The Urban Political Ecology of Post-industrial Scottish Towns: Examining Greengairs and Ravenscraig

Urban ecological politics is shaped by both moments of concerted action and more silent perceptions and responses. Instead of only being evident in situations of organised protest, the politics of urban ecology is also manifested, in material and symbolic terms, in the daily life of the residents. The fragmentation of urban political ecology turns out to be an important element in the affirmation of post-political forms of urban governance. Those issues were the object of fieldwork research carried out in Greengairs and Ravenscraig, two towns in North Lanarkshire, near Glasgow, with the goal of unravelling the understanding and the coping mechanisms of environmentally deprived residents. The towns are permeated by a widespread, often dissimulated, political ecology that is nonetheless always present. Empirical results demonstrate that a more comprehensive handling of the political ecology of the urban is crucial in order to halt the sources of marginalisation and ecological degradation

Weakly Supervised Semantic Segmentation via Progressive Patch Learning

Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel "Progressive Patch Learning" approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. "Patch Learning" destructs the feature maps into patches and independently processes each local patch in parallel before the final aggregation. Such a mechanism enforces the network to find weak information from the scattered discriminative local parts, achieving enhanced local details sensitivity. "Progressive Patch Learning" further extends the feature destruction and patch learning to multi-level granularities in a progressive manner. Cooperating with a multi-stage optimization strategy, such a "Progressive Patch Learning" mechanism implicitly provides the model with the feature extraction ability across different locality-granularities. As an alternative to the implicit multi-granularity progressive fusion approach, we additionally propose an explicit method to simultaneously fuse features from different granularities in a single model, further enhancing the CAM quality on the full object coverage. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset e.g., with 69.6$% mIoU on the test set), which surpasses most existing weakly supervised semantic segmentation methods. Code will be made publicly available here https://github.com/TyroneLi/PPL_WSSS.Comment: TMM2022 accepte

Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential of 'prompt to masks' from the powerful class-agnostic large segmentation model, segment-anything. Specifically, different weak labels are used as prompts to the segment-anything model, generating precise class masks. The class masks are utilized to generate pseudo labels to train the segmentation networks. We have conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments demonstrate that segment-anything can serve as a good pseudo-label generator. The code will be made publicly available.Comment: Technical repor

Identification of Kelvin waves: numerical challenges

Kelvin waves are expected to play an essential role in the energy dissipation for quantized vortices. However, the identification of these helical distortions is not straightforward, especially in case of vortex tangle. Here we review several numerical methods that have been used to identify Kelvin waves within the vortex filament model. We test their validity using several examples and estimate whether these methods are accurate enough to verify the correct Kelvin spectrum. We also illustrate how the correlation dimension is related to different Kelvin spectra and remind that the 3D energy spectrum E(k) takes the form 1/k in the high-k region, even in the presence of Kelvin waves.Comment: 6 pages, 5 figures. The final publication is available at http://www.springerlink.co

Relaciones entre historia de las matemáticas y formación de ingenieros

Integrating historical and epistemological aspects of Newton and Leibniz's mathematics to what is generally known as Fundamental Theorem of Calculus (FTC), allows to improve accessibility to engineering students (understanding it in the college context as "a tool for the preparation of professionals" [1]) to this mathematical object. Indeed, the way Newton and Leibniz found this theorem was a result that emerged when they were solving a problem [2]–[4] this is in conformity with the need of engineering to design artifacts that work in practice, fulfilling the purpose and specifications that motivated it [5]. This writing seeks to collaborate with the transition from Newton and Leibniz's scientific knowledge to current pedagogical knowledge, proposing a new entry to this theorem, via the GeoGebra software. Three relations of the FTC will be presented: its relation with current and previous university textbooks; the FTC in the works of Newton and Leibniz; and, finally, the history and teaching practices of engineering trainers.Integrar aspectos históricos y epistemológicos de las matemáticas de Newton y de Leibniz en lo que actualmente conocemos como Teorema Fundamental del Cálculo (TFC), permite mejorar la accesibilidad (entendiéndola como “una herramienta para la preparación de profesionales” [1]) de los estudiantes de ingeniería a este objeto matemático, por cuanto Newton y Leibniz se relacionaron con este teorema como un resultado que emergió al resolver un problema [2]–[4], en concordancia con la necesidad de la ingeniería de diseñar artefactos que funcionen en la práctica, cumpliendo con el propósito y especificaciones que lo motivaron [5]. Este escrito busca entonces colaborar con el tránsito del conocimiento científico de Newton y Leibniz al saber pedagógico actual, proponiendo una nueva entrada a dicho teorema, vía el software GeoGebra. Se presentarán tres relaciones del TC: su relación con los textos universitarios actuales y precedentes; el TFC en los trabajos de Newton y de Leibniz; y, por último, la historia y las prácticas docentes de los formadores de ingenieros

Making visible the invisible through the analysis of acknowledgements in the humanities

Purpose: Science is subject to a normative structure that includes how the contributions and interactions between scientists are rewarded. Authorship and citations have been the key elements within the reward system of science, whereas acknowledgements, despite being a well-established element in scholarly communication, have not received the same attention. This paper aims to put forward the bearing of acknowledgements in the humanities to bring to the foreground contributions and interactions that, otherwise, would remain invisible through traditional indicators of research performance. Design/methodology/approach: The study provides a comprehensive framework to understanding acknowledgements as part of the reward system with a special focus on its value in the humanities as a reflection of intellectual indebtedness. The distinctive features of research in the humanities are outlined and the role of acknowledgements as a source of contributorship information is reviewed to support these assumptions. Findings: Peer interactive communication is the prevailing support thanked in the acknowledgements of humanities, so the notion of acknowledgements as super-citations can make special sense in this area. Since single-authored papers still predominate as publishing pattern in this domain, the study of acknowledgements might help to understand social interactions and intellectual influences that lie behind a piece of research and are not visible through authorship. Originality/value: Previous works have proposed and explored the prevailing acknowledgement types by domain. This paper focuses on the humanities to show the role of acknowledgements within the reward system and highlight publication patterns and inherent research features which make acknowledgements particularly interesting in the area as reflection of the socio-cognitive structure of research.Comment: 14 page

A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.Comment: Accepted to IEEE TPAM