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

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Sonic Human-Robot Interaction aims at equipping robots with the ability to convey emotions and intentions via sounds. Such sounds are typically handcrafted by human experts, which results in expensive, small sound sets with limited expressivity. To overcom ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

The technological advancements of the past decades have allowed transforming an increasing part of our daily actions and decisions into storable data, leading to a radical change in the scale and scope of available data in relation to virtually any object ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

A data storage medium (2) comprising a stacked plurality of layers (9), each layer composed of a layer material selected from a group comprising at least two different dielectric materials, adjacent layers being formed of different materials, and at least ...

Extracting value and insights from increasingly heterogeneous data sources involves multiple systems combining and consuming the data. With multi-modal and context-rich data such as strings, text, videos, or images, the problem of standardizing the data mo ...

This dataset supports the publication 'Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid' by Lebo Molefe and John M. Kolinski, Physical Review E, (2023). The data are surface profiles of textured surf ...

Raw data and Matlab scripts from the publication at https://doi.org/10.1021/jacs.3c01279 All codes are provided free of charge and come with no warranty. Future use (original or modified) is required to cite: Zenedo data deposit at DOI 10.5281/zenodo. ...

. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

Controlling and shaping radiation beams is fundamental for a better understanding of radiation-matter interaction and advancing experimental techniques for material characterization at high spatial resolution. In particular, the current trend in the miniat ...

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

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

Raw data associated with the publication "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis". Temperature profile, daily measurements, physiological measurements, element ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

We develop a reduced-order model for thin plates made of hard magnetorheological elastomers (hard-MREs), which are composed of hard-magnetic particles embedded in a polymeric matrix. First, we propose a new magnetic potential, as an alternative to an exist ...

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

Chattez avec Graph Search

Automated Generation of Emotion-Conveying Sounds for Robots

Spectral Estimators for High-Dimensional Matrix Inference

Robust machine learning for neuroscientific inference

Data-driven statistical optimization of a groundwater monitoring network

Discrete choice modeling in the era of big data

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Non-volatile data storage medium and system

E-Scan: Consuming Contextual Data with Model Plugins

Dataset for "Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid"

Imaging radial distribution functions of complex particles by relayed dynamic nuclear polarization

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Scalable and Privacy-Preserving Federated Principal Component Analysis

Quantum engineering and detection of advanced radiation beams

HPCache: memory-efficient OLAP through proportional caching revisited

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Equivariant Neural Architectures for Representing and Generating Graphs

Data for "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis"

Transfer learning application of self-supervised learning in ARPES

A reduced-order, rotation-based model for thin hard-magnetic plates