Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light–matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell’s equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

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Binary vectors are an indispensable component of modern Agrobacterium tumefaciens-mediated plant genetic transformation systems. A remarkable variety of binary plasmids have been developed to support the cloning and transfer of foreign genes into plant cells. The majority of these systems, however, are limited to the cloning and transfer of just a single gene of interest. Thus, plant biologists and biotechnologists face a major obstacle when planning the introduction of multigene traits into transgenic plants. Here, we describe the assembly of multitransgene binary vectors by using a combination of engineered zinc finger nucleases (ZFNs) and homing endonucleases. Our system is composed of a modified binary vector that has been engineered to carry an array of unique recognition sites for ZFNs and homing endonucleases and a family of modular satellite vectors. By combining the use of designed ZFNs and commercial restriction enzymes, multiple plant expression cassettes were sequentially cloned into the acceptor binary vector. Using this system, we produced binary vectors that carried up to nine genes. Arabidopsis (Arabidopsis thaliana) protoplasts and plants were transiently and stably transformed, respectively, by several multigene constructs, and the expression of the transformed genes was monitored across several generations. Because ZFNs can potentially be engineered to digest a wide variety of target sequences, our system allows overcoming the problem of the very limited number of commercial homing endonucleases. Thus, users of our system can enjoy a rich resource of plasmids that can be easily adapted to their various needs, and since our cloning system is based on ZFN and homing endonucleases, it may be possible to reconstruct other types of binary vectors and adapt our vectors for cloning on multigene vector systems in various binary plasmids.

These authors contributed equally to this workOur visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has revolutionized the field of optics in recent years by enabling the manipulation of light-matter interaction with subwavelength structures (1-3). However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages (4-6).But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high nonlinearity of the problem (7-8). Here, we harness the power of Deep Learning, a new path in modern machine learning, and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach also addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmon's mediated cancer thermotherapy.While computer science has been harnessed to address the diffraction limit in imaging and characterization on one hand (super-resolution techniques such as PALM and STORM techniques and more (9-12)) and to assist with the design process on the other hand (13-19) to date no computational technique is capable of addressing both aspects in an integrated manner.Here, we present an integrated deep learning (DL) approach and show how deep neural networks

We experimentally demonstrate coherent control of the nonlinear response of optical second harmonic generation in resonant nanostructures beyond the weak-field regime. Contrary to common perception, we show that maximizing the intensity of the pulse does not yield the strongest nonlinear power-law response. We show this effect emerges from the temporally asymmetric photo-induced response in a resonant mediated non-instantaneous interaction. We develop a novel theoretical approach which captures the photoinduced nonlinearities in resonant nanostructures beyond the two photon description and give an intuitive picture to the observed non-instantaneous phenomena.Nanostructures (NS) have revolutionized light matter interaction allowing for on demand control of unique optical [1,2], electrical [3,4] and mechanical properties [5], both in linear and nonlinear regimes [6][7][8][9]. In the past decade, much research has been performed on the optical nonlinearity of NSs emerging from their energy confinement [10] and geometrical architecture [11,12] contributing in both their single and collective responses [13][14][15]. Commonly enhanced by resonant NSs, the photoinduced nonlinear interaction in NSs has been mostly studied within the framework of the instantaneous response of these materials [16][17][18], meaning that the nonlinear medium interacts simultaneously with all interacting waves. While this instantaneous picture has provided a model describing the observations of rich nonlinear phenomena, it does not capture the full nonlinear dynamical response, which is fundamentally non-instantaneous. This is apparent in current research frontiers, where the study of the ultrafast, out-of-equilibrium, electronic dynamics in NSs has gained much attention [19][20][21][22][23][24][25][26][27][28]. However, the non-instantaneous contribution inherent to resonant interaction in these systems has been so far mostly overlooked.The non-instantaneous contribution inherent to nonlinear resonant dynamics is well known in atomic and molecular systems, which is of particular importance in multiphoton processes [29]. Enhancement by orders of magnitude of electronic transitions in atomic systems [30,31] as well as for large organic molecules [32,33], has been enabled by spectrally shaping the pulse to be compatible with the non-instantaneous response in resonant mediated interactions, via coherent control schemes. However, for resonant NSs, applying such pulse shaping methods to enhance nonlinear processes have been so far limited, since these require the interacting pulse spectrum to be much broader than the resonant linewidth, which is typically not fulfilled for NSs. Therefore, pulse shaping has been mostly shown for controlling the linear response in plasmonic systems [34][35][36][37][38] or for multicolor second harmonic generation (SHG) imaging [39]. As the resonant mediated non-instantaneous process also contributes to the power-law response, it is expected that this fundamental characteristic of the nonlinear dynamical response wil...