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Wei Han

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preprint2026arXiv

LithoBench: Benchmarking Large Multimodal Models for Remote-Sensing Lithology Interpretation

Remote sensing lithology interpretation is fundamental to geological surveys, mineral exploration, and regional geological mapping. Unlike general land-cover recognition, lithology interpretation is a knowledge-intensive task that requires experts to infer rock types from various features, e.g., subtle visual, spectral, textural, geomorphological, and contextual cues, making reliable automated interpretation highly challenging. Geological knowledge-guided large multimodal models offer new opportunities, yet their evaluation remains constrained by the lack of benchmarks that capture lithological annotations, multi-level geological semantics, and expert-informed assessment. Here, we propose LithoBench, a multi-level benchmark for evaluating geological semantic understanding in remote sensing lithology interpretation. LithoBench contains 10,000 expert-annotated interpretation instances across 12 representative lithological categories, including 4,000 multiple-choice and 6,000 open-ended tasks organized into five cognitive levels: Identification and Description, Comparative Analysis, Mechanism Explanation, Practical Application, and Comprehensive Reasoning. We further develop an expert-in-the-loop, knowledge-grounded semi-automated construction pipeline, coupling multi sub-processes, e.g., structured geological image descriptions, to enhance geological validity and evaluation reliability. Experiments with multiple large vision-language models eveal substantial limitations in geological semantic understanding, particularly on higher-order explanation, application, and reasoning tasks.

preprint2023arXiv

Electromagnetic-Compliant Channel Modeling and Performance Evaluation for Holographic MIMO

Recently, the concept of holographic multiple-input multiple-output (MIMO) is emerging as one of the promising technologies beyond massive MIMO. Many challenges need to be addressed to bring this novel idea into practice, including electromagnetic (EM)-compliant channel modeling and accurate performance evaluation. In this paper, an EM-compliant channel model is proposed for the holographic MIMO systems, which is able to model both the characteristics of the propagation channel and the non-ideal factors caused by mutual coupling at the transceivers, including the antenna pattern distortion and the decrease of antenna efficiency. Based on the proposed channel model, a more realistic performance evaluation is conducted to show the performance of the holographic MIMO system in both the single-user and the multi-user scenarios. Key challenges and future research directions are further provided based on the theoretical analyses and numerical results.

preprint2022arXiv

Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data

Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varieties, and also an understanding of model configuration that is the most helpful in achieving robust understanding of all types of speech. However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition. In this paper, we discuss recent progress towards developing more inclusive ASR systems, namely, the importance of building new data sets representing linguistic diversity, and exploring novel training approaches to improve performance for all users. We address recent directions within benchmarking ASR systems for accented speech, measure the effects of wav2vec 2.0 pre-training on accented speech recognition, and highlight corpora relevant for diverse ASR evaluations.

preprint2022arXiv

An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access

In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for the inner SPARC with practical concerns. A two-stage method is proposed to achieve near-optimal performance while maintaining low computational complexity. Specifically, a one-step thresholding-based algorithm is first used for reducing large dimensions of the SPARC decoding, after which a relaxed maximum-likelihood estimator is employed for refinement. Adequate simulation results are provided to validate the near-optimal performance and the low computational complexity. Besides, for covariance-based sparse recovery method, theoretical analyses are given to characterize the upper bound of the number of active users supported when convex relaxation is considered, and the probability of successful dimension reduction by the one-step thresholding-based algorithm.

preprint2022arXiv

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.

preprint2022arXiv

DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix first leverages a couple of simple augmentation operations to generate several perturbed samples for each training data, and then uses the perturbed data and original data to carry out a two-step interpolation in the hidden space of neural models. Concretely, it first mixes up the perturbed data to a synthetic sample and then mixes up the original data and the synthetic perturbed data. DoubleMix enhances models' robustness by learning the "shifted" features in hidden space. On six text classification benchmark datasets, our approach outperforms several popular text augmentation methods including token-level, sentence-level, and hidden-level data augmentation techniques. Also, experiments in low-resource settings show our approach consistently improves models' performance when the training data is scarce. Extensive ablation studies and case studies confirm that each component of our approach contributes to the final performance and show that our approach exhibits superior performance on challenging counterexamples. Additionally, visual analysis shows that text features generated by our approach are highly interpretable. Our code for this paper can be found at https://github.com/declare-lab/DoubleMix.git.

preprint2022arXiv

Lossy Computing with Side Information via Multi-Hypergraphs

We consider a problem of coding for computing, where the decoder wishes to estimate a function of its local message and the source message at the encoder within a given distortion. We show that the rate-distortion function can be characterized through a characteristic multi-hypergraph, which simplifies the evaluation of the rate-distortion function.

preprint2022arXiv

Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset. More precisely, we carry out noisy student training with SpecAugment using giant Conformer models pre-trained using wav2vec 2.0 pre-training. By doing so, we are able to achieve word-error-rates (WERs) 1.4%/2.6% on the LibriSpeech test/test-other sets against the current state-of-the-art WERs 1.7%/3.3%.

preprint2022arXiv

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty aspects. We also explore and highlight limitations of our models in order to define and exemplify key areas of focus for further improvements. See https://parti.research.google/ for high-resolution images.

preprint2022arXiv

Semantic Compression with Side Information: A Rate-Distortion Perspective

We consider the semantic rate-distortion problem motivated by task-oriented video compression. The semantic information corresponding to the task, which is not observable to the encoder, shows impacts on the observations through a joint probability distribution. The similarities among intra-frame segments and inter-frames in video compression are formulated as side information available at both the encoder and the decoder. The decoder is interested in recovering the observation and making an inference of the semantic information under certain distortion constraints. We establish the information-theoretic limits for the tradeoff between compression rates and distortions by fully characterizing the rate-distortion function. We further evaluate the rate-distortion function under specific Markov conditions for three scenarios: i) both the task and the observation are binary sources; ii) the task is a binary classification of an integer observation as even and odd; iii) Gaussian correlated task and observation. We also illustrate through numerical results that recovering only the semantic information can reduce the coding rate comparing to recovering the source observation.

preprint2022arXiv

Simulation and detection of Weyl fermions in ultracold Fermi gases with Raman-assisted spin-orbit coupling

Weyl fermion, also referred to as pseudo-magnetic monopole in momentum space, is an undiscovered massless elementary particle with half-integer spin predicted according to relativistic quantum field theory. Motivated by the recent experimental observation of Weyl semimetal band in ultracold Bose gases with Raman-assisted 3D spin-orbit coupling, we investigate the properties and possible observation of Weyl fermions in the low-energy quasi-particle excitations of ultracold Fermi gases. Following a previous suggestion that the existing Raman lattice scheme can be readily generalized to fermionic systems, here we discuss the movement of the Weyl points in the Brillouin Zone, as well as the creation and annihilation of Weyl fermions by adjusting the effective Zeeman field. The relevant topological properties are also demonstrated by calculating the Chern number. Furthermore, we propose how to experimentally verify the existence of the Weyl fermions and the associated quantum phase transition via density profile measurements.

