Synthetic data generation for the optimization of strains in metabolic engineering using latent space representations derived from a Conditional Variational Autoencoder

This study investigates the application of generative models for synthetic data generation in pathway optimization experiments within the field of metabolic engineering. Conditional Variational Autoencoders (CVAEs) use neural networks and latent variable distributions to generate new, plausible data samples. We adapt this model by conditioning...

Optimal Robust Decision Trees: A dynamic programming approach

Decision trees are integral to machine learning, with their robustness being a critical measure of effectiveness against adversarial data manipulations. Despite advancements in algorithms, current solutions are either optimal but lack scalability or scale well, but do not guarrantee optimality. This paper presents a novel adaptation of the...

User Interface Optimization for Quantified Self

Self-tracking has expanded exponentially in an era defined by the ubiquitous presence of wearable technologies and smart devices. From health and fitness to finances and productivity, these devices empower users to delve into their quantified self (QS) through an almost infinite amount of visualizations. However, a user has limited time to...

QAOA Mixing Hamiltonians for MinVertexCover

The minimum vertex cover problem (MinVertexCover) is an important optimization problem in graph theory, with applications in numerous fields outside of mathematics. As MinVertexCover is an NP-hard problem, there currently exists no efficient algorithm to find an optimal solution on arbitrary graphs. We consider quantum optimization algorithms,...

Predict+optimize for combinatorial optimization with uncertainty in the constraints

In this work, it is investigated whether the predict+optimize framework could be utilized for combinatorial optimization problems with a linear objective that have uncertainty in the constraint parameters, such that it outperforms prediction-error-based training. To this end, a predict+optimize formulation of the 0-1 knapsack problem is used,...

SAT-based optimisation for the resource-constrained project scheduling problem with time-dependent resource capacities and requests

In this paper, a variant of the resource-constrained project scheduling problem is discussed. This variant introduces time-dependence for resource capacities and requests, making the problem a more realistic model for many practical applications such as production scheduling and medical research project planning. The main aim of this paper is to...

Quantum Annealing for Seismic Imaging: Exploring Quantum Annealing Possibilities For Residual Statics Estimation using the D-Wave Advantage System and Hybrid Solver

Recent developments in quantum annealing have shown promising results in logistics, life sciences, machine learning and more. However, in the field of geophysical sciences the applications have been limited. A quantum annealing application was developed for residual statics estimation. Residual statics estimation is a highly non-linear problem...

An Investigation Into Predict-and-Optimize Machine Learning

Predict-and-Optimize (PnO) is a relatively new machine learning paradigm that has attracted recent interest: it concerns the prediction of parameters that determine the value of solutions to an optimization problem, such that the optimizer ends up picking a good solution. Training estimators with standard loss functions like mean squared error...

Deepification: Learning Variable Ordering Heuristics in Constraint Optimization Problems

Constraint programming is a paradigm for solving combinatorial problems by checking whether constraints are satisfied in a constraint satisfaction problem or by optimizing an objective in a constraint optimization problem. To find solutions, the solver needs to find a variable and value ordering. Numerous heuristics designed by human experts...

Optimizing Memory Mapping for Dataflow Inference Accelerators: Efficient Memory Utilization on FPGAs

Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the...

Learning variable selection rules for the branch-and-bound algorithm using reinforcement learning

Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...

Relocatable Energy Storage Systems for Congestion Management

In 2018 the Dutch government, businesses and other stakeholders started negotiating an agreement to combat climate change (het Klimaatakkoord). Energy consumption is changing and more Renewable Energy Sources (RES) are implemented. While these changes are ongoing, the Distribution System Operators (DSOs) must ensure no congestion occurs in the...

Optimal scheduling for agile Earth observation satellites

The past decade has seen a continuous increase of Earth observation missions, since they are regarded as an important tool to address global problems such as climate change or disaster mitigation. A commercial trend exists now towards higher resolution imagery, which drives the use of agile satellites. Nevertheless, a disadvantage of agile...