Passive frequency comb generation at radiofrequency for ranging applications

Optical frequency combs, featuring evenly spaced spectral lines, have been extensively studied and applied to metrology, signal processing, and sensing. Recently, frequency comb generation has been also extended to MHz frequencies by harnessing nonlinearities in microelectromechanical membranes. However, the generation of frequency combs at radio frequencies (RF) has been less explored, together with their potential application in wireless technologies. In this work, we demonstrate an RF system able to wirelessly and passively generate frequency combs. This circuit, which we name quasi-harmonic tag (qHT), offers a battery-free solution for far-field ranging of unmanned vehicles (UVs) in GPS-denied settings, and it enables a strong immunity to multipath interference, providing better accuracy than other RF approaches to far-field ranging. Here, we discuss the principle of operation, design, implementation, and performance of qHTs used to remotely measure the azimuthal distance of a UV flying in an uncontrolled electromagnetic environment. We show that qHTs can wirelessly generate frequency combs with μWatt-levels of incident power by leveraging the nonlinear interaction between an RF parametric oscillator and a high quality factor piezoelectric microacoustic resonator. Our technique for frequency comb generation opens new avenues for a wide range of RF applications beyond ranging, including timing, computing and sensing.

Beyond MD17: the reactive xxMD dataset

System specific neural force fields (NFFs) have gained popularity in computational chemistry. One of the most popular datasets as a bencharmk to develop NFF models is the MD17 dataset and its subsequent extension. These datasets comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampled from direct adiabatic dynamics. However, many chemical reactions involve significant molecular geometrical deformations, for example, bond breaking. Therefore, MD17 is inadequate to represent a chemical reaction. To address this limitation in MD17, we introduce a new dataset, called Extended Excited-state Molecular Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from direct nonadiabatic dynamics, and the energies are computed at both multireference wavefunction theory and density functional theory. We show that the xxMD dataset involves diverse geometries which represent chemical reactions. Assessment of NFF models on xxMD dataset reveals significantly higher predictive errors than those reported for MD17 and its variants. This work underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability.

Thermodynamic behavior of correlated electron-hole fluids in van der Waals heterostructures

Coupled two-dimensional electron-hole bilayers provide a unique platform to study strongly correlated Bose-Fermi mixtures in condensed matter. Electrons and holes in spatially separated layers can bind to form interlayer excitons, composite Bosons expected to support high-temperature exciton condensates. The interlayer excitons can also interact strongly with excess charge carriers when electron and hole densities are unequal. Here, we use optical spectroscopy to quantitatively probe the local thermodynamic properties of strongly correlated electron-hole fluids in MoSe2/hBN/WSe2 heterostructures. We observe a discontinuity in the electron and hole chemical potentials at matched electron and hole densities, a definitive signature of an excitonic insulator ground state. The excitonic insulator is stable up to a Mott density of ~0.8 × 1012 cm-2 and has a thermal ionization temperature of ~70 K. The density dependence of the electron, hole, and exciton chemical potentials reveals strong correlation effects across the phase diagram. Compared with a non-interacting uniform charge distribution, the correlation effects lead to significant attractive exciton-exciton and exciton-charge interactions in the electron-hole fluid. Our work highlights the unique quantum behavior that can emerge in strongly correlated electron-hole systems.

Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo

To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.

Large-scale optical characterization of solid-state quantum emitters

Solid-state quantum emitters have emerged as a leading quantum memory for quantum networking applications. However, standard optical characterization techniques are neither efficient nor repeatable at scale. Here we introduce and demonstrate spectroscopic techniques that enable large-scale, automated characterization of colour centres. We first demonstrate the ability to track colour centres by registering them to a fabricated machine-readable global coordinate system, enabling a systematic comparison of the same colour centre sites over many experiments. We then implement resonant photoluminescence excitation in a widefield cryogenic microscope to parallelize resonant spectroscopy, achieving two orders of magnitude speed-up over confocal microscopy. Finally, we demonstrate automated chip-scale characterization of colour centres and devices at room temperature, imaging thousands of microscope fields of view. These tools will enable the accelerated identification of useful quantum emitters at chip scale, enabling advances in scaling up colour centre platforms for quantum information applications, materials science and device design and characterization.

An end-to-end pipeline for succinic acid production at an industrially relevant scale using Issatchenkia orientalis

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34-90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.

