Efficient Bundled Spatial Range Queries

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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 ...

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about ne ...

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

The use of aerial vehicles for exploration and data collection has the potential to significantly aid environmental monitoring in environments which are dangerous and hard to navigate. However, within these environments navigation can often be restricted b ...

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

Acoustic emission (AE) monitoring is a useful technique to monitor the health of a structure continuously, helping to prevent potential failure. AE are elastic waves produced and emitted during fracture processes inside a material and are recorded by senso ...

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

Efficient Bundled Spatial Range Queries

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

Shared metadata for data-centric materials science

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

On Artificial Intelligence and Manipulation

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Heading for the Abyss: Control Strategies for Exploiting Swinging of a Descending Tethered Aerial Robot

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

An Unbiased Method of Measuring the Ratio of Two Data Sets

Automatic and high-precision acoustic emission-based structural health monitoring of concrete structures

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data