Colorization as a Proxy Task for Visual Understanding

We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using ImageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of self-supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining.Comment: CVPR 2017 (Project page: http://people.cs.uchicago.edu/~larsson/color-proxy/

System concept for small-scale biological methanation using electrolysis and trickle bed reactor

In the present study, the concept of a biological H2 methanation (BHM) system was created for four cases of scale which are determined by electrolyser scale. The system design is aimed to upgrade existing biogas to vehicle fuel quality, with a concentration of CH4 above 95% and H2S removal. The cases of scale and type of electrolyser are: 4.8 kW AEM electrolyser, 20 kW AEL electrolyser, 100 kWe AEL electrolyser and 550 kWe PEM electrolyser. Each case of scale can upgrade a biogas flow of 0.7, 2.4, 14.5 and 73.9 Nm3 respectively. A trickle bed reactor design at thermophilic conditions was chosen for the systems methanation process. A MATLAB model was created to simulate energy- and mass flows for the system. The simulation also includes economic parameters such as OPEX and CAPEX. Results of the simulation are presented as levelized cost of CH4 production (€/kWh) and specific CAPEX (€/kWe). Simulations of the system show a high upgrading performance with an output gas of above 95% CH4 with H2S removal. The system also increases CH4 yield of 60%. The system performs comparatively to traditional upgrading method. The economic results show that the system has an upgrading cost of 0.37 to 0.089 €/kWh and specific CAPEX of 3830 to 22 500 €/kW. The system cannot be considered economically competitive to traditional upgrading when no additional cost reductions are applied. The concept of upgrading by BHM also reduces carbon emissions from biogas production giving the system a good chance of receiving subsidies from greenhouse gas reduction initiatives. Larger scales of the system can then reach competitive upgrading costs by utilizing subsidies, electricity price reductions and heat recovery

Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning

The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To address this concern, with the long-term goal of leveraging the abundance of cheap unlabeled data, we explore methods of unsupervised "pre-training." In particular, we propose to use self-supervised automatic image colorization. We show that traditional methods for unsupervised learning, such as layer-wise clustering or autoencoders, remain inferior to supervised pre-training. In search for an alternative, we develop a fully automatic image colorization method. Our method sets a new state-of-the-art in revitalizing old black-and-white photography, without requiring human effort or expertise. Additionally, it gives us a method for self-supervised representation learning. In order for the model to appropriately re-color a grayscale object, it must first be able to identify it. This ability, learned entirely self-supervised, can be used to improve other visual tasks, such as classification and semantic segmentation. As a future direction for self-supervision, we investigate if multiple proxy tasks can be combined to improve generalization. This turns out to be a challenging open problem. We hope that our contributions to this endeavor will provide a foundation for future efforts in making self-supervision compete with supervised pre-training.Comment: Ph.D. thesi

Realistic Modeling of Thermal Effects in Concrete Bridges

Thermal actions have a signicant eect on bridge structures and can at some sections account for more than 20% of the reinforcement. Such actions are especially important in certain types of concrete bridges, e.g. portal frame bridges. Thermal actions dier from other load types considered during bridge design through being a constraining load. A temperature prole can be divided into a uniform part that aect the bridge with a linear expansion and a non-uniform part that will induce an arch shape of the bridge deck. Using the current building code, thermal actions can account for more than 20% of the total demand of reinforcement at midsection making them so large that they cannot be neglected. This makes thermal actions in bridge design interesting for further investigation

Finding differences in perspectives between designers and engineers to develop trustworthy AI for autonomous cars

In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles

Dags att bryta "Anders kretslopp" - En studie om mångfald och finansiell prestation i svenska börsbolag

Studien syftar till att undersöka hur svenska börsbolags finansiella prestation påverkas av mångfald i dess styrelse, sett till kön, ålder, nationalitet, beroendeställning, utbildning och ämbetstid