Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification

In this letter, we propose a multitask deep learning method for classification of multiple hyperspectral data in a single training. Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images. However, samples from multiple data sets might be sufficient to train one deep learning model, thereby improving its performance. To do so, we trained an identical feature extractor for all data, and the extracted features were fed into corresponding Softmax classifiers. Spectral knowledge was introduced to ensure that the shared features were similar across domains. Four hyperspectral data sets were used in the experiments. We achieved higher classification accuracies on three data sets (Pavia University, Pavia Center, and Indian Pines) and competitive results on the Salinas Valley data compared with the baseline. Spectral knowledge was useful to prevent the deep network from overfitting when the data shared similar spectral response. The proposed method tested on two deep CNNs successfully shows its ability to utilize samples from multiple data sets and enhance networks' performance.Comment: Accepted by IEEE GRS

Greedy Signal Space Recovery Algorithm with Overcomplete Dictionaries in Compressive Sensing

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms

Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning

Current hyperspectral image classification assumes that a predefined classification system is closed and complete, and there are no unknown or novel classes in the unseen data. However, this assumption may be too strict for the real world. Often, novel classes are overlooked when the classification system is constructed. The closed nature forces a model to assign a label given a new sample and may lead to overestimation of known land covers (e.g., crop area). To tackle this issue, we propose a multitask deep learning method that simultaneously conducts classification and reconstruction in the open world (named MDL4OW) where unknown classes may exist. The reconstructed data are compared with the original data; those failing to be reconstructed are considered unknown, based on the assumption that they are not well represented in the latent features due to the lack of labels. A threshold needs to be defined to separate the unknown and known classes; we propose two strategies based on the extreme value theory for few-shot and many-shot scenarios. The proposed method was tested on real-world hyperspectral images; state-of-the-art results were achieved, e.g., improving the overall accuracy by 4.94% for the Salinas data. By considering the existence of unknown classes in the open world, our method achieved more accurate hyperspectral image classification, especially under the few-shot context.Comment: Accepted by IEEE TGR

Learning-Based Massive Beamforming

Developing resource allocation algorithms with strong real-time and high efficiency has been an imperative topic in wireless networks. Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems. This paper studies learning-based efficient massive beamforming methods for multi-user MIMO networks. The considered massive beamforming problem is challenging in two aspects. First, the beamforming matrix to be learned is quite high-dimensional in case with a massive number of antennas. Second, the objective is often time-varying and the solution space is not fixed due to some communication requirements. All these challenges make learning representation for massive beamforming an extremely difficult task. In this paper, by exploiting the structure of the most popular WMMSE beamforming solution, we propose convolutional massive beamforming neural networks (CMBNN) using both supervised and unsupervised learning schemes with particular design of network structure and input/output. Numerical results demonstrate the efficacy of the proposed CMBNN in terms of running time and system throughput

Enhancing spin-orbit torque by strong interfacial scattering from ultra-thin insertion layers

Increasing dampinglike spin-orbit torque (SOT) is both of fundamental importance for enabling new research into spintronics phenomena and also technologically urgent for advancing low-power spin-torque memory, logic, and oscillator devices. Here, we demonstrate that enhancing interfacial scattering by inserting ultra-thin layers within a spin Hall metals with intrinsic or side-jump mechanisms can significantly enhance the spin Hall ratio. The dampinglike SOT was enhanced by a factor of 2 via sub-monolayer Hf insertion, as evidenced by both harmonic response measurements and current-induced switching of in-plane magnetized magnetic memory devices with the record low critical switching current of ~73 uA (switching current density of 3.6x10^6 A/cm^2). This work demonstrates a very effective strategy for maximizing dampinglike SOT for low-power spin-torque devices

Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Frechet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: https://fei-hdu.github.io/ca-gan/.Comment: 10 pages, 8 figures, journa

Anisotropic Charge Carrier and Coherent Acoustic Phonon Dynamics of Black Phosphorus Studied by Transient Absorption Microscopy

Due to its corrugated hexagonal lattice structure, Black phosphorus (BP) has unique anisotropic physical properties, which provides an additional freedom for designing devices. Many body interactions, including interactions with phonon, is crucial for heat dissipation and charge carrier mobility in device. However, the rich properties of the coherent acoustic phonon, including anisotropy, propagation and generation were not fully interrogated. In this paper, the polarization-resolved transient absorption microscopy was conducted on BP flakes to study the dynamics of photoexcited charge carriers and coherent acoustic phonon. Polarization-resolved transient absorption images and traces were recorded and showed anisotropic and thickness-dependent charge carriers decay dynamics. The damping of the coherent acoustic phonon oscillation was found to be anisotropic, which was attributed to the polarization-dependent absorption length of the probe pulse. From the analysis of initial oscillation amplitude and phase of coherent acoustic phonon oscillation, we proposed that the direct deformation potential mechanism dominated the generation of coherent acoustic phonons in our experiment. Besides, we obtained the sound velocity of the coherent acoustic phonon from the oscillation frequency and the acoustic echo, respectively, which agreed well with each other. These findings provide significant insights into the rich acoustic phonon properties of BP, and promise important application for BP in polarization-sensitive optical and optoelectronic devices.Comment: just accepte

Energy-efficient ultrafast SOT-MRAMs based on low-resistivity spin Hall metal Au0.25Pt0.75

Many key electronic technologies (e.g., large-scale computing, machine learning, and superconducting electronics) require new memories that are fast, reliable, energy-efficient, and of low-impedance at the same time, which has remained a challenge. Non-volatile magnetoresistive random access memories (MRAMs) driven by spin-orbit torques (SOTs) have promise to be faster and more energy-efficient than conventional semiconductor and spin-transfer-torque magnetic memories. This work reports that the spin Hall effect of low-resistivity Au0.25Pt0.75 thin films enables ultrafast antidamping-torque switching of SOT-MRAM devices for current pulse widths as short as 200 ps. If combined with industrial-quality lithography and already-demonstrated interfacial engineering, our results show that an optimized MRAM cell based on Au0.25Pt0.75 can have energy-efficient, ultrafast, and reliable switching, e.g. a write energy of < 1 fJ (< 50 fJ) for write error rate of 50% (<1e-5) for 1 ns pulses. The antidamping torque switching of the Au0.25Pt0.75 devices is 10 times faster than expected from a rigid macrospin model, most likely because of the fast micromagnetics due to the enhanced non-uniformity within the free layer. These results demonstrate the feasibility of Au0.25Pt0.75-based SOT-MRAMs as a candidate for ultrafast, reliable, energy-efficient, low-impedance, and unlimited-endurance memory

Fast, low-current spin-orbit torque switching of magnetic tunnel junctions through atomic modifications of the free layer interfaces

Future applications of spin-orbit torque will require new mechanisms to improve the efficiency for switching nanoscale magnetic tunnel junctions (MTJs), while also controlling the magnetic dynamics to achieve fast, nanosecond scale performance with low write error rates. Here we demonstrate a strategy to simultaneously enhance the interfacial magnetic anisotropy energy and suppress interfacial spin memory loss by introducing sub-atomic and monatomic layers of Hf at the top and bottom interfaces of the ferromagnetic free layer of an in-plane magnetized three-terminal MTJ device. When combined with a beta-W spin Hall channel that generates spin-orbit torque, the cumulative effect is a switching current density of 5.4 x 106 A/cm2, more than a factor of 3 lower than demonstrated in any other spin-orbit-torque magnetic memory device at room temperature, and highly reliable switching with current pulses only 2 ns long

Nanosecond Reversal of Three-Terminal Spin Hall Effect Memories Sustained at Cryogenic Temperatures

We characterize the nanosecond pulse switching performance of the three-terminal magnetic tunnel junctions (MTJs), driven by the spin Hall effect (SHE) in the channel, at a cryogenic temperature of 3 K. The SHE-MTJ devices exhibit reasonable magnetic switching and reliable current switching by as short pulses as 1 ns of $<10^{12}$ A/m$^{2}$ magnitude, exceeding the expectation from conventional macrospin model. The pulse switching bit error rates reach below $10^{-6}$ for < 10 ns pulses. Similar performance is achieved with exponentially decaying pulses expected to be delivered to the SHE-MTJ device by a nanocryotron device in parallel configuration of a realistic memory cell structure. These results suggest the viability of the SHE-MTJ structure as a cryogenic memory element for exascale superconducting computing systems.Comment: 12 pages, 5 figure