Towards Visually Explaining Variational Autoencoders

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g. variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset

Learning Similarity Attention

We consider the problem of learning similarity functions. While there has been substantial progress in learning suitable distance metrics, these techniques in general lack decision reasoning, i.e., explaining why the input set of images is similar or dissimilar. In this work, we solve this key problem by proposing the first method to generate generic visual similarity explanations with gradient-based attention. We demonstrate that our technique is agnostic to the specific similarity model type, e.g., we show applicability to Siamese, triplet, and quadruplet models. Furthermore, we make our proposed similarity attention a principled part of the learning process, resulting in a new paradigm for learning similarity functions. We demonstrate that our learning mechanism results in more generalizable, as well as explainable, similarity models. Finally, we demonstrate the generality of our framework by means of experiments on a variety of tasks, including image retrieval, person re-identification, and low-shot semantic segmentation.Comment: 10 pages, 7 figures, 4 table

Combined physeal fractures of the distal radius and ulna: complications associated with K-wire fixation and long-term prognosis in six cats

Objectives The objective was to describe the complications and long-term outcome associated with Kirschner (K)-wire fixation of combined distal radial and ulnar physeal fractures in six cats. Methods Medical records (2002-2014) of six referral institutions were searched for cats with combined distal radial and ulnar physeal fractures. Cases with complete clinical files, radiographs and surgical records were retrospectively reviewed. Long-term outcome was assessed via telephone interviews using an owner questionnaire. Results Complete files were available for 6/9 identified cases (cases 1-6). All fractures were classified as Salter-Harris type I or II. Five cases underwent open reduction and internal fixation via cross-pinning of the distal radius and intramedullary pinning of the ulna (cases 1-3); fixation of the distal radial and ulnar physes with one K-wire each (case 4); and K-wire fixation of the radial physis in combination with two transulnoradial K-wires (case 5). One case underwent closed reduction and percutaneous cross-pinning of the distal radius under fluoroscopic guidance (case 6). The complications encountered were: reduced radiocarpal range of motion (ROM) (cases 1, 3, 4, 5); implant loosening/migration (cases 1, 2, 5); and radioulnar synostosis (case 4). None of the cats developed angular limb deformity. Long-term outcome (12 months to 7 years after surgery) was graded as 'excellent' by the owners in all cases. Conclusions and relevance Prognosis is favourable for feline combined distal radial and ulnar physeal fractures following K-wire fixation in cats over 7 months of age. Implant removal after bony union is recommended to minimise reduction in ROM and to prevent implant loosening/migration

Multimodality in Group Communication Research

Team interactions are often multisensory, requiring members to pick up on verbal, visual, spatial and body language cues. Multimodal research, research that captures multiple modes of communication such as audio and visual signals, is therefore integral to understanding these multisensory group communication processes. This type of research has gained traction in biomedical engineering and neuroscience, but it is unclear the extent to which communication and management researchers conduct multimodal research. Our study finds that despite its' utility, multimodal research is underutilized in the communication and management literature's. This paper then covers introductory guidelines for creating new multimodal research including considerations for sensors, data integration and ethical considerations.Comment: 27 pages, 3 figure

Laboratory Focus on Improving the Culture of Biosafety: Statewide Risk Assessment of Clinical Laboratories That Process Specimens for Microbiologic Analysis

The Wisconsin State Laboratory of Hygiene challenged Wisconsin laboratories to examine their biosafety practices and improve their culture of biosafety. One hundred three clinical and public health laboratories completed a questionnaire-based, microbiology-focused biosafety risk assessment. Greater than 96% of the respondents performed activities related to specimen processing, direct microscopic examination, and rapid nonmolecular testing, while approximately 60% performed culture interpretation. Although they are important to the assessment of risk, data specific to patient occupation, symptoms, and travel history were often unavailable to the laboratory and, therefore, less contributory to a microbiology-focused biosafety risk assessment than information on the specimen source and test requisition. Over 88% of the respondents complied with more than three-quarters of the mitigation control measures listed in the survey. Facility assessment revealed that subsets of laboratories that claim biosafety level 1, 2, or 3 status did not possess all of the biosafety elements considered minimally standard for their respective classifications. Many laboratories reported being able to quickly correct the minor deficiencies identified. Task assessment identified deficiencies that trended higher within the general (not microbiology-specific) laboratory for core activities, such as packaging and shipping, direct microscopic examination, and culture modalities solely involving screens for organism growth. For traditional microbiology departments, opportunities for improvement in the cultivation and management of highly infectious agents, such as acid-fast bacilli and systemic fungi, were revealed. These results derived from a survey of a large cohort of small- and large-scale laboratories suggest the necessity for continued microbiology-based understanding of biosafety practices, vigilance toward biosafety, and enforcement of biosafety practices throughout the laboratory setting

Socio-economic vision graph generation and handover in distributed smart camera networks

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras