The Auto'Mate technologies were tested on several DT-25 target drones, and during almost six hours of flight testing, the four successively launched receivers were sequentially controlled and commanded without human interaction. These cutting-edge technologies demonstrate a significant breakthrough for future aerial operations involving manned and unmanned assets, and could reduce crew fatigue, minimize crew-training costs, and provide more effective operations. A second campaign is planned towards the end of 2023, which will explore the use of navigation sensors based on artificial intelligence and enhanced algorithms for autonomous formation flight. This groundbreaking achievement is a significant step towards autonomous formation flight and autonomous air-to-air refueling (A4R), and holds great potential for future aerial operations involving both manned and unmanned assets. "The success of this first flight-test campaign paves the way for developing autonomous and unmanned air-to-air refuelling technologies," said Jean Brice Dumont, Head of Military Air Systems at Airbus Defence and Space.

Most representative decision tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3 month to 6 month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.

In this paper it is reconsidered the prediction problem in time series framework by using a new non-parametric approach. Through this reconsideration, the prediction is obtained by a weighted sum of past observed data. These weights are obtained by solving a constrained linear optimization problem that minimizes an outer bound of the prediction error. The innovation is to consider both deterministic and stochastic assumptions in order to obtain the upper bound of the prediction error, a tuning parameter is used to balance these deterministic-stochastic assumptions in order to improve the predictor performance. A benchmark is included to illustrate that the proposed predictor can obtain suitable results in a prediction scheme, and can be an interesting alternative method to the classical non-parametric methods. Besides, it is shown how this model can outperform the preexisting ones in a short term forecast.

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

Octopus-like robots are plucking strawberries in Spain, in the US machines are vacuuming apples off the trees, and in the UK they are feeding and milking cows. Robots are taking over fields around the world, and last week food and rural affairs secretary Andrea Leadsom suggested they could help replace the thousands of EU workers who currently help put food on British tables. And it is not just Brexit that is forcing the agricultural industry to embrace the next phase of mechanisation. Farmers are already having to rethink their operations in the face of higher minimum pay – mainly a result of the national living wage for over-25s, which came into effect last year. Robotic milking machines, in which cows queue up to milk themselves, are now mainstream, while systems tat automatically feed or track the health of livestock are on the rise.