Cardiology

The use of cardiac imaging modalities has grown steadily, and cardiac nuclear studies constitute a large part of this number. Nuclear Cardiology is often mistakenly considered a synonym of myocardial perfusion imaging (MPI), but has broader applications, including metabolic imaging, innervation imaging, among other technologies. MPI has been a powerful diagnostic and prognostic tool in the assessment of patients for known or suspected CAD for decades, and is now increasingly used for the evaluation of the anti-ischemic effects of various therapies, according to changes in left ventricular perfusion defect size defined by sequential MPI. Neuronal dysfunction identified with iodine-123-metaiodobenzylguanidine may give information on prognosis in different disease conditions, such as after myocardial infarction, in diabetes and dilated cardiomyopathy. Molecular imaging may identify the predominant cellular population in the atherosclerotic plaque and help predict the likelihood of clinical events. Therefore, although its usefulness is well established, Nuclear Cardiology remains a moving science, whose roles keep in pace with evolving clinical needs and expectations

Issue Yield and Party Strategy in Multiparty Competition

The issue yield model introduced a theory of the herestethic use of policy issues as strategic resources in multidimensional party competition. We extend the model by systematically addressing the specificities of issue yield dynamics in multiparty systems, with special regard to parties\u2019 issue yield rankings (relative position) and issue yield heterogeneity (differentiation) on each issue. Second, we introduce a novel research design for original data collection that allows for a more systematic testing of the model, by featuring (a) a large number of policy issues, (b) the use of Twitter content for coding parties\u2019 issue emphasis, and (c) an appropriate time sequence for measuring issue yield configurations and issue emphasis. We finally present findings from a pilot implementation of such design, performed on the occasion of the 2014 European Parliament election in Italy. Findings confirm the soundness of the design and provide support for the newly introduced hypotheses about multiparty competition

Effect of the Counterion on Circularly Polarized Luminescence of Europium(III) and Samarium(III) Complexes

Each enantiopure europium(III) and samarium(III) nitrate and triflate complex of the ligand L, with L = N,N'-bis(2-pyridylmethylidene)-1,2-(R,R + S,S)-cyclohexanediamine ([LnL(tta)2]·NO3 and [LnL(tta)2(H2O)]·CF3SO3, where tta = 2-thenoyltrifluoroacetylacetonate) has been synthesized and characterized from a spectroscopic point of view, using a chiroptical technique such as electronic circular dichroism (ECD) and circularly polarized luminescence (CPL). In all cases, both ligands are capable of sensitizing the luminescence of both metal ions upon absorption of light around 280 and 350 nm. Despite small differences in the total luminescence (TL) and ECD spectra, the CPL activity of the complexes is strongly influenced by a concurrent effect of the solvent and counterion. This particularly applies to europium(III) complexes where the CPL spectra in acetonitrile can be described as a weighed linear combination of the CPL spectra in dichloromethane and methanol, which show nearly opposite signatures when their ligand stereochemistries are the same. This phenomenon could be related to the presence of equilibria interconverting solvated, anion-coordinated complexes and isomers differing by the relative orientation of the tta ligands. The difference between some bond lengths (M-N bonds, in particular) in the different species could be at the basis of such an unusual CPL activity

