Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

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Data repository for the following publication: A. Duque-Prata*, H.-Y. Chen*, L. Müller, M. Oppermann, M. Puppin, C. Serpa, C. Bostedt and M. Chergui, “Ultrafast deep-ultraviolet study of photoexcited aqueous Ferrioxalate”, submitted to ChemPhotoCh ...

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

Systems and methods for monitoring penile tumescence are provided that overcome the drawbacks of previously known systems by providing a wearable formed of a flexible and elastic tube having a plurality of sensors disposed on or embedded within it, the wea ...

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

Systems and methods for data storage and readout using hybrid nucleic acid- polymeric molecules are herein disclosed. A molecular data storage medium is presented comprising a header and a footer, each comprising or consisting of at least one unit of a fir ...

Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collec ...

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Chat with Graph Search

Data for "Ultrafast deep-ultraviolet study of photoexcited aqueous Ferrioxalate"

Timing and spatial selection bias in rapid extreme event attribution

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Tumescense monitoring system for diagnosing erectile dysfunction and methods of use

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Systems and methods for digital information decoding and data storage in hybrid macromolecules

Graph Embedding for Retrieval

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

High-dimensional Data Cubes

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

HPCache: Memory-Efficient OLAP Through Proportional Caching