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Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is either assumed to be known a priori or estimated from data through set-membership identification. However, the problem of computing a suitable input disturbance set in case the set of possible output values is assigned a priori has received relatively little attention. This problem arises in many contexts, such as in supervisory control, actuator design, decentralized control, and others. In this paper, we propose a method to compute input disturbance sets (and the corresponding set of states) such that the resulting set of outputs matches as closely as possible a given set of outputs, while additionally satisfying strict (inner or outer) inclusion constraints. We formulate the problem as an optimization problem by relying on the concept of robust invariance. The effectiveness of the approach is demonstrated in numerical examples that illustrate how to solve safe reference set and input-constraint set computation problems.

In robust control under state constraints the set of admissible inputs is usually considered as given, under the assumption that the actuators have been already designed. However, if the input set is too small any controller will fail in stabilizing the closed-loop system while satisfying all prescribed constraints for some initial states of interest, or vice versa the chosen actuators may be over-sized. To handle this issue, in this paper we address the problem of computing the smallest input constraint set such that the closed-loop system is stabilizable from a prescribed set of initial states while respecting all constraints. We focus our attention on linear systems with additive disturbances, and develop the algorithm based on recursive feasibility of robust model predictive control. We demonstrate the results using numerical examples, in which we consider different metrics for the input constraint set selection.

Preference-based global optimization algorithms minimize an unknown objective function only based on whether the function is better, worse, or similar for given pairs of candidate optimization vectors. Such optimization problems arise in many real-life examples, such as finding the optimal calibration of the parameters of a control law. The calibrator can judge whether a particular combination of parameters leads to a better, worse, or similar closed-loop performance. Often, the search for the optimal parameters is also subject to unknown constraints. For example, the vector of calibration parameters must not lead to closed-loop instability. This paper extends an active preference learning algorithm introduced recently by the authors to handle unknown constraints. The proposed method, called C-GLISp, looks for an optimizer of the problem only based on preferences expressed on pairs of candidate vectors, and on whether a given vector is reported feasible and/or satisfactory. C-GLISp learns a surrogate of the underlying objective function based on the expressed preferences, and a surrogate of the probability that a sample is feasible and/or satisfactory based on whether each of the tested vectors was judged as such. The surrogate functions are used iteratively to propose a new candidate vector to test and judge. Numerical benchmarks and a semi-automated control calibration task demonstrate the effectiveness of C-GLISp, showing that it can reach near-optimal solutions within a small number of iterations., Comment: A MATLAB and a Python implementation of C-GLISp is available at http://cse.lab.imtlucca.it/~bemporad/glis

In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particular, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complexity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner. Funding Agencies|Swedish Research Council (VR) [2017-04710]

Le présent article concerne une analyse empirique effectuée au sein du département de Langue et Littérature Italienne de l’Université de Lausanne en 2006. Il s’intéresse à la comparaison des pratiques de lecture de textes littéraires en langue italienne des étudiants pour qui l’italien est la langue maternelle, avec d’autres pour lesquels il est langue étrangère. Il s’interroge sur le rôle de l’enseignement de la littérature italienne dans les écoles secondaires du canton de Vaud et sur les enjeux didactiques dérivant du rapport complexe induit par l’attention aux attitudes face à la langue et aux représentations sociales et culturelles de la littérature en langue étrangère.

Par son titre, Lecture littéraire et appropriation des langues étrangères, ce volume souhaite s’inscrire dans la suite des travaux qui, depuis quelques années, affirment la concomitance possible et souhaitable dans l’enseignement des langues étrangères, de la lecture littéraire et des processus d’appropriation langagière. Loin de considérer la lecture d’œuvres littéraires comme une ou même l’étape ultime de l’appropriation, il nous semblait, au moment de concevoir la journée scientifique qui ...

