Adaptive Recursive Query Optimization

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This book outlines a methodology to develop formally verified Just-in-Time compilers. Just-in-Time compilation is a technique to execute programs, where execution is interleaved with optimizations of the program itself. These compilers often produce fast e ...

Large language models (LLMs) hold great promise in solving many challenges arising from software complexity, including the possibility of automating code generation and repair. Although we cannot deny the groundbreaking nature of LLM-based code repair, we ...

As data sizes continue to expand, the challenge of conducting meaningful analysis grows. Utilizing I/O (Input/Output) libraries, such as HDF5 (Hierarchical Data Format) and ADIOS2 (Adaptable IO System), facilitates the filtering of raw data, with prior res ...

Combined passive ultrasonic (US) stress wave [better known as acoustic emission (AE)] and active US stress wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring agin ...

A hybrid-twin for gas-bearing supported, high-speed turbocompressor is suggested to be an aide to increase the operational reliability and to provide valuable insights to improve its design. The bearing clearances of a few micrometers imply that excessive ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

This is a README file for a data repository originating from the DCML corpus initiative and serves as welcome page for both the GitHub repo

To benefit from the cloud’s higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parque ...

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

Amid a data revolution that is transforming industries around the globe, computing systems have undergone a paradigm shift where many applications are scaled out to run on multiple computers in a computing cluster. As the storage and processing capabilitie ...

Renewable energy supply and energy storage in a closed materials cycle are the urgent global challenges of the 21st century. Carbon dioxide (CO2) hydrogenation over catalysts is a method to produce synthetic fuels from renewable energy in a CO2 neutral cyc ...

Adaptive Recursive Query Optimization

Chattez avec Graph Search

Formal Verification of Just-in-Time Compilation

Challenges to Using Large Language Models in Code Generation and Repair

DAI: How Pre-computation Speeds up Data Analysis

Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations

Towards a real-time capable hybrid-twin for gas-bearing supported high-speed turbocompressors

Maximum Independent Set: Self-Training through Dynamic Programming

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Johann Sebastian Bach – The Chorales

Pixels: An Efficient Column Store for Cloud Data Lakes

Advances and Open Problems in Federated Learning

An Architecture for Load Balance in Computer Cluster Applications

Mechanisms of the Heterogeneously Catalyzed Reaction of CO2 Hydrogenation on Transition Metal Surfaces