Smooth Scan: Statistics-Oblivious Access Paths

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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 ...

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 ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

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 ...

In this thesis we will present two results on global existence for nonlinear dispersive equations with data at or below the scaling regularity. In chapter 1 we take a probabilistic perspective to study the energy-critical nonlinear Schrödinger equation in ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

Smooth Scan: Statistics-Oblivious Access Paths

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

Robust machine learning for neuroscientific inference

Data-driven Methods for Control: from Linear to Lifting

Data set for control of Ge island coalescence for the formation of nanowires on silicon

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

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

A Framework for Autonomic Computing for In Situ Imageomics

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Validating functional redundancy with mixed generative adversarial networks