As a key feature, NASA's Parker Solar Probe (PSP) and ESA-NASA's Solar Orbiter (SO) missions cooperate to trace solar wind and transients from their sources on the Sun to the inner interplanetary space. The goal of this work is to accurately reconstruct the interplanetary Parker spiral and the connection between coronal features observed remotely by the Metis coronagraph on-board SO and those detected in situ by PSP at the time of the first PSP-SO quadrature of January 2021. We use the Reverse In-situ and MHD Approach (RIMAP), a hybrid analytical-numerical method performing data-driven reconstructions of the Parker spiral. RIMAP solves the MHD equations on the equatorial plane with the PLUTO code, using the measurements collected by PSP between 0.1 and 0.2 AU as boundary conditions. Our reconstruction connects density and wind speed measurements provided by Metis (3-6 solar radii) to those acquired by PSP (21.5 solar radii) along a single streamline. The capability of our MHD model to connect the inner corona observed by Metis and the super Alfv\'enic wind measured by PSP, not only confirms the research pathways provided by multi-spacecraft observations, but also the validity and accuracy of RIMAP reconstructions as a possible test bench to verify models of transient phenomena propagating across the heliosphere, such as coronal mass ejections, solar energetic particles and solar wind switchbacks., Comment: Astronomy & Astrophysics, Solar Orbiter First Results (Nominal Mission Phase), (in press) DOI: 10.1051/0004-6361/202244535

Peer reviewed: True, Acknowledgements: We thank Dr Luigi Vitagliano (IBB-CNR, Italy) for discussions and critical reading of the manuscript., Proteins from hyperthermophilic organisms are evolutionary optimised to adopt functional structures and dynamics under conditions in which their mesophilic homologues are generally inactive or unfolded. Understanding the nature of such adaptation is of crucial interest to clarify the underlying mechanisms of biological activity in proteins. Here we measured NMR residual dipolar couplings of a hyperthermophilic acylphosphatase enzyme at 80°C and used these data to generate an accurate structural ensemble representative of its native state. The resulting energy landscape was compared to that obtained for a human homologue at 37°C, and additional NMR experiments were carried out to probe fast (15N relaxation) and slow (H/D exchange) backbone dynamics, collectively sampling fluctuations of the two proteins ranging from the nanosecond to the millisecond timescale. The results identified key differences in the strategies for protein-protein and protein-ligand interactions of the two enzymes at the respective physiological temperatures. These include the dynamical behaviour of a β-strand involved in the protection against aberrant protein aggregation and concerted motions of loops involved in substrate binding and catalysis. Taken together these results elucidate the structure-dynamics-function relationship associated with the strategies of thermal adaptation of protein molecules.

We consider the problem of estimating the numerical accuracy of programs with operations in fixed-point arithmetic and variables of arbitrary, mixed precision, and possibly non-deterministic value. By applying a set of parameterised rewrite rules, we transform the relevant fragments of the program under consideration into sequences of operations in integer arithmetic over vectors of bits, thereby reducing the problem as to whether the error enclosures in the initial program can ever exceed a given order of magnitude to simple reachability queries on the transformed program. We describe a possible verification flow and a prototype analyser that implements our technique. We present an experimental evaluation on a particularly complex industrial case study, including a preliminary comparison between bit-level and word-level decision procedures.

In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and suboptimality results are available for a wide range of optimization algorithms. However, a typical underlying assumption is that the operations performed by the algorithm are exact, i.e., that there is no numerical error during the mathematical operations, which is hardly a valid assumption in a real hardware implementation. This is particularly true in the case of fixed-point hardware, where computational inaccuracies are not uncommon. This article presents a certification procedure for the proximal gradient method for box-constrained QP problems implemented in fixed-point arithmetic. The procedure provides a method to select the minimal fractional precision required to obtain a certain suboptimality bound, indicating the maximum number of iterations of the optimization method required to obtain it. The procedure makes use of formal verification methods to provide arbitrarily tight bounds on the suboptimality guarantee. We apply the proposed certification procedure on the implementation of a non-trivial model predictive controller on $32$-bit fixed-point hardware., 8 pages

To test automated driving systems, we present a case study for finding critical scenarios in driving environments guided by formal specifications. To that aim, we devise a framework for critical scenario identification, which we base on open-source libraries that combine scenario specification, testing, formal methods, and optimization.

Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy implementation on a microcontroller, and the others can convert the C code into hardware description language code for implementation on a field programmable gate array (FPGA). However, a unified tool allowing one to generate efficient embedded MPC for an FPGA, starting from the definition of the plant and its constraints, was still missing. The MOBY-DIC2 toolbox described in this brief bridges this gap. To illustrate its functionalities, the tool is exploited to embed the controller and observer for a real buck power converter in an FPGA. This implementation achieves a latency of about 30 µs with the implicit controller and 240 μs with the approximate explicit controller.

On February 21 and March 21 – 22, 2021, the Extreme Ultraviolet Imager (EUI) onboard Solar Orbiter observed three prominence eruptions. The eruptions were associated with coronal mass ejections (CMEs) observed by Metis, Solar Orbiter’s coronagraph. All three eruptions were also observed by instruments onboard the Solar–TErrestrial RElations Observatory (Ahead; STEREO-A), the Solar Dynamics Observatory (SDO), and the Solar and Heliospheric Observatory (SOHO). Here we present an analysis of these eruptions. We investigate their morphology, direction of propagation, and 3D properties. We demonstrate the success of applying two 3D reconstruction methods to three CMEs and their corresponding prominences observed from three perspectives and different distances from the Sun. This allows us to analyze the evolution of the events, from the erupting prominences low in the corona to the corresponding CMEs high in the corona. We also study the changes in the global magnetic field before and after the eruptions and the magnetic field configuration at the site of the eruptions using magnetic field extrapolation methods. This work highlights the importance of multi-perspective observations in studying the morphology of the erupting prominences, their source regions, and associated CMEs. The upcoming Solar Orbiter observations from higher latitudes will help to constrain this kind of study better.

We introduce an approach to efficiently tune LQR controllers for linear time-invariant systems to match a prescribed closed-loop behavior, such as the one given by a reference model. The proposed approach is able to efficiently tune the LQR controller, even for high dimensional systems and is superior in terms of achieved tracking performance and other criteria with respect to global optimization methods commonly used for black-box, simulation-based, automated tuning.

We propose a linear programming method that is based on active-set changes and proximal-point iterations. The method solves a sequence of least-distance problems using a warm-started quadratic programming solver that can reuse internal matrix factorizations from the previously solved least-distance problem. We show that the proposed method terminates in a finite number of iterations and that it outperforms state-of-the-art LP solvers in scenarios where an extensive number of small/medium scale LPs need to be solved rapidly, occurring in, for example, multi-parametric programming algorithms. In particular, we show how the proposed method can accelerate operations such as redundancy removal, computation of Chebyshev centers and solving linear feasibility problems. Funding Agencies|Swedish Research Council (VR)Swedish Research Council [2017-04710]

We present a field-programmable gate array (FPGA)-based technique for the on-line identification of highly ionizing particles in a liquid xenon (LXe) detector. The method was developed and successfully exploited to select $\alpha $ particles emitted by 241Am sources submerged in LXe in an overwhelming, mostly beam-related, $\gamma $ -ray background. After revising the main features of xenon and other liquid noble gases as ultra-violet (UV) scintillating media, we describe the algorithm idea and its firmware implementation. We then present the results in terms of efficiency and background suppression for the real-time $\alpha $ -particle tagging and the limits of the MEG trigger configuration. Finally, we show that in MEG II we are going to overcome the main issues and further improve the performances.

Cet article analyse des verbalisations d’enseignantes de Suisse romande qui ont participé en 2019 à la lecture publique de l’Iliade organisée en de nombreux endroits dans le cadre du Festival Européen latin grec. Comme on l’apprendra, les enseignantes de deux groupes d’élèves ont préparé la performance que constitue la lecture publique par le biais de scénarios didactiques convoquant à plusieurs reprises la notion de «détour par d’autres langues et cultures», qui se trouvera donc au centre de cette communication. Celle-ci se divise en quatre parties: la première décrit le contexte de la recherche; la deuxième définit la notion de détour en s’appuyant sur des études de référence; la troisième analyse les entretiens avec les enseignantes décrivant leur scénario didactique ; la dernière, la conclusion, discute la notion de «détour» à la lumière des données analysées et en identifie quatre typologies : les détours relatifs à la culture, aux formes linguistiques, à la construction textuelle et à la dimension performative.

The coronal magnetic field, despite its overwhelming importance to the physics and dynamics of the corona, has only rarely been measured. Here, the electron density maps derived from images acquired during the total solar eclipse of August 21st, 2017 are employed to demonstrate a new technique to measure the coronal magnetic fields. The strength of the coronal magnetic fields is derived with a semiempirical formula relating the plasma magnetic energy density to the gravitational potential energy. The resulting values are compared with those provided by more advanced coronal field reconstruction methods based on MHD simulations of the whole corona starting from photospheric field measurements, finding a very good agreement. Other parameters such as the plasma-$\beta$ and Alfv\'en velocity are also derived and compared with those of MHD simulations. Moreover, the plane-of-sky (POS) orientation of the coronal magnetic fields is derived from the observed inclination of the coronal features in the filtered images, also finding a close agreement with magnetic field reconstructions. Hence, this work demonstrates for the first time that the 2D distribution of coronal electron densities measured during total solar eclipses is sufficient to provide the coronal magnetic field strengths and inclinations projected on the POS. These are among the main missing pieces of information that limited so far our understanding of physical phenomena going on in the solar corona., Comment: 20 pages, 11 figures

We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape. In the CEGIS framework, a learner iteratively proposes a candidate control Lyapunov function and a tailored controller by solving a linear matrix inequality (LMI) feasibility problem, while a verifier either falsifies the current candidate by producing a counter-example to be considered at the next iteration, or it certifies that the tentative control Lyapunov function actually enjoys such feature. We investigate the Lipschitz continuity of the objective function of the global optimization problem solved by the verifier, which is key to establish the convergence of our method in a finite number of iterations. Numerical simulations confirm the effectiveness of the proposed approach.

Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are scientifically relevant because they are involved in the physical mechanisms characterizing the active Sun. However, more recently CMEs have attracted attention for their impact on space weather, as they are correlated to geomagnetic storms and may induce the generation of Solar Energetic Particles streams. In this space weather framework, the present paper introduces a physics-driven artificial intelligence (AI) approach to the prediction of CMEs travel time, in which the deterministic drag-based model is exploited to improve the training phase of a cascade of two neural networks fed with both remote sensing and in-situ data. This study shows that the use of physical information in the AI architecture significantly improves both the accuracy and the robustness of the travel time prediction.

Optimization problems involving mixed variables, i.e., variables of numerical and categorical nature, can be challenging to solve, especially in the presence of complex constraints. Moreover, when the objective function is the result of a complicated simulation or experiment, it may be expensive to evaluate. This paper proposes a novel surrogate-based global optimization algorithm to solve linearly constrained mixed-variable problems up to medium-large size (around 100 variables after encoding and 20 constraints) based on constructing a piecewise affine surrogate of the objective function over feasible samples. We introduce two types of exploration functions to efficiently search the feasible domain via mixed-integer linear programming solvers. We also provide a preference-based version of the algorithm, which can be used when only pairwise comparisons between samples can be acquired while the underlying objective function to minimize remains unquantified. The two algorithms are tested on mixed-variable benchmark problems with and without constraints. The results show that, within a small number of acquisitions, the proposed algorithms can often achieve better or comparable results than other existing methods., Comment: code available at https://github.com/mjzhu-p/PWAS

We propose a learning-based methodology to reconstruct private information held by a population of interacting agents in order to predict an exact outcome of the underlying multi-agent interaction process, here identified as a stationary action profile. We envision a scenario where an external observer, endowed with a learning procedure, is allowed to make queries and observe the agents' reactions through private action-reaction mappings, whose collective fixed point corresponds to a stationary profile. By adopting a smart query process to iteratively collect sensible data and update parametric estimates, we establish sufficient conditions to assess the asymptotic properties of the proposed learning-based methodology so that, if convergence happens, it can only be towards a stationary action profile. This fact yields two main consequences: i) learning locally-exact surrogates of the action-reaction mappings allows the external observer to succeed in its prediction task, and ii) working with assumptions so general that a stationary profile is not even guaranteed to exist, the established sufficient conditions hence act also as certificates for the existence of such a desirable profile. Extensive numerical simulations involving typical competitive multi-agent control and decision making problems illustrate the practical effectiveness of the proposed learning-based approach.

