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Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star formation, which ultimately determine the features of the stars, including positions, velocities, and masses. To mitigate this issue, we introduce a new method for sampling physically realistic initial conditions from a limited set of simulations using Gaussian processes. Results. We evaluated the resulting sets of initial conditions based on whether they meet tests for physical realism. We find that direct sampling based on the learned distribution of the star features fails to reproduce binary systems. Consequently, we show that physics-informed sampling algorithms solve this issue, as they are capable of generating realisations closer to reality., Comment: Accepted to A&A (4th Sep 2024)

A typical crystal is a finite piece of a material which may be invariant under some point symmetry group. If it is a so-called intrinsic higher-order topological insulator or superconductor then it displays boundary modes at hinges or corners protected by the crystalline symmetry and the bulk topology. We explain the mechanism behind that using operator K-theory. Specifically, we derive a groupoid $C^\ast$-algebra that 1) encodes the dynamics of the electrons in the infinite size limit of a crystal; 2) remembers the boundary conditions at the crystal's boundaries, and 3) accepts a natural action by the point symmetries of the atomic lattice. The filtrations of the groupoid's unit space by closed subsets that are invariant under the groupoid and point group actions supply equivariant co-filtrations of the groupoid $C^\ast$-algebra. We show that specific derivations of the induced spectral sequences in twisted equivariant K-theories enumerate all non-trivial higher-order bulk-boundary correspondences.

Improvements in networking technologies and the steadily increasing numbers of users, as well as the shift from traditional broadcasting to streaming content over the Internet, have made video applications (e.g., live and Video-on-Demand (VoD)) predominant sources of traffic. Recent advances in Artificial Intelligence (AI) and its widespread application in various academic and industrial fields have focused on designing and implementing a variety of video compression and content delivery techniques to improve user Quality of Experience (QoE). However, providing high QoE services results in more energy consumption and carbon footprint across the service delivery path, extending from the end user's device through the network and service infrastructure (e.g., cloud providers). Despite the importance of energy efficiency in video streaming, there is a lack of comprehensive surveys covering state-of-the-art AI techniques and their applications throughout the video streaming lifecycle. Existing surveys typically focus on specific parts, such as video encoding, delivery networks, playback, or quality assessment, without providing a holistic view of the entire lifecycle and its impact on energy consumption and QoE. Motivated by this research gap, this survey provides a comprehensive overview of the video streaming lifecycle, content delivery, energy and Video Quality Assessment (VQA) metrics and models, and AI techniques employed in video streaming. In addition, it conducts an in-depth state-of-the-art analysis focused on AI-driven approaches to enhance the energy efficiency of end-to-end aspects of video streaming systems (i.e., encoding, delivery network, playback, and VQA approaches). Finally, it discusses prospective research directions for developing AI-assisted energy-aware video streaming systems., Comment: 33 pages, 7 figures, 6 Tables, Journal paper

The lack of both time-reversal and spatial inversion symmetry in polar magnets is a prerequisite for the occurrence of optical magnetoelectric effects such as nonreciprocal directional dichroism with the potential for the realization of optical diodes. In particular, antiferromagnetic materials with magnetic excitations in the THz range such as Fe2Mo3O8 are promising candidates for next-generation spintronic applications. In a combined experimental and theoretical effort we investigated the THz excitations of the polar honeycomb antiferromagnet Fe2Mo3O8 in external magnetic fields and their nonreciprocal directional dichroism, together with the temperature dependence of the electronic transitions in the mid- and near-infrared frequency range. Using an advanced single-ion approach for the Fe ions, we are able to describe optical excitations from the THz to the near-infrared frequency range quantitatively and successfully model the observed nonreciprocal directional dichroism in the THz regime.

In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involves a comprehensive procedure, encompassing the approximation of constraints through a regression process. Subsequently, we transform these constraints into an equivalent representation of mixed-integer linear constraints, seamlessly integrating them into other stabilizing control architectures. The advantage resides in the compatibility with the linear control design and the constraint satisfaction in the model predictive control setup, even for forecasted trajectories. Simulations are provided to validate the proposed constraint reformulation.

