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A collaboration between Belgian artist François Schuiten and French writer Benoît Peeters, The Obscure Cities is one of the few comics series to achieve massive popularity while remaining highly experimental in form and content. Set in a parallel world, full of architecturally distinctive city-states, The Obscure Cities also represents one of the most impressive pieces of world-building in any form of literature. Rebuilding Story Worlds offers the first full-length study of this seminal series, exploring both the artistic traditions from which it emerges and the innovative ways it plays with genre, gender, and urban space. Comics scholar Jan Baetens examines how Schuiten’s work as an architectural designer informs the series’ concerns with the preservation of historic buildings. He also includes an original interview with Peeters, which reveals how poststructuralist critical theory influenced their construction of a rhizomatic fictional world, one which has made space for fan contributions through the Alta Plana website. Synthesizing cutting-edge approaches from both literary and visual studies, Rebuilding Story Worlds will give readers a new appreciation for both the aesthetic ingenuity of The Obscure Cities and its nuanced conception of politics.

The development of critical thinking in technical drawing is achieved through active methods that involve students in the analysis, interpretation and evaluation of graphic representations. The application of critical thinking development methods in technical drawing contributes significantly to the improvement of students' conceptual understanding and practical skills. These strategies stimulate the formulation of questions, the argumentation of solutions, the identification of errors and the reflection on their own learning process. By using them, technical drawing becomes a favourable context for the formation of critical thinking and technical skills necessary in professional practice.

The development of critical thinking in technical drawing is achieved through active methods that involve students in the analysis, interpretation and evaluation of graphic representations. The application of critical thinking development methods in technical drawing contributes significantly to the improvement of students' conceptual understanding and practical skills. These strategies stimulate the formulation of questions, the argumentation of solutions, the identification of errors and the reflection on their own learning process. By using them, technical drawing becomes a favourable context for the formation of critical thinking and technical skills necessary in professional practice.

Many libraries and librarians have embraced graphic novels. A number of books, articles, and presentations focus on the history of the medium and offer advice on building and maintaining collections. Few, however, give attention to the integration of graphic novels into a library's instructional efforts. This paper explores the characteristics of graphic novels that make them a valuable resource for research and information literacy instruction, identifies skills and competencies that can be taught through the study of graphic novels, and provides specific examples of how to incorporate graphic novels into instruction. Adapted from the source document.

The evolution of multimedia document production and diffusion technologies has lead to a significant spread of knowledge in form of pictures and recordings. However, scholarly reading tasks are still principally performed on textual contents. We argue that this is due to a lack of critical and structured tools: (1) to handle the wide spectrum of interpretive operations involved by the polymorphous scholarly reading process; (2) to perform these operations on a heterogeneous multimedia corpus. This firstly calls for identifying fundamental document requirements for such reading practices. Then, we present a flexible model and a software environment which enable the reader to structure, annotate, link, fragment, compare, freely organise and spatially lay out documents, and to prepare the writing of their critical comment. We eventually discuss experiments with humanities scholars, and explore new academic reading practices which take advantage of document engineering principles such as multimedia document structuring, publication or sharing.

This survey reviews explainability methods for vision-based self-driving systems trained with behavior cloning. The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application. Gathering contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI), this survey tackles several points. First, it discusses definitions, context, and motivation for gaining more interpretability and explainability from self-driving systems, as well as the challenges that are specific to this application. Second, methods providing explanations to a black-box self-driving system in a post-hoc fashion are comprehensively organized and detailed. Third, approaches from the literature that aim at building more interpretable self-driving systems by design are presented and discussed in detail. Finally, remaining open-challenges and potential future research directions are identified and examined.

Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for anomaly detection in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects. We adapt state-of-the-art methods to recent semantic segmentation models and compare uncertainty estimation approaches based on softmax confidence, Bayesian learning, density estimation, image resynthesis, as well as supervised anomaly detection methods. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art. Results, data and submission information can be found at https://fishyscapes.com/.

Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors’ inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a “technological loss”, incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task. Bayesian networks gain importance in safety-critical applications. Authors conducted experiments with a memristor-based Bayesian network trained with variational inference with technological loss, achieving accurate heartbeats classification and prediction certainty.

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates.

Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.