Type U radio bursts are impulsive coherent radio emissions produced by the Sun that indicate the presence of subrelativistic electron beams propagating along magnetic loops in the solar corona. In this work, we present the analysis of a type U radio burst that was exceptionally imaged on 2011 March 22 by the Nan\c{c}ay Radioheliograph (NRH) at three different frequencies (298.7, 327.0, and 360.8 MHz). Using a novel modelling approach, we show for the first time that the use of high-resolution radio heliograph images of type U radio bursts can be sufficient to both accurately reconstruct the 3D morphology of coronal loops (without recurring to triangulation techniques) and to fully constrain their physical parameters. At the same time, we can obtain unique information on the dynamics of the accelerated electron beams, which provides important clues as to the plasma mechanisms involved in their acceleration and as to why type U radio bursts are not observed as frequently as type III radio bursts. We finally present plausible explanations for a problematic aspect related to the apparent lack of association between the modeled loop as inferred from radio images and the extreme-ultraviolet (EUV) structures observed from space in the same coronal region, Comment: A&A, in press (8 pages, 6 figures)

This study describes the investigations of failure in an e-type clip at the inside of rear arch, where the stresses are maximum owing to in-service loading and bending that occurs during forming operations. Visual inspection, stereomicroscopy, optical-microscopy and Scanning Electron Microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) have been used as the characterization techniques. The microstructural, morphological changes were compared both in the central area and close to the location of failure in e-clip. During primary heating for forming steel rod into e-clip shape, the oxidation and decarburization occurs which caused the formation and penetration of iron oxide. The average thickness of oxide layer was found to be 20 µm with localized areas, where the oxides penetrated up to depth of 50 µm. During forming and shot peening operations, the iron oxide ingested in material at the inside circumferential region of rear arch, which triggered the initiation and propagation of fatigue phenomenon in service. The SEM-EDS analysis and fractography confirmed the presence of iron oxide and pearlite lamellar tearing at the crack initiation site, respectively. It is suggested to use electric induction heating for forming of steel rod into e-clip shape or increased the silicon content from two to three weight percent, which minimize the thickness of oxide layer. The finishing process by adding air spray or tapping of the rod after heating will further help to remove the scale at the critical region before taking it up for mechanical forming operations.

The magnetic activity of the Sun directly impacts the Earth and human life. Likewise, other stars will have an impact on the habitability of planets orbiting these host stars. The lack of information on the magnetic field in the higher atmospheric layers hampers our progress in understanding solar magnetic activity. Overcoming this limitation would allow us to address four paramount long-standing questions: (1) How does the magnetic field couple the different layers of the atmosphere, and how does it transport energy? (2) How does the magnetic field structure, drive and interact with the plasma in the chromosphere and upper atmosphere? (3) How does the magnetic field destabilise the outer solar atmosphere and thus affect the interplanetary environment? (4) How do magnetic processes accelerate particles to high energies? New ground-breaking observations are needed to address these science questions. We suggest a suite of three instruments that far exceed current capabilities in terms of spatial resolution, light-gathering power, and polarimetric performance: (a) A large-aperture UV-to-IR telescope of the 1-3 m class aimed mainly to measure the magnetic field in the chromosphere by combining high spatial resolution and high sensitivity. (b) An extreme-UV-to-IR coronagraph that is designed to measure the large-scale magnetic field in the corona with an aperture of about 40 cm. (c) An extreme-UV imaging polarimeter based on a 30 cm telescope that combines high throughput in the extreme UV with polarimetry to connect the magnetic measurements of the other two instruments. This mission to measure the magnetic field will unlock the driver of the dynamics in the outer solar atmosphere and thereby greatly advance our understanding of the Sun and the heliosphere., Comment: Submitted to Experimental Astronomy (on 28. Jul. 2020). Based on a proposal submitted in response to a call for white papers in the Voyage 2050 long-term plan in the ESA science programme. 36 pages, 10 figures

