Efficient Processing of Ranking Queries in Novel Applications

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Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

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 ...

This study presents a novel framework for evaluating the luminance measurement capabilities of High Dynamic Range (HDR) sensor cameras in indoor glare conditions. Results indicate that the practical usage range of the CSEM VIP camera is significantly lower ...

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 construction industry is a significant contributor to resource consumption and waste generation. To address this issue, component reuse has been proposed as a way to prevent valuable building elements from being discarded and avoid producing new ones. ...

In high energy physics, semiconductor-based sensors are widely used in particle tracking applications. These sensors are typically glued on low-mass cooling substrates, which guarantee the correct thermal management and, at the same time, minimise their in ...

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

As it has become easier and cheaper to collect big datasets in the last few decades, designing efficient and low-cost algorithms for these datasets has attracted unprecedented attention. However, in most applications, even storing datasets as acquired has ...

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

This paper describes the second iteration in the development of a simulation tool that provides trade-offs of avionic architectures for future Active Debris Removal (ADR) space missions. ...

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the c ...

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and ve ...

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processin ...

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

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 ...

In the era of "big data" finding solutions for data storage alternative to those based on silicon or magnetic tapes is an urgent need for our society. The development of polymers that can store information at the molecular level has opened up new opportuni ...

Efficient Processing of Ranking Queries in Novel Applications

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Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

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

Performance investigation of cameras using HDR sensors for daylight glare evaluations

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

A Reuse-Ready Timber Slab-and-Column System for Modular Building Structures

Interlocking modular microfluidic cooling substrates for future high energy physics experiments

Dynamic Personalized Ranking

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Sketches, metrics and fast algorithms

Landscape and training regimes in deep learning

Simulation Tool : Resources Management in High Performance Avionic for ADR Missions

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Propagation on Multi-relational Graphs for Node Regression

MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

Efficient GPU-accelerated Join Optimization for Complex Queries

Chemiscope: interactive structure-property explorer for materials and molecules

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

Bacterial nanopores open the future of data storage