Embodying Meaning Visually: From Perceptual Dynamics to Motion Kinematics

This paper adopts an embodied cognitive perspective to review the significance of dynamic patterns in the visual expression of meaning. Drawing upon the work of Rudolf Arnheim we first show how perceptual dynamics of inanimate objects might be extended in order to structure abstract meaning in fixed images such as paintings. Second, we evaluate existing experimental work that shows how simple kinematic structures within a stationary frame might embody such high-level properties as perceptual causality and animacy. Third and last, we take inspiration from these experiments to shed light on the expressiveness of dynamic patterns that unfold once the frame itself becomes a mobile entity (i.e., camera movement). In the latter case we will also present a filmic case study, showing how filmmakers might resort to these dynamic patterns so as to embody a film’s story content, while simultaneously offering a further avenue for film scholars to deepen their engagement with the experimental method.

A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

Objective Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of DCE-MRI can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors. Approach To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype. Main results The decomposition performance of DMFDE was evaluated by the root mean square error (RMSE) and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model with K=3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC=0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC= 0.813), which is significantly higher than that based on CAM. Conclusion This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.

Risk-utility tradeoff shapes memory strategies for evolving patterns

Keeping a memory of evolving stimuli is ubiquitous in biology, an example of which is immune memory for evolving pathogens. However, learning and memory storage for dynamic patterns still pose challenges in machine learning. Here, we introduce an analytical energy-based framework to address this problem. By accounting for the tradeoff between utility in keeping a high-affinity memory and the risk in forgetting some of the diverse stimuli, we show that a moderate tolerance for risk enables a repertoire to robustly classify evolving patterns, without much fine-tuning. Our approach offers a general guideline for learning and memory storage in systems interacting with diverse and evolving signals.

The Double Life of Dissent: Art, Politics and the Predicaments of Democracy in India

The article focuses on two moments in India’s political history, in which out-rightly expressed dissent underlines analytical shifts in the nature and course of the country’s democracy. It asks two questions: First, what does a self-proclaimed, democratic state do with peaceful dissenting artists? The second question follows from this. If indeed the state stigmatizes and suppresses that dissent, what does the artist do? By foregrounding the relationship between the dissent and offence-taking, the article shows the increasingly complex changes in the nature of the democratic state, role of the art market therein, the dynamic patterns of dissent itself, which underline the cyclic outbursts of violence against artists.