Design study of time-preserving grating monochromators for ultrashort pulses in the extreme-ultraviolet and soft X-rays

The design of grating-based instruments to handle and condition coherent ultrafast pulses in the extreme-ultraviolet is discussed. The main application of such instruments is the monochromatization of high-order laser harmonics and free-electron-laser pulses in the femtosecond time scale. Broad-band monochromators require the use of diffraction gratings at grazing incidence. A grating can be used for the spectral selection of ultrashort pulses without altering the pulse duration in a significant way, provided that the number of illuminated grooves is equal to the resolution. We discuss here the design conditions to be fulfilled by a grating monochromator that does not increase the pulse duration significantly longer than the Fourier limit

Meta-Learning for Hierarchical Classification and Applications in Bioinformatics

Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation

New Probabilistic Graphical Models and Meta-Learning Approaches for Hierarchical Classification, with Applications in Bioinformatics and Ageing

This interdisciplinary work proposes new hierarchical classification algorithms and evaluates them on biological datasets, and specifically on ageing-related datasets. Hierarchical classification is a type of classification task where the classes to be predicted are organized into a hierarchical structure. The focus on ageing is justified by the increasing impact that ageing-related diseases have on the human population and by the increasing amount of freely available ageing-related data. The main contributions of this thesis are as follows. First, we improve the running time of a previously proposed hierarchical classification algorithm based on an extension of the well-known Naive Bayes classification algorithm. We show that our modification greatly improves the runtime of the hierarchical classification algorithm, maintaining its predictive performance. We also propose four new hierarchical classification algorithms. The focus on hierarchical classification algorithms and their evaluation on biological data is justified as the class labels of biological data are commonly organized into class hierarchies. Two of our four new hierarchical classification algorithms - the "Hierarchical Dependence Network" (HDN) and the "Hierarchical Dependence Network algorithm based on finding non-Hierarchically related Predictive Classes'' (HDN-nHPC) - are based on Dependence Networks, a relatively new type of probabilistic graphical model that has not yet received a lot of attention from the classification community. The other two hierarchical classification algorithms we proposed are hybrid algorithms that use the hierarchical classification models produced by the Predictive Clustering Tree (PCT) algorithm. One of the hybrids combines the models produced by the PCT algorithm and a Local Hierarchical Classification (LHC) algorithm (which basically induces a local model for each class in the hierarchy). The other hybrid combines the models produced by the PCT and HDN algorithms. We have tested our four proposed algorithms and four other commonly used hierarchical classification algorithms on 42 hierarchical classification datasets. 20 of these datasets were created by us and are freely available for researchers. We have concluded that, for one out of the three hierarchical predictive accuracy measures used in our experiments, one of our four new algorithms (the HDN-nHPC algorithm) outperforms all other seven algorithms in terms of average rank across the 42 hierarchical classification datasets. We have also proposed the first meta-learning approach for hierarchical classification problems. In meta-learning, each meta-instance represents a dataset, meta-features represent dataset properties, and meta-classes represent the best classification algorithm for the corresponding dataset (meta-instance). Hence, meta-learning techniques for classification use the predictive performance of some candidate classification algorithms in previously tested datasets, and dataset descriptors (the meta-features), to infer the performance of those candidate classification algorithms in new datasets, given the meta-features of those new datasets. The predictions of our meta-learning system can be used as a guide to choose which hierarchical classification algorithm (out of a set of candidate ones) to use on a new dataset, without the need for time-consuming trial and error experiments with those candidate algorithms. This is particularly important for hierarchical classification problems, as the training time of hierarchical classification algorithms tends to be much greater than the training time of 'flat' classification algorithms. This increased training time is mainly due to the typically much greater number of class labels that annotate the instances of hierarchical classification problems. We have tested the predictive power of our meta-learning system and interpreted some generated meta-models. We have concluded that our meta-learning system had good predictive performance when compared to other baseline meta-learning approaches. We have also concluded that the meta-rules generated by our meta-learning system were useful to identify dataset characteristics to assist the choice of hierarchical classification algorithm. Finally, we have reviewed the current practice of applying supervised machine learning (classification and regression) algorithms to study the biology of ageing. This review discusses the main findings of such algorithms, in the context of the ageing biology literature. We have also interpreted some of the hierarchical classification models generated in our experiments. Both the above literature review and the interpretation of some models were performed in collaboration with an ageing expert, in order to extract relevant information for ageing research

An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes

This study comprehensively evaluates the performance of 5 types of probabilistic hierarchical classification methods used for predicting Gene Ontology (GO) terms related to ageing. Of those tested, a new hybrid of a Local Hierarchical Classifier (LHC) and the Predictive Clustering Tree algorithm (LHC-PCT) had the best predictive accuracy results. We also tested the impact of two types of variations in most hierarchical classification algorithms, namely: (a) changing the base algorithm (we tested Naive Bayes and Support Vector Machines), and the impact of (b) using or not the Correlation based Feature Selection (CFS) algorithm in a pre-processing step. In total, we evaluated the predictive performance of 17 variations of hierarchical classifiers across 15 datasets of ageing and longevityrelated genes. We conclude that the LHC-PCT algorithm ranks better across several tests (7 out of 12). In addition, we interpreted the models generated by the PCT algorithm to show how hierarchical classification algorithms can be used to extract biological insights out of the ageing-related datasets that we compiled

A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins

Motivation: The incidence of ageing-related diseases has been constantly increasing in the last decades, raising the need for creating effective methods to analyze ageing-related protein data. These methods should have high predictive accuracy and be easily interpretable by ageing experts. To enable this, one needs interpretable classification models (supervised machine learning) and features with rich biological meaning. In this paper we propose two interpretable feature types based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compare them with traditional feature types in hierarchical classification (a more challenging classification task regarding predictive performance) and binary classification (a classification task producing easier to interpret classification models). As far as we know, this work is the first to: (i) explore the potential of the KEGG pathway data in the hierarchical classification setting, (i) use the graph structure of KEGG pathways to create a feature type that quantifies the influence of a current protein on another specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the classification models induced using KEGG features. Results: We performed tests measuring predictive accuracy considering hierarchical and binary class labels extracted from the Mouse Phenotype Ontology. One of the KEGG feature types leads to the highest predictive accuracy among five individual feature types across three hierarchical classification algorithms. Additionally, the combination of the two KEGG feature types proposed in this work results in one of the best predictive accuracies when using the binary class version of our datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information

A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti- Longevity Genes

Understanding the ageing process is a very challenging problem for biologists. To help in this task, there has been a growing use of classification methods (from machine learning) to learn models that predict whether a gene influences the process of ageing or promotes longevity. One type of predictive feature often used for learning such classification models is Protein-Protein Interaction (PPI) features. One important property of PPI features is their uncertainty, i.e., a given feature (PPI annotation) is often associated with a confidence score, which is usually ignored by conventional classification methods. Hence, we propose the Lazy Feature Selection for Uncertain Features (LFSUF) method, which is tailored for coping with the uncertainty in PPI confidence scores. In addition, following the lazy learning paradigm, LFSUF selects features for each instance to be classified, making the feature selection process more flexible. We show that our LFSUF method achieves better predictive accuracy when compared to other feature selection methods that either do not explicitly take PPI confidence scores into account or deal with uncertainty globally rather than using a per-instance approach. Also, we interpret the results of the classification process using the features selected by LFSUF, showing that the number of selected features is significantly reduced, assisting the interpretability of the results. The datasets used in the experiments and the program code of the LFSUF method are freely available on the web at http://github.com/pablonsilva/FSforUncertainFeatureSpaces