Mechanical robustness, biocompatibility, and antibacterial performances are key features for materials suitable to be used in tissue engineering applications. In this work, we investigated the link existing between structural and functional properties of TiCu(Ag) thin films deposited by physical vapor deposition magnetron sputtering on Si substrates. The thin films were characterized by X-ray diffraction (XRD), nanoindentation, atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS). The TiCu(Ag) thin films showed complete amorphous structure and improved mechanical properties in comparison with pure Ti films. However, for contents in excess of 20% Ag, we observed the appearance of nanometric Ag crystallite. The TiCu(Ag) thin films displayed excellent biocompatibility properties, allowing adhesion and proliferation of the human fibroblasts MRC-5 cell line. Moreover, all the investigated TiCu(Ag) alloy display bactericidal properties, preventing the growth of both Pseudomonas aeruginosa and Staphylococcus aureus. Results obtained from biological tests have been correlated to the surface structure and microstructure of films. The excellent biocompatibility and bactericidal properties of these multifunctional thin films opens to their use in tissue engineering applications.

PROBA-3 is an ESA mission aimed at the demonstration of formation flying performance of two satellites that will form a giant coronagraph in space. The first spacecraft will host a telescope imaging the solar corona in visible light, while the second, the external occulter, will produce an artificial eclipse. This instrument is named ASPIICS (Association of Spacecraft for Polarimetric and Imaging Investigation of the Corona of the Sun). To accomplish the payload's scientific tasks, PROBA-3 will ensure sub-millimeter reciprocal positioning of its two satellites using closed-loop on-board metrology. Several metrology systems will be used and the Shadow Position Sensors (SPS) subsystem senses the penumbra around the instrument aperture and returns the 3-D displacement of the coronagraph satellite, with respect to its nominal position, by running a dedicated algorithm. In this paper, we describe how the SPS works and the choices made to accomplish the mission objectives.

For model-free optimal control design, this paper proposes an approach based on optimizing the reference model that is used in direct data-driven controller synthesis. Optimality is defined with respect to suitable cost functions reflecting desired performance and control objectives. We rely on the well-known Virtual Reference Feedback Tuning technique and on a direct control design approach that ensures stability of the resulting closed-loop system. The proposed design method leads to a non-convex optimization problem with a small number of variables that can be easily solved by a global optimizer, such as by particle swarm optimization. The effectiveness of the proposed solution is illustrated in simulation examples.

Surface free energy (SFE) of titanium surfaces plays a significant role in tissue engineering, as it affects the effectiveness and long-term stability of both active coatings and functionalization and the establishment of strong bonds to the newly growing bone. A new contact–mechanics methodology based on high-resolution non-destructive elastic contacting nanoindentation is applied here to study SFE of micro- and nano-structured titanium surfaces, right after their preparation and as a function of exposure to air. The effectiveness of different surface treatments in enhancing SFE is assessed. A time-dependent decay of SFE within a few hours is observed, with kinetics related to the sample preparation. The fast, non-destructive method adopted allowed for SFE measurements in very hydrophilic conditions, establishing a reliable comparison between surfaces with different properties.

The release and acceleration of the solar wind is still an outstanding question. There are several aspects related to this phenomenon that still need to be investigated, and one of these is the identification of the region within the inner corona where the larger fraction of acceleration occurs. To address this matter, it is necessary to have reliable measurements of the solar wind speed between 1 and 10 solar radii (R ⊙). Moreover, in order to describe the coronal plasma behavior, it is important to consider its interaction with the magnetic field. Within this context, our intent is to investigate a method to combine measurements of the solar wind with the extrapolated magnetic field in the corona to derive how the solar wind velocity evolves along the magnetic field lines, aiming at better understanding the sources, origins, and acceleration of the solar wind. To this purpose, we used outflow speed measurements of the coronal plasma derived by applying the Doppler dimming technique, as well as the global magnetic field configuration derived from the measured photospheric magnetic field by using the Wang–Sheeley–Arge model. These two sets of data are then combined for heliocentric distances between 2.6 and 5 R ⊙. This paper presents the proposed method and the results obtained over two different Carrington rotations (CR 1923 and CR 1924), demonstrating the applicability of the method and the capability to link measured solar wind velocity to the extrapolated coronal magnetic field in order to derive the velocity profile.

