A First-Order Explicit-Implicit Splitting Method for a Convection-Diffusion Problem

We analyze a second-order in space, first-order in time accurate finite difference method for a spatially periodic convection-diffusion problem. This method is a time stepping method based on the first-order Lie splitting of the spatially semidiscrete solution. In each time step, on an interval of length k, of this solution, the method uses the backward Euler method for the diffusion part, and then applies a stabilized explicit forward Euler approximation on m >= 1 intervals of length k/m for the convection part. With h the mesh width in space, this results in an error bound of the form C(0)h(2) + C(m)k for appropriately smooth solutions, where C-m <= C\u27 + C-\u27\u27/m. This work complements the earlier study [V. Thomee and A. S. Vasudeva Murthy, An explicit- implicit splitting method for a convection-diffusion problem, Comput. Methods Appl. Math. 19 (2019), no. 2, 283-293] based on the second-order Strang splitting

An Explicit-Implicit Splitting Method for a Convection-Diffusion Problem

We analyze a second-order accurate finite difference method for a spatially periodic convection-diffusion problem. The method is a time stepping method based on the Strang splitting of the spatially semidiscrete solution, in which the diffusion part uses the Crank-Nicolson method and the convection part the explicit forward Euler approximation on a shorter time interval. When the diffusion coefficient is small, the forward Euler method may be used also for the diffusion term

“When Was This Picture Taken?” – Image Date Estimation in the Wild

The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans

New trends and ideas in visual concept detection

The MIR Flickr collection consists of 25000 high-quality photographic images of thousands of Flickr users, made available under the Creative Commons license. The database includes all the original user tags and EXIF metadata. Additionally, detailed and accurate annotations are provided for topics corresponding to the most prominent visual concepts in the user tag data. The rich metadata allow for a wide variety of image retrieval benchmarking scenarios. In this paper, we provide an overview of the various strategies that were devised for automatic visual concept detection using the MIR Flickr collection. In particular we discuss results from various experiments in combining social data and low-level content-based descriptors to improve the accuracy of visual concept classifiers. Additionally, we present retrieval result

The network structure of visited locations according to geotagged social media photos

Businesses, tourism attractions, public transportation hubs and other points of interest are not isolated but part of a collaborative system. Making such collaborative network surface is not always an easy task. The existence of data-rich environments can assist in the reconstruction of collaborative networks. They shed light into how their members operate and reveal a potential for value creation via collaborative approaches. Social media data are an example of a means to accomplish this task. In this paper, we reconstruct a network of tourist locations using fine-grained data from Flickr, an online community for photo sharing. We have used a publicly available set of Flickr data provided by Yahoo! Labs. To analyse the complex structure of tourism systems, we have reconstructed a network of visited locations in Europe, resulting in around 180,000 vertices and over 32 million edges. An analysis of the resulting network properties reveals its complex structure.Comment: 8 pages, 3 figure

Shonan Rotation Averaging: Global Optimality by Surfing SO(p)nSO(p)^n

Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of current SFM methods, while recovering globally optimal solutions.Comment: 30 pages (paper + supplementary material). To appear at the European Conference on Computer Vision (ECCV) 202

Rethinking summarization and storytelling for modern social multimedia

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to re-focus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanatio

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Patients' views on responsibility for the management of musculoskeletal disorders – A qualitative study

<p>Abstract</p> <p>Background</p> <p>Musculoskeletal disorders are very common and almost inevitable in an individual's lifetime. Enabling self-management and allowing the individual to take responsibility for care is stated as desired in the management of these disorders, but this may be asking more than people can generally manage. A willingness among people to take responsibility for musculoskeletal disorders and not place responsibility out of their hands or on employers but to be shared with medical professionals has been shown. The aim of the present study was to describe how people with musculoskeletal disorders think and reason regarding responsibility for prevention, treatment and management of the disorder.</p> <p>Methods</p> <p>Individual interviews with a strategic sample of 20 individuals with musculoskeletal disorders were performed. The interviews were tape-recorded, transcribed verbatim and analysed according to qualitative content analysis.</p> <p>Results</p> <p>From the interviews an overarching theme was identified: own responsibility needs to be met. The analysis revealed six interrelated categories: Taking on responsibility, Ambiguity about responsibility, Collaborating responsibility, Complying with recommendations, Disclaiming responsibility, and Responsibility irrelevant. These categories described different thoughts and reasoning regarding the responsibility for managing musculoskeletal disorders. Generally the responsibility for prevention of musculoskeletal disorders was described to lie primarily on society/authorities as they have knowledge of what to prevent and how to prevent it. When musculoskeletal disorders have occurred, health care should provide fast accessibility, diagnosis, prognosis and support for recovery. For long-term management, the individuals themselves are responsible for making the most out of life despite disorders.</p> <p>Conclusion</p> <p>No matter what the expressions of responsibility for musculoskeletal disorders are, own responsibility needs to be met by society, health care, employers and family in an appropriate way, with as much or as little of the "right type" of support needed, based on the individual's expectations.</p