preprint2022arXiv

Spin Seebeck effect in quantum magnet Pb2V3O9

Spin Seebeck effect (SSE), the generation of spin current from heat, has been extensively studied in a large variety of magnetic materials, including ferromagnets, antiferromagnets, paramagnets, and quantum spin liquids. In this paper, we report the study of the SSE in the single crystalline Pb2V3O9, a spin-gapped quantum magnet candidate with quasi-one-dimensional spin-1/2 chain. Detailed temperature and magnetic field dependences of the SSE are investigated, and the temperature-dependent critical magnetic fields show a strong correlation to the Bose-Einstein condensation phase of the quantum magnet Pb2V3O9. This work shows the potential of using spin current as a probe to study the spin correlation and phase transition properties in quantum magnets.

preprint2022arXiv

Unsupervised Data Selection via Discrete Speech Representation for ASR

Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR). In this paper, we show that data selection is important for self-supervised learning. We propose a simple and effective unsupervised data selection method which selects acoustically similar speech to a target domain. It takes the discrete speech representation available in common self-supervised learning frameworks as input, and applies a contrastive data selection method on the discrete tokens. Through extensive empirical studies we show that our proposed method reduces the amount of required pre-training data and improves the downstream ASR performance. Pre-training on a selected subset of 6% of the general data pool results in 11.8% relative improvements in LibriSpeech test-other compared to pre-training on the full set. On Multilingual LibriSpeech French, German, and Spanish test sets, selecting 6% data for pre-training reduces word error rate by more than 15% relatively compared to the full set, and achieves competitive results compared to current state-of-the-art performances.

preprint2021arXiv

A Better and Faster End-to-End Model for Streaming ASR

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by word error rate (WER)) and endpointer latency [2]. However, the model still tends to delay the predictions towards the end and thus has much higher partial latency compared to a conventional ASR model. To address this issue, we look at encouraging the E2E model to emit words early, through an algorithm called FastEmit [3]. Naturally, improving on latency results in a quality degradation. To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR. Secondly, we also explore running a 2nd-pass beam search to improve quality. In order to ensure the 2nd-pass completes quickly, we explore non-causal Conformer layers that feed into the same 1st-pass RNN-T decoder, an algorithm called Cascaded Encoders [5]. Overall, we find that the Conformer RNN-T with Cascaded Encoders offers a better quality and latency tradeoff for streaming ASR.

preprint2021arXiv

Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses. In this work, we propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation during the training. The Dual-mode ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks. We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and a large-scale dataset MultiDomain. Experiments and ablation studies demonstrate that Dual-mode ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR. With Dual-mode ASR, we achieve new state-of-the-art streaming ASR results on both LibriSpeech and MultiDomain in terms of accuracy and latency.

preprint2021arXiv

FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measured by word error rate (WER), is highly challenging. Existing approaches including Early and Late Penalties and Constrained Alignments penalize emission delay by manipulating per-token or per-frame probability prediction in sequence transducer models. While being successful in reducing delay, these approaches suffer from significant accuracy regression and also require additional word alignment information from an existing model. In this work, we propose a sequence-level emission regularization method, named FastEmit, that applies latency regularization directly on per-sequence probability in training transducer models, and does not require any alignment. We demonstrate that FastEmit is more suitable to the sequence-level optimization of transducer models for streaming ASR by applying it on various end-to-end streaming ASR networks including RNN-Transducer, Transformer-Transducer, ConvNet-Transducer and Conformer-Transducer. We achieve 150-300 ms latency reduction with significantly better accuracy over previous techniques on a Voice Search test set. FastEmit also improves streaming ASR accuracy from 4.4%/8.9% to 3.1%/7.5% WER, meanwhile reduces 90th percentile latency from 210 ms to only 30 ms on LibriSpeech.

preprint2021arXiv

Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data

Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech with minimal latency, they are constrained to be causal with no future context, compared to their non-streaming counterparts. Consequently, streaming models usually perform worse than non-streaming models. We propose a novel and effective learning method by leveraging a non-streaming ASR model as a teacher to generate transcripts on an arbitrarily large data set, which is then used to distill knowledge into streaming ASR models. This way, we scale the training of streaming models to up to 3 million hours of YouTube audio. Experiments show that our approach can significantly reduce the word error rate (WER) of RNNT models not only on LibriSpeech but also on YouTube data in four languages. For example, in French, we are able to reduce the WER by 16.4% relatively to a baseline streaming model by leveraging a non-streaming teacher model trained on the same amount of labeled data as the baseline.

preprint2021arXiv

Structural Entropy of the Stochastic Block Models

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the "structural information" only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős--Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the Stochastic Block Models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for Stochastic Block Models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the Stochastic Block Models, and provide a compression scheme that asymptotically achieves this entropy limit.

preprint2021arXiv

TAPL: Dynamic Part-based Visual Tracking via Attention-guided Part Localization

Holistic object representation-based trackers suffer from performance drop under large appearance change such as deformation and occlusion. In this work, we propose a dynamic part-based tracker and constantly update the target part representation to adapt to object appearance change. Moreover, we design an attention-guided part localization network to directly predict the target part locations, and determine the final bounding box with the distribution of target parts. Our proposed tracker achieves promising results on various benchmarks: VOT2018, OTB100 and GOT-10k

preprint2020arXiv

Conformer: Convolution-augmented Transformer for Speech Recognition

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters.

preprint2020arXiv

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1%/4.6% without external language model (LM), 1.9%/4.1% with LM and 2.9%/7.0% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0%/4.6% with LM and 3.9%/11.3% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.

preprint2020arXiv

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides

Multi-focus image fusion technologies compress different focus depth images into an image in which most objects are in focus. However, although existing image fusion techniques, including traditional algorithms and deep learning-based algorithms, can generate high-quality fused images, they need multiple images with different focus depths in the same field of view. This criterion may not be met in some cases where time efficiency is required or the hardware is insufficient. The problem is especially prominent in large-size whole slide images. This paper focused on the multi-focus image fusion of cytopathological digital slide images, and proposed a novel method for generating fused images from single-focus or few-focus images based on conditional generative adversarial network (GAN). Through the adversarial learning of the generator and discriminator, the method is capable of generating fused images with clear textures and large depth of field. Combined with the characteristics of cytopathological images, this paper designs a new generator architecture combining U-Net and DenseBlock, which can effectively improve the network's receptive field and comprehensively encode image features. Meanwhile, this paper develops a semantic segmentation network that identifies the blurred regions in cytopathological images. By integrating the network into the generative model, the quality of the generated fused images is effectively improved. Our method can generate fused images from only single-focus or few-focus images, thereby avoiding the problem of collecting multiple images of different focus depths with increased time and hardware costs. Furthermore, our model is designed to learn the direct mapping of input source images to fused images without the need to manually design complex activity level measurements and fusion rules as in traditional methods.

