Hyperscaling in the Coherent Hyperspin Machine.

Classical and quantum systems are used to simulate the Ising Hamiltonian, an essential component in large-scale optimization and machine learning. However, as the system size increases, devices like quantum annealers and coherent Ising machines face an exponential drop in their success rate. Here, we introduce a novel approach involving high-dimensional embeddings of the Ising Hamiltonian and a technique called "dimensional annealing" to counteract the decrease in performance. This approach leads to an exponential improvement in the success rate and other performance metrics, slowing down the decline in performance as the system size grows. A thorough examination of convergence dynamics in high-performance computing validates the new methodology. Additionally, we suggest practical implementations using technologies like coherent Ising machines, all-optical systems, and hybrid digital systems. The proposed hyperscaling heuristics can also be applied to other quantum or classical Ising devices by adjusting parameters such as nonlinear gain, loss, and nonlocal couplings.

Referenceless characterization of complex media using physics-informed neural networks.

In this work, we present a method to characterize the transmission matrices of complex scattering media using a physics-informed, multi-plane neural network (MPNN) without the requirement of a known optical reference field. We use this method to accurately measure the transmission matrix of a commercial multi-mode fiber without the problems of output-phase ambiguity and dark spots, leading to up to 58% improvement in focusing efficiency compared with phase-stepping holography. We demonstrate how our method is significantly more noise-robust than phase-stepping holography and show how it can be generalized to characterize a cascade of transmission matrices, allowing one to control the propagation of light between independent scattering media. This work presents an essential tool for accurate light control through complex media, with applications ranging from classical optical networks, biomedical imaging, to quantum information processing.

Extreme transport of light in spheroids of tumor cells.

Extreme waves are intense and unexpected wavepackets ubiquitous in complex systems. In optics, these rogue waves are promising as robust and noise-resistant beams for probing and manipulating the underlying material. Localizing large optical power is crucial especially in biomedical systems, where, however, extremely intense beams have not yet been observed. We here discover that tumor-cell spheroids manifest optical rogue waves when illuminated by randomly modulated laser beams. The intensity of light transmitted through bio-printed three-dimensional tumor models follows a signature Weibull statistical distribution, where extreme events correspond to spatially-localized optical modes propagating within the cell network. Experiments varying the input beam power and size indicate that the rogue waves have a nonlinear origin. We show that these nonlinear optical filaments form high-transmission channels with enhanced transmission. They deliver large optical power through the tumor spheroid, and can be exploited to achieve a local temperature increase controlled by the input wave shape. Our findings shed light on optical propagation in biological aggregates and demonstrate how nonlinear extreme event formation allows light concentration in deep tissues, paving the way to using rogue waves in biomedical applications, such as light-activated therapies.

Observation of terahertz transition from Fano resonances to bound states in the continuum.

Bound states in the continuum (BIC) in metamaterials have recently attracted attention for their promising applications in photonics. Here, we investigate the transition from Fano resonances to BIC, at terahertz (THz) frequencies, of a one-dimensional photonic crystal slab made of rectangular dielectric rods. Simulations performed by an analytical exact solution of the Maxwell equations showed that symmetry-protected, high-quality factor (Q), BIC emerge at normal incidence. For non-normal incidence, BIC couple with the freely propagating waves and appear in the scattering field as a Fano resonance. Simulations were verified by realizing the photonic crystal slab by 3D-printing technique. THz time-domain spectroscopy measurements as a function of the incidence angle matched the simulation to good accuracy and confirmed the evolution of Fano resonances to high-Q resonances typical of BIC. These results point out the design of highly sensitive and low-cost THz devices for sensing for a wide range of applications.

Single-shot polarimetry of vector beams by supervised learning.

States of light encoding multiple polarizations - vector beams - offer unique capabilities in metrology and communication. However, their practical application is limited by the lack of methods for measuring many polarizations in a scalable and compact way. Here we demonstrate polarimetry of vector beams in a single shot without any polarization optics. We map the beam polarization content into a spatial intensity distribution through light scattering and exploit supervised learning for single-shot measurements of multiple polarizations. We characterize structured light encoding up to nine polarizations with accuracy beyond 95% on each Stokes parameter. The method also allows us to classify beams with an unknown number of polarization modes, a functionality missing in conventional techniques. Our findings enable a fast and compact polarimeter for polarization-structured light, a general tool that may radically impact optical devices for sensing, imaging, and computing.

