Abstract
Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.

Abstract
AbstractOne of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact on performance. Drug features appeared to be more predictive of drug response. Molecular fingerprint-based drug representations performed slightly better than learned representations, and gene expression data of cancer or drug response-specific genes also improved performance. In general, fully connected feature-encoding subnetworks outperformed other architectures, with DL outperforming other ML methods. Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.Author summaryCancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future.

Abstract
Although machine learning (ML) is a field that has been the subject of research for decades, a large number of applications with high computational power have recently emerged. Usually, we only focus on solving machine learning problems without considering how much energy has been consumed by the different frameworks used for such applications. This study aims to provide a comparison among four widely used frameworks such as Tensorflow, Keras, Pytorch, and Scikit-learn in terms of many aspects, including energy efficiency, memory usage, execution time, and accuracy. We monitor the performance of such frameworks using different well-known machine learning benchmark problems. Our results show interesting findings, such as slower and faster frameworks consuming less or more energy, higher or lower memory usage, etc. We show how to use our results to provide machine learning developers with information to decide which framework to use for their applications when energy efficiency is a concern.

Abstract

Abstract
Computer science education has always been a challenging topic for both sides of the trench: educators and learners. Nowadays, with the pandemic state that we are facing, these challenges are even greater, leading educators to look for strategies that promote effective virtual learning. One of such strategies includes the use of game mechanics to improve student engagement and motivation. This design strategy is typically called gamification. Nowadays, gamification is being seen as the solution to solve most of the issues related to demotivation, complexity, or tedious tasks. In the latest years, we saw thousands of educational applications being created with gamification in mind. Nevertheless, this has been an unsustainable growth with ad hoc designs and implementations of educational gamified applications, hampering interoperability and the reuse of good practices. This chapter presents a systematic study on gamification standardization aiming to characterize the status of the field, namely describing existing frameworks, languages, services, and platforms.

Abstract
This work addresses the fabrication of straight silica-core liquid-cladding suspended waveguides inside a microfluidic channel through fs-laser micromachining. These structures enable the reconfiguration of the waveguide's mode profile and enhance the evanescent interaction between light and analyte. Further, their geometry resembles a tapered optical fiber with the added advantage of being monolithically integrated within a microfluidic platform. The fabrication process includes an additional post-processing thermal treatment responsible for smoothening the waveguide surface and reshaping it into a circular cross-section. Suspended waveguides with a minimum core diameter of 3.8 mu m were fabricated. Their insertion losses can be tuned and are mainly affected by mode mismatch between the coupling and suspended waveguides. The transmission spectrum was studied and it was numerically confirmed that it consists of interference between the guided LP01 mode and uncoupled light and of modal interference between the LP01 and LP02 modes.