preprint2020arXiv

Rate-Splitting Multiple Access for Downlink Multi-Antenna Communications: Physical Layer Design and Link-level Simulations

Rate-Splitting Multiple Access (RSMA) is an emerging flexible, robust and powerful multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) strategies at the transmitter and Successive Interference Cancellation (SIC) at the receivers, and has the unique ability to partially decode interference and partially treat interference as noise so as to softly bridge the two extremes of fully decoding interference (as in Non-Orthogonal Multiple Access, NOMA) and treating interference as noise (as in Space Division Multiple Access, SDMA or Multi-User Multiple-Input Multiple-Output, MU-MIMO). RSMA has been shown to provide significant room for spectral efficiency, energy efficiency, Quality-of-Service enhancements, robustness to Channel State Information (CSI) imperfections, as well as feedback overhead and complexity reduction, in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities). RSMA is also deeply rooted and motivated by recent advances in understanding the fundamental limits of multi-antenna networks with imperfect CSI at the Transmitter (CSIT). In this work, we leverage recent results on the optimization of RSMA and design for the first time its physical layer, accounting for modulation, coding (using polar codes), message split, adaptive modulation and coding, and SIC receiver. Link-level evaluations confirm the significant throughput benefits of RSMA over various baselines as SDMA and NOMA.

preprint2020arXiv

Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G

In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from conventional interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in Non-Orthogonal Multiple Access, NOMA). This paper is dedicated to the theory and applications of a more general and powerful transmission framework based on Rate-Splitting Multiple Access (RSMA) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect Channel State Information at the Transmitter (CSIT), and complexity reduction. We give an overview of RSMA and its potential to address the requirements of 6G. This paper provides an overview of RSMA and its potential to address the requirements of 6G.

preprint2020arXiv

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open.

preprint2020arXiv

Streaming Object Detection for 3-D Point Clouds

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a prominent sensory modality that informs many existing perceptual systems including object detection, segmentation, motion estimation, and action recognition. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan (e.g. 100 ms). This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures. However, unlike camera sensors, most LiDAR point cloud data is natively a streaming data source in which laser reflections are sequentially recorded based on the precession of the laser beam. In this work, we explore how to build an object detector that removes this artificial latency constraint, and instead operates on native streaming data in order to significantly reduce latency. This approach has the added benefit of reducing the peak computational burden on inference hardware by spreading the computation over the acquisition time for a scan. We demonstrate a family of streaming detection systems based on sequential modeling through a series of modifications to the traditional detection meta-architecture. We highlight how this model may achieve competitive if not superior predictive performance with state-of-the-art, traditional non-streaming detection systems while achieving significant latency gains (e.g. 1/15'th - 1/3'rd of peak latency). Our results show that operating on LiDAR data in its native streaming formulation offers several advantages for self driving object detection -- advantages that we hope will be useful for any LiDAR perception system where minimizing latency is critical for safe and efficient operation.

preprint2016arXiv

Comparison of OFDM and Single-Carrier for Large-Scale Antenna Systems

Large-scale antenna (LSA) or massive multiple-input multiple-output (MIMO) has gained a lot of attention due to its potential to significantly improve system throughput. As a natural evolution from traditional MIMO-orthogonal frequency division multiplexing (OFDM), LSA has been combined with OFDM to deal with frequency selectivity of wireless channels in most existing works. As an alternative approach, single-carrier (SC) has also been proposed for LSA systems due to its low implementation complexity. In this article, a comprehensive comparison between LSA-OFDM and LSA-SC is presented, which is of interest to the waveform design for the next generation wireless systems.

preprint2016arXiv

Crystal Structure Manipulation of the Exchange Bias in an Antiferromagnetic Film

Exchange bias is one of the most extensively studied phenomena in magnetism, since it exerts a unidirectional anisotropy to a ferromagnet (FM) when coupled to an antiferromagnet (AFM) and the control of the exchange bias is therefore very important for technological applications, such as magnetic random access memory and giant magnetoresistance sensors. In this letter, we report the crystal structure manipulation of the exchange bias in epitaxial hcp Cr2O3 films. By epitaxially growing twined (10-10) oriented Cr2O3 thin films, of which the c axis and spins of the Cr atoms lie in the film plane, we demonstrate that the exchange bias between Cr2O3 and an adjacent permalloy layer is tuned to in-plane from out-of-plane that has been observed in (0001) oriented Cr2O3 films. This is owing to the collinear exchange coupling between the spins of the Cr atoms and the adjacent FM layer. Such a highly anisotropic exchange bias phenomenon is not possible in polycrystalline films.

preprint2016arXiv

Device-to-device Cooperation in Massive MIMO Systems with Cascaded Precoding

This paper investigates user cooperation in massive multiple-input multiple-output (MIMO) systems with cascaded precoding. The high-dimensional physical channel in massive MIMO systems can be converted into a low-dimensional effective channel through the inner precoder to reduce the overhead of channel estimation and feedback. The inner precoder depends on the spatial covariance matrix of the channels, and thus the same precoder can be used for different users as long as they have the same spatial covariance matrix. Spatial covariance matrix is determined by the surrounding environment of user terminals. Therefore, the users that are close to each other will share the same spatial covariance matrix. In this situation, it is possible to achieve user cooperation by sharing receiver information through some dedicated link, such as device-to-device communications. To reduce the amount of information that needs to be shared, we propose a decoding codebook based scheme, which can achieve user cooperation without the need of channel state information. Moreover, we also investigate the amount of bandwidth required to achieve efficient user cooperation. Simulation results show that user cooperation can improve the capacity compared to the non-cooperation scheme.

preprint2016arXiv

Double-Quantum Spin Vortices in SU(3) Spin-Orbit Coupled Bose Gases

We show that double-quantum spin vortices, which are characterized by doubly quantized circulating spin currents and unmagnetized filled cores, can exist in the ground states of SU(3) spin-orbit coupled Bose gases. It is found that the SU(3) spin-orbit coupling and spin-exchange interaction play important roles in determining the ground-state phase diagram. In the case of effective ferromagnetic spin interaction, the SU(3) spin-orbit coupling induces a three-fold degeneracy to the magnetized ground state, while in the antiferromagnetic spin interaction case, the SU(3) spin-orbit coupling breaks the ordinary phase rule of spinor Bose gases, and allows the spontaneous emergence of double-quantum spin vortices. This exotic topological defect is in stark contrast to the singly quantized spin vortices observed in existing experiments, and can be readily observed by the current magnetization-sensitive phase-contrast imaging technique.