Work is fundamental to societal prosperity and mental health, providing financial security, identity, purpose, and social integration. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6% - 31.8%), and decrease the consumption index by 21% (95% interval, 13.6% - 28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital- to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for governments, civic organisations, and business to work together to ensure a smooth transition to an AI- dominated economy to safeguard the Mental Wealth of nations., Comment: 10 pages, 5 figures, 1 table, an Appendix

Antiferromagnetic FeSn is considered to be a close realization of the ideal two-dimensional (2D) kagome lattice, hosting Dirac cones, van Hove singularities, and flat bands, as it comprises Fe$_3$Sn kagome layers well separated by Sn buffer layers. We observe a pronounced optical anisotropy, with the low-energy optical conductivity being surprisingly higher perpendicular to the kagome planes than along the layers. This finding contradicts the prevalent picture of dominantly 2D electronic structure for FeSn. Our material-specific theory reproduces the measured conductivity spectra remarkarbly well. A site-specific decomposition of the optical response to individual excitation channels shows that the optical conductivity for polarizations both parallel and perpendicular to the kagome plane is dominated by interlayer transitions between kagome layers and adjacent Sn-based layers. Moreover, the matrix elements corresponding to these transitions are highly anisotropic, leading to larger out-of-plane conductivity. Our results evidence the crucial role of interstitial layers in charge dynamics even in seemingly 2D systems., Comment: 7 pages, 2 figures. Accepted for publication in Phys. Rev. B

Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement are unfounded, while others contend that generative AI`s unique capacity to undertake non-routine cognitive tasks sets it apart from other forms of automation capital and presents a threat to the quality and availability of work that underpin stable societies. This paper explores the conditions under which both may be true. We posit the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability. To prevent this, the paper underscores the urgent need for proactive policy responses, making recommendations to reduce these risks through robust regulatory frameworks and a new social contract characterised by progressive social and economic policies. This approach aims to ensure a sustainable, inclusive, and resilient economic future where human contribution to the economy is retained and integrated with generative AI to enhance the Mental Wealth of nations., Comment: 7 pages, 3 figures

This paper addresses the particularities in optimal control of the uranium extraction-scrubbing operation in the PUREX process. The control problem requires optimally stabilizing the system at a desired solvent saturation level, guaranteeing constraints, disturbance rejection, and adapting to set point variations. A qualified simulator named PAREX was developed by the French Alternative Energies and Atomic Energy Commission (CEA) to simulate liquid-liquid extraction operations in the PUREX process. However, since the mathematical model is complex and is described by a system of nonlinear, stiff, high-dimensional differential-algebraic equations (DAE), applying optimal control methods will lead to a large-scale nonlinear programming problem with a huge computational burden. The solution we propose in this work is to train a neural network to predict the process outputs using the measurement history. This neural network architecture, which employs the long short-term memory (LSTM), linear regression and logistic regression networks, allows reducing the number of state variables, thus reducing the complexity of the optimization problems in the control scheme. Furthermore, nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) problems are developed and solved using the PSO (Particle Swarm Optimization) algorithm. Simulation results show that the proposed adaptive optimal control scheme satisfies the requirements of the control problem and provides promise for experimental testing.

Understanding the interaction of real- and reciprocal space topology in skyrmion crystals is an open problem. We approach it from the viewpoint of $C^\ast$-algebras and calculate all admissible Chern numbers of a strongly coupled tight-binding skyrmion system on a triangular lattice as a function of Fermi energy and texture parameters. Our analysis reveals the topological complexity of electronic states coupled to spin textures, and the failure of the adiabatic picture to account for it in terms of emergent electromagnetism. On the contrary, we explain the discontinuous jumps in the real-space winding number in terms of collective evolution in real-, reciprocal, and mixed space Chern numbers. Our work sets the stage for further research on topological dynamics in complex dynamic spin textures coupled to external fields., Comment: 39 pages, 13 figures, Submission to SciPost, typos corrected, acknowledgement added

In this paper, we present a stabilizing Nonlinear Model Predictive Control (NMPC) scheme tailored for a class of nonholonomic systems with drift, where the acceleration is laterally restrained. Examples include a mobile robot with drifting wheels on a planar surface or a spacecraft maneuvering in a vacuum. The novelty lies in the formulation of the terminal set, reachable from a significant distance from the equilibrium, and the terminal cost, represented as the integration of the stage cost. The proposed approach establishes essential steps for ensuring stability and feasibility guarantees. Simulation results substantiate the viability and effectiveness of the NMPC scheme., Comment: Submitted to an international conference

