The use of soft and compliant manipulators in marine environments represents a promising paradigm shift for subsea inspection, with devices better suited to tasks owing to their ability to safely conform to items during contact. However, limitations driven by material characteristics often restrict the reach of such devices, with the complexity of obtaining state estimations making control non-trivial. Here, a detailed analysis of a 1m long compliant manipulator prototype for subsea inspection tasks is presented, including its mechanical design, state estimation technique, closed-loop control strategies, and experimental performance evaluation in underwater conditions. Results indicate that both the configuration-space and task-space controllers implemented are capable of positioning the end effector to desired locations, with deviations of <5% of the manipulator length spatially and to within 5^{o} of the desired configuration angles. The manipulator was also tested when subjected to various disturbances, such as loads of up to 300g and random point disturbances, and was proven to be able to limit displacement and restore the desired configuration. This work is a significant step towards the implementation of compliant manipulators in real-world subsea environments, proving their potential as an alternative to classical rigid-link designs.

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.

In this paper we introduce a word embedding composition method based on the intuitive idea that a fair embedding representation for a given set of words should satisfy that the new vector will be at the same distance of the vector representation of each of its constituents, and this distance should be minimized. The embedding composition method can work with static and contextualized word representations, it can be applied to create representations of sentences and learn also representations of sets of words that are not necessarily organized as a sequence. We theoretically characterize the conditions for the existence of this type of representation and derive the solution. We evaluate the method in data augmentation and sentence classification tasks, investigating several design choices of embeddings and composition methods. We show that our approach excels in solving probing tasks designed to capture simple linguistic features of sentences.

Santa Cruz Cabralia, Bahia, Brazil – With a loud whir, the drone takes flight. Minutes later, the humming sound gives way to a distinctive rattling as the machine, hovering about 20 metres above the ground, begins unloading its precious cargo and a cocktail of seeds rains down onto the land below. Given time, these seeds will grow into trees and, eventually, it is hoped, a thriving forest will stand where there was once just sparse vegetation. That is what the startup which operates this drone, a large contraption that looks a bit like a Pokemon ball with antennae, hopes. The 54 hectares (133 acres) here which have been badly degraded by agriculture and cattle farming in the Brazilian state of Bahia are just the start.

Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks which limit the scale of continuum designs, preventing widespread adoption of this technology. To tackle these problems, this work details the design and experimental testing of a modular, tendon driven bead-style continuum manipulator with tunable stiffness. By embedding the ability to independently control the stiffness of distinct sections of the structure, the manipulator can regulate it's posture under greater loads of up to 1kg at the end-effector, with reference to the flexible state. Likewise, an internal routing scheme vastly improves the stability of the proximal segment when operating the distal segment, reducing deviations by at least 70.11%. Operation is validated when gravity is both tangential and perpendicular to the manipulator backbone, a feature uncommon in previous designs. The findings presented in this work are key to the development of larger scale continuum designs, demonstrating that flexibility and tip stability under loading can co-exist without compromise.

The infection dynamics of Zika virus (ZIKV) are difficult to characterize. Many ZIKV infections are asymptomatic, and the clinical presentation of ZIKV is nonspecific. Rodriguez-Barraquer et al. took advantage of a long-term health study under way in Salvador, Brazil, the epicenter of the recent outbreak in the Americas. They used multiple serological assays, from before and after the emergence of ZIKV in October 2015, to distinguish ZIKV immune responses from those against Dengue virus (DENV). About 73% of the population was attacked by ZIKV.

An important open problem in robotic planning is the autonomous generation of 3D inspection paths -- that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously - and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together ensures a good balance between the two - both when 100% coverage is feasible, and when large parts of the object are hidden.

Stories of how students and developers get started in applied machine learning are an inspiration. In this post, you will hear about Álvaro Lemos story and his transition from student to getting a machine learning internship. I'm from Salvador, Bahia (Brazil), but currently, I live in Belo Horizonte, Minas Gerais (also in Brazil). I am studying Electrical Engineering at the Federal University of Minas Gerais and since the beginning of my undergraduation course, I've been involved with software development in some way. On my first week as a freshman, I joined a research group called LabCOM to help a colleague on his master's degree project.

Artificial intelligence could be heading to Australian classrooms -- and in schools overseas, it's already there. In Bahia, Brazil, 15-year-old students David and Roama from Colegio Perfil often start their school day at home, or on the bus. They pick up their phones, log into the education app Geekie Lab, and begin their classes from wherever they are. "You can access it everywhere, as long as you have your phone with you," David said. Students from Colegio Perfil in Bahia use phones or computers to access the Geekie app.

Artificial intelligence could be heading to Australian classrooms -- and in schools overseas, it's already there. In Bahia, Brazil, 15-year-old students David and Roama from Colegio Perfil often start their school day at home, or on the bus. They pick up their phones, log into the education app Geekie Lab, and begin their classes from wherever they are. "You can access it everywhere, as long as you have your phone with you," David said.