preprint2016arXiv

Equilibrium vortex lattices of a binary rotating atomic Bose-Einstein condensate with unequal atomic masses

We perform a detailed numerical study of the equilibrium ground-state structures of a binary rotating Bose-Einstein condensate with unequal atomic masses. Our results show that the ground-state distribution and its related vortex configurations are complex events that differ markedly depending strongly on the strength of rotation frequency, as well as on the ratio of atomic masses. We also discuss the structure and radius of the clouds, the number and the size of the core region of the vortices, as a function of the rotation frequency, and of the ratio of atomic masses, and the analytical results agree well with our numerical simulations. This work may open an alternate way in the quantum control of the binary rotating quantum gases with unequal atomic masses.

preprint2016arXiv

Experimental Investigation of Temperature-Dependent Gilbert Damping in Permalloy Thin Films

The Gilbert damping of ferromagnetic materials is arguably the most important but least understood phenomenological parameter that dictates real-time magnetization dynamics. Understanding the physical origin of the Gilbert damping is highly relevant to developing future fast switching spintronics devices such as magnetic sensors and magnetic random access memory. Here, we report an experimental study of temperature-dependent Gilbert damping in permalloy (Py) thin films of varying thicknesses by ferromagnetic resonance. From the thickness dependence, two independent contributions to the Gilbert damping are identified, namely bulk damping and surface damping. Of particular interest, bulk damping decreases monotonically as the temperature decreases, while surface damping shows an enhancement peak at the temperature of ~50 K. These results provide an important insight to the physical origin of the Gilbert damping in ultrathin magnetic films.

preprint2016arXiv

Facet-dependent giant spin orbit torque in single crystalline antiferromagnetic Ir-Mn / ferromagnetic permalloy bilayers

There has been considerable interest in spin-orbit torques for the purpose of manipulating the magnetization of ferromagnetic (FM) films or nano-elements for spintronic technologies. Spin-orbit torques are derived from spin currents created from charge currents in materials with significant spin-orbit coupling that diffuse into an adjacent FM material. There have been intensive efforts to search for candidate materials that exhibit large spin Hall angles, i.e. efficient charge to spin current conversion. Here we report, using spin torque ferromagnetic resonance, the observation of a giant spin Hall angle of up to ~0.35 in (100) oriented single crystalline antiferromagnetic (AF) IrMn3 thin films, coupled to ferromagnetic permalloy layers, and a spin Hall angle that is about three times smaller in (111) oriented films. For the (100) oriented samples we show that the magnitude of the spin Hall angle can be significantly changed by manipulating the populations of the various AF domains through field annealing. Using ab-initio calculations we show that the triangular AF structure of IrMn3 gives rise to a substantial intrinsic spin Hall conductivity that is three times larger for the (100) than for the (111) orientations, consistent with our experimental findings.

preprint2016arXiv

Localized spatially nonlinear matter waves in atomic-molecular Bose-Einstein condensates with space-modulated nonlinearity

The intrinsic nonlinearity is the most remarkable characteristic of the Bose-Einstein condensates (BECs) systems. Many studies have been done on atomic BECs with time- and space- modulated nonlinearities, while there is few work considering the atomic-molecular BECs with space-modulated nonlinearities. Here, we obtain two kinds of Jacobi elliptic solutions and a family of rational solutions of the atomic-molecular BECs with trapping potential and space-modulated nonlinearity and consider the effect of three-body interaction on the localized matter wave solutions. The topological properties of the localized nonlinear matter wave for no coupling are analysed: the parity of nonlinear matter wave functions depends only on the principal quantum number $n$, and the numbers of the density packets for each quantum state depend on both the principal quantum number $n$ and the secondary quantum number $l$. When the coupling is not zero,the localized nonlinear matter waves given by the rational function, their topological properties are independent of the principal quantum number $n$, only depend on the secondary quantum number $l$. The Raman detuning and the chemical potential can change the number and the shape of the density packets. The stability of the Jacobi elliptic solutions depends on the principal quantum number $n$, while the stability of the rational solutions depends on the chemical potential and Raman detuning.

preprint2016arXiv

Low-Complexity Recursive Convolutional Precoding for OFDM-based Large-Scale Antenna System

Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.

preprint2016arXiv

Magnetic anisotropy of the single-crystalline ferromagnetic insulator Cr2Ge2Te6

Cr2Ge2Te6 (CGT), a layered ferromagnetic insulator, has attracted a great deal of interest recently owing to its potential for integration with Dirac materials to realize the quantum anomalous Hall effect (QAHE) and to develop novel spintronics devices. Here, we study the uniaxial magnetic anisotropy energy of single-crystalline CGT and determine that the magnetic easy axis is directed along the c-axis in its ferromagnetic phase. In addition, CGT is an insulator below the Curie temperature. These properties make CGT a potentially promising candidate substrate for integration with topological insulators for the realization of the high-temperature QAHE.

preprint2016arXiv

Perspectives for Spintronics in 2D Materials

The past decade has been especially creative for spintronics since the (re)discovery of various two dimensional (2D) materials. Due to the unusual physical characteristics, 2D materials have provided new platforms to probe the spin interaction with other degrees of freedom for electrons, as well as to be used for novel spintronics applications. This review briefly presents the most important recent and ongoing research for spintronics in 2D materials.

preprint2016arXiv

Positive Exchange Bias between Permalloy and Twined (10-10)-Cr2O3 Films

We report the discovery of a positive exchange bias between Ni80Fe20 (Py) and twined (10-10)-Cr2O3 film near its blocking temperature (TB) when it is cooled in an in-plane magnetic field applied along 45 degrees from the two spin configurations of the Cr atoms. This is an abnormal behavior compared to the negative exchange bias at all temperatures below TB when the cooling and measuring magnetic fields are applied along one of the two spin configurations of the Cr atoms. We speculate these results could be related to the exchange interactions between the twined structure of the (10-10)-Cr2O3 film epitaxially grown on the rutile (001)-TiO2 substrate.

preprint2016arXiv

Quantization and Feedback of Spatial Covariance Matrix for Massive MIMO Systems with Cascaded Precoding

In this paper, we investigate the quantization and the feedback of downlink spatial covariance matrix for massive multiple-input multiple-output (MIMO) systems with cascaded precoding. Massive MIMO has gained a lot of attention recently because of its ability to significantly improve the network performance. To reduce the overhead of downlink channel estimation and uplink feedback in frequency-division duplex massive MIMO systems, cascaded precoding has been proposed, where the outer precoder is implemented using traditional limited feedback while the inner precoder is determined by the spatial covariance matrix of the channels. In massive MIMO systems, it is difficult to quantize the spatial covariance matrix because of its large size caused by the huge number of antennas. In this paper, we propose a spatial spectrum based approach for the quantization and the feedback of the spatial covariance matrix. The proposed inner precoder can be viewed as modulated discrete prolate spheroidal sequences and thus achieve much smaller spatial leakage than the traditional discrete Fourier transform submatrix based precoding. Practical issues for the application of the proposed approach are also addressed in this paper.

