Equivariant Representation Learning in the Presence of Stabilizers

We introduce Equivariant Isomorphic Networks (EquIN) -- a method for learning representations that are equivariant with respect to general group actions over data. Differently from existing equivariant representation learners, EquIN is suitable for group actions that are not free, i.e., that stabilize data via nontrivial symmetries. EquIN is theoretically grounded in the orbit-stabilizer theorem from group theory. This guarantees that an ideal learner infers isomorphic representations while trained on equivariance alone and thus fully extracts the geometric structure of data. We provide an empirical investigation on image datasets with rotational symmetries and show that taking stabilizers into account improves the quality of the representations.Comment: NeurIPS Workshop on Symmetry and Geometry in Neural Representations (v1), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (v2

Quantifying and Learning Disentangled Representations with Limited Supervision

Learning low-dimensional representations that disentangle the underlying factors of variation in data has been posited as an important step towards interpretable machine learning with good generalization. To address the fact that there is no consensus on what disentanglement entails, Higgins et al. (2018) propose a formal definition for Linear Symmetry-Based Disentanglement, or LSBD, arguing that underlying real-world transformations give exploitable structure to data. Although several works focus on learning LSBD representations, such methods require supervision on the underlying transformations for the entire dataset, and cannot deal with unlabeled data. Moreover, none of these works provide a metric to quantify LSBD. We propose a metric to quantify LSBD representations that is easy to compute under certain well-defined assumptions. Furthermore, we present a method that can leverage unlabeled data, such that LSBD representations can be learned with limited supervision on transformations. Using our LSBD metric, our results show that limited supervision is indeed sufficient to learn LSBD representations

Introduction

In recent years, a considerable amount of effort has been devoted, both in industry and academia, to improving maintenance. Time is a critical factor in maintenance, and efforts are placed to monitor, analyze, and visualize machine or asset data in order to anticipate to any possible failure, prevent damage, and save costs. The MANTIS Book aims to highlight the underpinning fundamentals of Condition-Based Maintenance related conceptual ideas, an overall idea of preventive maintenance, the economic impact and technical solution. The core content of this book describes the outcome of the Cyber-Physical System based Proactive Collaborative Maintenance project, also known as MANTIS, and funded by EU ECSEL Joint Undertaking under Grant Agreement nº 662189. The ambition has been to support the creation of a maintenance-oriented reference architecture that support the maintenance data lifecycle, to enable the use of novel kinds of maintenance strategies for industrial machinery. The key enabler has been the fine blend of collecting data through Cyber-Physical Systems, and the usage of machine learning techniques and advanced visualization for the enhanced monitoring of the machines. Topics discussed include, in the context of maintenance: Cyber-Physical Systems, Communication Middleware, Machine Learning, Advanced Visualization, Business Models, Future Trends. An important focus of the book is the application of the techniques in real world context, and in fact all the work is driven by the pilots, all of them centered on real machines and factories. This book is suitable for industrial and maintenance managers that want to implement a new strategy for maintenance in their companies. It should give readers a basic idea on the first steps to implementing a maintenance-oriented platform or information system.info:eu-repo/semantics/publishedVersio

Pattern-based feature extraction for fault detection in quality relevant process control

\u3cp\u3eStatistical quality control (SQC) applies multivariate statistics to monitor production processes over time and detect changes in their performance in terms of meeting specification limits on key product quality metrics. These limits are imposed by customers and typically assumed to be a single target value, however, for some products, it is more reasonable to target a range of values. Under this assumption we propose a multi-stage approach for mapping operating conditions to product quality classes. We use principal component analysis (PCA) and a pattern mining algorithm to reduce dimensionality and identify predictive patterns in time series of operating conditions in order to improve the performance of the classifier. We apply this approach to an industrial machining process and obtain significant improvements over models trained using features based on the last value of each process variable.\u3c/p\u3

Using artificial neurons in evidence based trust computation

This paper proposes an alternative approach for evidence based trust computation where the relationship between evidence and trust is learned using an artificial neuron, making it possible to automatically adapt trust computation to different use cases. Computational trust aims to quantify trust based on ever increasing evidence on observations. In the literature a trust value is seen as a posterior subjective probability, computed using Bayesian inference on evidence, a prior and a weight of the prior. This provides a fixed mapping between evidence and trust, which may not be suitable for every case study, e.g. when positive and negative evidences are not equally important. The proposed solution is also a first step towards our future work to replace complex and case-specific trust fusion operators proposed in the literature with a generic case-independent artificial neural network solution. Our experiments on example cases of coin toss prediction and occupancy detection show that for sufficiently large data sets, i.e. given sufficient evidence based on a history of observations, the proposed learning approach yields comparable results and in some cases beats the existing approach

A formalization of computational trust

Computational trust aims to quantify trust and is studied by many disciplines including computer science, social sciences and business science. We propose a formal computational trust model, including its parameters and operations on these parameters, as well as a step by step guide to compute trust in a real application. We make a distinction between trust statements that aim to capture the truth of a dynamic phenomenon and trust statements that aim to capture the truth of a static phenomenon. We elaborate on how this difference should be reflected in trust computation. To this end we apply a dynamic base rate (prior trust) as an alternative to the widely used fixed base rate

Swift mode changes in memory constrained real-time systems

A method for \preempting memory is presented, where (parts of) the memory allocated to an active task may be reallocated to another task, without corrupting the state of the active task's job. The method is based on combining scalable components with Fixed-Priority Scheduling with Deferred Preemption (FPDS). Real-time systems composed of scalable components are investigated. A scalable component can operate in one of several modes, where each mode defines certain trade off between the resource requirements and output quality. The focus of this paper is on memory constrained systems, with modes limited to memory requirements. During runtime the system may decide to reallocate the resources between the components, resulting in a mode change. The latency of a mode change should satisfy timing constraints expressed by upper bounds. A modeling framework is presented combining scalable components with FPDS. A quantitive analysis comparing Fixed-Priority Preemptive Scheduling (FPPS) and FPDS is provided, showing that FPDS sets a lower bound on the mode change latency. The analytical results are verified by simulation. The results for both FPPS and FPDS are applied to improve the existing latency bound for mode changes in the processor domain. Next to improving the latency bound of mode changes, the presented architecture offers a simple protocol and avoids the complication and overheads associated with tasks signaling during a mode change, necessary in existing mode change protocols. The architecture is especially suited for pipelined applications, allowing to perform the mode change without the need to first clear the whole pipeline