Multidimensional hyperspin machine.

From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and show that the hyperspin machine finds to a very good approximation the ground state of complex graphs. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call "dimensional annealing". When interpolating between the XY and the Ising model, the dimensional annealing substantially increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies.

Early Dietary Exposures Epigenetically Program Mammary Cancer Susceptibility through Igf1-Mediated Expansion of the Mammary Stem Cell Compartment.

Diet is a critical environmental factor affecting breast cancer risk, and recent evidence shows that dietary exposures during early development can affect lifetime mammary cancer susceptibility. To elucidate the underlying mechanisms, we used our established crossover feeding mouse model, where exposure to a high-fat and high-sugar (HFHS) diet during defined developmental windows determines mammary tumor incidence and latency in carcinogen-treated mice. Mammary tumor incidence is significantly increased in mice receiving a HFHS post-weaning diet (high-tumor mice, HT) compared to those receiving a HFHS diet during gestation (low-tumor mice, LT). The current study revealed that the mammary stem cell (MaSC) population was significantly increased in mammary glands from HT compared to LT mice. Igf1 expression was increased in mammary stromal cells from HT mice, where it promoted MaSC self-renewal. The increased Igf1 expression was induced by DNA hypomethylation of the Igf1 Pr1 promoter, mediated by a decrease in Dnmt3b levels. Mammary tissues from HT mice also had reduced levels of Igfbp5, leading to increased bioavailability of tissue Igf1. This study provides novel insights into how early dietary exposures program mammary cancer risk, demonstrating that effective dietary intervention can reduce mammary cancer incidence.

Terahertz waves dynamic diffusion in 3D printed structures.

Applications of metamaterials in the realization of efficient devices in the terahertz band have recently been considered to achieve wave deflection, focusing, amplitude manipulation and dynamical modulation. Terahertz metamaterials offer practical advantages since their structures have typical sizes of hundreds microns and are within the reach of current three-dimensional (3D) printing technologies. Here, we propose terahertz photonic structures composed of dielectric rods layers made of acrylonitrile styrene acrylate realized by low-cost, rapid, and versatile fused deposition modeling 3D-printing. Terahertz time-domain spectroscopy is employed for the experimental study of their spectral and dynamic response. Measured spectra are interpreted by using simulations performed by an analytical exact solution of the Maxwell equations for a general incidence geometry, by a field expansion as a sum over reciprocal lattice vectors. Results show that the structures possess specific spectral forbidden bands of the incident THz radiation depending on their optical and geometrical parameters. We also find evidence of disorder in the 3D printed structure resulting in the closure of the forbidden bands at frequencies above 0.3 THz. The size disorder of the structures is quantified by studying the dynamics diffusion of THz pulses as a function of the numbers of layers of dielectric rods. Comparison with simulations of light diffusion in photonic crystals with increasing disorder allows estimating the size distributions of elements. By using a Mean Squared Displacement model, from the broadening of the pulses' widths it is also possible to estimate the diffusion coefficient of the terahertz radiation in the photonic structures.

Metriplectic Structure of a Radiation-Matter-Interaction Toy Model.

A dynamical system defined by a metriplectic structure is a dissipative model characterized by a specific pair of tensors, which defines a Leibniz bracket; and a free energy, formed by a "Hamiltonian" and an entropy, playing the role of dynamics generator. Generally, these tensors are a Poisson bracket tensor, describing the Hamiltonian part of the dynamics, and a symmetric metric tensor, that models purely dissipative dynamics. In this paper, the metriplectic system describing a simplified two-photon absorption by a two-level atom is disclosed. The Hamiltonian component is sufficient to describe the free electromagnetic radiation. The metric component encodes the radiation-matter coupling, driving the system to an asymptotically stable state in which the excited level of the atom is populated due to absorption, and the radiation has disappeared. First, a description of the system is used, based on the real-imaginary decomposition of the electromagnetic field phasor; then, the whole metriplectic system is re-written in terms of the phase-amplitude pair, named Madelung variables. This work is intended as a first result to pave the way for applying the metriplectic formalism to many other irreversible processes in nonlinear optics.

