Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition

If an unknown example that is not seen during training appears, most recognition systems usually produce overgeneralized results and determine that the example belongs to one of the known classes. To address this problem, teacher-explorer-student (T/E/S) learning, which adopts the concept of open set recognition (OSR) that aims to reject unknown samples while minimizing the loss of classification performance on known samples, is proposed in this study. In this novel learning method, overgeneralization of deep learning classifiers is significantly reduced by exploring various possibilities of unknowns. Here, the teacher network extracts some hints about unknowns by distilling the pretrained knowledge about knowns and delivers this distilled knowledge to the student. After learning the distilled knowledge, the student network shares the learned information with the explorer network. Then, the explorer network shares its exploration results by generating unknown-like samples and feeding the samples to the student network. By repeating this alternating learning process, the student network experiences a variety of synthetic unknowns, reducing overgeneralization. Extensive experiments were conducted, and the experimental results showed that each component proposed in this paper significantly contributes to the improvement in OSR performance. As a result, the proposed T/E/S learning method outperformed current state-of-the-art methods.

Actionable Cognitive Twins for Decision Making in Manufacturing

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

Segmentation of EM showers for neutrino experiments with deep graph neural networks

We introduce a novel method for showers reconstruction from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and kinematics of in-going particles. In this work, we consider the case when a large number of particles pass through an Emulsion Cloud Chamber (ECC) brick, generating electromagnetic showers. This situation can be observed with long exposure times or large input particle flux. For example, SHiP experiment is planning to use emulsion detectors for dark matter search and neutrino physics investigation. The expected full flux of SHiP experiment is about $10^{20}$ particles over five years. Because of the high amount of in-going particles, we will observe a lot of overlapping showers. It makes EM showers reconstruction a challenging segmentation problem. Our reconstruction pipeline consists of a Graph Neural Network that predicts an adjacency matrix for the clustering algorithm. To improve Graph Neural Network's performance, we propose a new layer type (EmulsionConv) that takes into account geometrical properties of shower development in ECC brick. For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm. Our method does not use any prior information about the incoming particles and identifies up to 82% of electromagnetic showers in emulsion detectors. The mean energy resolution over $17,715$ showers is 27%. The main test bench for the algorithm for reconstructing electromagnetic showers is going to be SND@LHC.

Fatigued Random Walks in Hypergraphs: A Neuronal Analogy to Improve Retrieval Performance

Hypergraphs are data structures capable of capturing supra-dyadic relations. We can use them to model binary relations, but also to model groups of entities, as well as the intersections between these groups or the contained subgroups. In previous work, we explored the usage of hypergraphs as an indexing data structure, in particular one that was capable of seamlessly integrating text, entities and their relations to support entity-oriented search tasks. As more information is added to the hypergraph, however, it not only increases in size, but it also becomes denser, making the task of efficiently ranking nodes or hyperedges more complex. Random walks can effectively capture network structure, without compromising performance, or at least providing a tunable balance between efficiency and effectiveness, within a nondeterministic universe. For a higher effectiveness, a higher number of random walks is usually required, which often results in lower efficiency. Inspired by von Neumann and the neuron in the brain, we propose and study the usage of node and hyperedge fatigue as a way to temporarily constrict random walks during keyword-based ad hoc retrieval. We found that we were able to improve search time by a factor of 32, but also worsen MAP by a factor of 8. Moreover, by distinguishing between fatigue in nodes and hyperedges, we are able to find that, for hyperedge ranking tasks, we consistently obtained lower MAP scores when increasing fatigue for nodes. On the other hand, the overall impact of hyperedge fatigue was slightly positive, although it also slightly worsened efficiency.

Denoise and Contrast for Category Agnostic Shape Completion

In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region around it. Local and global information are encoded in a combined embedding. A denoising pretext task provides the network with the needed local cues, decoupled from the high-level semantics and naturally shared over multiple classes. On the other hand, contrastive learning maximizes the agreement between variants of the same shape with different missing portions, thus producing a representation which captures the global appearance of the shape. The combined embedding inherits category-agnostic properties from the chosen pretext tasks. Differently from existing approaches, this allows to better generalize the completion properties to new categories unseen at training time. Moreover, while decoding the obtained joint representation, we better blend the reconstructed missing part with the partial shape by paying attention to its known surrounding region and reconstructing this frame as auxiliary objective. Our extensive experiments and detailed ablation on the ShapeNet dataset show the effectiveness of each part of the method with new state of the art results. Our quantitative and qualitative analysis confirms how our approach is able to work on novel categories without relying neither on classification and shape symmetry priors, nor on adversarial training procedures.