The discovery of interstellar comet 2I/Borisov offered the unique opportunity to obtain a detailed analysis of an object coming from another planetary system, and leaving behind material in our interplanetary space. We continuously observed 2I/Borisov between October 3 and December 13, 2019 using the 1.52-m Telescopio Carlos S\'{a}nchez equipped with MuSCAT2 instrument, and the 2.54-m Isaac Newton Telescope with Wide Field Camera. We characterize its morphology and spectro-photometric features using the data gathered during this extended campaign. Simultaneous imaging in four bands ($g$, $r$, $i$, and $z_s$) reveals a homogeneous composition and a reddish hue, resembling Solar System comets, and as well a diffuse profile exhibiting familiar cometary traits. We discern a stationary trend fluctuating around a constant activity level throughout October and November 2019. Subsequently, a reduction in activity is observed in December. Dust production and mass loss calculations indicate approximately an average of 4 kg/s before perihelion, while after perihelion the net mass loss is about 0.6 kg/s. Our simulations indicate the most probable size of coma dust particles should be in the range 200-250 nm, and the terminal speed around 300 m/s. The spectrum acquired with the 4.2-m William Herschel Telescope shows the presence of a strong CN line for which we find a gas production rate of $1.2 \times 10^{24}~s^{-1}$. We also detected NH$_2$ and OI bands. The ratio between NH$_2$ and CN productions is $\log (NH_2/CN) =-0.2$. Overall, this observing campaign provides a new understanding of 2I/Borisov's unique characteristics and activity patterns., Comment: accepted to MNRAS on 12th Feb 2024

This paper addresses the particularities of adaptive optimal control of the uranium extraction-scrubbing operation in the PUREX process. The process dynamics are nonlinear, high dimensional, and have limited online measurements. In addition, analysis and developments are based on a qualified simulation program called PAREX, which was validated with laboratory and industrial data. The control objective is to stabilize the process at a desired solvent saturation level, guaranteeing constraints and handling disturbances. The developed control strategy relies on optimization-based methods for computing control inputs and estimates, i.e., Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimation (NMHE). The designs of these two associated algorithms are tailored for this process's particular dynamics and are implemented through an enhanced Particle Swarm Optimization (PSO) to guarantee constraint satisfaction. Software-in-the-loop simulations using PAREX show that the designed control scheme effectively satisfies control objectives and guarantees constraints during operation.

We present muon spin rotation and relaxation ($\mu$SR) results for chalcogenide spinels CuCr$_2$X$_4$ with X= S and Se. Both compounds are known as ferromagnetic metals with high Curie temperatures. Our $\mu$SR and magnetization data show clear signatures for additional magnetic transitions far below the respective Curie temperatures. They can be related to changes in the Cr valence system from the mixed valence between Cr$^{3+}$ and Cr$^{4+}$ at high temperatures with collinear ferromagnetism to a charge-ordered state at low temperatures with a different ferromagnetic structure. Our results demonstrate that the electronic systems and the related spin structures of both compounds are more complex than assumed so far., Comment: 17 pages, 10 Figures, experimental results

The demand for distributed applications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. These applications predominantly utilize Cloud data centers for high-performance computing and Fog and Edge devices for low-latency communication for small-size machine learning model training and inference. The challenge of executing applications with different requirements on heterogeneous devices requires effective methods for solving NP-hard resource allocation and application scheduling problems. The state-of-the-art techniques primarily investigate conflicting objectives, such as the completion time, energy consumption, and economic cost of application execution on the Cloud, Fog, and Edge computing infrastructure. Therefore, in this work, we review these research works considering their objectives, methods, and evaluation tools. Based on the review, we provide a discussion on the scheduling methods in the Computing Continuum., Comment: 7 pages, 3 figures, 3 tables

In NiTe$_3$O$_6$ with a chiral crystal structure, we report on a giant natural optical rotation of the lowest-energy magnon. This polarization rotation, as large as 140 deg/mm, corresponds to a path difference between right and left circular polarizations that is comparable to the sample thickness. Natural optical rotation, being a measure of structural chirality, is highly unusual for long-wavelength magnons. The collinear antiferromagnetic order of NiTe$_3$O$_6$ makes this giant effect even more peculiar: Chirality of the crystal structure does not affect the magnetic ground state but is strongly manifested in the lowest excited state. We show that the dynamic magnetoelectric effect, turning this magnon to a magnetic- and electric-dipole active hybrid mode, generates the giant natural optical rotation. In finite magnetic fields, it also leads to a strong optical magnetochiral effect., Comment: 9 pages, 4 figures

Adaptive live video streaming applications use a fixed predefined configuration for the bitrate ladder with constant framerate and encoding presets in a session. However, selecting optimized framerates and presets for every bitrate ladder representation can enhance perceptual quality, improve computational resource allocation, and thus, the streaming energy efficiency. In particular, low framerates for low-bitrate representations reduce compression artifacts and decrease encoding energy consumption. In addition, an optimized preset may lead to improved compression efficiency. To this light, this paper proposes a Content-adaptive Variable Framerate (CVFR) encoding scheme, which offers two modes of operation: ecological (ECO) and high-quality (HQ). CVFR-ECO optimizes for the highest encoding energy savings by predicting the optimized framerate for each representation in the bitrate ladder. CVFR-HQ takes it further by predicting each representation's optimized framerate-encoding preset pair using low-complexity discrete cosine transform energy-based spatial and temporal features for compression efficiency and sustainable storage. We demonstrate the advantage of CVFR using the x264 open-source video encoder. The results show that CVFR-ECO yields an average PSNR and VMAF increase of 0.02 dB and 2.50 points, respectively, for the same bitrate, compared to the fastest preset highest framerate encoding. CVFR-ECO also yields an average encoding and storage energy consumption reduction of 34.54% and 76.24%, considering a just noticeable difference (JND) of six VMAF points. In comparison, CVFR-HQ yields an average increase in PSNR and VMAF of 2.43 dB and 10.14 points, respectively, for the same bitrate. Finally, CVFR-HQ resulted in an average reduction in storage energy consumption of 83.18%, considering a JND of six VMAF points.

On 26 September 2022, the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the satellite of binary near-Earth asteroid (65803) Didymos. This demonstrated the efficacy of a kinetic impactor for planetary defense by changing the orbital period of Dimorphos by 33 minutes (Thomas et al. 2023). Measuring the period change relied heavily on a coordinated campaign of lightcurve photometry designed to detect mutual events (occultations and eclipses) as a direct probe of the satellite's orbital period. A total of 28 telescopes contributed 224 individual lightcurves during the impact apparition from July 2022 to February 2023. We focus here on decomposable lightcurves, i.e. those from which mutual events could be extracted. We describe our process of lightcurve decomposition and use that to release the full data set for future analysis. We leverage these data to place constraints on the post-impact evolution of ejecta. The measured depths of mutual events relative to models showed that the ejecta became optically thin within the first ~1 day after impact, and then faded with a decay time of about 25 days. The bulk magnitude of the system showed that ejecta no longer contributed measurable brightness enhancement after about 20 days post-impact. This bulk photometric behavior was not well represented by an HG photometric model. An HG1G2 model did fit the data well across a wide range of phase angles. Lastly, we note the presence of an ejecta tail through at least March 2023. Its persistence implied ongoing escape of ejecta from the system many months after DART impact., Comment: 52 pages, 5 tables, 9 figures, accepted to PSJ

Understanding the exotic properties of quantum materials, including high-temperature superconductors, remains a formidable challenge that demands direct insights into electronic conductivity. Current methodologies either capture a bulk average or near-atomically-resolved information, missing direct measurements at the critical intermediate length scales. Here, using the superconductor Fe(Se,Te) as a model system, we use low-temperature conductive atomic force microscopy (cAFM) to bridge this gap. Contrary to the uniform superconductivity anticipated from bulk assessments, cAFM uncovers micron-scale conductive intrusions within a relatively insulating matrix. Subsequent compositional mapping through atom probe tomography, shows that differences in conductivity correlated with local changes in composition. cAFM, supported by advanced microscopy and microanalysis, represents a methodological breakthrough that can be used to navigate the intricate landscape of high-temperature superconductors and the broader realm of quantum materials. Such fundamental information is critical for theoretical understanding and future guided design.

Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating the scalability requirements. We apply the linear regression (LR), multilayer perception (MLP), and gradient boosting regression (GBR) models on the Alibaba microservice traces. The prediction results reveal that the LR model reaches a lower training time than the GBR and MLP models. However, the GBR reduces the mean absolute error and the mean absolute percentage error compared to LR and MLP models. Moreover, the prediction results show that the required number of replicas for each microservice by the gradient boosting model is close to the actual test data without any prediction., Comment: 7 pages, 5 figures, 4 tables

Quasimolecular orbitals in cluster Mott insulators provide a route to tailor exchange interactions, which may yield novel quantum phases of matter. We demonstrate the cluster Mott character of the lacunar spinel GaTa$_4$Se$_8$ using resonant inelastic x-ray scattering (RIXS) at the Ta $L_3$ edge. Electrons are fully delocalized over Ta$_4$ tetrahedra, forming quasimolecular $J_{\rm tet}$=3/2 moments. The modulation of the RIXS intensity as function of the transferred momentum q allows us to determine the cluster wavefunction, which depends on competing intracluster hopping terms that mix states with different character. This mixed wavefunction is decisive for the macroscopic properties since it affects intercluster hopping and exchange interactions and furthermore renormalizes the effective spin-orbit coupling constant. The versatile wavefunction, tunable via intracluster hopping, opens a new perspective on the large family of lacunar spinels and cluster Mott insulators in general., Comment: 7 pages, 4 figures, plus supplementary information

This paper addresses the particularities of the uranium extraction-scrubbing operation in a spent nuclear fuel treatment process (PUREX-Plutonium Uranium Refining by Extraction) through the use of set-point tracking MPC (Model Predictive Control). The presented controller uses the feed solution flow rate as the manipulated variable to control the saturation of the solvent at the extraction step. In addition, it guarantees not to loose uranium in the raffinates, and ensures equipment limitations during operation time. Simulation results show that the tracking NMPC effectively ensures accurate set point tracking and constraints guarantee. As a result, the system can be driven to its optimal working condition, avoid and recover from constraint violations. The control performance was compared with PID and openloop controllers.

Due to the nonlinearities and operational constraints typical to quadcopter missions, Model Predictive Control (MPC) encounters the major challenge of high computational power necessary for the online implementation. This problem may prove impractical, especially for a hardware-limited or small-scale setup. By removing the need for online solvers while keeping the constraint satisfaction and optimality, Explicit MPC (ExMPC) stands out as a strong candidate for this application. Yet, the formulation was usually hindered by the two main problems: nonlinearity and dimensionality. In this paper, we propose an ExMPC solution for the quadcopter position stabilization by analyzing its description (dynamics and constraints) in the flat output space. With the former issue, the system is exactly linearized into a concatenation of three double integrators at a price of cumbersome constraints in the new coordinates. For the latter, with a suitable characterization of these constraints, the stabilizing ExMPC can be computed for each double integrator separately. The controller is then validated via simulations and experimental tests. The proposed scheme achieves similar performance and guarantees to the state-of-the-art solution but with notably less computational effort, allowing scalability in a centralized manner

This paper extends our previous study on an explicit saturated control for a quadcopter, which ensures both constraint satisfaction and stability thanks to the linear representation of the system in the flat output space. The novelty here resides in the adaptivity of the controller's gain to enhance the system's performance without exciting its parasitic dynamics and avoid lavishing the input actuation with excessively high gain parameters. Moreover, we provide a thorough robustness analysis of the proposed controller when additive disturbances are affecting the system behavior. Finally, simulation and experimental tests validate the proposed controller.

We consider synthetic materials consisting of self-coupled identical resonators carrying classical internal degrees of freedom. The architecture of such material is specified by the positions and orientations of the resonators. Our goal is to calculate the smallest C*-algebra that covers the dynamical matrices associated to a fixed architecture and adjustable internal structures. We give the answer in terms of a groupoid C*-algebra that can be canonically associated to a uniformly discrete subset of the group of isometries of the Euclidean space. Our result implies that the isomorphism classes of these C*-algebras split these architected materials into classes containing materials that are identical from the dynamical point of view.

In this paper, we introduce the novel concept of economic ports, allowing modular and distributed optimal operation of networked microgrids. Firstly, we design a novel price-based controller for optimal operation of a single microgrid and show asymptotic stability. Secondly, we define novel physical and economic interconnection ports for the microgrid and study the dissipativity properties of these ports. Lastly, we propose an interconnection scheme for microgrids via the economic ports. This interconnection scheme requires only an exchange of the local prices and allows a globally economic optimal operation of networked microgrids at steady state, while guaranteeing asymptotic stability of the networked microgrids via the passivity properties of economic ports. The methods are demonstrated through various academic examples.