ReCANVo: A database of real-world communicative and affective nonverbal vocalizations

Nonverbal vocalizations, such as sighs, grunts, and yells, are informative expressions within typical verbal speech. Likewise, individuals who produce 0-10 spoken words or word approximations ("minimally speaking" individuals) convey rich affective and communicative information through nonverbal vocalizations even without verbal speech. Yet, despite their rich content, little to no data exists on the vocal expressions of this population. Here, we present ReCANVo: Real-World Communicative and Affective Nonverbal Vocalizations - a novel dataset of non-speech vocalizations labeled by function from minimally speaking individuals. The ReCANVo database contains over 7000 vocalizations spanning communicative and affective functions from eight minimally speaking individuals, along with communication profiles for each participant. Vocalizations were recorded in real-world settings and labeled in real-time by a close family member who knew the communicator well and had access to contextual information while labeling. ReCANVo is a novel database of nonverbal vocalizations from minimally speaking individuals, the largest available dataset of nonverbal vocalizations, and one of the only affective speech datasets collected amidst daily life across contexts.

Explainable AI via learning to optimize

Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the "learn to optimize" (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy .

Immune resilience despite inflammatory stress promotes longevity and favorable health outcomes including resistance to infection

Some people remain healthier throughout life than others but the underlying reasons are poorly understood. Here we hypothesize this advantage is attributable in part to optimal immune resilience (IR), defined as the capacity to preserve and/or rapidly restore immune functions that promote disease resistance (immunocompetence) and control inflammation in infectious diseases as well as other causes of inflammatory stress. We gauge IR levels with two distinct peripheral blood metrics that quantify the balance between (i) CD8+ and CD4+ T-cell levels and (ii) gene expression signatures tracking longevity-associated immunocompetence and mortality-associated inflammation. Profiles of IR metrics in ~48,500 individuals collectively indicate that some persons resist degradation of IR both during aging and when challenged with varied inflammatory stressors. With this resistance, preservation of optimal IR tracked (i) a lower risk of HIV acquisition, AIDS development, symptomatic influenza infection, and recurrent skin cancer; (ii) survival during COVID-19 and sepsis; and (iii) longevity. IR degradation is potentially reversible by decreasing inflammatory stress. Overall, we show that optimal IR is a trait observed across the age spectrum, more common in females, and aligned with a specific immunocompetence-inflammation balance linked to favorable immunity-dependent health outcomes. IR metrics and mechanisms have utility both as biomarkers for measuring immune health and for improving health outcomes.

Coherent antiferromagnetic spintronics

Antiferromagnets have attracted extensive interest as a material platform in spintronics. So far, antiferromagnet-enabled spin-orbitronics, spin-transfer electronics and spin caloritronics have formed the bases of antiferromagnetic spintronics. Spin transport and manipulation based on coherent antiferromagnetic dynamics have recently emerged, pushing the developing field of antiferromagnetic spintronics towards a new stage distinguished by the features of spin coherence. In this Review, we categorize and analyse the critical effects that harness the coherence of antiferromagnets for spintronic applications, including spin pumping from monochromatic antiferromagnetic magnons, spin transmission via phase-correlated antiferromagnetic magnons, electrically induced spin rotation and ultrafast spin-orbit effects in antiferromagnets. We also discuss future opportunities in research and applications stimulated by the principles, materials and phenomena of coherent antiferromagnetic spintronics.

Electro-thermal actuation in percolative ferroelectric polymer nanocomposites

The interconversion between electrical and mechanical energies is pivotal to ferroelectrics to enable their applications in transducers, actuators and sensors. Ferroelectric polymers exhibit a giant electric-field-induced strain (>4.0%), markedly exceeding the actuation strain (≤1.7%) of piezoelectric ceramics and crystals. However, their normalized elastic energy densities remain orders of magnitude smaller than those of piezoelectric ceramics and crystals, severely limiting their practical applications in soft actuators. Here we report the use of electro-thermally induced ferroelectric phase transition in percolative ferroelectric polymer nanocomposites to achieve high strain performance in electric-field-driven actuation materials. We demonstrate a strain of over 8% and an output mechanical energy density of 11.3 J cm^-3 at an electric field of 40 MV m^-1 in the composite, outperforming the benchmark relaxor single-crystal ferroelectrics. This approach overcomes the trade-off between mechanical modulus and electro-strains in conventional piezoelectric polymer composites and opens up an avenue for high-performance ferroelectric actuators.

Influence maximization in Boolean networks

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network

Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive, free-space optical layers. Here, we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple, arbitrarily-selected linear transformations through a single diffractive network trained using deep learning. In this framework, an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic, and different target linear transformations (complex-valued) are uniquely assigned to different combinations of input/output polarization states. The transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations. Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons (N) approaches [Formula: see text], where N_i and N_o represent the number of pixels at the input and output fields-of-view, respectively, and N_p refers to the number of unique linear transformations assigned to different input/output polarization combinations. This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.