preprint2016arXiv

Relative contravariantly finite subcategories and relative tilting modules

Let $A$ be a finite dimensional algebra over an algebraically closed field $k$. Let $T$ be a tilting $A$-module and $B={\rm End}_A\ T$ be the endomorphism algebra of $T$. In this paper, we consider the correspondence between the tilting $A$-modules and the tilting $B$-modules, and we prove that there is a one-one correspondence between the basic $T$-tilting $A$-modules in $T^{\perp}$ and the basic tilting $B$-modules in $^{\perp}(D_BT)$. Moreover, we show that there is a one-one correspondence between the $T$-contravariantly finite $T$-resolving subcategories of $T^{\perp}$ and the basic $T$-tilting $A$-modules contained in $T^{\perp}$. As an application, we show that there is a one-one correspondence between the basic tilting $A$-modules in $T^{\perp}$ and the basic tilting $B$-modules in $^{\perp}(D_BT)$ if $A$ is a $1$-Gorenstein algebra or a $m$-replicated algebra over a finite dimensional hereditary algebra.

preprint2016arXiv

Seq-NMS for Video Object Detection

Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have been major advances for doing object detection in a single image. These methods typically contain three phases: (i) object proposal generation (ii) object classification and (iii) post-processing. We propose a modification of the post-processing phase that uses high-scoring object detections from nearby frames to boost scores of weaker detections within the same clip. We show that our method obtains superior results to state-of-the-art single image object detection techniques. Our method placed 3rd in the video object detection (VID) task of the ImageNet Large Scale Visual Recognition Challenge 2015 (ILSVRC2015).

preprint2016arXiv

Spin Injection and Inverse Edelstein Effect in the Surface States of Topological Kondo Insulator SmB6

There has been considerable interest in exploiting the spin degrees of freedom of electrons for potential information storage and computing technologies. Topological insulators (TI), a class of quantum materials, have special gapless edge/surface states, where the spin polarization of the Dirac fermions is locked to the momentum direction. This spin-momentum locking property gives rise to very interesting spin-dependent physical phenomena such as the Edelstein and inverse Edelstein effects. However, the spin injection in pure surface states of TI is very challenging because of the coexistence of the highly conducting bulk states. Here, we experimentally demonstrate the spin injection and observe the inverse Edelstein effect in the surface states of a topological Kondo insulator, SmB6. At low temperatures when only surface carriers are present, a clear spin signal is observed. Furthermore, the magnetic field angle dependence of the spin signal is consistent with spin-momentum locking property of surface states of SmB6.

preprint2015arXiv

An Analysis of Unsupervised Pre-training in Light of Recent Advances

Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to improve performance. Unfortunately, unsupervised pre-training is not used by state-of-the-art methods leading to the following question: Is unsupervised pre-training still useful given recent advances? If so, when? We answer this in three parts: we 1) develop an unsupervised method that incorporates ReLUs and recent unsupervised regularization techniques, 2) analyze the benefits of unsupervised pre-training compared to data augmentation and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised samples, 3) verify our findings on STL-10. We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low. We also use unsupervised pre-training with additional color augmentation to achieve near state-of-the-art performance on STL-10.

preprint2015arXiv

Deep Networks for Image Super-Resolution with Sparse Prior

Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality.

preprint2015arXiv

Enhancing entanglement trapping by weak measurement and quantum measurement reversal

In this paper, we propose a scheme to enhance trapping of entanglement of two qubits in the environment of a photonic band gap material. Our entanglement trapping promotion scheme makes use of combined weak measurements and quantum measurement reversals. The optimal promotion of entanglement trapping can be acquired with a reasonable finite success probability by adjusting measurement strengths.

preprint2015arXiv

Epitaxial growth and properties of La0.7Sr0.3MnO3 thin films with micrometer wide atomic terraces

La0.7Sr0.3MnO3 (LSMO) films with extraordinarily wide atomic terraces are epitaxially grown on SrTiO3 (100) substrates by pulsed laser deposition. Atomic force microscopy measurements on the LSMO films show that the atomic step is ~ 4 Å and the atomic terrace width is more than 2 micrometers. For a 20 monolayers (MLs) LSMO film, the magnetization is determined to be 255 +- 15 emu/cm3 at room temperature, corresponding to 1.70 + - 0.11 Bohr magneton per Mn atom. As the thickness of LSMO increases from 8 MLs to 20 MLs, the critical thickness for the temperature dependent insulator-to-metal behavior transition is shown to be 9 MLs. Furthermore, post-annealing in oxygen environment improves the electron transport and magnetic properties of the LSMO films.

preprint2015arXiv

Graphene Spintronics

The isolation of graphene has triggered an avalanche of studies into the spin-dependent physical properties of this material, as well as graphene-based spintronic devices. Here we review the experimental and theoretical state-of-art concerning spin injection and transport, defect-induced magnetic moments, spin-orbit coupling and spin relaxation in graphene. Future research in graphene spintronics will need to address the development of applications such as spin transistors and spin logic devices, as well as exotic physical properties including topological states and proximity-induced phenomena in graphene and other 2D materials.

preprint2015arXiv

Role of initial system-bath correlation on coherence trapping

We study the coherence trapping of a qubit correlated initially with a non-Markovian bath in a pure dephasing channel. By considering the initial qubit-bath correlation and the bath spectral density, we find that the initial qubit-bath correlation can lead to a more efficient coherence trapping than that of the initially separable qubit-bath state. The stationary coherence in the long time limit can be maximized by optimizing the parameters of the initially correlated qubit-bath state and the bath spectral density. In addition, the effects of this initial correlation on the maximal evolution speed for the qubit trapped to its stationary coherence state are also explored.

preprint2015arXiv

Role of transparency of platinum-ferromagnet interface in determining intrinsic magnitude of spin Hall effect

The spin Hall effect (SHE) converts charge current to pure spin currents in orthogonal directions in materials that have significant spin-orbit coupling.The efficiency of the conversion is described by the spin Hall Angle (SHA). The SHA can most readily be inferred by using the generated spin currents to excite or rotate the magnetization of ferromagnetic films or nano-elements via spin-transfer torques.Some of the largest spin torque derived spin Hall angles (ST-SHA) have been reported in platinum. Here we show, using spin torque ferromagnetic resonance (ST-FMR) measurements, that the transparency of the Pt-ferromagnet interface to the spin current plays a central role in determining the magnitude of the ST-SHA. We measure a much larger ST-SHA in Pt/cobalt (~0.11) compared to Pt/permalloy (~0.05) bilayers when the interfaces are assumed to be completely transparent. Taking into account the transparency of these interfaces, as derived from spin-mixing conductances, we find that the intrinsic SHA in platinum has a much higher value of 0.19 +- 0.04 as compared to the ST-SHA. The importance of the interface transparency is further exemplified by the insertion of atomically thin magnetic layers at the Pt/permalloy interface that we show strongly modulates the magnitude of the ST-SHA.