Model predictive control (MPC) is a very attractive candidate to replace standard field-oriented control algorithms for electrical motors. We demonstrate that it is possible to implement an MPC algorithm for continuous control set (CCS-MPC), with both inputs and states constraints, in which the associated quadratic programming (QP) problem is solved online, even on the computationally limited platforms used in control of electrical motors. We detail the implementation of an active-set algorithm to solve efficiently the associated QP problem. Moreover, by exploiting recent results on active-set solver certification, we are able to assess the computational complexity of the online optimization algorithm, providing the exact worst-case solution time. The controller is experimentally tested on an embedded control unit for the torque regulation of a permanent magnet synchronous motor and benchmarked against explicit MPC. Computational feasibility, low-memory occupancy, and worst-case certification are achieved, fulfilling all the requirements of embedded control.

TAR DNA-binding protein 43 (TDP-43) forms intraneuronal cytoplasmic inclusions associated with amyotrophic lateral sclerosis and ubiquitin-positive frontotemporal lobar degeneration. Its N-terminal domain (NTD) can dimerise/oligomerise with the head-to-tail arrangement, which is essential for function but also favours liquid-liquid phase separation and inclusion formation of full-length TDP-43. Using various biophysical approaches, we identified an alternative conformational state of NTD in the presence of Sulfobetaine 3-10 (SB3-10), with higher content of α-helical structure and tryptophan solvent exposure. NMR shows a highly mobile structure, with partially folded regions and β-sheet content decrease, with a concomitant increase of α-helical structure. It is monomeric and reverts to native oligomeric NTD upon SB3-10 dilution. The equilibrium GdnHCl-induced denaturation shows a cooperative folding and a somewhat lower conformational stability. When the aggregation processes were compared with and without pre-incubation with SB3-10, but at the identical final SB3-10 concentration, a slower aggregation was found in the former case, despite the reversible attainment of the native conformation in both cases. This was attributed to protein monomerization and oligomeric seeds disruption by the conditions promoting the alternative conformation. Overall, the results show a high plasticity of TDP-43 NTD and identify strategies to monomerise TDP-43 NTD for methodological and biomedical applications.

In this work we performed a polarimetric study of a fast and wide coronal mass ejection (CME) observed on 12 July 2012 by the COR1 and COR2 instruments onboard the Solar TErrestrial RElations Observatory (STEREO) mission. The CME source region was an X1.4 flare located at approximately S15W01 on the solar disk, as observed from the Earth's perspective. The position of the CME as derived from the 3D Graduated Cylindrical Shell (GCS) reconstruction method was at around S18W00 at 2.5 solar radii and S07W00 at 5.7 solar radii, meaning that the CME was deflected towards the Equator while propagating outward in the corona. The projected speed of the leading edge of the CME also evolved from around 200 km s−1 in the lower corona to around 1000 km s−1 in the COR2 field of view. The degree of polarisation of the CME is around 65% but it can go as high as 80% in some CME regions. The CME showed deviation of the polarisation angle from the tangential in the range of 10 - 15∘ (or more). Our analysis showed that this is mostly due to the fact that the sequence of three polarised images from where the polarised parameters are derived is not taken simultaneously, but at a difference of a few seconds in time. In this interval of time, the CME moves by at least two pixels in the FOV of the instruments and this displacement results in uncertainties in the polarisation parameters (degree of polarisation, polarisation angle, etc.). We propose some steps forward to improve the derivation of the polarisation. This study is important for analysing the future data from instruments with polarisation capabilities.

