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Van der Waals magnets are an emerging material family for investigating light-matter interactions and spin-correlated excitations. Here, we report the discovery of a photo-induced state with a lifetime of 17 ps in the van der Waals antiferromagnet NiPS$_3$, which appears exclusively with resonant pumping at 1.476 eV in the antiferromagnetic state. The long-lived state comes with a negative photoconductivity, a characteristic optical response of population inversion. Our findings demonstrate a promising pathway to potentially achieve long-lived lasing at terahertz frequencies in reduced dimensions., Comment: 6 pages, 3 figures. Accepted for publication in Physical Review Letters

Based on experimental data, we propose a model to evaluate the energy dissipated during quantum tunneling processes in solid-state junctions. This model incorporates a nonlinear friction force expressed in the general form f(x)={\gamma} v(x)^{\alpha}, where {\gamma} is the frictional coefficient, which is fitted to data. We study this by applying voltages just below the barrier height up to near break down voltages. Furthermore, by lowering the temperature and adjusting the applied voltage to the junction, the effect on dissipation caused by the variation in barrier height is examined. We underline that the crucial dependency of dissipation on the fraction of particle energy lost is modulated by two primary mechanisms: the application of voltage and the variation of temperature. The fraction of energy dissipated decreases in general for increasing energies of the tunneling particles at a given temperature. However, for a given energy of the tunneling particle, the present work demonstrates a turning point at a temperature of 137 K, after which the dissipated energy starts increasing for higher temperatures. The latter can possibly be due to the increase of electron-phonon interactions which become predominant over barrier height reduction at higher temperatures and hence we identify T = 137 K as a critical temperature for change in dissipative characteristics of the solid-state junction under consideration. Notably, also the study identifies significant changes in dissipation parameters, {\gamma} and {\alpha}, above 137 K, exhibiting a linear decline and underscoring the importance of further research at higher temperatures., Comment: 10 pages, 9 figures

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

Many ligands commonly used to prepare nanoparticle catalysts with precise nanoscale features contain nitrogen (e.g., oleylamine); here, we found that the use of nitrogen-containing ligands during the synthesis of metal oxide nanoparticle catalysts substantially impacted product analysis during photocatalytic studies. We confirmed these experimental results via hybrid Density Functional Theory computations of the materials' electronic properties to evaluate their viability as photocatalysts for nitrogen reduction. This nitrogen ligand contamination, and subsequent interference in photocatalytic studies, is avoidable through the careful design of synthetic pathways that exclude nitrogen-containing constituents. This result highlights the urgent need for careful evaluation of catalyst synthesis protocols, as contamination by nitrogen-containing ligands may go unnoticed since the presence of nitrogen is often not detected or probed., Comment: 13 pages, 3 figures

Photonic neural networks offer a promising alternative to traditional electronic systems for machine learning accelerators due to their low latency and energy efficiency. However, the challenge of implementing the backpropagation algorithm during training has limited their development. To address this, alternative machine learning schemes, such as extreme learning machines (ELMs), have been proposed. ELMs use a random hidden layer to increase the feature space dimensionality, requiring only the output layer to be trained through linear regression, thus reducing training complexity. Here, we experimentally demonstrate a programmable photonic extreme learning machine (PPELM) using a hexagonal waveguide mesh, and which enables to program directly on chip the input feature vector and the random hidden layer. Our system also permits to apply the nonlinearity directly on-chip by using the systems integrated photodetecting elements. Using the PPELM we solved successfully three different complex classification tasks. Additioanlly, we also propose and demonstrate two techniques to increase the accuracy of the models and reduce their variability using an evolutionary algorithm and a wavelength division multiplexing approach, obtaining excellent performance. Our results show that programmable photonic processors may become a feasible way to train competitive machine learning models on a versatile and compact platform.

Nonlinear frequency conversion by optical rectification, as well as difference- and sum-frequency generation are fundamental processes for producing electromagnetic radiation at different frequencies. Here, we demonstrate that coherently excited infrared-active phonons can be used as transducers for generating nonlinear electric polarizations and magnetizations via phonon-phonon and phonon-magnon interactions, in a way similar to nonlinear optical frequency conversion. We derive analytical solutions for the time-dependent polarizations and magnetizations for the second-order response to the electric field component of an ultrashort laser pulse. These allow us to define second-order nonlinear electric and magneto-electric susceptibilities that capture the rectification, as well as the impulsive and sum-frequency excitation of coherent phonons and magnons. Our theoretical framework naturally incorporates existing mechanisms and further leads to the prediction of a hybrid magneto-opto-phononic inverse Faraday effect involving photon-phonon-magnon scattering. Our work demonstrates nonlinear phononics as a pathway to controlling the electric polarization and magnetization in solids.

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications., Comment: Submitted to Information Fusion

Photonic integrated circuits play an important role in the field of optical computing, promising faster and more energy-efficient operations compared to their digital counterparts. This advantage stems from the inherent suitability of optical signals to carry out matrix multiplication. However, even deterministic phenomena such as thermal crosstalk make precise programming of photonic chips a challenging task. Here, we train and experimentally evaluate three models incorporating varying degrees of physics intuition to predict the effect of thermal crosstalk in different locations of an integrated programmable photonic mesh. We quantify the effect of thermal crosstalk by the resonance wavelength shift in the power spectrum of a microring resonator implemented in the chip, achieving modelling errors <0.5 pm. We experimentally validate the models through compensation of the crosstalk-induced wavelength shift. Finally, we evaluate the generalization capabilities of one of the models by employing it to predict and compensate for the effect of thermal crosstalk for parts of the chip it was not trained on, revealing root-mean-square-errors of <2.0 pm., Comment: 10 pages, 10 figures. The paper has been submitted to the Journal of Lightwave Technology, a special issue on Photonic Computing, as an invited paper. It is under review

As optical telecommunication networks become more complex, there is an emerging need for systems capable of very complex switching and manipulation of large numbers of optical signals. MEMS enable these systems by combining excellent capabilities and optical properties of macroscopic optomechanics with dense integration of multiple actuators on a single chip. Such optical MEMS present common design and process challenges, such as multiple electrical and optical IO, optical surface quality, optical integration density (fill factor) and actuator performance and reliability. We have used general design approaches such as pure-flexure design, electrostatic actuation and residual stress engineering in addressing these challenges. On several examples in this paper we illustrate these approaches along with underlying design tradeoffs and process requirements. We also describe specific numerical techniques useful for electrostatic actuator optimization and for analyzing the effects of residual stress., Comment: https://www.transducer-research-foundation.org/technical_digests/past_digests.html

Data center networks are experiencing unprecedented exponential growth, mostly driven by the continuous computing demands in machine learning and artificial intelligence algorithms. Within this realm, optical networking offers numerous advantages, including low latency, energy efficiency, and bandwidth transparency, positioning it as a compelling alternative to its electronic counterparts. In this work, we showcase a range of software-defined optical networking applications deployed on a general-purpose programmable integrated photonic processor. Leveraging graph-based theory, we experimentally demonstrate dynamic optical interconnects, circuit switching, and multicasting on the same photonic platform, yielding remarkable results in terms of crosstalk and reconfiguration speed. Our approach harnesses the benefits of reconfigurability and reliability, paving the way for a new generation of high-performance optical devices tailored for data center and computing clusters.

One key piece of evidence for dark matter is the flat rotation curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here. A model of relativistic frame effects on Doppler shifts due to the slightly curved frames of an emitting galaxy and the Milky Way is derived. This model predicts observed Doppler shifted spectra (in excess of the luminous mass) based only on the observed luminous matter profile and one free model parameter. Fits to the 175 galaxies reported in the SPARC database of galactic rotation profiles and accurate photometry measurements are compared between this novel model and dark matter and MOND (RAR) models. We find on the SPARC sample of 175 galaxies; that MOND-RAR has an average reduced chisquare of $\chi^2_r = 4.22$ for 175 galaxies fitted, the isothermal dark matter model has $\chi^2_r = 1.90$ for 165 galaxies fitted, and the new model we present has $\chi^2_r = 2.39$ for 172 galaxies fitted. Implications of this model are discussed., Comment: 19 pages, 19 figures

A variety of topological Hamiltonians have been demonstrated in photonic platforms, leading to fundamental discoveries and enhanced robustness in applications such as lasing, sensing, and quantum technologies. To date, each topological photonic platform implements a specific type of Hamiltonian with inexistent or limited reconfigurability. Here, we propose and demonstrate different topological models by using the same reprogrammable integrated photonics platform, consisting of a hexagonal mesh of silicon Mach-Zehnder interferometers with phase-shifters. We specifically demonstrate a one-dimensional Su-Schrieffer-Heeger Hamiltonian supporting a localized topological edge mode and a higher-order topological insulator based on a two-dimensional breathing Kagome Hamiltonian with three corner states. These results highlight a nearly universal platform for topological models that may fast-track research progress toward applications of topological photonics and other coupled systems.

Rectification describes the generation of a quasistatic component from an oscillating field, such as an electric polarization in optical rectification, or a structural distortion in nonlinear phononic rectification. Here, we present a third fundamental process for magnetization, in which spin precession is rectified along the coordinates of a nonlinearly driven magnon mode in an antiferromagnet. We demonstrate theoretically that a quasistatic magnetization can be induced by transient spin canting in response to the coherent excitation of a chiral phonon mode that produces an effective magnetic field for the spins. This mechanism, which we call nonlinear magnonic rectification, is generally applicable to magnetic systems that exhibit infrared-active chiral phonon modes. Our results serve as an example of light-induced weak ferromagnetism and open a promising avenue towards creating dynamical spin configurations that are not accessible in equilibrium.

Real-time semantic segmentation is a challenging task that requires high-accuracy models with low-inference times. Implementing these models on embedded systems is limited by hardware capability and memory usage, which produces bottlenecks. We propose an efficient model for real-time semantic segmentation called JetSeg, consisting of an encoder called JetNet, and an improved RegSeg decoder. The JetNet is designed for GPU-Embedded Systems and includes two main components: a new light-weight efficient block called JetBlock, that reduces the number of parameters minimizing memory usage and inference time without sacrificing accuracy; a new strategy that involves the combination of asymmetric and non-asymmetric convolutions with depthwise-dilated convolutions called JetConv, a channel shuffle operation, light-weight activation functions, and a convenient number of group convolutions for embedded systems, and an innovative loss function named JetLoss, which integrates the Precision, Recall, and IoUB losses to improve semantic segmentation and reduce computational complexity. Experiments demonstrate that JetSeg is much faster on workstation devices and more suitable for Low-Power GPU-Embedded Systems than existing state-of-the-art models for real-time semantic segmentation. Our approach outperforms state-of-the-art real-time encoder-decoder models by reducing 46.70M parameters and 5.14% GFLOPs, which makes JetSeg up to 2x faster on the NVIDIA Titan RTX GPU and the Jetson Xavier than other models. The JetSeg code is available at https://github.com/mmontielpz/jetseg.

Recent experimental studies have demonstrated the possibility of utilizing strong terahertz pulses to manipulate individual ferroic orders on pico- and femtosecond timescales. Here, we extend these findings and showcase the simultaneous manipulation of multiple ferroic orders in BiFeO$_3$, a material that is both ferroelectric and antiferromagnetic at room temperature. We find a concurrent enhancement of ferroelectric and antiferromagnetic second-harmonic generation (SHG) following the resonant excitation of a high-frequency fully-symmetric phonon mode. Based on first-principles calculations and phenomenological modeling, we ascribe this observation to the inherent coupling of the two ferroic orders to the nonequilibrium distortions induced in the crystal lattice by nonlinearly driven phonons. Our finding highlights the potential of nonlinear phononics as a technique for manipulating multiple ferroic order parameters at once. In addition, this approach provides a promising avenue to studying the dynamical magnetization and polarization behavior, as well as their intrinsic coupling, on ultrashort timescales., Comment: 20 pages, 6 figures

I propose modified set of creation and annihilation operators for the Schr\"odinger representation which is compatible with the Fock representation which differs from previous works. I take into account the relation between different non-unitary vacuums obtained from restricted frameworks like the relation between the Minkowski and Unruh vacuums., Comment: 6 pages

The electronic structure and the magnetic properties of iron-based superconductors are highly sensitive to the pnictogen height. Coherent excitation of the $A_{1g}$ phonon by femtosecond laser directly modulates the pnictogen height, which has been used to control the physical properties of iron-based superconductors. Previous studies show that the driven $A_{1g}$ phonon resulted in a transient increase of the pnictogen height in BaFe$_2$As$_2$, favoring an enhanced Fe magnetic moment. Here, we use time-resolved broadband terahertz spectroscopy to investigate the dynamics of BaFe$_2$As$_2$ in the $A_{1g}$ phonon driven state. Below the spin-density wave (SDW) transition temperature, we observe a transient gap generation at early time delays. A similar transient feature is observed in the normal state up to room temperature., Comment: 6 pages, 4 figures

Synchrotrons are powerful and productive in revealing the spatiotemporal complexities in matter. However, X-ray pulses produced by the synchrotrons are predetermined in specific patterns and widths, limiting their operational flexibility and temporal resolution. Here, we introduce the on-chip picosecond synchrotron pulse shaper that shapes the sub-nm-wavelength hard X-ray pulses at individual beamlines, flexibly and efficiently beyond the synchrotron pulse limit. The pulse shaper is developed using the widely available silicon-on-insulator technology, oscillates in torsional motion at the same frequency or at harmonics of the storage ring, and manipulates X-ray pulses through the narrow Bragg peak of the crystalline silicon. Stable pulse manipulation is achieved by synchronizing the shaper timing to the X-ray timing using electrostatic closed-loop control. Tunable shaping windows down to 40 $ps$ are demonstrated, allowing X-ray pulse picking, streaking, and slicing in the majority of worldwide synchrotrons. The compact, on-chip shaper offers a simple but versatile approach to boost synchrotron operating flexibility and to investigate structural dynamics from condensed matter to biological systems beyond the current synchrotron-source limit.

The linearization of a microwave photonic link based on a dual-parallel Mach-Zehnder modulator is theoretically described and experimentally demonstrated. Up to four different radio frequency tones are considered in the study, which allow us to provide a complete mathematical description of all third-order distortion terms that arise at the photodetector. Simulations show that a complete linearization is obtained by properly tuning the DC bias voltages and processing the optical carrier and. As a result, a suppression of 17 dBm is experimentally obtained for the third-order distortion terms, as well as a SDFR improvement of 3 dB. The proposed linearization method enables the simultaneous modulation of four different signals without the need of additional radio frequency components, which is desirable to its implementation in integrated optics and makes it suitable for several applications in microwave photonics.

A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can easily identify if an arm is pointing in some general direction. The Sawyer robotic arm is used for developing and training this intelligent algorithm. Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to cover the entire joint configuration space. In this work, orthogonal arrays are used, similar to the Taguchi method, to efficiently span the joint space with the minimal number of training images. This ``optimally'' generated database is used to train the custom ANN and its degree of accuracy is on average equal to twice the smallest joint displacement step used for database generation. A pre-trained ANN will be useful for estimating the postures of robotic manipulators used on space stations, spacecraft, and rovers as an auxiliary tool or for contingency plans.

Many nonlinear systems are described by eigenmodes with amplitude-dependent frequencies, interacting strongly whenever the frequencies become commensurate at internal resonances. Fast energy exchange via the resonances holds the key to rich dynamical behavior, such as time-varying relaxation rates and signatures of nonergodicity in thermal equilibrium, revealed in the recent experimental and theoretical studies of micro and nanomechanical resonators. However, a universal yet intuitive physical description for these diverse and sometimes contradictory experimental observations remains elusive. Here we experimentally reveal persistent nonlinear phase-locked states occurring at internal resonances and demonstrate that they are essential for understanding the transient dynamics of nonlinear systems with coupled eigenmodes. The measured dynamics of a fully observable micromechanical resonator system are quantitatively described by the lower frequency mode entering, maintaining, and exiting a persistent phase-locked period tripling state generated by the nonlinear driving force exerted by the higher frequency mode. This model describes the observed phase-locked coherence times, the direction and magnitude of the energy exchange, and the resulting non-monotonic mode energy evolution. Depending on the initial relative phase, the system selects distinct relaxation pathways, either entering or bypassing the locked state. The described persistent phase-locking is not limited to particular frequency fractions or types of nonlinearities and may advance nonlinear resonator systems engineering across physical domains, including photonics as well as nanomechanics.

Digital electronics is a technological cornerstone in our modern society which has covered the increasing demand in computing power during the last decades thanks to a periodic doubling of transistor density and power efficiency in integrated circuits. Currently, such scaling laws are reaching their fundamental limits, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real time analog multi-data processing, e.g., medical diagnostic imaging, robotic control and remote sensing, among others. In this scenario, an analog computing approach implemented in a real-time reconfigurable non-electronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the benefits of PIP technology (and integrated photonics in general). Here, we present the foundations of a new computation theory, termed Analog Programmable-Photonic Computation (APC), explicitly designed to unleash the full potential of PIP. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models, can be implemented in other technologies (e.g. in electronics, acoustics or using metamaterials) and, consequently, exhibits the potential to spark a ground-breaking impact on our information society.

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.

Optical scanning is a prevalent technique for optical neural interfaces where light delivery with high spatial and temporal precision is desired. However, due to the sequential nature of point-scanning techniques, the settling time of optical modulators is a major bottleneck for throughput and limits random-access targeting capabilities. While fast lateral scanners exist, commercially available varifocal elements are constrained to >3ms settling times, limiting the speed of the overall system to hundreds of Hz. Faster focusing methods exist but cannot combine fast operation and dwelling capability with electrical and optical efficiency. Here, we present a varifocal mirror comprised of an array of piston-motion MEMS micromirrors and a custom driver ASIC, offering fast operation with dwelling capability while maintaining high diffraction efficiency. The ASIC features a reconfigurable nonlinear DAC to simultaneously compensate for the built-in nonlinearity of electrostatic actuators and the global process variations in MEMS mirrors. Experimental results demonstrate a wide continuous sweeping range that spans 22 distinctly resolvable depth planes with refresh rates greater than 12 kHz., Comment: This work has been submitted to the IEEE for possible publication