preprint2015arXiv

Self-Tuned Deep Super Resolution

Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images.

preprint2015arXiv

Supersolid with nontrivial topological spin textures in spin-orbit-coupled Bose gases

Supersolid is a long-sought exotic phase of matter, which is characterized by the coexistence of a diagonal long-range order of solid and an off-diagonal long-range order of superfluid. Possible candidates to realize such a phase have been previously considered, including hard-core bosons with long-range interaction and soft-core bosons. Here we demonstrate that an ultracold atomic condensate of hard-core bosons with contact interaction can establish a supersolid phase when simultaneously subjected to spin-orbit coupling and a spin-dependent periodic potential. This supersolid phase is accompanied by topologically nontrivial spin textures, and is signaled by the separation of momentum distribution peaks, which can be detected via time-of-flight measurements. We also discuss possibilities to produce and observe the supersolid phase for realistic experimental situations.

preprint2014arXiv

Classical-driving-assisted quantum speed-up

We propose a method of accelerating the speed of evolution of an open system by an external classical driving field for a qubit in a zero-temperature structured reservoir. It is shown that, with a judicious choice of the driving strength of the applied classical field, a speed-up evolution of an open system can be achieved in both the weak system-environment couplings and the strong system-environment couplings. By considering the relationship between non-Makovianity of environment and the classical field, we can drive the open system from the Markovian to the non-Markovian regime by manipulating the driving strength of classical field. That is the intrinsic physical reason that the classical field may induce the speed-up process. In addition, the roles of this classical field on the variation of quantum evolution speed in the whole decoherence process is discussed.

preprint2014arXiv

Quantum speed limits for Bell-diagonal states

Bounds of the minimum evolution time between two distinguishable states of a system can help to assess the maximal speed of quantum computers and communication channels. We study the quantum speed limit time of a composite quantum states in the presence of nondissipative decoherence. For the initial states with maximally mixed marginals, we obtain the exactly expressions of quantum speed limit time which mainly depend on the parameters of the initial states and the decoherence channels. Furthermore, by calculating quantum speed limit time for the time-dependent states started from a class of initial states, we discover that the quantum speed limit time gradually decreases in time, and the decay rate of the quantum speed limit time would show a sudden change at a certain critical time. Interestingly, at the same critical time, the composite system dynamics would exhibit a sudden transition from classical to quantum decoherence.

preprint2013arXiv

Competing With Strategies

We study the problem of online learning with a notion of regret defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and for deriving regret-minimization algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow the regularized leader strategies. In several cases we also derive efficient learning algorithms.

preprint2013arXiv

Effect of in-situ deposition of Mg adatoms on spin relaxation in graphene

We have systematically introduced charged impurity scatterers in the form of Mg adsorbates to exfoliated single layer graphene and observe little variation of the spin relaxation times despite pronounced changes in the charge transport behavior. All measurements are performed on non-local graphene tunneling spin valves exposed in-situ to Mg adatoms, thus systematically introducing atomic-scale charged impurity scattering. While charge transport properties exhibit decreased mobility and decreased momentum scattering times, the observed spin lifetimes are not significantly affected indicating that charged impurity scattering is inconsequential in the present regime of spin relaxation times.

preprint2013arXiv

Integrating MBE materials with graphene to induce novel spin-based phenomena

Magnetism in graphene is an emerging field that has received much theoretical attention. In particular, there have been exciting predictions for induced magnetism through proximity to a ferromagnetic insulator as well as through localized dopants and defects. Here, we discuss our experimental work using molecular beam epitaxy (MBE) to modify the surface of graphene and induce novel spin-dependent phenomena. First, we investigate the epitaxial growth the ferromagnetic insulator EuO on graphene and discuss possible scenarios for realizing exchange splitting and exchange fields by ferromagnetic insulators. Second, we investigate the properties of magnetic moments in graphene originating from localized p_z-orbital defects (i.e. adsorbed hydrogen atoms). The behavior of these magnetic moments is studied using non-local spin transport to directly probe the spin-degree of freedom of the defect-induced states. We also report the presence of enhanced electron g-factors caused by the exchange fields present in the system. Importantly, the exchange field is found to be highly gate dependent, with decreasing g-factors with increasing carrier densities.

preprint2013arXiv

Quantum speed limit for arbitrary initial states

We investigate the generic bound on the minimal evolution time of the open dynamical quantum system. This quantum speed limit time is applicable to both mixed and pure initial states. We then apply this result to the damped Jaynes-Cummings model and the Ohimc-like dephasing model starting from a general time-evolution state. The bound of this time-dependent state at any point in time can be found. For the damped Jaynes-Cummings model, the corresponding bound first decreases and then increases in the Markovian dynamics. While in the non-Markovian regime, the speed limit time shows an interesting periodic oscillatory behavior. For the case of Ohimc-like dephasing model, this bound would be gradually trapped to a fixed value. In addition, the roles of the relativistic effects on the speed limit time for the observer in non-inertial frames are discussed.

preprint2013arXiv

Spin injection and detection in lanthanum- and niobium-doped SrTiO3 using the Hanle technique

There has been much interest in the injection and detection of spin polarized carriers in semiconductors for the purposes of developing novel spintronic devices. Here we report the electrical injection and detection of spin-polarized carriers into Nb-doped strontium titanate (STO) single crystals and La-doped STO epitaxial thin films using MgO tunnel barriers and the three-terminal Hanle technique. Spin lifetimes of up to ~100 ps are measured at room temperature and vary little as the temperature is decreased to low temperatures. However, the mobility of the STO has a strong temperature dependence. This behavior and the carrier doping dependence of the spin lifetime suggest that the spin lifetime is limited by spin-dependent scattering at the MgO/STO interfaces, perhaps related to the formation of doping induced Ti3+. Our results reveal a severe limitation of the three-terminal Hanle technique for measuring spin lifetimes within the interior of the subject material.

