Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

The role of tetragonal side morphotropic phase boundary in modified relaxor-PbTiO3 crystals for high power transducer applications

Morphotropic phase boundary (MPB) in ferroelectric materials leads to improved properties due to the structural instability. The manganese modified Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb 2/3)O3-PbTiO3 crystals with MPB composition were investigated, the structure/property relationship was established. The tetragonal side MPB (coexistence of 91% tetragonal and 9% monoclinic phases) was confirmed by X-ray synchrotron data, while relaxor behavior was detected by Raman characterization and dielectric measurement. Crystals with such MPB composition possess high figure of merit (d33· Q33 ∼ 106 pC/N), being one order higher when compared with their pure rhombohedral counterparts. Together with high Curie temperature (∼229 °C) and temperature stability of properties, demonstrating a promising candidate for high power transducer applications. 2013 AIP Publishing LLC

Comparison of the Tribological Behaviour of Various Graphene Nano-Coatings as a Solid Lubricant for Copper

Among the amazing properties of graphene, superlubricity is one of the most promising properties. This property can be used in industrial field components to reduce friction without using liquid lubricants, and therefore, improve machines’ efficiency and reliability with low environmental impact thanks to the elimination of oil or grease lubricants. In this paper, copper alloy samples for electrical purposes were coated with graphene by four different deposition processes. The investigated synthesis processes are direct grown graphene on bulk Cu, transferred graphene, and self-assembled graphene from graphene flakes. Ball-on-disk tests were performed to evaluate the tribological performance of samples. The aim was to compare the effect on the tribological performance given by different types of coatings, taking also into consideration industrial scalability. Interestingly, not all graphene nano-coatings being compared proved effective in reducing friction and wear in gross sliding conditions. The results show that the cost-effective self-assembled graphene is the longer-lasting nano-coating among those investigated in this work, and can reduce both friction and wear. Tests revealed that graphene coatings can be applied as a solid lubricant, reducing friction up to 78%, and reducing the average wear volume up to 40%

Toward MXene interconnects

Performance challenges as electronics continue to scale down motivate searches for new interconnect materials. In a recent report in Matter, Lipatov and coworkers demonstrate that MXene may be a candidate for interconnects by measuring conductivity and breakdown current density of Ti[subscript 3]C[subscript 2]T[subscript x]

Ferroelectric memory field-effect transistors using CVD monolayer MoS2 as resistive switching channel

Ferroelectric field-effect transistors (FeFETs) have been considered as promising electrically switchable nonvolatile data storage elements due to their fast switching speed, programmable conductance, and high dynamic range for neuromorphic applications. Meanwhile, FeFETs can be aggressively shrunk to the atomic scale for a high density device integration, ideally, without comprising the performance by introducing two-dimensional (2D) materials. So far, the demonstrated 2D material-based FeFETs mainly rely on mechanically exfoliated flakes, which are not favorable for large-scale industrial applications, and FeFETs based on organic ferroelectrics typically show a large writing voltage (e.g., >±20 V), making these types of memory devices impractical to be commercially viable. Here, we demonstrate that monolayer MoS₂ grown by chemical vapor deposition (CVD) can be used as a resistive switching channel to fabricate FeFETs, in which the MoS2 channel is modulated by a hybrid gate stack of HfO₂/ferroelectric HfZrOx thin films. The programming processes in the 2D MoS₂ FeFETs originate from the ferroelectric polarization switching, yielding two distinct write and erase states for data storage and cumulative channel conductance for artificial synapse applications. Our 2D FeFETs show a low-voltage-driven feature (<±3 V) and gate-tunable ferroelectric hysteresis characteristics. The thin HfO₂ layer in the hybrid gate stack likely plays crucial roles in preserving the ferroelectricity of the device and lowering the threshold of switching voltages through energy redistribution. Our findings open an avenue for the use of CVD-grown layered materials as the resistive switching mediums combined with HfO₂-based ferroelectrics for future energy-efficient "brain-on-a-chip" hardware