Multiple instance learning under real-world conditions

Multiple instance learning (MIL) is a form of weakly-supervised learning that deals with data arranged in sets called bags. In MIL problems, a label is provided for bags, but not for each individual instance in the bag. Like other weakly-supervised frameworks, MIL is useful in situations where obtaining labels is costly. It is also useful in applications where instance labels cannot be observed individually. MIL algorithms learn from bags, however, prediction can be performed at instance- and bag-level. MIL has been used in several applications from drug activity prediction to object localization in image. Real-world data poses many challenges to MIL methods. These challenges arise from different problem characteristics that are sometimes not well understood or even completely ignored. This causes MIL methods to perform unevenly and often fail in real-world applications. In this thesis, we propose methods for both classification levels under different working assumptions. These methods are designed to address challenging problem characteristics that arise in real-world applications. As a first contribution, we survey these characteristics that make MIL uniquely challenging. Four categories of characteristics are identified: the prediction level, the composition of bags, the data distribution types and the label ambiguity. Each category is analyzed and related state-of-the-art MIL methods are surveyed. MIL applications are examined in light of these characteristics and extensive experiments are conducted to show how these characteristics affect the performance of MIL methods. From these analyses and experiments, several conclusions are drawn and future research avenues are identified. Then, as a second contribution, we propose a method for bag classification which relies on the identification of positive instances to train an ensemble of instance classifiers. The bag classifier uses the predictions made on instances to infer bag labels. The method identifies positive instances by projecting the instances into random subspaces. Clustering is performed on the data in these subspaces and positive instances are probabilistically identified based on the bag label of instances in clusters. Experiments show that the method achieves state-of-theart performance while being robust to several characteristics identified in the survey. In some applications, the instances cannot be assigned to a positive or negative class. Bag classes are defined by a composition of different types of instances. In such cases, interrelations between instances convey the information used to discriminate between positive and negative bags. As a third contribution, we propose a bag classification method that learns under these conditions. The method is a applied to predict speaker personality from speech signals represented as bags of instances. A sparse dictionary learning algorithm is used to learn a dictionary and encode instances. Encoded instances are embedded in a single feature vector summarizing the speech signal. Experimental results on real-world data reveal that the proposed method yields state-of-the-art accuracy results while requiring less complexity than commonly used methods in the field. Finally, we propose two methods for querying bags in a multiple instance active learning (MIAL) framework. In this framework the objective is to train a reliable instance classifier using a minimal amount of labeled data. Single instance methods are suboptimal is this framework because they do not account the bag structure of MIL. The proposed methods address the problem from different angles. One aims at directly refining the decision boundary, while the other leverage instance and bag labels to query instances in the most promising clusters. Experiments are conducted in an inductive and transductive setting. Results on data from 3 application domains show that leveraging bag structure in this MIAL framework is important to effectively reduce the number of queries necessary to attain a high level of classification accuracy. This thesis shows that real-world MIL problems pose a wide range of challenges. After an in-depth analysis, we show experimentally that these challenges have a profound impact on the performance of MIL algorithms. We propose methods to address some of these challenges and validate them on real-world data sets. We also identify future directions for research and remaining open problems

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Research on Teaching and Learning In Biology, Chemistry and Physics In ESERA 2013 Conference

This paper provides an overview of the topics in educational research that were published in the ESERA 2013 conference proceedings. The aim of the research was to identify what aspects of the teacher-student-content interaction were investigated frequently and what have been studied rarely. We used the categorization system developed by Kinnunen, Lampiselkä, Malmi and Meisalo (2016) and altogether 184 articles were analyzed. The analysis focused on secondary and tertiary level biology, chemistry, physics, and science education. The results showed that most of the studies focus on either the teacher’s pedagogical actions or on the student - content relationship. All other aspects were studied considerably less. For example, the teachers’ thoughts about the students’ perceptions and attitudes towards the goals and the content, and the teachers’ conceptions of the students’ actions towards achieving the goals were studied only rarely. Discussion about the scope and the coverage of the research in science education in Europe is needed.Peer reviewe

Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group

This is the Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG). This was the first PPIG to be held physically since 2019, following the two online-only PPIGs in 2020 and 2021, both during the Covid pandemic. It was also the first PPIG conference to be designed specifically for hybrid attendance. Reflecting the theme, it was hosted by Music Computing Lab at the Open University in Milton Keynes