preprint2013arXiv

Spin-orbit Coupled Bose-Einstein Condensates in Spin-dependent Optical Lattices

We investigate the ground-state properties of spin-orbit coupled Bose-Einstein condensates in spin-dependent optical lattices. The competition between the spin-orbit coupling strength and the depth of the optical lattice leads to a rich phase diagram. Without spin-orbit coupling, the spin-dependent optical lattices separate the condensates into alternating spin domains with opposite magnetization directions. With relatively weak spin-orbit coupling, the spin domain wall is dramatically changed from Néel wall to Bloch wall. For sufficiently strong spin-orbit coupling, vortex chains and antivortex chains are excited in the spin-up and spin-down domains respectively, corresponding to the formation of a lattice composed of meron-pairs and antimeron-pairs in the pseudospin representation. We also discuss how to observe these phenomena in real experiments.

preprint2013arXiv

The synthesis and characterization of 1111-type diluted magnetic semiconductors (La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co)

The doping effect of Sr and transition metals Mn, Fe, Co into the direct-gap semiconductor LaZnAsO has been investigated. Our results indicate that the single phase ZrCuSiAs-type tetragonal crystal structure is preserved in (La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co) with the doping level up to x = 0.1. While the system remains semiconducting, doping with Sr and Mn results in ferromagnetic order with TC ~ 30K, and doping with Sr and Fe results in a spin glass like state below ~6K with a saturation moment of ~0.02 muB/Fe, an order of magnitude smaller than the ~0.4 muB/Mn of Sr and Mn doped samples. The same type of magnetic state is observed neither for (Zn,Fe) substitution without carrier doping, nor for Sr and Co doped specimens.

preprint2012arXiv

Blow up for some semilinear wave equations in multi-space dimensions

In this paper, we discuss a new nonlinear phenomenon. We find that in $n\geq 2$ space dimensions, there exists two indexes $p$ and $q$ such that the cauchy problems for the nonlinear wave equations {equation} \label{0.1} \Box u(t,x) = |u(t,x)|^{q}, \ \ x\in R^{n}, {equation} and {equation} \label{0.2} \Box u(t,x) = |u_{t}(t,x)|^{p}, \ \ x\in R^{n} {equation} both have global existence for small initial data, while for the combined nonlinearity, the solutions to the Cauchy problem for the nonlinear wave equation {equation} \label{0.3} \Box u(t,x) = | u_{t}(t,x)|^{p} + |u(t,x)|^{q}, \ \ x\in R^{n}, {equation} with small initial data will blow up in finite time. In the two dimensional case, we also find that if $ q=4$, the Cauchy problem for the equation \eqref{0.1} has global existence, and the Cauchy problem for the equation {equation} \label{0.4} \Box u(t,x) = u (t,x)u_{t}(t,x)^{2}, \ \ x\in R^{2} {equation} has almost global existence, that is, the life span is at least $ \exp (c\varepsilon^{-2}) $ for initial data of size $ \varepsilon$. However, in the combined nonlinearity case, the Cauchy problem for the equation {equation} \label{0.5} \Box u(t,x) = u(t,x) u_{t}(t,x)^{2} + u(t,x)^{4}, \ \ x\in R^{2} {equation} has a life span which is of the order of $ \varepsilon^{-18} $ for the initial data of size $ \varepsilon$, this is considerably shorter in magnitude than that of the first two equations. This solves an open optimality problem for general theory of fully nonlinear wave equations (see \cite{Katayama}).

preprint2012arXiv

Electric Field Control of the Verwey Transition and Induced Magnetoelectric Effect in Magnetite

We incorporate single crystal Fe$_3$O$_4$ thin films into a gated device structure and demonstrate the ability to control the Verwey transition with static electric fields. The Verwey transition temperature ($T_V$) increases for both polarities of the electric field, indicating the effect is not driven by changes in carrier concentration. Energetics of induced electric polarization and/or strain within the Fe$_3$O$_4$ film provide a possible explanation for this behavior. Electric field control of the Verwey transition leads directly to a large magnetoelectric effect with coefficient of 585 pT m/V.

preprint2012arXiv

Magnetic Moment Formation in Graphene Detected by Scattering of Pure Spin Currents

Hydrogen adatoms are shown to generate magnetic moments inside single layer graphene. Spin transport measurements on graphene spin valves exhibit a dip in the non-local spin signal as a function of applied magnetic field, which is due to scattering (relaxation) of pure spin currents by exchange coupling to the magnetic moments. Furthermore, Hanle spin precession measurements indicate the presence of an exchange field generated by the magnetic moments. The entire experiment including spin transport is performed in an ultrahigh vacuum chamber, and the characteristic signatures of magnetic moment formation appear only after hydrogen adatoms are introduced. Lattice vacancies also demonstrate similar behavior indicating that the magnetic moment formation originates from pz-orbital defects.

preprint2012arXiv

Spin Relaxation in Single Layer Graphene with Tunable Mobility

Graphene is an attractive material for spintronics due to theoretical predictions of long spin lifetimes arising from low spin-orbit and hyperfine couplings. In experiments, however, spin lifetimes in single layer graphene (SLG) measured via Hanle effects are much shorter than expected theoretically. Thus, the origin of spin relaxation in SLG is a major issue for graphene spintronics. Despite extensive theoretical and experimental work addressing this question, there is still little clarity on the microscopic origin of spin relaxation. By using organic ligand-bound nanoparticles as charge reservoirs to tune mobility between 2700 and 12000 cm2/Vs, we successfully isolate the effect of charged impurity scattering on spin relaxation in SLG. Our results demonstrate that while charged impurities can greatly affect mobility, the spin lifetimes are not affected by charged impurity scattering.

preprint2011arXiv

Electrical spin injection and transport in Germanium

We report the first experimental demonstration of electrical spin injection, transport and detection in bulk germanium (Ge). The non-local magnetoresistance in n-type Ge is observable up to 225K. Our results indicate that the spin relaxation rate in the n-type Ge is closely related to the momentum scattering rate, which is consistent with the predicted Elliot-Yafet spin relaxation mechanism for Ge. The bias dependence of the nonlocal magnetoresistance and the spin lifetime in n-type Ge is also investigated.

preprint2011arXiv

Enhancement of Spin Injection into Graphene by Water Dipping

We immerse single layer graphene spin valves into purified water for a short duration (<1 min) and investigate the effect on spin transport. Following water immersion, we observe an enhancement in nonlocal magnetoresistance. Additionally, the enhancement of spin signal is correlated with an increase in junction resistance, which produces an increase in spin injection efficiency. This study provides a simple way to improve the signal magnitude and establishes the robustness of graphene spin valves to water exposure, which enables future studies involving chemical functionalization in aqueous solution.

preprint2011arXiv

Half-vortex sheets and domain-wall trains of rotating two-component Bose-Einstein condensates in spin-dependent optical lattices