Computational design of quantum defects in two-dimensional materials

Missing atoms or atom substitutions (point defects) in crystal lattices in two-dimensional (2D) materials are potential hosts for emerging quantum technologies, such as single-photon emitters and spin quantum bits (qubits). First-principles-guided design of quantum defects in 2D materials is paving the way for rational spin qubit discovery. Here we discuss the frontier of first-principles theory development and the challenges in predicting the critical physical properties of point defects in 2D materials for quantum information technology, in particular for optoelectronic and spin-optotronic properties. Strong many-body interactions at reduced dimensionality require advanced electronic structure methods beyond mean-field theory. The great challenges for developing theoretical methods that are appropriate for strongly correlated defect states, as well as general approaches for predicting spin relaxation and the decoherence time of spin defects, are yet to be addressed.

Principled approach to the selection of the embedding dimension of networks

Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension - small enough to be efficient and large enough to be effective - is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

Enhancing the graphene photocurrent using surface plasmons and a p-n junction

The recently proposed concept of graphene photodetectors offers remarkable properties such as unprecedented compactness, ultrabroadband detection, and an ultrafast response speed. However, owing to the low optical absorption of pristine monolayer graphene, the intrinsically low responsivity of graphene photodetectors significantly hinders the development of practical devices. To address this issue, numerous efforts have thus far been made to enhance the light-graphene interaction using plasmonic structures. These approaches, however, can be significantly advanced by leveraging the other critical aspect of graphene photoresponsivity enhancement-electrical junction control. It has been reported that the dominant photocarrier generation mechanism in graphene is the photothermoelectric (PTE) effect. Thus, the two energy conversion mechanisms involved in the graphene photodetection process are light-to-heat and heat-to-electricity conversions. In this work, we propose a meticulously designed device architecture to simultaneously enhance the two conversion efficiencies. Specifically, a gap plasmon structure is used to absorb a major portion of the incident light to induce localized heating, and a pair of split gates is used to produce a p-n junction in graphene to augment the PTE current generation. The gap plasmon structure and the split gates are designed to share common key components so that the proposed device architecture concurrently realizes both optical and electrical enhancements. We experimentally demonstrate the dominance of the PTE effect in graphene photocurrent generation and observe a 25-fold increase in the generated photocurrent compared to the un-enhanced cases. While further photocurrent enhancement can be achieved by applying a DC bias, the proposed device concept shows vast potential for practical applications.

A new brilliantly blue-emitting luciferin-luciferase system from Orfelia fultoni and Keroplatinae (Diptera)

Larvae of O. fultoni (Keroplatidae: Keroplatinae), which occur along river banks in the Appalachian Mountains in Eastern United States, produce the bluest bioluminescence among insects from translucent areas associated to black bodies, which are  located mainly in the anterior and posterior parts of the body. Although closely related to Arachnocampa spp (Keroplatidae: Arachnocampininae), O.fultoni has a morphologically and biochemically distinct bioluminescent system which evolved independently, requiring a luciferase enzyme, a luciferin, a substrate binding fraction (SBF) that releases luciferin in the presence of mild reducing agents, molecular oxygen, and no additional cofactors. Similarly, the closely related Neoceroplatus spp, shares the same kind of luciferin-luciferase system of Orfelia fultoni. However, the molecular properties, identities and functions of luciferases, SBF and luciferin of Orfelia fultoni and other  luminescent members of the Keroplatinae subfamily still remain to be fully elucidated. Using O. fultoni as a source of luciferase, and the recently discovered non-luminescent cave worm Neoditomiya sp as the main source of luciferin and SBF, we isolated and initially characterized these compounds. The luciferase of O. fultoni is a stable enzyme active as an apparent trimer (220 kDa) composed of ~70 kDa monomers, with an optimum pH of 7.8. The SBF, which is found in the black bodies in Orfelia fultoni and in smaller dark granules in Neoditomiya sp, consists of a high molecular weight complex of luciferin and proteins, apparently associated to mitochondria. The luciferin, partially purified from hot extracts by a combination of anion exchange chromatography and TLC, is a very polar and weakly fluorescent compound, whereas its oxidized product displays blue fluorescence with an emission spectrum matching the bioluminescence spectrum (~460 nm), indicating that it is oxyluciferin. The widespread occurrence of luciferin and SBF in both luminescent and non-luminescent Keroplatinae larvae indicate an additional important biological function for the substrate, and therefore the name keroplatin.