Re-configurable materials and meta-materials can jump between space symmetry classes during their deformations. Here, we introduce the concept of singular symmetry enhancement, which refers to an abrupt jump to a higher symmetry class accompanied by an un-avoidable reduction in the number of dispersion bands of the excitations of the material. Such phenomenon prompts closings of some of the spectral resonant gaps along singular manifolds in a parameter space. In this work, we demonstrate that these singular manifolds can carry topological charges. As a concrete example, we show that a deformation of an acoustic crystal that encircles a p11g-symmetric configuration of an array of cavity resonators results in an adiabatic cycle that carries a Chern number in the bulk and displays Thouless pumping at the edges. This points to a very general guiding principle for recognizing cyclic adiabatic processes with high potential for topological pumping in complex materials and meta-materials, which rests entirely on symmetry arguments.

Modern power systems are characterized by low inertia and fast voltage dynamics due to the increase of sources connecting via power electronics and the removal of large traditional thermal generators. Power electronics are commonly equipped with fast controllers that are able to reach a desired voltage setpoint within seconds. In this paper, we propose and compare two approaches using Model Predictive Control (MPC) to compute optimal voltage references for the power electronic devices in order to minimize the losses in a DC microgrid: i) a traditional setpoint-tracking MPC which receives a previously computed optimal setpoint; ii) an economic MPC which does not require a priori computed setpoints. We show that the economic MPC outperforms the setpoint-tracking MPC in simulations with the CIGRE benchmark system when multiple load disturbances occur. Some insights and discussions related to the stability of the closed-loop system using its dissipativity properties are highlighted for both approaches.

Electrical manipulation of antiferromagnetic states, a cornerstone of antiferromagnetic spintronics, is a great challenge, requiring novel material platforms. Here we report the full control over antiferromagnetic states by voltage pulses in the insulating Co$_3$O$_4$ spinel. We show that the strong linear magnetoelectric effect emerging in its antiferromagnetic state is fully governed by the orientation of the N\'eel vector. As a unique feature of Co$_3$O$_4$, the magnetoelectric energy can easily overcome the weak magnetocrystalline anisotropy, thus, the N\'eel vector can be manipulated on demand, either rotated smoothly or reversed suddenly, by combined electric and magnetic fields. We succeed with switching between antiferromagnetic states of opposite N\'eel vectors by voltage pulses within a few microsecond in macroscopic volumes. These observations render quasi-cubic antiferromagnets, like Co$_3$O$_4$, an ideal platform for the ultrafast (pico- to nanosecond) manipulation of microscopic antiferromagnetic domains and may pave the way for the realization of antiferromagnetic spintronic devices., Comment: 7 pages, 3 figures

Using the properties of differential flatness, a controllable system, such as a quadcoper model, may be transformed into a linear equivalent system via a coordinate change and an input mapping. This is a straightforward advantage for the quadcopter's controller design and its real-time implementation. However, one significant hindrance is that, while the dynamics become linear in the new coordinates (the flat output space), the input constraints become convoluted. This paper addresses an explicit pre-stabilization based control scheme which handles the input constraints for the quadcopter in the flat output space with a saturation component. The system's stability is shown to hold by Lyapunov-stability arguments. Moreover, the practical viability of the proposed method is validated both in simulation and experiments over a nano-drone platform. Hence, the flatness-based saturated controller not only ensures stability and constraints satisfaction, but also requires very low computational effort, allowing for embedded implementations.

Host load prediction is essential for dynamic resource scaling and job scheduling in a cloud computing environment. In this context, workload prediction is challenging because of several issues. First, it must be accurate to enable precise scheduling decisions. Second, it must be fast to schedule at the right time. Third, a model must be able to account for new patterns of workloads so it can perform well on the latest and old patterns. Not being able to make an accurate and fast prediction or the inability to predict new usage patterns can result in severe outcomes such as service level agreement (SLA) misses. Our research trains a fast model with the ability of online adaptation based on the gated recurrent unit (GRU) to mitigate the mentioned issues. We use a multivariate approach using several features, such as memory usage, CPU usage, disk I/O usage, and disk space, to perform the predictions accurately. Moreover, we predict multiple steps ahead, which is essential for making scheduling decisions in advance. Furthermore, we use two pruning methods: L1 norm and random, to produce a sparse model for faster forecasts. Finally, online learning is used to create a model that can adapt over time to new workload patterns.

