The first pieces of the gravitational-wave progenitor population puzzle

The field of gravitational wave astronomy is rapidly unfolding; between the start and finish of this thesis, the Gravitational-Wave Transient catalog has grown from order 10 to almost 100 merging double compact objects (pairs of merging black holes and neutron stars). The larger sample size has, for the first time, allowed us to infer properties of the entire population, rather than just individual sources. The observed population properties serve as the initial pieces of the “progenitor population puzzle”, that provides new insight into the question: ‘How do merging double compact objects form?’In this thesis, we set out to use the first pieces of this puzzle to form a picture of the massive stellar progenitors that give rise to merging double compact objects. We apply a combination of numerical population synthesis models, and analytical models to build intuition for the complex phenomena involved.Specifically, we show why the locations of features in the mass distribution of merging binary black holes are especially promising to constrain the physics of isolated binary stars. With this knowledge in mind, we explore features related to both the smallest-, and the largest-mass black holes formed from massive stars. We provide the first theoretical explanation for why lower-mass double compact objects (i.e., binary neutron stars) form through different formation channels than higher-mass systems (i.e., binary black holes), and produce testable results for the evolution of the merger rate with redshift. We conclude by looking ahead to the observational landscape of the next 20 years

A single-objective and a multi-objective genetic algorithm to generate accurate and interpretable fuzzy rule based classifiers for the analysis of complex financial data

Nowadays, organizations deal with rapidly increasing amount of data that is stored in their databases. It has therefore become of crucial importance for them to identify the necessary patterns in these large databases to turn row data into valuable and actionable information. By exploring these important datasets, the organizations gain competitive advantage against other competitors, based on the assumption that the added value of Knowledge Management Systems strength is first and foremost to facilitate the decision making process. Especially if we consider the importance of knowledge in the 21st century, data mining can be seen as a very effective tool to explore the essential data that foster competitive gain in a changing environment. The overall aim of this study is to design the rule base component of a fuzzy rule-based system (FRBS) through the use of genetic algorithms. The main objective is to generate accurate and interpretable models of the data trying to overcome the existing tradeoff between accuracy and interpretability. We propose two different approaches: an accuracy-driven single-objective genetic algorithm, and a three-objective genetic algorithm that produce a Pareto front approximation, composed of classifiers with different tradeoffs between accuracy and complexity. The proposed approaches have been compared with two other systems, namely a rule selection single-objective algorithm, and a three-objective algorithm. The latter has been developed by the University of Pisa and is able to generate the rule base, while simultaneously learning the definition points of the membership functions, by taking into account both the accuracy and the interpretability of the final model

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications