Incremental View Maintenance for Collection Programming

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently be ...

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

In this work we consider an online learning problem, called Online k-Clustering with Moving Costs, at which a learner maintains a set of k facilities over T rounds so as to minimize the connection cost of an adversarially selected sequence of clients. The ...

A strategic roadmap for noncarbonized fuels is a global priority, and the reduction of carbon dioxide emissions is a key focus of the Paris Agreement to mitigate the effects of rising temperatures. In this context, hydrogen is a promising noncarbonized fue ...

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

Time has always been a central factor in understanding the challenges of daily mobility. For a long time, and still today, methods of economic evaluation of transport projects have monetized time savings so that they can be included in the cost–benefit ana ...

In this paper, we consider electric vehicle charging facilities that offer various levels of service, i.e., charging rates, for varying prices such that rational users choose a level of service that minimizes the total cost to themselves including an oppor ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions ...

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

Background: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.Methods: In this retrospective study, an AI algorithm w ...

In this work, we develop a new framework for dynamic network flow pro-blems based on optimal transport theory. We show that the dynamic multicommodity minimum-cost network flow problem can be formulated as a multimarginal optimal transport problem, where t ...

Lead halide perovskites are promising semiconducting materials for solar energy harvesting. However, the presence of heavy-metal lead ions is problematic when considering potential harmful leakage into the environment from broken cells and also from a publ ...

Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization ...

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

We give a new characterization of the Sherali-Adams proof system, showing that there is a degree-d Sherali-Adams refutation of an unsatisfiable CNF formula C if and only if there is an ε > 0 and a degree-d conical junta J such that viol_C(x) - ε = J, where ...

Zero knowledge plays a central role in cryptography and complexity. The seminal work of Ben-Or et al. (STOC 1988) shows that zero knowledge can be achieved unconditionally for any language in NEXP, as long as one is willing to make a suitable physical assu ...

Incremental View Maintenance for Collection Programming

Chat with Graph Search

Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Planetary-Scale Byzantine Fault Tolerance

Scalable Quantum Algorithms for Noisy Quantum Computers

Efficient Online Clustering with Moving Costs

Updates on Hydrogen Value Chain: A Strategic Roadmap

Improving K-means Clustering Using Speculation

Appropriations and Uses of Travel Time

Sensitivity to user mischaracterizations in electric vehicle charging

Hardware-Software co-design Methodologies for Edge AI Optimization

Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Text Representation Learning for Low Cost Natural Language Understanding

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma

Scalable Computation of Dynamic Flow Problems via Multimarginal Graph-Structured Optimal Transport

Lead immobilization for environmentally sustainable perovskite solar cells

Modeling and mitigating supply chain disruptions as a bilevel network flow problem

Optimal Matching of Random Parts

On Semi-Algebraic Proofs and Algorithms

Spatial Isolation Implies Zero Knowledge Even in a Quantum World