Testing the Efficient Network TRaining (ENTR) Hypothesis: initially reducing training image size makes Convolutional Neural Network training for image recognition tasks more efficient

Convolutional Neural Networks (CNN) for image recognition tasks are seeing rapid advances in the available architectures and how networks are trained based on large computational infrastructure and standard datasets with millions of images. In contrast, performance and time constraints for example, of small devices and free cloud GPUs necessitate efficient network training (i.e., highest accuracy in the shortest inference time possible), often on small datasets. Here, we hypothesize that initially decreasing image size during training makes the training process more efficient, because pre-shaping weights with small images and later utilizing these weights with larger images reduces initial network parameters and total inference time. We test this Efficient Network TRaining (ENTR) Hypothesis by training pre-trained Residual Network (ResNet) models (ResNet18, 34, & 50) on three small datasets (steel microstructures, bee images, and geographic aerial images) with a free cloud GPU. Based on three training regimes of i) not, ii) gradually or iii) in one step increasing image size over the training process, we show that initially reducing image size increases training efficiency consistently across datasets and networks. We interpret these results mechanistically in the framework of regularization theory. Support for the ENTR hypothesis is an important contribution, because network efficiency improvements for image recognition tasks are needed for practical applications. In the future, it will be exciting to see how the ENTR hypothesis holds for large standard datasets like ImageNet or CIFAR, to better understand the underlying mechanisms, and how these results compare to other fields such as structural learning.Comment: 12 pages, 5 figures, 1 table +++ Keywords: Image recognition, Efficient Network Training hypothesis, image size increase, network efficiency, ResNet models, Google Colaboratory, free cloud GPU, material science, geoscience, environmental science, convolutional neural networks, regularizatio

A Calculus for Modular Loop Acceleration

Loop acceleration can be used to prove safety, reachability, runtime bounds, and (non-)termination of programs operating on integers. To this end, a variety of acceleration techniques has been proposed. However, all of them are monolithic: Either they accelerate a loop successfully or they fail completely. In contrast, we present a calculus that allows for combining acceleration techniques in a modular way and we show how to integrate many existing acceleration techniques into our calculus. Moreover, we propose two novel acceleration techniques that can be incorporated into our calculus seamlessly. An empirical evaluation demonstrates the applicability of our approach

A Calculus for Modular Loop Acceleration

Loop acceleration can be used to prove safety, reachability, runtime bounds, and (non-)termination of programs operating on integers. To this end, a variety of acceleration techniques has been proposed. However, all of them are monolithic: Either they accelerate a loop successfully or they fail completely. In contrast, we present a calculus that allows for combining acceleration techniques in a modular way and we show how to integrate many existing acceleration techniques into our calculus. Moreover, we propose two novel acceleration techniques that can be incorporated into our calculus seamlessly. An empirical evaluation demonstrates the applicability of our approach

Hope, Power and Precarity in Artisanal and Small-scale Mining

In dieser Doktorarbeit geht es um den Kleinbergbau (ASM) und wie dieser Leben und Landschaften verändert. ASM ist eine von vielen informellen Lebensgrundlagen, die in kapitalistische Wertschöpfungsketten eingebunden ist. ASM ist für schätzungsweise 40 Millionen Menschen im globalen Süden zu einer der wichtigsten nichtlandwirtschaftlichen Lebensgrundlagen geworden. Schlechte Arbeitsbedingungen, Umweltverschmutzung, Umweltzerstörung und Machtasymmetrien machen ASM jedoch zu einer prekären Lebensgrundlage, die Nachhaltigkeitstransformationen in Frage stellt. Daher wird mehr Forschung über die Nachhaltigkeitspotenziale von ASM gefordert. Wissenschaftler betonen insbesondere die Notwendigkeit, sich mit den Perspektiven von ASM-Akteuren sowie den Dynamiken der ASM-Wertschöpfungsketten auseinanderzusetzen. Auf der Grundlage einer sechsmonatigen ethnografischen Feldforschung in der Region Geita in Tansania untersuche ich in der Dissertation skalenübergreifende Beziehungen zwischen Akteuren und Agenden innerhalb von ASM und diskutiere die Herausforderungen und Möglichkeiten für einen nachhaltigeren Sektor. Die Arbeit ist zwischen Anthropologie, Human- und Wirtschaftsgeographie, politischer Ökologie und Landsystemwissenschaft situiert. Ich setze qualitative Methoden ein, darunter Interviews, teilnehmende Beobachtung und gemeinschaftliches Filmen, und beziehe dabei ein breites Spektrum von Akteuren ein. In vier Hauptkapiteln beschreibt die Arbeit die Ambivalenzen von ASM, die Hoffnung und Chancen, aber auch Prekarität und Degradierung die ASM mit sich bringt. Die Arbeit zeigt, wie konkurrierende Visionen und Machtasymmetrien die Ungleichheit verstärken, aber auch zum Widerstand und zu alternativen Visionen der Globalisierung aufrufen.This thesis is about artisanal and small-scale mining (ASM) and how it transforms lives and landscapes. ASM is one of many informal livelihoods spun in capitalist value chains. Engaging estimated 40 million people, ASM has become one of the most important non-farm rural livelihoods in the Global South. However, poor work conditions, pollution, environmental degradation and power asymmetries make ASM a precarious livelihood that challenge sustainability transitions. Consequently, calls have been made for more research on the sustainability potentials of ASM. Particularly, scholars emphasise the need for engaging the perspectives of ASM actors, along with a better understanding of the value chain dynamics of ASM. Based on 6 months of ethnographic fieldwork in the Geita region of Tanzania, I explore the cross-scalar relations between actors and agendas within ASM, and discuss the challenges and possibilities for a more sustainable sector. The thesis is situated between anthropology, human and economic geography, political ecology and land system science. I engage qualitative methods, including interviewing, participant observation and collaborative filmmaking, encompassing a broad range of actors. Through four core chapters, the thesis describes the ambiguities of ASM, creating hope and opportunity, but also precarity and degradation. It shows how competing visions and power asymmetries reinforce inequality, while also invoking resistance and alternative visions of globalisation

Wall House. Lampa, Chile

Indexación: Web of Science; ScieloEl control climático y lumínico es tomado como generatriz de una vivienda ubicada en la zona central de Chile. A través de la creación de capas sucesivas desde el interior, se conforman los espacios privados y comunes, en un contexto velado por los mantos exteriores que controlan la luz y las vistas.This thesis project, built thanks to the participation of locals and the recycling of abandoned material, proposes a contemplation space of a landscape previously exploited. The generated volumes are placed and give gratuity to the space, allowing one to reflect over history.http://ref.scielo.org/w8xtw