Using Cross-User Understanding to Develop Better User Embeddings

Digital payment apps, e.g., available via smartphones, can make multiple payment instruments available to a user; however, a user may not link all their cards to a payment app. Some payment apps include analytics features based on data from cards linked to the app. However, such data can give an incomplete financial picture of the user. This disclosure describes techniques to develop a more complete representation of a user that has provided only a partial view of their finances. Two embedding vector spaces are created, one trained over users with incomplete financial profiles and another trained over users with complete financial profiles. A map is created between the two vector spaces. The map is used to extend the representation of a user with a partial financial profile to that of a user with a complete financial profile

Templatized, Attribute-driven Search to Generate Stories

This disclosure describes techniques to automatically generate stories for users based on their spending histories. The stories can be formed by aggregating or clustering transactions around common themes or attributes using templatized search. Templatized searches are filled on the fly, and with user permission, are executed over the user’s transaction history to obtain matching results. The search results are used to form a story that can be displayed to the user in engaging and insightful ways, e.g., spending locations highlighted on maps; spending histograms; spending versus time; summary statistics (sum, average, max/min); etc

App Personalization with Images Generated Using Artificial Intelligence

Personalized content such as app backgrounds, profile pictures, etc. help in creating a rich and interactive user experience for an app, potentially driving an increase in the active usage. Recent advancements in generative imagery can be used to generate images that are personalized to the user. However, current image generation techniques require a user to explicitly provide prompts to the generation model. This disclosure describes techniques that use generative artificial intelligence to automatically generate customized images for use as an app background or in other contexts. Prompts for image creation are generated based on user-permitted contextual information by using a large language model or other technique. The generated images are filtered by an image recommendation model and provided for user selection to customize the app experience

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance

Geometry engine optimization: Cache friendly compressed representation of geometry

Recent advances in graphics architecture focus on improving texture performance and pixel processing. These have paralleled advances in rich pixel shading algorithms for realistic images. However, applications that require significantly more geometry processing than pixel processing suffer due to limited resource being devoted to the geometry processing part of the graphics pipeline. We present an algorithm to improve the effective geometry processing performance without adding significant hardware. This algorithm computes a representation for geometry that reduces the bandwidth required to transmit it to the graphics subsystem. It also reduces the total geometry processing requirement by increasing the effectiveness of the vertex cache. A goal of this algorithm is to keep the primitive assembly simple for easy hardware implementation

Budget Sampling of Parametric Surface Patches

We investigate choosing point samples on a model comprising parametric patches to meet a user specified budget. These samples may then be triangulated, rendered as points or ray-traced. The main idea is to pre-compute a set of samples on the surface and at the rendering time, use a subset that meets the total budget while reducing the screen-space error across the model. We have used this algorithm for interactive display of large spline models on low-end graphics workstations. This is done by distributing the points on the surface to minimize surface error. These points are then drawn as screen-space squares to fill the gaps between them. Our algorithm works well in practice and has a low memory footprint

Interactive Haptic Rendering of Deformable Surfaces Based On The . . .

We present a new method for interactive deformation and haptic rendering of viscoelastic surfaces. Objects are defined by a discretized Medial Axis Transform (MAT), which consists of an ordered set of circles (in 2D) or spheres (in 3D) whose centers are connected by a skeleton. The skeleton and physical properties of the object, including the radii of the spheres centered on the skeleton and material properties, are encapsulated in a single high dimensional parametric surface. To compute the force upon deformation, we use a mass-spring-damper model that takes into account both normal and shear surface forces. Our implementation attains real time 3D haptic and graphic rendering rates, making it appropriate to model deformation in complex haptic virtual environments. The algorithm is appealing because it takes advantage of single-point haptic interaction to render efficiently while maintaining a very low memory footprint