Tessellations of the hyperbolic spaces by regular polygons are becoming popular because they support discrete quantum and classical models displaying unique spectral and topological characteristics. Resolving the true bulk spectra and the thermodynamic response functions of these models requires converging periodic boundary conditions and our work delivers a practical solution for this open problem on generic {p,q}-tessellations. This enables us to identify the true spectral gaps of bulk Hamiltonians and, as an application, we construct all but one topological models that deliver the topological gaps predicted by the K-theory of the lattices. We demonstrate the emergence of the expected topological spectral flows whenever two such bulk models are deformed into each other and, additionally, we prove the emergence of topological channels whenever a soft physical interface is created between different topological classes of Hamiltonians.

Differential flatness has been used to provide diffeomorphic transformations for non-linear dynamics to become a linear controllable system. This greatly simplifies the control synthesis since in the flat output space, the dynamics appear in canonical form (as chains of integrators). The caveat is that mapping from the original to the flat output space often leads to nonlinear constraints. In particular, the alteration of the feasible input set greatly hinders the subsequent calculations. In this paper, we particularize the problem for the case of the quadcopter dynamics and investigate the deformed input constraint set. An optimization-based procedure will achieve a non-conservative, linear, inner-approximation of the non-convex, flat-output derived, input constraints. Consequently, a receding horizon problem (linear in the flat output space) is easily solved and, via the inverse flat mapping, provides a feasible input to the original, nonlinear, dynamics. Experimental validation and comparisons confirm the benefits of the proposed approach and show promise for other class of flat systems.

Topological pumping supplies a robust mechanism to steer waves across a sample without being affected by disorders and defects. For the first time, we demonstrate the pumping of elastic surface waves, achieved by a smart patterning of a surface that creates a synthetic dimension, which is explored by the wave as it is launched perpendicularly to the steering direction. Specifically, we design and fabricate an elastic medium decorated with arrays of pillar-type resonators whose eigenmodes are locate below the sound cone, together with coupling bridges edged according to a specific algorithm. We establish a connection between the collective dynamics of the pillars and that of electrons in a magnetic field by deriving an accurate tight-binding model and developing a WKB-type analysis suitable for such discrete aperiodic systems with spatially slow-varying couplings. This enable us to predict topological pumping pattern, which is numerically and experimentally demonstrated by steering waves from one edge of the system to the other. Finally, the immune character of the topologically pumped surface waves against disorder and defects is evidenced. The principle of surface patterning together with the WKB-analysis could provide a powerful new platform for surface wave control and exploration of topological matter in higher dimensions.

In this paper we investigate different types of couplings used in acoustic metamaterials requiring preservation of symmetries. For testing we use the SSH model to test whether topologically edge and interface modes are supported with the different types of connection. We observed that a modular platform where the resonators are coupled through the bottom is the simplest method that is accurate and flexible., Comment: 7 pages, 5 figures

We propose a circuit-level model combining the Marcus-Hush-Chidsey electron current equation and the Yakopcic equation for the state variable for describing resistive switching memory devices of the structure metal-ionic conductor-metal. We extend the dynamics of the state variable originally described by a first-order time derivative by introducing a fractional derivative with an arbitrary order between zero and one. We show that the extended model fits with great fidelity the current-voltage characteristic data obtained on a Si electrochemical metallization memory device with Ag-Cu alloy., Comment: 7 pages, 3 figures

Biological systems have been theoretically predicted to support phononic topological wave-modes, similar to the ones existing in meta-materials. The existing methods to measure these modes, however cannot be applied to biological systems; new techniques have to be developed to accommodate phononic measurements in life science. Motivated by this perspective, we report a nanofluidic device for studying one microtubule at a time. Microchannels etched into fused-silica using reactive ion etching were interfaced with nanochannels written and etched by electron beam lithography and reactive ion etching, and sealed with a PDMS-coated glass coverslip. The microchannels are 1 micron deep and 100 micron wide, and the nanochannels are 150 nm deep and 750 nm wide, they are tested to be effective for isolating microtubules. The methods presented here are for an adaptable nanofluidic platform for phonon measurements in biopolymers made of proteins or DNA., Comment: 6 pages, 4 figures