Abstract
This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gradient estimation, the efficient trainability of these policies remains an open question. Our findings reveal significant challenges, including standard Barren Plateaus with exponentially small gradients and gradient explosion. These phenomena depend on the type of basis-state partitioning and the mapping of these partitions onto actions. For a polynomial number of actions, a trainable window can be ensured with a polynomial number of measurements if a contiguous-like partitioning of basis-states is employed. These results are empirically validated in a multi-armed bandit environment.

Abstract
In our data-flooded age, an enormous amount of redundant, but also disparate textual data is collected on a daily basis on a wide variety of topics. Much of this information refers to documents related to the same theme, that is, different versions of the same document, or different documents discussing the same topic. Being aware of such differences turns out to be an important aspect for those who want to perform a comparative task. However, as documents increase in size and volume, keeping up-to-date, detecting, and summarizing relevant changes between different documents or versions of it becomes unfeasible. This motivates the rise of the contrastive or comparative summarization task, which attempts to summarize the text of different documents related to the same topic in a way that highlights the relevant differences between them. Our research aims to provide a systematic literature review on contrastive or comparative summarization, highlighting the different methods, data sets, metrics, and applications. Overall, we found that contrastive summarization is most commonly used in controversial news articles, controversial opinions or sentiments on a topic, and reviews of a product. Despite the great interest in the topic, we note that standard data sets, as well as a competitive task dedicated to this topic, are yet to come to be proposed, eventually impeding the emergence of new methods. Moreover, the great breakthrough of using deep learning-based language models for abstract summaries in contrastive summarization is still missing.

Abstract
Fiducial markers play a fundamental role in various fields in which precise localization and tracking are paramount. In Augmented Reality, they provide a known reference point in the physical world so that AR systems can accurately identify, track, and overlay virtual objects. This accuracy is essential for creating a seamless and immersive AR experience, particularly when prompted to cope with the sub-millimeter requirements of medical and industrial applications. This research article presents a comparative analysis of four fiducial marker tracking algorithms, aiming to assess and benchmark their accuracy and precision. The proposed methodology compares the pose estimated by four algorithms running on Hololens 2 with those provided by a highly accurate ground truth system. Each fiducial marker was positioned in 25 sampling points with different distances and orientations. The proposed evaluation method is not influenced by human error, relying only on a high-frequency and accurate motion tracking system as ground truth. This research shows that it is possible to track the fiducial markers with translation and rotation errors as low as 1.36 mm and 0.015 degrees using ArUco and Vuforia, respectively.

Abstract
As robots have limited power sources. Energy optimization is essential to ensure an extension for their operating periods without needing to be recharged, thus maximizing their uptime and minimizing their running costs. This paper compares the energy consumption of different mobile robotic platforms, including differential, omnidirectional 3-wheel, omnidirectional 4-wheel, and Mecanum platforms. The comparison is based on the RobotAtFactory 4.0 competition that typically takes place during the Portuguese Robotics Open. The energy consumption from the batteries for each platform is recorded and compared. The experiments were conducted in a validated simulation environment with dynamic and friction models to ensure that the platforms operated at similar speeds and accelerations and through a 5200 mAh battery simulation. Overall, this study provides valuable information on the energy consumption of different mobile robotic platforms. Among other findings, differential robots are the most energy-efficient robots, while 4-wheel omnidirectional robots may offer a good balance between energy efficiency and maneuverability.

Abstract
Expanding upon the potential of generative machine learning to create atemporal latent space representations of musical-theoretical and cognitive interest, we delve into their explainability by formulating and testing hypotheses on their alignment with DFT phase spaces from {0, 1}(12) pitch classes and {0, 1}(128) pitch distributions - capturing common-tone tonal functional harmony and parsimonious voice-leading principles, respectively. We use 371 J.S. Bach chorales as a benchmark to train a Variational Autoencoder on a representative piano roll encoding. The Spearman rank correlation between the latent space and the two before-mentioned DFT phase spaces exhibits a robust rank association of approximately .65 +/- .05 for pitch classes and .61 +/- .05 for pitch distributions, denoting an effective preservation of harmonic functional clusters per region and parsimonious voice-leading. Furthermore, our analysis prompts essential inquiries about the stylistic characteristics inferred from the rank deviations to the DFT phase space and the balance between the two DFT phase spaces.

Abstract
The objective of achieving carbon neutrality by 2050 requires the various sectors of the economy to actively participate in the decarbonisation of all their activities, from production to consumption and product distribution. The vineyard and wine production sector is no exception to this goal. This paper aims to evaluate the feasibility and efficiency that hybrid energy systems based on renewable energy sources, solar photovoltaic (PV) and wind, can contribute to energy efficiency in certain activities related to wine production. In this sense, this study presents results based on linear programming optimisation models, which show how effective they are in minimising the use of energy from the power grid. The results show that renewable hybrid energy systems based on PV and wind are an effective solution for achieving carbon neutrality in some agricultural sectors, particularly winemaking sector. Besides being able to minimise the energy bought from the grid, the hybrid renewable energy system (HRES) is almost self-sufficient, being able to produce 340,232 kWh over 25 years.