The Metis coronagraph of the Solar Orbiter (SolO) mission records full-Sun images of the solar corona in Lyman-α ultraviolet (UV) radiation and in visible light polarized brightness (pB). This work investigates the utility of a synoptic observational program of Metis in terms of using its pB-images for tomographic reconstruction of the three-dimensional (3D) distribution of the electron density of the global solar corona. During its lifetime, SolO’s distance to the Sun will range D ≈ 0.3−1.0 au, while its solar latitude will span θ ≈ ±33 •. The limitations that such orbit complexity poses on tomographic reconstructions is explored in this work. Using SolO’s predictive orbital information and 3D MHD simulations of the solar corona, time series of synthetic Metis pB-images were computed and used as data to attempt tomographic reconstruction of the model. These numerical experiments were implemented for two Carrington rotations, corresponding to a solar minimum and a solar maximum, representative of extreme conditions of coronal complexity. For each rotation images were synthesized from three orbital segments, corresponding to extreme geometrical conditions of observation by Metis. For the early phase of the mission (year 2023), simulations were carried from the largest aphelion (D ≈ 0.95 au) and the smallest perihelion (D ≈ 0.29 au), both cases corresponding to low latitude (|θ| < 10 •) positions. For the late phase of the mission (year 2029), a simulation was carried out from the maximum solar latitude position (θ ≈ +33 •), at an intermediate distance (D ≈ 0.5 au). The range of heights that can be reconstructed and the required data-gathering time, both dependent on D, are reported for the six experiments. The extension of the coronal region that can be reconstructed and the accuracy of the reconstruction, both decreasing with increasing solar latitude |θ| as well as with increasing coronal complexity, are discussed in detail in each case. As a general conclusion, a Metis synoptic observational program with a cadence of at least 4 images/day provides enough data to attempt tomographic reconstructions of the coronal electron density during the whole lifetime of the mission, a requirement well within the 2 − 3 hr cadence of the current synoptic program. This program will allow implementation of tomography experimenting with different values for the cadence of the time-series of images used to feed reconstructions. Its cadence will also provide continuous opportunity to select images avoiding highly dynamic events, which compromise the accuracy of tomographic reconstructions.

Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a variety of terrains. We introduce a mobility-based criterion to define an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and improves adaptation to the terrain features. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at $25\,\mathrm{Hz}$ with a prediction horizon of $2$ seconds. We use the single rigid body dynamic model defined in the center of mass frame in order to increase the computational efficiency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney,and to locomote over rough terrain. In real experiments, we demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT's $87\, \mathrm{kg}$ quadruped robot HyQ to achieve an omni-directional walk on flat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk., Comment: Video available on: https://youtu.be/r0-KIiw0eWM

We address the problem of synthesizing state-constraint sets for a fully decentralized Model Predictive Control (MPC) scheme. We consider linear time-invariant discrete time systems, with subsystems possibly coupled in both dynamics and state constraints. For each individual subsystem we employ a set-based framework to compute the state-constraint sets, that are used to synthesize local tube-based MPC controllers. The offline problem that computes the constraint sets explicitly ensures that the feasible regions of the MPC controllers are non-empty, and whenever the controllers are feasible, the overall system constraints are satisfied with the least conservativeness possible. We demonstrate the closed-loop performance of the decentralized scheme, assessed with respect to centralized MPC, using a numerical example.

This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express apreferencesuch as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available athttp://cse.lab.imtlucca.it/~bemporad/glis.

We present a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration. Our algorithm is very robust, placing no requirements on the problem data such as positive definiteness of the objective function or linear independence of the constraint functions. It can be configured to be division-free once an initial matrix factorization is carried out, making it suitable for real-time applications in embedded systems. In addition, our technique is the first operator splitting method for quadratic programs able to reliably detect primal and dual infeasible problems from the algorithm iterates. The method also supports factorization caching and warm starting, making it particularly efficient when solving parametrized problems arising in finance, control, and machine learning. Our open-source C implementation OSQP has a small footprint, is library-free, and has been extensively tested on many problem instances from a wide variety of application areas. It is typically ten times faster than competing interior-point methods, and sometimes much more when factorization caching or warm start is used. OSQP has already shown a large impact with tens of thousands of users both in academia and in large corporations. © 2020 Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society. ISSN:1867-2957 ISSN:1867-2949

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real-world datasets. An implementation of the algorithm, which we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab.imtlucca.it/~bemporad/ideal., 26 pages, 11 figures. Submitted for publication