Adaptive Playback Control: A Framework for Cinematic VR Creators to Embrace Viewer Interaction

Content creators have been trying to produce engaging and enjoyable Cinematic Virtual Reality (CVR) experiences using immersive media such as 360-degree videos. However, a complete and flexible framework, like the filmmaking grammar toolbox for film directors, is missing for creators working on CVR, especially those working on CVR storytelling with viewer interactions. Researchers and creators widely acknowledge that a viewer-centered story design and a viewer’s intention to interact are two intrinsic characteristics of CVR storytelling. In this paper, we stand on that common ground and propose Adaptive Playback Control (APC) as a set of guidelines to assist content creators in making design decisions about the story structure and viewer interaction implementation during production. Instead of looking at everything CVR covers, we set constraints to focus only at cultural heritage oriented content using a guided-tour style. We further choose two vital elements for interactive CVR: the narrative progression (director vs. viewer control) and visibility of viewer interaction (implicit vs. explicit) as the main topics at this stage. We conducted a user study to evaluate four variants by combining these two elements, and measured the levels of engagement, enjoyment, usability, and memory performance. One of our findings is that there were no differences in the objective results. Combining objective data with observations of the participants’ behavior we provide guidelines as a starting point for the application of the APC framework. Creators need to choose if the viewer will have control over narrative progression and the visibility of interaction based on whether the purpose of a piece is to invoke emotional resonance or promote efficient transfer of knowledge. Also, creators need to consider the viewer’s natural tendency to explore and provide extra incentives to invoke exploratory behaviors in viewers when adding interactive elements. We recommend more viewer control for projects aiming at viewer’s participation and agency, but more director control for projects focusing on education and training. Explicit (vs. implicit) control will also yield higher levels of engagement and enjoyment if the viewer’s uncertainty of interaction consequences can be relieved.

Beyond categories: dynamic qualitative analysis of visuospatial representation in arithmetic

Visuospatial representations of numbers and their relationships are widely used in mathematics education. These include drawn images, models constructed with concrete manipulatives, enactive/embodied forms, computer graphics, and more. This paper addresses the analytical limitations and ethical implications of methodologies that use broad categorizations of representations and argues the benefits of dynamic qualitative analysis of arithmetical-representational strategy across multiple semi-independent aspects of display, calculation, and interaction. It proposes an alternative methodological approach combining the structured organization of classification with the detailed nuance of description and describes a systematic but flexible framework for analysing nonstandard visuospatial representations of early arithmetic. This approach is intended for use by researchers or practitioners, for interpretation of multimodal and nonstandard visuospatial representations, and for identification of small differences in learners’ developing arithmetical-representational strategies, including changes over time. Application is illustrated using selected data from a microanalytic study of struggling students’ multiplication and division in scenario tasks.

CSNet: Estimating cell-type-specific gene co-expression networks from bulk gene expression data

Inferring and characterizing gene co-expression networks have led to important insights on the molecular mechanisms and functional pathways in healthy and diseased individuals. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples, resulting in co-expression estimates confounded by heterogeneity in cell type proportions. To address this limitation in co-expression analysis, we propose a flexible framework that estimates cell-type-specific gene co-expressions from bulk sample data, where the cell-type-specific distributions of gene expression levels are not assumed known. To overcome the computational challenge in estimating covariances and correlations from a convolution of high dimensional densities, we develop a novel thresholded least squares estimator, named CSNet, that is efficient to implement and has good theoretical properties. We further investigate the convergence rate of CSNet. The utility and efficacy of CSNet is demonstrated through simulation studies and an application to a gene co-expression study with bulk samples from Alzheimer's disease patients, where our analysis identified new cell-type-specific modules of AD risk genes.

A Bayesian Semiparametric Realized Stochastic Volatility Model

This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by Bayesian nonparametric mixtures not only improves the fitting of asymmetric and leptokurtic densities of asset returns and logarithmic realized volatility but also enables flexible adjustments for estimation bias in realized volatility. Applications to equity data show that the proposed model offers superior density forecasts for returns and improved estimates of parameters and latent volatility compared with existing alternatives.

Interactive visualization of climate model data via Python or GUI with psyplot

<div> <p><span data-contrast="auto">psyplot (</span><span data-contrast="none">https://psyplot.github.io</span><span data-contrast="auto">) is an open-source data visualization framework that integrates rich computational and mathematical software packages (such as xarray and matplotlib) into a flexible framework for visualization. It differs from most of the visual analytic software such that it focuses on extensibility in order to flexibly tackle the different types of analysis questions that arise in pioneering research. The design of the high-level API of the framework enables a simple and standardized usage from the command-line, python scripts or Jupyter notebooks. A modular plugin framework enables a flexible development of the framework that can potentially go into many different directions. The additional enhancement with a graphical user interface (GUI) makes it the only visualization framework that can be handled from the convenient command-line or scripts, as well as via point-click handling. It additionally allows to build further desktop applications on top of the existing framework.</span><span data-ccp-props="{"201341983":0,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">In this presentation, I will show the main functionalities of psyplot, with a special focus on the visualization of unstructured grids (such as the ICON model by the German Weather Service (DWD)), and the usage of psyplot on the HPC facilities of the DKRZ (mistral, jupyterhub, remote desktop, etc.). My demonstration will cover the basic structure of the psyplot framework and how to use psyplot in python scripts (and Jupyter notebooks). I will demonstrate a quick demo of to the psyplot GUI and psy-view, a ncview-like interface built upon psyplot, and talk about different features such as reusing plot configurations and exporting figures.</span></p> </div>

Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010

A long-term, high-resolution cropland dataset plays an essential part in accurately and systematically understanding the mechanisms that drive cropland change and its effect on biogeochemical processes. However, current widely used spatially explicit cropland databases are developed according to a simple downscaling model and are associated with low resolution. By combining historical county-level cropland archive data with natural and anthropogenic variables, we developed a random forest model to spatialize the cropland distribution in the Tuojiang River Basin (TRB) during 1911–2010, using a resolution of 30 m. The reconstruction results showed that the cropland in the TRB increased from 1.13 × 104 km2 in 1911 to 1.81 × 104 km2. In comparison with satellite-based data for 1980, the reconstructed dataset approximated the remotely sensed cropland distribution. Our cropland map could capture cropland distribution details better than three widely used public cropland datasets, due to its high spatial heterogeneity and improved spatial resolution. The most critical factors driving the distribution of TRB cropland include nearby-cropland, elevation, and climatic conditions. This newly reconstructed cropland dataset can be used for long-term, accurate regional ecological simulation, and future policymaking. This novel reconstruction approach has the potential to be applied to other land use and cover types via its flexible framework and modifiable parameters.