Graphene and the following derivative 2D materials have been demonstrated to exhibit rich distinct optoelectronic properties, such as broadband optical response, strong and tunable light–mater interactions, and fast relaxations in the flexible nanoscale. Combining with optical platforms like fibers, waveguides, grating, and resonators, these materials has spurred a variety of active and passive applications recently. Herein, the optical and electrical properties of graphene, transition metal dichalcogenides, black phosphorus, MXene, and their derivative van der Waals heterostructures are comprehensively reviewed, followed by the design and fabrication of these 2D material‐based optical structures in implementation. Next, distinct devices, ranging from lasers to light emitters, frequency convertors, modulators, detectors, plasmonic generators, and sensors, are introduced. Finally, the state‐of‐art investigation progress of 2D material‐based optoelectronics offers a promising way to realize new conceptual and high‐performance applications for information science and nanotechnology. The outlook on the development trends and important research directions are also put forward.

Optical frequency combs, which emit pulses of light at discrete, equally spaced frequencies, are cornerstones of modern-day frequency metrology, precision spectroscopy, astronomical observations, ultrafast optics and quantum information. Chip-scale frequency combs, based on the Kerr and Raman nonlinearities in monolithic microresonators with ultrahigh quality factors, have recently led to progress in optical clockwork and observations of temporal cavity solitons. But the chromatic dispersion within a laser cavity, which determines the comb formation, is usually difficult to tune with an electric field, whether in microcavities or fibre cavities. Such electrically dynamic control could bridge optical frequency combs and optoelectronics, enabling diverse comb outputs in one resonator with fast and convenient tunability. Arising from its exceptional Fermi-Dirac tunability and ultrafast carrier mobility, graphene has a complex optical dispersion determined by its optical conductivity, which can be tuned through a gate voltage. This has brought about optoelectronic advances such as modulators, photodetectors and controllable plasmonics. Here we demonstrate the gated intracavity tunability of graphene-based optical frequency combs, by coupling the gate-tunable optical conductivity to a silicon nitride photonic microresonator, thus modulating its second- and higher-order chromatic dispersions by altering the Fermi level. Preserving cavity quality factors up to 10 in the graphene-based comb, we implement a dual-layer ion-gel-gated transistor to tune the Fermi level of graphene across the range 0.45-0.65 electronvolts, under single-volt-level control. We use this to produce charge-tunable primary comb lines from 2.3 terahertz to 7.2 terahertz, coherent Kerr frequency combs, controllable Cherenkov radiation and controllable soliton states, all in a single microcavity. We further demonstrate voltage-tunable transitions from periodic soliton crystals to crystals with defects, mapped by our ultrafast second-harmonic optical autocorrelation. This heterogeneous graphene microcavity, which combines single-atomic-layer nanoscience and ultrafast optoelectronics, will help to improve our understanding of dynamical frequency combs and ultrafast optics.

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Abstract-In this paper, we present an image parsing to text description (I2T) framework that generates text descriptions of image and video content based on image understanding. The proposed I2T framework follows three steps: 1) Input images (or video frames) are decomposed into their constituent visual patterns by an image parsing engine, in a spirit similar to parsing sentences in natural language.2) The image parsing results are converted into semantic representation in the form of Web Ontology Language (OWL), which enables seamless integration with general knowledge bases. 3) A text generation engine converts the results from previous steps into semantically meaningful, human readable and query-able text reports. The centerpiece of the I2T framework is an And-or Graph (AoG) visual knowledge representation, which provides a graphical representation serving as prior knowledge for representing diverse visual patterns and provides top-down hypotheses during the image parsing. The AoG embodies vocabularies of visual elements including primitives, parts, objects, scenes as well as a stochastic image grammar that specifies syntactic relations (i.e. compositional) and semantic relations (e.g. categorical, spatial, temporal and functional) between these visual elements. Therefore, the AoG is a unified model of both categorical and symbolic representation of visual knowledge. The proposed I2T framework has two objectives. First, we use semi-automatic method to parse images from the Internet in order to build an AoG for visual knowledge representation. Our goal is to make the parsing process more and more automatic using the learned AoG model. Second, we use automatic methods to parse image/video in specific domains and generate text reports that are useful for real-world applications. In the case studies at the end of this paper, we demonstrate two automatic I2T systems: a maritime and urban scene video surveillance system and a real-time automatic driving scene understanding system.

Graphene, a unique two-dimensional material of carbon in a honeycomb lattice [1], has brought remarkable breakthroughs across the domains of electronics, mechanics, and thermal transport, driven by the quasiparticle Dirac fermions obeying a linear dispersion [2-3]. Here we demonstrate a counter-pumped all-optical difference frequency process to coherently generate and control THz plasmons in atomic layer graphene with an octave tunability and high efficiency. We leverage the inherent surface asymmetry of graphene for a strong second-order nonlinear polarizability  (2) [4-5], which together with tight plasmon field confinement, enables a robust difference frequency signal at THz frequencies. The counter-pumped resonant process on graphene uniquely achieves both energy and momentum conservation. Consequently we demonstrate a dual-layer graphene heterostructure that achieves the charge-and gate-tunability of the THz plasmons over an octave, from 9.4 THz to 4.7 THz, bounded only by the pump amplifier optical bandwidth. Theoretical modeling supports our single-volt-level gate tuning and optical-bandwidth-bounded 4.7 THz phase-matching measurements, through the random phase approximation with phonon coupling, saturable absorption, and below the Landau damping, to predict and understand the graphene carrier plasmon physics. 2The discovery of graphene spurred dramatic advances ranging from condensed matter physics, materials science to physical electronics, mechanics, and thermal processes. In optics [6][7], the additional chiral symmetry of the Dirac fermion quasiparticles of graphene [8] enables an optical conductivity defined only by the fine structure constant  [9], one that is remarkably charge-density tunable [10][11] and with broadband nonlinearities [12][13][14][15]. The collective oscillations of the two-dimensional correlated quasiparticles in graphene [16] naturally make for a fascinating cross-disciplinary field in graphene plasmonics [17], with applications ranging from tight-field-enhanced modulators, detectors, lasers, polarizers, to biochemical sensors [18][19][20][21][22]. Different from conventional noble metal plasmons, graphene plasmons are dominant in the terahertz and far-infrared frequencies [23]. To excite and detect these plasmons, specialized techniques such as resonant scattering nanoscale antennae near-field microscopy or micro-and nano-scale scattering arrays have been pursued, albeit still using terahertz/far-infrared sources [24][25][26][27][28]. Recently nonlinear optical processes, only with free-space experiments, have proven especially effective in generating graphene plasmons with efficiencies up to 10 -5 [4][5]. However, to date, it is challenging to generate, detect, and control on-chip graphene plasmons all-optically, a key step towards planar integration and next-generation high-density optoelectronics.Concurrently THz generation has recently been revisited by a number of studies for imaging, spectroscopy, and communications [29]. While a wide tunability in THz can provide new g...

A graphene coated microfiber Bragg grating (GMFBG) for gas sensing is reported in this Letter. Taking advantage of the surface field enhancement and gas absorption of a GMFBG, we demonstrate an ultrasensitive approach to detect the concentration of chemical gas. The obtained sensitivities are 0.2 and 0.5 ppm for NH3 and xylene gas, respectively, which are tens of times higher than that of a GMFBG without graphene for tiny gas concentration change detection. Experimental results indicate that the GMFBG-based NH3 gas sensor has fast response due to its highly compact structure. Such a miniature fiber-optic element may find applications in high sensitivity gas sensing and trace analysis.

Knowledge distillation is typically conducted by training a small model (the student) to mimic a large and cumbersome model (the teacher). The idea is to compress the knowledge from the teacher by using its output probabilities as soft-labels to optimize the student. However, when the teacher is considerably large, there is no guarantee that the internal knowledge of the teacher will be transferred into the student; even if the student closely matches the soft-labels, its internal representations may be considerably different. This internal mismatch can undermine the generalization capabilities originally intended to be transferred from the teacher to the student. In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it. We formulate two ways to distill such representations and various algorithms to conduct the distillation. We experiment with datasets from the GLUE benchmark and consistently show that adding knowledge distillation from internal representations is a more powerful method than only using soft-label distillation.

Chemical sensing is one of the most important applications of nanoscience, whose ultimate aim is to seek higher sensitivity. In recent years, graphene with intriguing quantum properties has spurred dramatic advances ranging from materials science to optoelectronics and mechanics, showing its potential to realize individual molecule solid-state sensors. However, for optical sensing the single atom thickness of graphene greatly limits the light-graphene interactions, bottlenecking their performances. Here we demonstrate a novel approach based on the forward phase-matched Brillouin optomechanics in a graphene inner-deposited high Q (>2 × 10) microfluidic resonator, expanding the "electron-photon" interaction in conventional graphene optical devices to the "electron-phonon-photon" process. The molecular adsorption induced surface elastic modulation in graphene enables the Brillouin optomechanical modes (mechanical Q ≈ 43,670) extremely sensitive (200 kHz/ppm) in ammonia gas detection, achieving a noise equivalent detection limit down to 1 ppb and an unprecedented dynamic range over five orders-of-magnitude with fast response. This work provides a new platform for the researches of graphene-based optomechanics, nanophotonics, and optical sensing.