Hyperbolic optics and superlensing in room-temperature KTN from self-induced k-space topological transitions.

A hyperbolic medium will transfer super-resolved optical waveforms with no distortion, support negative refraction, superlensing, and harbor nontrivial topological photonic phases. Evidence of hyperbolic effects is found in periodic and resonant systems for weakly diffracting beams, in metasurfaces, and even naturally in layered systems. At present, an actual hyperbolic propagation requires the use of metamaterials, a solution that is accompanied by constraints on wavelength, geometry, and considerable losses. We show how nonlinearity can transform a bulk KTN perovskite into a broadband 3D hyperbolic substance for visible light, manifesting negative refraction and superlensing at room-temperature. The phenomenon is a consequence of giant electro-optic response to the electric field generated by the thermal diffusion of photogenerated charges. Results open new scenarios in the exploration of enhanced light-matter interaction and in the design of broadband photonic devices.

Evidence of Chaotic Dynamics in Three-Soliton Collisions.

We observe chaotic optical wave dynamics characterized by erratic energy transfer and soliton annihilation and creation in the aftermath of a three-soliton collision in a photorefractive crystal. Irregular dynamics are found to be mediated by the nonlinear Raman effect, a coherent interaction that leads to nonreciprocal soliton energy exchange. Results extend the analogy between solitons and particles to the emergence of chaos in three-body physics and provide new insight into the origin of the irregular dynamics that accompany extreme and rogue waves.

Topological nanophotonics and artificial neural networks.

We propose the use of artificial neural networks to design and characterize photonic topological insulators. As a hallmark, the band structures of these systems show the key feature of the emergence of edge states, with energies lying within the energy gap of the bulk materials and localized at the boundary between regions of distinct topological invariants. We consider different structures such as one-dimensional photonic crystals, [Formula: see text]-symmetric chains and cylindrical systems and show how, through a machine learning application, one can identify the parameters of a complex topological insulator to obtain protected edge states at target frequencies. We show how artificial neural networks can be used to solve the long-standing quest for a solution to inverse problems solution and apply this to the cutting edge topic of topological nanophotonics.

Theory of Neuromorphic Computing by Waves: Machine Learning by Rogue Waves, Dispersive Shocks, and Solitons.

We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layered model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave. For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schrödinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization.

Firm characteristics and capabilities that enable superior performance in recessions.

Recessions are recurring events in which most firms suffer severe impacts while others are affected less or may even prosper. Notwithstanding, strategic management scholars have made little progress in understanding the reasons for these differences in performance, particularly in unstable macroeconomic environments such as Latin America. In this study, we link literatures on entrepreneurship and improvisation to create an integrative model that indicates characteristics and capabilities that enable a firm to adapt successfully to the recessionary environment. We use survey data from Brazilian firms on the 2008-2009 global recession, and we find that the firms that have superior performance in recessions are those that had, before the recession, 1) a propensity to recognize opportunities and 2) improvisation capabilities for fast and creative actions. We also find a moderating effect of entrepreneurial orientation.

The importance of immunity in the development of reliable animal models for psoriasis and atopic dermatitis.

Psoriasis (PS) and atopic dermatitis (AD) are common inflammatory skin diseases characterized by an imbalance in specific T-cell subsets, resulting in a specific cytokine profile in patients. Obtaining models closely resembling both pathologies along with a relevant clinical impact is crucial for the development of new therapies because of the high prevalence of these diseases. Single-gene mouse models developed until now do not fully reflect the complexity of these disorders, in part not only because of inherent differences between mice and humans but also because of the multifactorial nature of these pathologies. The skin-humanized mouse model developed by our group, based on a tissue engineering approach, has been used to test therapeutic strategies, although this methodology is still technically challenging and not widely available. The skin-humanized mouse models for PS and AD reproduce human skin phenotypes, providing valuable tools for drug development and testing in the preclinical setting. The tissue engineering approach allows the development of personalized medicine, covering the broad genotypic spectrum of these pathologies. This review highlights the main differences between available murine models focusing on the tissue-specific immunity of PS and AD. We discuss their contribution to unravel the complex pathophysiology of these diseases and to translate this knowledge into more accurate therapies.

