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Plasmonic near-field transducers (NFTs) play a key role in administering nanoscale heating for a number of applications ranging from medical devices to next generation data processing technology. We present a novel multi-scale approach, combining quantum many-body perturbation theory with finite-element modelling, to predict the electric and thermal material parameters of various Au-based, noble metal (M) alloys. Specifically, we focus on modelling their performance within an NFT designed to focus high-intensity, sub-diffracted light for technologies such as nanoscale etching, manipulation, sensing, and heat-assisted magnetic recording (HAMR). Elemental Au is the long-standing general-purpose NFT medium due its excellent plasmonic performance at relevant wavelengths. However, elemental Au is a soft, ductile material that tends to extrude and deform in response to extreme temperature gradients. Therefore, alloying Au with other noble metals such as Ag, Cu, Pd or Pt, has attracted considerable interest for improved mechanical and thermal robustness while reaching threshold plasmonic generation at standard optoelectronics operating wavelengths (e.g., $\approx 830$~nm) and approximate high-power NFT temperatures ($\approx 400$~K). We predict that certain Au-Ag alloys may offer improved thermal stability as whole-NFT media compared to elemental Au, alongside plasmonic figures of merit comparable to that of Au. Simulations of certain solid solution Au-Pd/Pt alloys enable us to predict significantly enhanced thermal conductivity. We predict that alloying with Pd at low concentrations $\sim 10\%$ may preserve the NFT performance of Au, while offering the benefits of improved thermal and mechanical stability., Comment: 8 pages, 4 figures, 1 table. Includes Supplemental Material with 10 tables

A modified form of Legendre-Gauss orthogonal direct collocation is developed for solving optimal control problems whose solutions are nonsmooth due to control discontinuities. This new method adds switch-time variables, control variables, and collocation conditions at both endpoints of a mesh interval, whereas these new variables and collocation conditions are not included in standard Legendre-Gauss orthogonal collocation. The modified Legendre-Gauss collocation method alters the search space of the resulting nonlinear programming problem and enables determining accurately the location of the nonsmoothness in the optimal control. The transformed adjoint system of the modified Legendre-Gauss collocation method is then derived and shown to satisfy a discrete form of the continuous variational necessary conditions for optimality. The method is motivated via a control-constrained triple-integrator minimum-time optimal control problem where the solution possesses a two-switch bang-bang optimal control structure. In addition, the method developed in this paper is compared with existing Gaussian quadrature collocation methods. The method developed in this paper is shown to be capable of accurately solving optimal control problems with a discontinuous optimal control., Comment: 23 pages, 7 figures. Submitted for publication consideration in the IEEE Transactions on Automatic Control. Also, an earlier version has been submitted for publication consideration in the 2024 IEEE Conference on Decision and Control to be held in Milan, Italy, 16-19 December 2024

This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iteratively forecasting the temporal evolution of flow dynamics. Three autoregressive models were compared: a convolutional autoencoder combined with a convolutional LSTM (ConvLSTM), a variational autoencoder (VAE) combined with a ConvLSTM and a hybrid model that combines proper orthogonal decomposition (POD) with a LSTM (POD-DL). These models were tested on two high-dimensional, nonlinear datasets representing the velocity field of flow past a circular cylinder in both laminar and turbulent regimes. The study used latent dimension methods, enabling a bijective reduction of high-dimensional dynamics into a lower-order space to facilitate future predictions. While the VAE and ConvLSTM models accurately predicted laminar flow, the hybrid POD-DL model outperformed the others across both laminar and turbulent flow regimes. This success is attributed to the model's ability to incorporate modal decomposition, reducing the dimensionality of the data, by a non-parametric method, and simplifying the forecasting component. By leveraging POD, the model not only gained insight into the underlying physics, improving prediction accuracy with less training data, but also reduce the number of trainable parameters as POD is non-parametric. The findings emphasize the potential of hybrid models, particularly those integrating modal decomposition and deep learning, in predicting complex flow dynamics., Comment: 18 pages, two column, 21 figures and 10 tables