Radar SLAM: A Robust SLAM System for All Weather Conditions

A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade their imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensing geometry and noise characteristics bring open challenges when being exploited for SLAM. % However, there are still open challenges since most existing visual and LiDAR SLAM systems do not operate in bad weathers. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion tracking algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. % while a scheme to reject ambiguous loop closure candidates is also designed specifically for radar. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions, e.g. heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

Dialogue Discourse-Aware Graph Convolutional Networks for Abstractive Meeting Summarization

Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization. Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information. In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus, which can be used to pre-train our model. Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.

Automatic Flare Spot Artifact Detection and Removal in Photographs

Flare spot is one type of flare artifact caused by a number of conditions, frequently provoked by one or more high-luminance sources within or close to the camera field of view. When light rays coming from a high-luminance source reach the front element of a camera, it can produce intra-reflections within camera elements that emerge at the film plane forming non-image information or flare on the captured image. Even though preventive mechanisms are used, artifacts can appear. In this paper, we propose a robust computational method to automatically detect and remove flare spot artifacts. Our contribution is threefold: firstly, we propose a characterization which is based on intrinsic properties that a flare spot is likely to satisfy; secondly, we define a new confidence measure able to select flare spots among the candidates; and, finally, a method to accurately determine the flare region is given. Then, the detected artifacts are removed by using exemplar-based inpainting. We show that our algorithm achieve top-tier quantitative and qualitative performance.

Two-Level K-FAC Preconditioning for Deep Learning

In the context of deep learning, many optimization methods use gradient covariance information to accelerate the convergence of Stochastic Gradient Descent. In particular, starting with Adagrad~\cite{duchi2011adaptive}, a seemingly endless line of research advocates the use of diagonal approximations of the so-called empirical Fisher matrix in stochastic gradient-based algorithms, with the most prominent one arguably being Adam. However, in recent years, several works cast doubt on the theoretical basis for preconditioning with the empirical Fisher matrix, and it has been shown that more sophisticated approximations of the actual Fisher matrix more closely resemble the theoretically well-motivated Natural Gradient Descent. One particularly successful variant of such methods is the so-called K-FAC optimizer~\cite{martens2015optimizing}, which uses a Kronecker-factored block-diagonal Fisher approximation as preconditioner. In this work, drawing inspiration from two-level domain decomposition methods used as preconditioners in the field of scientific computing, we extend K-FAC by enriching it with off-diagonal (i.e. global) curvature information in a computationally efficient way. We achieve this by adding a coarse-space correction term to the preconditioner, which captures the global Fisher information matrix at a coarser scale. We present a small set of experimental results suggesting improved convergence behaviour of our proposed method.

Contrastive Neural Architecture Search with Neural Architecture Comparators

One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of candidate architectures. Existing methods either directly use the validation performance or learn a predictor to estimate the performance. However, these methods can be either computationally expensive or very inaccurate, which may severely affect the search efficiency and performance. Moreover, as it is very difficult to annotate architectures with accurate performance on specific tasks, learning a promising performance predictor is often non-trivial due to the lack of labeled data. In this paper, we argue that it may not be necessary to estimate the absolute performance for NAS. On the contrary, we may need only to understand whether an architecture is better than a baseline one. However, how to exploit this comparison information as the reward and how to well use the limited labeled data remains two great challenges. In this paper, we propose a novel Contrastive Neural Architecture Search (CTNAS) method which performs architecture search by taking the comparison results between architectures as the reward. Specifically, we design and learn a Neural Architecture Comparator (NAC) to compute the probability of candidate architectures being better than a baseline one. Moreover, we present a baseline updating scheme to improve the baseline iteratively in a curriculum learning manner. More critically, we theoretically show that learning NAC is equivalent to optimizing the ranking over architectures. Extensive experiments in three search spaces demonstrate the superiority of our CTNAS over existing methods.