Abstract
Aerial networks, composed of Unmanned Aerial Vehicles (UAVs) acting as Wi-Fi access points or cellular base stations, are emerging as an interesting solution to provide on-demand wireless connectivity to users, when there is no network infrastructure available, or to enhance the network capacity. This article proposes a traffic aware topology control solution for aerial networks that holistically combines the placement of UAVs with a predictive and centralized routing protocol. The synergy created by the combination of the UAV placement and routing solutions allows the aerial network to seamlessly update its topology according to the users' traffic demand, whilst minimizing the disruption caused by the movement of the UAVs. As a result, the Quality of Service (QoS) provided to the users is improved. The components of the proposed solution are described and evaluated in this article by means of simulation and an experimental testbed. The results show that the QoS provided to the users is significantly improved when compared to the corresponding baseline solutions.

Abstract
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.

Abstract
MPC-in-the-Head (MitH) is a general framework that enables constructing efficient zero-knowledge (ZK) protocols for NP relations from secure multiparty computation (MPC) protocols. In this paper we present the first machine-checked implementations of MitH. We begin with an EasyCrypt formalization that preserves the modular structure of the original construction and can be instantiated with arbitrary MPC protocols, and secret sharing and commitment schemes satisfying standard notions of security. We then formalize various suitable components, which we use to obtain full-fledged ZK protocols for general relations. We compare two approaches for obtaining verified executable implementations. The first uses a fully automated extraction from EasyCrypt to OCaml. The second reduces the trusted computing base (TCB) and provides better performance by combining code extraction with formally verified manual low-level components implemented in the Jasmin language. We conclude with a discussion of the trade-off between the formal verification effort and the performance of resulting executables, and how our approach opens the way for fully verified implementations of state-of the-art optimized protocols based on MitH.

Abstract
Hybrid-material optical fibers enhance the capabilities of fiber-optics technologies, extending current functionalities to several emerging application areas. Such platforms rely on the integration of novel materials into the fiber core or cladding, thereby supporting hybrid modes with new characteristics. Here we present experiments that reveal hybrid mode interactions within a doped-core silica fiber containing a central high-index nanofluidic channel. Compared with a standard liquid-filled capillary, calculations predict modes with unique properties emerging as a result of the doped core/cladding interface, possessing a high power fraction inside and outside the nanofluidic channel. Our experiments directly reveal the beating pattern in the fluorescent liquid resulting from the excitation of the first two linearly polarized hybrid modes in this system, being in excellent agreement with theoretical predictions. The efficient excitation and beat of such modes in such an off-resonance situation distinguishes our device from regular directional mode couplers and can benefit applications that demand strong coupling between fundamental- and higher-order- modes, e.g. intermodal third-harmonic generation, bidirectional coupling, and nanofluidic sensing. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Abstract
eSports is a rapidly growing industry with increasing investment and large-scale international tournaments offering significant prizes. This has led to an increased focus on individual and team performance with factors such as communication, concentration, and team intelligence identified as important to success. Over a similar period of time, personal physiological monitoring technologies have become commonplace with clinical grade assessment available across a range of parameters that have evidenced utility. The use of physiological data to assess concentration is an area of growing interest in eSports. However, body-worn devices, typically used for physiological data collection, may constitute a distraction and/or discomfort for the subjects. To this end, in this work we devise a novel "invisible " sensing approach, exploring new materials, and proposing a proof-of-concept data collection system in the form of a keyboard armrest and mouse. These enable measurements as an extension of the interaction with the computer. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard device, involving 7 healthy subjects. A particularly advantageous characteristic of our setup is the use of conductive nappa leather, as it preserves the standard look and feel of the keyboard and mouse. According to the results obtained, this approach shows 3-15% signal loss, with a mean difference in heart rate between the reference and experimental device of -1.778 & PLUSMN; 4.654 beats per minute (BPM); in terms of ECG waveform morphology, the best cases show a Pearson correlation coefficient above 0.99.

Abstract
Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts' information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters.