Observation of Exchange Interaction in Iron(II) Spin Crossover Molecules in Contact with Passivated Ferromagnetic Surface of Co/Au(111).

Spin crossover (SCO) complexes sensitively react on changes of the environment by a change in the spin of the central metallic ion making them ideal candidates for molecular spintronics. In particular, the composite of SCO complexes and ferromagnetic (FM) surfaces would allow spin-state switching of the molecules in combination with the magnetic exchange interaction to the magnetic substrate. Unfortunately, when depositing SCO complexes on ferromagnetic surfaces, spin-state switching is blocked by the relatively strong interaction between the adsorbed molecules and the surface. Here, the Fe(II) SCO complex [FeII (Pyrz)2 ] (Pyrz = 3,5-dimethylpyrazolylborate) with sub-monolayer thickness in contact with a passivated FM film of Co on Au(111) is studied. In this case, the molecules preserve thermal spin crossover and at the same time the high-spin species show a sizable exchange interaction of > 0.9 T with the FM Co substrate. These observations provide a feasible design strategy in fabricating SCO-FM hybrid devices.

SmartDeal: Remodeling Deep Network Weights for Efficient Inference and Training.

The record-breaking performance of deep neural networks (DNNs) comes with heavy parameter budgets, which leads to external dynamic random access memory (DRAM) for storage. The prohibitive energy of DRAM accesses makes it nontrivial for DNN deployment on resource-constrained devices, calling for minimizing the movements of weights and data in order to improve the energy efficiency. Driven by this critical bottleneck, we present SmartDeal, a hardware-friendly algorithm framework to trade higher-cost memory storage/access for lower-cost computation, in order to aggressively boost the storage and energy efficiency, for both DNN inference and training. The core technique of SmartDeal is a novel DNN weight matrix decomposition framework with respective structural constraints on each matrix factor, carefully crafted to unleash the hardware-aware efficiency potential. Specifically, we decompose each weight tensor as the product of a small basis matrix and a large structurally sparse coefficient matrix whose nonzero elements are readily quantized to the power-of-2. The resulting sparse and readily quantized DNNs enjoy greatly reduced energy consumption in data movement as well as weight storage, while incurring minimal overhead to recover the original weights thanks to the required sparse bit-operations and cost-favorable computations. Beyond inference, we take another leap to embrace energy-efficient training, by introducing several customized techniques to address the unique roadblocks arising in training while preserving the SmartDeal structures. We also design a dedicated hardware accelerator to fully utilize the new weight structure to improve the real energy efficiency and latency performance. We conduct experiments on both vision and language tasks, with nine models, four datasets, and three settings (inference-only, adaptation, and fine-tuning). Our extensive results show that 1) being applied to inference, SmartDeal achieves up to 2.44× improvement in energy efficiency as evaluated using real hardware implementations and 2) being applied to training, SmartDeal can lead to 10.56× and 4.48× reduction in the storage and the training energy cost, respectively, with usually negligible accuracy loss, compared to state-of-the-art training baselines. Our source codes are available at: https://github.com/VITA-Group/SmartDeal.

A very low frequency (VLF) antenna based on clamped bending-mode structure magnetoelectric laminates.

As the development of wireless communication devices tends to be highly integrated, the miniaturization of very low frequency (VLF) antenna units has always been an unresolved issue. Here, a novel VLF mechanical communication antenna using magnetoelectric (ME) laminates with bending-mode structure is realized. ME laminates combines magnetostrictive Metglas amorphous ribbons and piezoelectric 0.7Pb(Mg1/3Nb2/3)O3-0.3PbTiO3single crystal plates. From the simulation, we confirmed that the ME laminates can reduce the resonance peak from 18 kHz to 7.5 kHz by bending-mode structure. Experiment results show the resonance frequency can be farther reduced to 6.3 kHz by clamping one end of the ME antenna. The ME laminate exhibits a giant converse ME coefficient of 6 Oe cm V-1at 6.3 kHz. The magnetic flux density generated by the ME antenna has been tested along with distance ranging from 0 to 60 cm and it is estimated that a 1 fT flux could be detected around 100 m with an excitation power of 10 mW.

Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling.

Recent techniques built on generative adversarial networks (GANs), such as cycle-consistent GANs, are able to learn mappings among different domains built from unpaired data sets, through min-max optimization games between generators and discriminators. However, it remains challenging to stabilize the training process and thus cyclic models fall into mode collapse accompanied by the success of discriminator. To address this problem, we propose an novel Bayesian cyclic model and an integrated cyclic framework for interdomain mappings. The proposed method motivated by Bayesian GAN explores the full posteriors of cyclic model via sampling latent variables and optimizes the model with maximum a posteriori (MAP) estimation. Hence, we name it Bayesian CycleGAN. In addition, original CycleGAN cannot generate diversified results. But it is feasible for Bayesian framework to diversify generated images by replacing restricted latent variables in inference process. We evaluate the proposed Bayesian CycleGAN on multiple benchmark data sets, including Cityscapes, Maps, and Monet2photo. The proposed method improve the per-pixel accuracy by 15% for the Cityscapes semantic segmentation task within origin framework and improve 20% within the proposed integrated framework, showing better resilience to imbalance confrontation. The diversified results of Monet2Photo style transfer also demonstrate its superiority over original cyclic model. We provide codes for all of our experiments in https://github.com/ranery/Bayesian-CycleGAN.