Programming multi-level quantum gates in disordered computing reservoirs via machine learning.

Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates, including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates. We study various qudit gates, including the scaling properties of the algorithms with the size of the reservoir. Despite an initial low slop learning stage, TensorFlow turns out to be an extremely versatile resource for designing gates with complex media, including different models that use spatial light modulators with quantized modulation levels.

Nonlinear Optics: feature issue introduction.

This joint issue of Optics Express and Optical Materials Express features 18 state-of-the art articles that witness actual developments in nonlinear optics, including those by authors who participated in the international conference Nonlinear Optics held in Waikoloa, Hawaii from July 15 to 19, 2019. As an introduction, the editors provide a summary of these articles that cover all aspects of nonlinear optics, from basic nonlinear effects and novel frequency windows to innovative nonlinear materials and devices, thereby paving the way for new nonlinear optical concepts and forthcoming applications.

Topological photonic crystal fibers and ring resonators.

With an exact recursive approach, we study photonic crystal fibers and resonators with topological features induced by Aubry-Andre-Harper cladding modulation. We find nontrivial gaps and edge states at the interface between regions with different topological invariants. These structures show topological protection against symmetry-preserving local perturbations that do not close the gap and sustain strong field localization and energy concentration at a given radial distance. As topological light guiding and trapping devices, they may bring about many opportunities for both fundamentals and applications unachievable with conventional devices.

Humanization of Tumor Stroma by Tissue Engineering as a Tool to Improve Squamous Cell Carcinoma Xenograft.

The role of stroma is fundamental in the development and behavior of epithelial tumors. In this regard, limited growth of squamous cell carcinomas (SCC) or cell-lines derived from them has been achieved in immunodeficient mice. Moreover, lack of faithful recapitulation of the original human neoplasia complexity is often observed in xenografted tumors. Here, we used tissue engineering techniques to recreate a humanized tumor stroma for SCCs grafted in host mice, by combining CAF (cancer associated fibroblasts)-like cells with a biocompatible scaffold. The stroma was either co-injected with epithelial cell lines derived from aggressive SCC or implanted 15 days before the injection of the tumoral cells, to allow its vascularization and maturation. None of the mice injected with the cell lines without stroma were able to develop a SCC. In contrast, tumors were able to grow when SCC cells were injected into previously established humanized stroma. Histologically, all of the regenerated tumors were moderately differentiated SCC with a well-developed stroma, resembling that found in the original human neoplasm. Persistence of human stromal cells was also confirmed by immunohistochemistry. In summary, we provide a proof of concept that humanized tumor stroma, generated by tissue engineering, can facilitate the development of epithelial tumors in immunodeficient mice.

Anisotropic Optical Shock Waves in Isotropic Media with Giant Nonlocal Nonlinearity.

Dispersive shock waves in thermal optical media are nonlinear phenomena whose intrinsic irreversibility is described by time asymmetric quantum mechanics. Recent studies demonstrated that the nonlocal wave breaking evolves in an exponentially decaying dynamics ruled by the reversed harmonic oscillator, namely, the simplest irreversible quantum system in the rigged Hilbert spaces. The generalization of this theory to more complex scenarios is still an open question. In this work, we use a thermal third-order medium with an unprecedented giant Kerr coefficient, the m-cresol/nylon mixed solution, to access an extremely nonlinear, highly nonlocal regime and realize anisotropic shock waves with internal gaps. We compare our experimental observations to results obtained under similar conditions but in hemoglobin solutions from human red blood cells, and found that the gap formation strongly depends on the nonlinearity strength. We prove that a superposition of Gamow vectors in an ad hoc rigged Hilbert space, that is, a tensorial product between the reversed and the standard harmonic oscillators spaces, describes the beam propagation beyond the shock point. The anisotropy turns out from the interaction of trapping and antitrapping potentials. Our work furnishes the description of novel intriguing shock phenomena mediated by extreme nonlinearities.