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

The bottom-up synthesis of carbon based nanomaterials directly on semiconductor surfaces allows to decouple their electronic and magnetic properties from the substrates. However, the lack of reactivity on these non-metallic surfaces hinders or reduces significantly the yield of these reactions. Such hurdles practically precludes transferring bottom-up synthesis strategies onto semiconducting and insulating surfaces. Here, we achieve a high polymerization yield of terphenyl molecules on the semiconductor TiO$_2$(110) surface by incorporating cobalt atoms as catalysts in the Ullmann coupling reaction. Cobalt atoms trigger the debromination of 4,4-dibromo-p-terphenyl (DBTP) molecules on TiO$_2$(110) and the formation of an intermediate organometallic phase already at room-temperature (RT). As the debromination temperature is drastically reduced, the homo-coupling temperature is also significantly lowered, preventing the desorption of DBTP molecules from the TiO$_2$(110) surface and leading to a radical improvement on the poly-para-phenylene (PPP) polymerization yield. The universality of this mechanism is demonstrated with an iodinated terphenyl derivative (DITP), which shows analogous dehalogenation and polymerization temperatures with a very similar reaction yield. Consequently, we propose to use minute amounts of metal catalyst to drive forward generic bottom-up synthesis strategies on non-metallic surfaces.

This article presents an innovative open-source software named ModelFLOWs-app, written in Python, which has been created and tested to generate precise and robust hybrid reduced order models (ROMs) fully data-driven. By integrating modal decomposition and deep learning methods in diverse ways, the software uncovers the fundamental patterns in dynamic systems. This acquired knowledge is then employed to enrich the comprehension of the underlying physics, reconstruct databases from limited measurements, and forecast the progression of system dynamics. These hybrid models combine experimental and numerical database, and serve as accurate alternatives to numerical simulations, effectively diminishing computational expenses, and also as tools for optimization and control. The ModelFLOWs-app software has demonstrated in a wide range of applications its great capability to develop reliable data-driven hybrid ROMs, highlighting its potential in understanding complex non-linear dynamical systems and offering valuable insights into various applications. This article presents the mathematical background, review some examples of applications and introduces a short tutorial of ModelFLOWs-app.

This work aims to improve fuel chamber injectors' performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of models that allow real-time prediction and improvement of the fuel/air mixture. However, the work carried out to date involves using experimental data (complicated to measure) or the numerical resolution of the complete problem (computationally prohibitive). The latter involves the resolution of a system of partial differential equations (PDE). These problems make difficult to develop a real-time prediction tool. Therefore, in this work, we propose using machine learning in conjunction with (complementarily cheaper) single-phase flow numerical simulations in the presence of tangential discontinuities to estimate the mixing process in two-phase flows. In this meaning we study the application of two proposed neural network (NN) models as PDE surrogate models. Where the future dynamics is predicted by the NN, given some preliminary information. We show the low computational cost required by these models, both in their training and inference phases. We also show how NN training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures. This reconstruction has the same number of samples and spatial dimension as the original flow, but with a less complex dynamics and preserving its main features. The core idea of this work is to test the limits of applicability of deep learning models to data forecasting in complex fluid dynamics problems. Generalization capabilities of the models are demonstrated by using the same NN architectures to forecast the future dynamics of four different two-phase flows., Comment: 48 pages, 21 figures, 13 tables. Submitted to Expert Systems with Applications

The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing information to researchers who wish to share and/or use sensitive data in a cloud environment in general, and the European Open Science Cloud in particular. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox will provide links to recommendations, procedures, and best practices, as well as to software (tools) to support data sharing and reuse. It will be based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources. The current design document provides an outline for the anticipated toolbox, as well as its basic principles regarding content and sustainability.

In this paper we report on a low energy consumption CMOS-compatible plasmonic modulator based on Franz-Keldysh effect in germanium on silicon. We performed integrated electro-optical simulations in order to optimize the main characteristics of the modulator. A 3.3 $dB$ extinction ratio for a 30 ${\mu}m$ long modulator is demonstrated under 3 $V$ bias voltage at an operation wavelength of 1647 $nm$. The estimated energy consumption is as low as 20 $fJ/bit$., Comment: 8 pages, 6 figures, and 1 table. Published version: see https://doi.org/10.1364/OE.22.011236. Related works: see https://doi.org/10.1364/OE.25.010070, https://doi.org/10.1364/OE.26.030292, and https://doi.org/10.1364/OE.455491. Keywords: Integrated Optics; Silicon Photonics; Plasmonics; Modulators. arXiv admin note: text overlap with arXiv:2207.09310

This work aims to design a CMOS compatible, low-electrical power consumption modulator assisted by plasmons. For compactness and reduction of the electrical power consumption, electro-absorption based on the Franz-Keldysh effect in Germanium was chosen for modulation. It consists in the change of the absorption coefficient of the material near the band edge under the application of a static electric field, hence producing a direct modulation of the light intensity. The use of plasmons allows enhancing the electro-optical effect due to the high field confinement. An integrated electro-optical simulation tool was developed to design and optimize the modulator. The designed plasmonic modulator has an extinction ratio of 3.3 dB with insertion losses of 13.2 dB and electrical power consumption as low as 20 fJ/bit, i.e. the lowest electrical power consumption reported for silicon photonic modulators. In- and out-coupling to a standard silicon waveguide was also engineered by the means of an optimized Si-Ge taper, reducing the coupling losses to only 1 dB per coupler. Besides, an experimental work was carried out to try to shift the Franz-Keldysh effect, which is maximum at 1650 nm, to lower wavelength close to 1.55 {\mu}m for telecommunication applications., Comment: 235 pages, 155 figures, and 28 tables. Published version: see https://tel.archives-ouvertes.fr/tel-01154052, https://www.theses.fr/2014PA112353, and https://hal.archives-ouvertes.fr/tel-01154052. Related works: see https://doi.org/10.1364/oe.22.011236, https://doi.org/10.1364/OE.25.010070, and https://doi.org/10.1364/OE.455491. Keywords: Silicon Photonics; Plasmonics; Modulators

We show the possibility of tailoring the molecular arrangement, as well as the chemical and structural modifications, of porphyrins at the monolayer saturation coverage on TiO2(110) by Synchrotron photoemission, electron diffraction, STM topography and DFT calculations. Free-base tetra-phenyl-porphyrins (2H-TPP) adsorb on the oxygen rows, where they can spontaneously capture two additional hydrogen atoms at their iminic nitrogens (4H-TPP). Both 2H-TPP and 4H-TPP molecules aggregate into a commensurate phase at the saturation coverage of one monolayer. Upon sample heating, a self-metalation reaction sets in at 100C, yielding full metalation of the saturated monolayer at ~200C. The Ti atoms are extracted from the substrate and, by simultaneous dehydrogenation of the pyrrolic nitrogen atoms, incorporated into the porphyrin macrocycle, where they remain coordinated to two oxygen atoms underneath. Neither the adsorption geometry (on-bridge, atop the oxygen rows) nor the molecular arrangement change across the self-metalation transition up to 300C. On one side, the robustness of this saturation phase makes it a promising system for its implementation into applications for photocatalysis and photovoltaic devices. On the other side, the possible manifestation of metal exchange with the very reactive Ti atoms must be taken into account when designing porphyrin-sensitized solar cells since the critical temperature for the onset of self-metalation is very close to the normal operating temperature of photovoltaic devices., Comment: Final Title and Abstract; Pre-final manuscript

The quantum-confined Stark effect in InAs/In(Ga)As quantum dots (QDs) using non-intentionally doped and p-doped QD barriers was investigated to compare their performance for use in optical modulators. The measurements indicate that the doped QD barriers lead to a better figure of merit $(FoM)$, defined as the ratio of the change in absorption $\Delta\alpha$ for a reverse bias voltage swing to the loss at $1 V$ $\alpha(1 V)$, $FoM=\Delta\alpha/\alpha (1 V)$. The improved performance is due to the absence of the ground-state absorption peak and an additional component to the Stark shift. Measurements indicate that p-doping the QD barriers can lead to more than a 3$\times$ increase in FoM modulator performance between temperatures of -73 $\deg$C to 100 $\deg$C when compared with the stack with NID QD barriers., Comment: https://doi.org/10.1364/OE.455491

We investigate a tapered, hybrid plasmonic waveguide which has previously been proposed as an optically efficient near-field transducer (NFT), or component thereof, in several devices which aim to exploit nanofocused light. We numerically analyze how light is transported through the waveguide and ultimately focused via effective-mode coupling and taper optimization. Crucial dimensional parameters in this optimization process are identified that are not only necessary to achieve maximum optical throughput, but also optimum thermal performance with specific application towards heat-assisted magnetic recording (HAMR). It is shown that existing devices constructed on similar waveguides may benefit from a heat spreader to avoid deformation of the plasmonic element which we achieve with no cost to the optical efficiency. For HAMR, our design is able to surpass many industry requirements in regard to both optical and thermal efficiency using pertinent figure of merits like 8.5% optical efficiency., Comment: 14 pages, 7 figures, and 3 tables. Published version: see https://doi.org/10.1364/OE.26.001752. Related works: see https://doi.org/10.1364/oe.22.011236, https://doi.org/10.1364/oe.26.030292, and https://doi.org/10.1063/5.0044490. Keywords: Integrated Optics; Components; Integrated Optics Devices; Surface Plasmons; Plasmonics

A CMOS-compatible plasmonic TE-pass polarizer capable of working in the O, E, S, C, L, and U bands is numerically analyzed. The device is based on an integrated hybrid plasmonic waveguide (HPW) with a segmented metal design. The segmented metal will avoid the propagation of the TM mode, confined in the slot of the HPW, while the TE fundamental mode will pass. The TE mode is not affected by the metal segmentation since it is confined in the core of the HPW. The concept of the segmented metal can be exploited in a plasmonic circuit with HPWs as the connecting waveguides between parts of the circuit and in a silicon photonics circuit with strip or slab waveguides connecting the different parts of the circuit. Using 3D FDTD simulations, it is shown that for a length of 5.5 {\mu}m the polarization extinction ratios are better than 20 dB and the insertion losses are less than 1.7 dB over all the optical communication bands., Comment: 13 pages, 12 figures, and 5 tables. Published version: see https://doi.org/10.1364/OE.26.030292. Related works: see https://doi.org/10.1364/oe.22.011236, https://doi.org/10.1364/oe.25.010070, https://doi.org/10.1364/oe.26.001752, and https://doi.org/10.1063/5.0044490. Keywords: Integrated Optics; Integrated Optics Devices; Polarization-Selective Devices; Plasmonics; Components

In this work, a novel highly fabrication tolerant polarization beam splitter (PBS) is presented on an InP platform. To achieve the splitting, we combine the Pockels effect and the plasma dispersion effect in a symmetric 1x2 Mach-Zehnder interferometer (MZI). One p-i-n phase shifter of the MZI is driven in forward bias to exploit the plasma dispersion effect and modify the phase of both the TE and TM mode. The other arm of the MZI is driven in reverse bias to exploit the Pockels effect which affects only the TE mode. By adjusting the voltages of the two phase shifters, a different interference condition can be set for the TE and the TM modes thereby splitting them at the output of the MZI. By adjusting the voltages, the very tight fabrication tolerances known for fully passive PBS are eased. The experimental results show that an extinction ratio better than 15 dB and an on-chip loss of 3.5 dB over the full C-band (1530-1565nm) are achieved., Comment: 8 pages, 7 figures, and 2 tables. Published version: see https://doi.org/10.1364/OE.25.010070. Related works: see https://doi.org/10.1364/OE.22.011236, https://doi.org/10.1364/oe.26.030292, and https://doi.org/10.1063/5.0044490. Keywords: Photonic Integrated Circuits; Integrated Optics Devices; Beam Splitters; Polarization-Selective Devices

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bio inspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

We report on the on-surface synthesis of 7 armchair graphene nanoribbons (7-AGNRs) substituted with nitrile (CN) functional groups. The CN groups are attached to the GNR backbone by modifying the 7-AGNR precursor. While many of these groups survive the on-surface synthesis, the reaction process causes the cleavage of some CN from the ribbon backbone and the on-surface cycloisomerization of few nitriles onto pyridine rings. Scanning Tunneling Spectroscopy and Density Functional Theory reveal that CN groups behave as very efficient n-dopants, significantly downshifting the bands of the ribbon, and introducing deep impurity levels associated to the nitrogen electron lone pairs.

In this paper, we present a complete spectral research of generalized Ces\`aro operators on Sobolev-Lebesgue sequence spaces. The main idea is to subordinate such operators to suitable $C_0$-semigroups on these sequence spaces. We introduce that family of sequence spaces using the fractional finite differences and we prove some structural properties similar to classical Lebesgue sequence spaces. In order to show the main results about fractional finite differences, we state equalities involving sums of quotients of Euler's Gamma functions. Finally, we display some graphical representations of the spectra of generalized Ces\`aro operators., Comment: 32 pages, 7 Figures

We convert a strongly interacting ultracold Bose gas into a mixture of atoms and molecules by sweeping the interactions from resonant to weak. By analyzing the decay dynamics of the molecular gas, we show that in addition to Feshbach dimers it contains Efimov trimers. Typically around 8\% of the total atomic population is bound into trimers, identified by their density-independent lifetime of about 100~$\mu$s. The lifetime of the Feshbach dimers shows a density dependence due to inelastic atom-dimer collisions, in agreement with theoretical calculations. We also vary the density of the gas across a factor of 250 and investigate the corresponding atom loss rate at the interaction resonance., Comment: 6 pages, 4 figures

Surface confined dehalogenation reactions are versatile bottom-up approaches for the synthesis of carbon-based nanostructures with predefined chemical properties. However, for devices generally requiring low conductivity substrates, potential applications are so far severely hampered by the necessity of a metallic surface to catalyze the reactions. In this work we report the synthesis of ordered arrays of poly(p-phenylene) chains on the surface of semiconducting TiO2(110) via a dehalogenative homocoupling of 4,4"-dibromoterphenyl precursors. The supramolecular phase is clearly distinguished from the polymeric one using low energy electron diffraction and scanning tunneling microscopy as the substrate temperature used for deposition is varied. X ray photoelectron spectroscopy of C 1s and Br 3d core levels traces the temperature of the onset of dehalogenation to around 475 K. Moreover, angle-resolved photoemission spectroscopy and tight-binding calculations identify a highly dispersive band characteristic of a substantial overlap between the precursor's {\pi} states along the polymer, considered as the fingerprint of a successful polymerization. Thus, these results establish the first spectroscopic evidence that atomically precise carbon based nanostructures can readily be synthesized on top of a transition-metal oxide surface, opening the prospect for the bottom-up production of novel molecule-semiconductor devices.

We report measurements of a new type of magnetoresistance in Pt and Ta thin films. The spin accumulation created at the surfaces of the film by the spin Hall effect decreases in a magnetic field because of the Hanle effect, resulting in an increase of the electrical resistance as predicted by Dyakonov [PRL 99, 126601 (2007)]. The angular dependence of this magnetoresistance resembles the recently discovered spin Hall magnetoresistance in Pt/Y3Fe5O12 bilayers, although the presence of a ferromagnetic insulator is not required. We show that this Hanle magnetoresistance is an alternative, simple way to quantitatively study the coupling between charge and spin currents in metals with strong spin-orbit coupling., Comment: 13 pages, 3 figures

In this paper, we will review the process of evidence accumulation in the PSEIKI system for expectation-driven interpretation of images of 3-D scenes. Expectations are presented to PSEIKI as a geometrical hierarchy of abstractions. PSEIKI's job is then to construct abstraction hierarchies in the perceived image taking cues from the abstraction hierarchies in the expectations. The Dempster-Shafer formalism is used for associating belief values with the different possible labels for the constructed abstractions in the perceived image. This system has been used successfully for autonomous navigation of a mobile robot in indoor environments., Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)