Refining, Implementing, and Evaluating the Extended Continuous Variable-Specific Resolutions of Feature Interactions

Systems that involve feature-oriented software development suffer from feature interactions, in which features affect one another’s behaviour in surprising ways. As the number of features increases, the complexity of examining feature combinations and fixing undesired interactions increases exponentially, such that the workload of resolving interactions comes to dominate feature development. The Feature Interaction Problem results from aiming resolve feature interaction by providing optimal resolutions. Resolution strategies combat the Feature Interaction Problem by offering default strategies that resolve entire classes of interactions, thereby reducing the work of the developer who is charged with the task of resolving interactions. However, most such approaches employ coarse-grained resolution strategies (e.g., feature priority) or a centralized arbitrator. This thesis focuses on evaluating and refining a proposed architecture that resolves features’ conflicting actions on system’s outputs. In this thesis, we extend a proposed architecture based on variable-specific resolution to enable co-resolution of related outputs and to promote smooth continuous resolutions over execution sequences. We implemented our approach within the PreScan simulator for advanced driver assistance systems, and performed a case study involving 15 automotive features that we implemented. We also devised and implemented three resolution strategies for the features’ outputs. The results of the case study show that the approach produces smooth and continuous resolutions of interactions throughout interesting scenarios

Continuous Variable-Specic Resolutions of Feature Interactions

© ACM 2019 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected] that are assembled from independently developed features suffer from feature interactions, in which features affect one another's behaviour in surprising ways. The Feature Interaction Problem results from trying to implement an appropriate resolution for each interaction within each possible context, because the number of possible contexts to consider increases exponentially with the number of features in the system. Resolution strategies aim to combat the Feature Interaction Problem by offering default strategies that resolve entire classes of interactions, thereby reducing the work needed to resolve lots of interactions. However most such approaches employ coarse-grained resolution strategies (e.g., feature priority) or a centralized arbitrator. Our work focuses on employing variable-specific default-resolution strategies that aim to resolve at runtime features- conflicting actions on a system's outputs. In this paper, we extend prior work to enable co-resolution of interactions on coupled output variables and to promote smooth continuous resolutions over execution paths. We implemented our approach within the PreScan simulator and performed a case study involving 15 automotive features; this entailed our devising and implementing three resolution strategies for three output variables. The results of the case study show that the approach produces smooth and continuous resolutions of interactions throughout interesting scenarios.NSERC Discovery Grant, 155243-12 || Ontario Research Fund, RE05-044 || NSERC / Automotive Partnership Canada, APCPJ 386797 - 0

Subtle changes in chromatin loop contact propensity are associated with differential gene regulation and expression.

While genetic variation at chromatin loops is relevant for human disease, the relationships between contact propensity (the probability that loci at loops physically interact), genetics, and gene regulation are unclear. We quantitatively interrogate these relationships by comparing Hi-C and molecular phenotype data across cell types and haplotypes. While chromatin loops consistently form across different cell types, they have subtle quantitative differences in contact frequency that are associated with larger changes in gene expression and H3K27ac. For the vast majority of loci with quantitative differences in contact frequency across haplotypes, the changes in magnitude are smaller than those across cell types; however, the proportional relationships between contact propensity, gene expression, and H3K27ac are consistent. These findings suggest that subtle changes in contact propensity have a biologically meaningful role in gene regulation and could be a mechanism by which regulatory genetic variants in loop anchors mediate effects on expression

DELPHES 3, A modular framework for fast simulation of a generic collider experiment

The version 3.0 of the DELPHES fast-simulation is presented. The goal of DELPHES is to allow the simulation of a multipurpose detector for phenomenological studies. The simulation includes a track propagation system embedded in a magnetic field, electromagnetic and hadron calorimeters, and a muon identification system. Physics objects that can be used for data analysis are then reconstructed from the simulated detector response. These include tracks and calorimeter deposits and high level objects such as isolated electrons, jets, taus, and missing energy. The new modular approach allows for greater flexibility in the design of the simulation and reconstruction sequence. New features such as the particle-flow reconstruction approach, crucial in the first years of the LHC, and pile-up simulation and mitigation, which is needed for the simulation of the LHC detectors in the near future, have also been implemented. The DELPHES framework is not meant to be used for advanced detector studies, for which more accurate tools are needed. Although some aspects of DELPHES are hadron collider specific, it is flexible enough to be adapted to the needs of electron-positron collider experiments.Comment: JHEP 1402 (2014

Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.Comment: 19 pages, 11 figure

A Self-Organizing Neural System for Learning to Recognize Textured Scenes

A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition

Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte