Views and Synthesis of Cognitive Maps

A cognitive map represent concepts linked by influences. This paper introduces a taxonomic cognitive map model that allows to define a cognitive map on a taxonomy. This taxonomy is used to provide simplified views of a map to help a user to understand it. The model we present also provides a mechanism to help a group of people to build a map. Our proposal consists of a method for building a synthesized map from individual cognitive maps provided by each participant, called designer, by exploiting the taxonomy and preferences over these designers.

An evidence map of the effect of Tai Chi on health outcomes.

BackgroundThis evidence map describes the volume and focus of Tai Chi research reporting health outcomes. Originally developed as a martial art, Tai Chi is typically taught as a series of slow, low-impact movements that integrate the breath, mind, and physical activity to achieve greater awareness and a sense of well-being.MethodsThe evidence map is based on a systematic review of systematic reviews. We searched 11 electronic databases from inception to February 2014, screened reviews of reviews, and consulted with topic experts. We used a bubble plot to graphically display clinical topics, literature size, number of reviews, and a broad estimate of effectiveness.ResultsThe map is based on 107 systematic reviews. Two thirds of the reviews were published in the last five years. The topics with the largest number of published randomized controlled trials (RCTs) were general health benefits (51 RCTs), psychological well-being (37 RCTs), interventions for older adults (31 RCTs), balance (27 RCTs), hypertension (18 RCTs), fall prevention (15 RCTs), and cognitive performance (11 RCTs). The map identified a number of areas with evidence of a potentially positive treatment effect on patient outcomes, including Tai Chi for hypertension, fall prevention outside of institutions, cognitive performance, osteoarthritis, depression, chronic obstructive pulmonary disease, pain, balance confidence, and muscle strength. However, identified reviews cautioned that firm conclusions cannot be drawn due to methodological limitations in the original studies and/or an insufficient number of existing research studies.ConclusionsTai Chi has been applied in diverse clinical areas, and for a number of these, systematic reviews have indicated promising results. The evidence map provides a visual overview of Tai Chi research volume and content.Systematic review registrationPROSPERO CRD42014009907

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

It is common to implicitly assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations? We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training. As a result, 1) the learned "look around" behavior is relevant even for new tasks in unseen environments, and 2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments. Completion episodes are shown at https://goo.gl/BgWX3W

Brain serotonin synthesis capacity in obsessive-compulsive disorder: effects of cognitive behavioral therapy and sertraline.

Cognitive behavioral therapy (CBT) and selective serotonin reuptake inhibitors (SSRIs) are both effective treatments for some patients with obsessive-compulsive disorder (OCD), yet little is known about the neurochemical changes related to these treatment modalities. Here, we used positron emission tomography and the α-[11C]methyl-L-tryptophan tracer to examine the changes in brain regional serotonin synthesis capacity in OCD patients following treatment with CBT or SSRI treatment. Sixteen medication-free OCD patients were randomly assigned to 12 weeks of either CBT or sertraline treatment. Pre-to-post treatment changes in the α-[11C]methyl-L-tryptophan brain trapping constant, K* (ml/g/min), were assessed as a function of symptom response, and correlations with symptom improvement were examined. Responders/partial responders to treatment did not show significant changes in relative regional tracer uptake; rather, in responders/partial responders, 12 weeks of treatment led to serotonin synthesis capacity increases that were brain-wide. Irrespective of treatment modality, baseline serotonin synthesis capacity in the raphe nuclei correlated positively with clinical improvement. These observations suggest that, for some patients, successful remediation of OCD symptoms might be associated with greater serotonergic tone