Arindam Dey

Elliott Wen, Chitralekha Gupta, Prasanth Sasikumar et al.

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches demand an accurately-labeled, real-world, and diverse dataset for high accuracy and generalizability. As a starting point to address this need, we introduce `VR.net', a dataset offering approximately 12-hour gameplay videos from ten real-world games in 10 diverse genres. For each video frame, a rich set of motion sickness-related labels, such as camera/object movement, depth field, and motion flow, are accurately assigned. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we utilize a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We continuously expand VR.net and envision its next version offering 10X more data than the current form. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.

Naveen K., Michael C. Koch, Kazunori Fujisawa et al.

Field measurements for direct current (DC) resistivity imaging, used for subsurface profiling, are frequently conducted over undulating terrain. Accurately incorporating such topographic variations in its forward modelling is essential for reliable inversion and interpretation. Singularity removal techniques provide a computationally efficient framework by analytically representing the singular component of the electric potential. Existing secondary potential formulations use the analytical solution for a flat homogeneous half space, but this assumption is realistic only when the source lies on a locally smooth, flat planar surface. In practice, natural topography often contains sharp corners or regions of high curvature, and additional slope discontinuities arise from linear finite element discretization. These conditions invalidate the flat-surface analytical primary field and lead to substantial modelling errors. These errors originate from a fundamental geometric mismatch between the flat half-space analytical primary field and the true solid angle subtended by the topography at the source. This study presents an improved singularity removal strategy for 2.5-D forward modelling by deriving a new analytical primary potential for a V-shaped wedge. The formulation remains valid for sharply varying surfaces and accurately captures the singular behaviour without requiring geometric smoothing or excessive mesh refinement. By embedding the correct geometric singularity into the primary field, the proposed formulation remains consistent with both the discretized surface geometry and the physical boundary conditions. Numerical experiments on flat, V-shaped trench, and sinusoidal hill-valley models reveal that the proposed method consistently achieves errors below 0.1 per cent, even when using coarse linear finite element meshes.