Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201

Modulation of ecdysal cyst and toxin dynamics of two Alexandrium (Dinophyceae) species under small-scale turbulence

Some dinoflagellate species have shown different physiological responses to certain turbulent conditions. Here we investigate how two levels of turbulent kinetic energy dissipation rates (epsilon = 0.4 and 27 cm(2) s(-3)) affect the PSP toxins and ecdysal cyst dynamics of two bloom forming species, Alexandrium minutum and A. catenella. The most striking responses were observed at the high epsilon generated by an orbital shaker. In the cultures of the two species shaken for more than 4 days, the cellular GTX(1+4) toxin contents were significantly lower than in the still control cultures. In A. minutum this trend was also observed in the C(1+2) toxin content. For the two species, inhibition of ecdysal cyst production occurred during the period of exposure of the cultures to stirring (4 or more days) at any time during their growth curve. Recovery of cyst abundances was always observed when turbulence stopped. When shaking persisted for more than 4 days, the net growth rate significantly decreased in A. minutum (from 0.25 +/- 0.01 day(-1) to 0.19 +/- 0.02 day(-1)) and the final cell numbers were lower (ca. 55.4%) than in the still control cultures. In A. catenella, the net growth rate was not markedly modified by turbulence although under long exposure to shaking, the cultures entered earlier in the stationary phase and the final cell numbers were significantly lower (ca. 23%) than in the control flasks. The described responses were not observed in the experiments performed at the low turbulence intensities with an orbital grid system, where the population development was favoured. In those conditions, cells appeared to escape from the zone of the influence of the grids and concentrated in calmer thin layers either at the top or at the bottom of the containers. This ecophysiological study provides new evidences about the sensitivity to high levels of small-scale turbulence by two life cycle related processes, toxin production and encystment, in dinoflagellates. This can contribute to the understanding of the dynamics of those organisms in nature

Technology foresight: a bibliometric analysis to identify leading and emerging methods

Foresight studies provide essential information used by the government, industry and academia for technology planning and knowledge expansion. They are complicated, resource-intensive, and quite expensive. The approach, methods, and techniques must be carefully identified and selected. Despite the global importance of foresight activities, there are no frameworks to help one develop and plan a proper foresight study. This paper begins to close this gap by analyzing and comparing different schools of thought and updating the literature with the most current tools and methods. Data mining techniques are used to identify articles through an extensive literature review. Social Network Analysis (SNA) techniques are used to identify and analyze leading journals, articles, and researchers. A framework is developed here to provide a guide to help in the selection of methods and tools for different approaches

Class 1 PI3K clinical candidates and recent inhibitor design strategies: a medicinal chemistry perspective

Phosphatidylinositol 3-kinases (PI3Ks) are a family of lipid kinases that phosphorylate the 3-OH of the inositol ring of phosphoinositides, and deregulation of this pathway has implications in many diseases. The search for novel PI3K inhibitors has been at the forefront of academic and industrial medicinal chemistry with over 600 medicinal chemistry-based publications and patents appearing to date, leading to 38 clinical candidates and the launch of two drugs, idelalisib in 2014 and copanlisib in 2017. This Perspective will discuss medicinal chemistry design approaches to novel isoform-selective inhibitors through consideration of brief case histories of compounds that have progressed into clinical development or that have revealed new structural motifs in this highly competitive area of research

A three decade mixed-method bibliometric investigation of the IEEE Transactions on Engineering Management

This paper offers a comprehensive overview of the IEEE Transactions on Engineering Management (IEEE TEM) from 1985 to 2017. This paper employs a mixed-method examination based on an in-depth interview with the new editor-in-chief regarding the challenges for the future of IEEE TEM, along with a bibliometric analysis of the journal. By using Web of Science Core Collection data, the analysis maps the knowledge produced and disseminated by IEEE TEM, revealing the most cited papers, the most frequently occurring keywords and the interconnection between them, the most prolific authors and their coauthorship network, and the most prolific countries for published articles. This paper also shows the main avenues of research covered by IEEE TEM and their evolution through the analysis of the correlation of keywords. This paper offers an example application of a mixed-method bibliometric analysis, seeking to extend the quantitative findings by including other sources of data

Applied Force and sEMG Muscle Activity Required To Operate Pistol Grip Control in an Electric Utility Aerial Bucket

Electric utility line workers report high levels of fatigue in forearm muscles when operating a conventional pistol grip control in aerial buckets. This study measured the applied force and surface electromyographic (sEMG) signals from four upper extremity muscles required to operate the pistol grip control in two tasks. The first task was movement of the pistol grip in six directions (up/down, forward/rearward, clockwise/counter-clockwise), and the second task was movement of the bucket from its resting position on the truck bed to an overhead conductor on top of a 40 ft tall pole. The force applied to the pistol grip was measured in 14 aerial bucket trucks, and sEMG activity was measured on eight apprentice line workers. The applied force required to move the pistol grip control in the six directions ranged from 12 to 15 lb. The sEMG activity in the extensor digitorum communis (EDC) forearm muscle was approximately twice as great or more than the other three muscles (flexor digitorum superficialis, triceps, and biceps). Line workers exerted 14 to 30% MVCEMG to move the pistol grip in the six directions. Average %MVCEMG of the EDC to move the bucket from the truck platform to an overhead line ranged from 26 to 30% across the four phases of the task. The sEMG findings from this study provide physiologic evidence to support the anecdotal reports of muscle fatigue from line workers after using the pistol grip control for repeated, long durations

Lessons from last mile electrification in Colombia: Examining the policy framework and outcomes for sustainability

More than a decade ago, Colombia reached a 95% electrification rate. Despite efforts from multiple actors, including government, private sector companies, communities and donors, this rate has only barely improved. In 2020, around 1.9 million Colombians – all residing in rural areas – lacked access to electricity. The electrification challenge is compounded by the geographical isolation of these last mile communities, which makes interconnection to the national electricity grid infeasible. Even where off-grid communities do have access to electricity, supply is often limited to less than six hours per day raising questions about the adequacy of provision. This paper investigates last mile electrification in Colombia, specifically examining the policy framework and the outcomes for the sustainability of last mile projects. Drawing on document analysis, expert interviews and case studies, this paper finds that the government has created an overly complex policy environment which hinders rather than facilitates electrification efforts. It also continues prioritizing the use of diesel generators through costly supply-side subsidies, resulting in high operating costs and inadequate service. More recently, although renewable sources have shown good outcomes, for instance in the case studies examined here, these experiences have not been extensible disseminated. Finally, this paper argues that changes are required to the institutional framework to deliver electricity to last mile communities in Colombia. Specifically, if the multidimensional benefits of electricity are to be realized, changes will need to include improvements in public infrastructure to promoting intersectoral work that promotes socio-economic development of last mile communities and beyond

Atomistic Modeling of Semiconductors: Si, C, and 3C-SiC

An ongoing task of the Computational Materials Group (CMG) at the NASA Glenn Research Center is to enhance the role of atomistic simulations based on quantum-approximate methods in the study of new materials and their properties. One of the main goals of the activity continues to be breaching limitations that arise from the natural balance between accuracy, range of application, and computational simplicity. Whether that balance can be maintained while breaking new ground depends on the methods available with a minimum of constraints and limitations for the study of the energetics of arbitrary systems. The main tool used in CMG research, the Bozzolo- Ferrante-Smith (BFS) method for alloys, has no inherent constraint in its formulation, a feature that has allowed for successful research on various topics. In this article, we report on the latest development of the CMG program, namely, the extension and application of the BFS method to compound semiconductors, a departure from our previous research based primarily on metallic alloys