Ab initio electron-two-phonon scattering in GaAs from next-to-leading order perturbation theory

Electron-phonon (e-ph) interactions are usually treated in the lowest order of perturbation theory. Here we derive next-to-leading order e-ph interactions, and compute from first principles the associated electron-two-phonon (2ph) scattering rates. The derivations involve Matsubara sums of two-loop Feynman diagrams, and the numerical calculations are challenging as they involve Brillouin zone integrals over two crystal momenta and depend critically on the intermediate state lifetimes. Using Monte Carlo integration together with a self-consistent update of the intermediate state lifetimes, we compute and converge the 2ph scattering rates, and analyze their energy and temperature dependence. We apply our method to GaAs, a weakly polar semiconductor with dominant optical-mode long-range e-ph interactions. We find that the 2ph scattering rates are as large as nearly half the value of the one-phonon rates, and that including the 2ph processes is necessary to accurately predict the electron mobility in GaAs from first principles.

Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing

Optical coherence tomography (OCT) suffers from speckle noise due to the high spatial coherence of the utilized light source, leading to significant reductions in image quality and diagnostic capabilities. In the past, angular compounding techniques have been applied to suppress speckle noise. However, existing image registration methods usually guarantee pure angular compounding only within a relatively small field of view in the focal region, but produce spatial averaging in the other regions, resulting in resolution loss and image blur. This work develops an image registration model to correctly localize the real-space location of every pixel in an OCT image, for all depths. The registered images captured at different angles are fused into a speckle-reduced composite image. Digital focusing, based on the convolution of the complex OCT images and the conjugate of the point spread function (PSF), is studied to further enhance lateral resolution and contrast. As demonstrated by experiments, angular compounding with our improved image registration techniques and digital focusing, can effectively suppress speckle noise, enhance resolution and contrast, and reveal fine structures in ex-vivo imaged tissue.

Edible unclonable functions

Counterfeit medicines are a fundamental security problem. Counterfeiting medication poses a tremendous threat to patient safety, public health, and the economy in developed and less developed countries. Current solutions are often vulnerable due to the limited security levels. We propose that the highest protection against counterfeit medicines would be a combination of a physically unclonable function (PUF) with on-dose authentication. A PUF can provide a digital fingerprint with multiple pairs of input challenges and output responses. On-dose authentication can verify every individual pill without removing the identification tag. Here, we report on-dose PUFs that can be directly attached onto the surface of medicines, be swallowed, and digested. Fluorescent proteins and silk proteins serve as edible photonic biomaterials and the photoluminescent properties provide parametric support of challenge-response pairs. Such edible cryptographic primitives can play an important role in pharmaceutical anti-counterfeiting and other security applications requiring immediate destruction or vanishing features.

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

Nature-inspired chiral metasurfaces for circular polarization detection and full-Stokes polarimetric measurements

The manipulation and characterization of light polarization states are essential for many applications in quantum communication and computing, spectroscopy, bioinspired navigation, and imaging. Chiral metamaterials and metasurfaces facilitate ultracompact devices for circularly polarized light generation, manipulation, and detection. Herein, we report bioinspired chiral metasurfaces with both strong chiral optical effects and low insertion loss. We experimentally demonstrated submicron-thick circularly polarized light filters with peak extinction ratios up to 35 and maximum transmission efficiencies close to 80% at near-infrared wavelengths (the best operational wavelengths can be engineered in the range of 1.3-1.6 µm). We also monolithically integrated the microscale circular polarization filters with linear polarization filters to perform full-Stokes polarimetric measurements of light with arbitrary polarization states. With the advantages of easy on-chip integration, ultracompact footprints, scalability, and broad wavelength coverage, our designs hold great promise for facilitating chip-integrated polarimeters and polarimetric imaging systems for quantum-based optical computing and information processing, circular dichroism spectroscopy, biomedical diagnosis, and remote sensing applications.

High-efficiency, large-area, topology-optimized metasurfaces

Metasurfaces are ultrathin optical elements that are highly promising for constructing lightweight and compact optical systems. For their practical implementation, it is imperative to maximize the metasurface efficiency. Topology optimization provides a pathway for pushing the limits of metasurface efficiency; however, topology optimization methods have been limited to the design of microscale devices due to the extensive computational resources that are required. We introduce a new strategy for optimizing large-area metasurfaces in a computationally efficient manner. By stitching together individually optimized sections of the metasurface, we can reduce the computational complexity of the optimization from high-polynomial to linear. As a proof of concept, we design and experimentally demonstrate large-area, high-numerical-aperture silicon metasurface lenses with focusing efficiencies exceeding 90%. These concepts can be generalized to the design of multifunctional, broadband diffractive optical devices and will enable the implementation of large-area, high-performance metasurfaces in practical optical systems.