Genetic Programming Techniques in Engineering Applications

2012/2013Machine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples. Machine learning systems, thus, may automatically synthesize programs from data. This approach is often feasible and cost-effective where manual programming or manual algorithm design is not. In the last decade techniques based on machine learning have spread in a broad range of application domains. In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of engineering applications grounded in real world problems. The problems treated in this work range from the automatic synthesis of regular expressions, to the generation of electricity price forecast, to the synthesis of a model for the tracheal pressure in mechanical ventilation. The results demonstrate that Genetic Programming is indeed a suitable tool for solving complex problems of practical interest. Furthermore, several results constitute a significant improvement over the existing state-of-the-art. The main contribution of this thesis is the design and implementation of a framework for the automatic inference of regular expressions from examples based on Genetic Programming. First, we will show the ability of such a framework to cope with the generation of regular expressions for solving text-extraction tasks from examples. We will experimentally assess our proposal comparing our results with previous proposals on a collection of real-world datasets. The results demonstrate a clear superiority of our approach. We have implemented the approach in a web application that has gained considerable interest and has reached peaks of more 10000 daily accesses. Then, we will apply the framework to a popular "regex golf" challenge, a competition for human players that are required to generate the shortest regular expression solving a given set of problems. Our results rank in the top 10 list of human players worldwide and outperform those generated by the only existing algorithm specialized to this purpose. Hence, we will perform an extensive experimental evaluation in order to compare our proposal to the state-of-the-art proposal in a very close and long-established research field: the generation of a Deterministic Finite Automata (DFA) from a labelled set of examples. Our results demonstrate that the existing state-of-the-art in DFA learning is not suitable for text extraction tasks. We will also show a variant of our framework designed for solving text processing tasks of the search-and-replace form. A common way to automate search-and-replace is to describe the region to be modified and the desired changes through a regular expression and a replacement expression. We will propose a solution to automatically produce both those expressions based only on examples provided by user. We will experimentally assess our proposal on real-word search-and-replace tasks. The results indicate that our proposal is indeed feasible. Finally, we will study the applicability of our framework to the generation of schema based on a sample of the eXtensible Markup Language documents. The eXtensible Markup Language documents are largely used in machine-to-machine interactions and such interactions often require that some constraints are applied to the contents of the documents. These constraints are usually specified in a separate document which is often unavailable or missing. In order to generate a missing schema, we will apply and will evaluate experimentally our framework to solve this problem. In the final part of this thesis we will describe two significant applications from different domains. We will describe a forecasting system for producing estimates of the next day electricity price. The system is based on a combination of a predictor based on Genetic Programming and a classifier based on Neural Networks. Key feature of this system is the ability of handling outliers-i.e., values rarely seen during the learning phase. We will compare our results with a challenging baseline representative of the state-of-the-art. We will show that our proposal exhibits smaller prediction error than the baseline. Finally, we will move to a biomedical problem: estimating tracheal pressure in a patient treated with high-frequency percussive ventilation. High-frequency percussive ventilation is a new and promising non-conventional mechanical ventilatory strategy. In order to avoid barotrauma and volutrauma in patience, the pressure of air insufflated must be monitored carefully. Since measuring the tracheal pressure is difficult, a model for accurately estimating the tracheal pressure is required. We will propose a synthesis of such model by means of Genetic Programming and we will compare our results with the state-of-the-art.XXVI Ciclo198

Your Paper has been Accepted, Rejected, or Whatever: Automatic Generation of Scientific Paper Reviews

4noPeer review is widely viewed as an essential step for ensuring scientific quality of a work and is a cornerstone of scholarly publishing. On the other hand, the actors involved in the publishing process are often driven by incentives which may, and increasingly do, undermine the quality of published work, especially in the presence of unethical conduits. In this work we investigate the feasibility of a tool capable of generating fake reviews for a given scientific paper automatically. While a tool of this kind cannot possibly deceive any rigorous editorial procedure, it could nevertheless find a role in several questionable scenarios and magnify the scale of scholarly frauds. A key feature of our tool is that it is built upon a small knowledge base, which is very important in our context due to the difficulty of finding large amounts of scientific reviews. We experimentally assessed our method 16 human subjects. We presented to these subjects a mix of genuine and machine generated reviews and we measured the ability of our proposal to actually deceive subjects judgment. The results highlight the ability of our method to produce reviews that often look credible and may subvert the decision.partially_openembargoed_20160915Bartoli, Alberto; De Lorenzo, Andrea; Medvet, Eric; Tarlao, FabianoBartoli, Alberto; DE LORENZO, Andrea; Medvet, Eric; Tarlao, Fabian

On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0)

Regular expressions are systematically used in a number of different application domains. Writing a regular expression for solving a specific task is usually quite difficult, requiring significant technical skills and creativity. We have developed a tool based on Genetic Programming capable of constructing regular expressions for text extraction automatically, based on examples of the text to be extracted. We have recently demonstrated that our tool is human-competitive in terms of both accuracy of the regular expressions and time required for their construction. We base this claim on a large-scale experiment involving more than 1700 users on 10 text extraction tasks of realistic complexity. The F-measure of the expressions constructed by our tool was almost always higher than the average F-measure of the expressions constructed by each of the three categories of users involved in our experiment (Novice, Intermediate, Experienced). The time required by our tool was almost always smaller than the average time required by each of the three categories of users. The experiment is described in full detail in "Can a machine replace humans? A case study. IEEE Intelligent Systems, 2016

Can a Machine Replace Humans in Building Regular Expressions? A Case Study

Regular expressions are routinely used in a variety of different application domains. But building a regular expression involves a considerable amount of skill, expertise, and creativity. In this work, the authors investigate whether a machine can surrogate these qualities and automatically construct regular expressions for tasks of realistic complexity. They discuss a large-scale experiment involving more than 1,700 users on 10 challenging tasks. The authors compare the solutions constructed by these users to those constructed by a tool based on genetic programming that they recently developed and made publicly available. The quality of automatically constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; the time for automatic construction was likewise similar to the time required by human users

Regex-based Entity Extraction with Active Learning and Genetic Programming

We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behavior. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required