Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages appendix

Sagittal realignment osteotomy for increased posterior tibial slope after opening-wedge high tibial osteotomy: a case report

A 40 year old welder who underwent opening-wedge high tibial osteotomy for correction of alignment in a varus knee developed persistent pain with loss of knee extension. The posterior tibial slope increased from 9 degrees to 20 degrees after the osteotomy and caused the anteromedial knee pain and limited extension. The patient then underwent a revision osteotomy using a closing wedge technique to correct tibial slope. The osteotomy was performed, first from the medial cortex in the lateral direction, and second in the anteroposterior direction to remove the tibial bone in wedge shape and obtain full extension of the knee. The posterior tibial slope decreased to 8 degrees after the revision osteotomy and the patients returned to pain-free daily life. We reviewed this unique technique for correction of sagittal malalignment using a closing-wedge osteotomy for revision after opening-wedge osteotomy

Painting the Nation:Examining the Intersection Between Politics and the Visual Arts Market in Emerging Economies

Politics and art have throughout history, intersected in diverse and complex ways. Ideologies and political systems have used the arts to create a certain image and, depending on the form of government this has varied from clear-cut state propaganda, to patronage, to more indirect arms-length funding procedures. Therefore, artists working within the macro-level socio-political context cannot help but be influenced, inspired and sometimes restricted by these policies and political influences. This article examines the contemporary art markets of two emerging, Socialist economies to investigate the relationship between state pol-itics and the contemporary visual arts market. We argue that the respective governments and art worlds are trying to construct a brand narrative for their nations, but that these discourses are often at cross-purposes. In doing so, we illustrate that it is impos-sible to separate a consideration of the artwork from the macro-level context in which it is produced, distributed, and consumed

Art Fairs as a Medium for Branding Young and Emerging Artists: The Case of Frieze London

While previous researchers have attempted to explain the uncertain quality of visual arts with reference to branding theory, they have overlooked the role of art fairs. Socio-cultural approaches to branding allow us to explore the function of intermediaries in valuing contemporary arts. This article aims to analyze the role of art fairs in the process of branding young and emerging artists. In particular, a prestigious art fair, Frieze London, serves as an instrumental case study for developing a systematic understanding of art fairs in terms of valuing and branding contemporary art

Role of high tibial osteotomy in chronic injuries of posterior cruciate ligament and posterolateral corner

High tibial osteotomy (HTO) is a surgical procedure used to change the mechanical weight-bearing axis and alter the loads carried through the knee. Conventional indications for HTO are medial compartment osteoarthritis and varus malalignment of the knee causing pain and dysfunction. Traditionally, knee instability associated with varus thrust has been considered a contraindication. However, today the indications include patients with chronic ligament deficiencies and malalignment, because an HTO procedure can change not only the coronal but also the sagittal plane of the knee. The sagittal plane has generally been ignored in HTO literature, but its modification has a significant impact on biomechanics and joint stability. Indeed, decreased posterior tibial slope causes posterior tibia translation and helps the anterior cruciate ligament (ACL)-deficient knee. Vice versa, increased tibial slope causes anterior tibia translation and helps the posterior cruciate ligament (PCL)-deficient knee. A review of literature shows that soft tissue procedures alone are often unsatisfactory for chronic posterior instability if alignment is not corrected. Since limb alignment is the most important factor to consider in lower limb reconstructive surgery, diagnosis and treatment of limb malalignment should not be ignored in management of chronic ligamentous instabilities. This paper reviews the effects of chronic posterior instability and tibial slope alteration on knee and soft tissues, in addition to planning and surgical technique for chronic posterior and posterolateral instability with HTO

Maximally divergent intervals for extreme weather event detection

We approach the task of detecting anomalous or extreme events in multivariate spatio-temporal climate data using an unsupervised machine learning algorithm for detection of anomalous intervals in time-series. In contrast to many existing algorithms for outlier and anomaly detection, our method does not search for point-wise anomalies, but for contiguous anomalous intervals. We demonstrate the suitability of our approach through numerous experiments on climate data, including detection of hurricanes, North Sea storms, and low-pressure fields

Large-scale Gaussian process inference with generalized histogram intersection kernels for visual recognition tasks

We present new methods for fast Gaussian process (GP) inference in large-scale scenarios including exact multi-class classification with label regression, hyperparameter optimization, and uncertainty prediction. In contrast to previous approaches, we use a full Gaussian process model without sparse approximation techniques. Our methods are based on exploiting generalized histogram intersection kernels and their fast kernel multiplications. We empirically validate the suitability of our techniques in a wide range of scenarios with tens of thousands of examples. Whereas plain GP models are intractable due to both memory consumption and computation time in these settings, our results show that exact inference can indeed be done efficiently. In consequence, we enable every important piece of the Gaussian process framework—learning, inference, hyperparameter optimization, variance estimation, and online learning—to be used in realistic scenarios with more than a handful of data

Chimpanzee faces in the wild: Log-Euclidean CNNs for predicting identities and attributes of primates

In this paper, we investigate how to predict attributes of chimpanzees such as identity, age, age group, and gender. We build on convolutional neural networks, which lead to significantly superior results compared with previous state-of-the-art on hand-crafted recognition pipelines. In addition, we show how to further increase discrimination abilities of CNN activations by the Log-Euclidean framework on top of bilinear pooling. We finally introduce two curated datasets consisting of chimpanzee faces with detailed meta-information to stimulate further research. Our results can serve as the foundation for automated large-scale animal monitoring and analysis

Detecting multivariate biosphere extremes

The detection of anomalies in multivariate time series is crucial to identify changes in the ecosystems. We propose an intuitive methodology to assess the occurrence of tail events of multiple biosphere variables