We investigate half-vortex sheets and domain-wall trains of rotating two-component Bose-Einstein condensates in spin-dependent optical lattices. The two-component condensates undergo phase separation in the form of stripes arranged alternatively. The vortices of one component are aligned in lines in the low-density regions and filled with the other component, which results in a stable vortex configuration, straight half-vortex sheets. A train of novel domain walls, with spatially periodic "eyebrow-like" spin textures embedded on them, are formed at the interfaces of the two components. We reveal that these spatially periodic textures on the domain walls result from the linear gradient of the relative phase, which is induced by the alternating arrangement of the vortex sheets in the two components. An accurate manipulation of the textures can be realized by adjusting the intercomponent interaction strength, the rotating angular frequency and the period of the optical lattices.

preprint2011arXiv

Life-Span of Solutions to Critical Semilinear Wave Equations

The final open part of the famous Strauss conjecture on semilinear wave equations of the form \Box u=|u|^{p}, i.e., blow-up theorem for the critical case in high dimensions was solved by Yordanov and Zhang, or Zhou independently. But the estimate for the lifespan, the maximal existence time, of solutions was not clarified in both papers. Recently, Takamura and Wakasa have obtained the sharp upper bound of the lifespan of the solution to the critical semilinear wave equations, and their method is based on the method in Yordanov and Zhang. In this paper, we give a much simple proof of the result of Takamura and Wakasa by using the method in Y. Zhou for space dimensions n\geq 2. Simultaneously, this estimate of the life span also proves the last open optimality problem of the general theory for fully nonlinear wave equations with small initial data in the case n=4 and quadratic nonlinearity(One can see Li and Chen for references on the whole history).

preprint2011arXiv

Quantum correlations dynamics under different non-markovian environmental models

We investigate the roles of different environmental models on quantum correlation dynamics of two-qubit composite system interacting with two independent environments. The most common environmental models (the single-Lorentzian model, the squared-Lorentzian model, the two-Lorentzian model and band-gap model) are analyzed. First, we note that for the weak coupling regime, the monotonous decay speed of the quantum correlation is mainly determined by the spectral density functions of these different environments. Then, by considering the strong coupling regime we find that, contrary to what is stated in the weak coupling regime, the dynamics of quantum correlation depends on the non-Markovianity of the environmental models, and is independent of the environmental spectrum density functions.

preprint2011arXiv

Spin Relaxation in Single Layer and Bilayer Graphene

We investigate spin relaxation in graphene spin valves and observe strongly contrasting behavior for single layer graphene (SLG) and bilayer graphene (BLG). In SLG, the spin lifetime (τs) varies linearly with the momentum scattering time (τp) as carrier concentration is varied, indicating the dominance of Elliot-Yafet (EY) spin relaxation at low temperatures. In BLG, τs and τp exhibit an inverse dependence, which indicates the dominance of Dyakonov-Perel spin relaxation at low temperatures. The different behavior is due to enhanced screening and/or reduced surface sensitivity of BLG, which greatly reduces the impurity-induced EY spin relaxation.

preprint2011arXiv

Spin Transport and Relaxation in Graphene

We review our recent work on spin injection, transport and relaxation in graphene. The spin injection and transport in single layer graphene (SLG) were investigated using nonlocal magnetoresistance (MR) measurements. Spin injection was performed using either transparent contacts (Co/SLG) or tunneling contacts (Co/MgO/SLG). With tunneling contacts, the nonlocal MR was increased by a factor of ~1000 and the spin injection/detection efficiency was greatly enhanced from ~1% (transparent contacts) to ~30%. Spin relaxation was investigated on graphene spin valves using nonlocal Hanle measurements. For transparent contacts, the spin lifetime was in the range of 50-100 ps. The effects of surface chemical doping showed that for spin lifetimes on the order of 100 ps, impurity scattering (Au) was not the dominant mechanism for spin relaxation. While using tunneling contacts to suppress the contact-induced spin relaxation, we observed the spin lifetimes as long as 771 ps at room temperature, 1.2 ns at 4 K in SLG, and 6.2 ns at 20 K in bilayer graphene (BLG). Furthermore, contrasting spin relaxation behaviors were observed in SLG and BLG. We found that Elliot-Yafet spin relaxation dominated in SLG at low temperatures whereas Dyakonov-Perel spin relaxation dominated in BLG at low temperatures. Gate tunable spin transport was studied using the SLG property of gate tunable conductivity and incorporating different types of contacts (transparent and tunneling contacts). Consistent with theoretical predictions, the nonlocal MR was proportional to the SLG conductivity for transparent contacts and varied inversely with the SLG conductivity for tunneling contacts. Finally, bipolar spin transport in SLG was studied and an electron-hole asymmetry was observed for SLG spin valves with transparent contacts...

preprint2011arXiv

Suppression of FM-to-AM conversion in third-harmonic generation at the retracing point of a crystal

FM-to-AM conversion can cause many negative effects (e.g., reducing of margin against damage to the optics, and etc.) on performances of third-harmonic conversion system. In this letter, the FM-to-AM conversion effect in third-harmonic generation is investigated both at and away from the retracing point of type-II KDP crystal. Obtained results indicate that the FM-to-AM conversion can be suppressed effectively when the crystal works at the retracing point.

preprint2011arXiv

Suppression of FM-to-AM conversion in third-harmonic generation by tuning the ratio of modulation depth

Issues of Frequency-to-Amplitude modulation (FM-to-AM) conversion occurred in phase-modulated third-harmonic generation (THG) process are investigated. An expression about group-velocity is theoretically derived to suppress the FM-to-AM conversion, which appears to be dependant on the ratio of modulation depth of fundamental to second-harmonic when given the same modulation frequencies of them. Simulation results indicate that the induced AM in THG process can be suppressed effectively when the expression about group-velocity is satisfied.

preprint2010arXiv

Blow up of Solutions to Semilinear Wave Equations with variable coefficients and boundary

This paper is devoted to studying the following two initial-boundary value problems for semilinear wave equations with variable coefficients on exterior domain with subcritical exponent in $n$ space dimensions: u_{tt}-partial_{i}(a_{ij}(x)\partial_{j}u)=|u|^{p}, (x,t)\in Ω^{c}\times(0,+\infty), n\geq 3 and u_{tt}-\partial_{i}(a_{ij}(x)\partial_{j}u)=|u_{t}|^{p}, (x,t)\in Ω^{c}\times (0,+\infty), n\geq 1, where $a_{ij}(x)=δ_{ij}, when |x|\geq R. The exponents $p$ satisfies $ 1<p<p_{1}(n)$ in (0.1), and $p \leq p_{2}(n)$ in (0.2), where $p_{1}(n)$ is the larger root of the quadratic equation (n-1)p^{2}-(n+1)p-2=0, and p_{2}(n)=\frac{2}{n-1}+1, respectively. It is well-known that the numbers p_{1}(n) and p_{2}(n) are the critical exponents. We will establish two blowup results for the above two initial-boundary value problems, it is proved that there can be no global solutions no matter how small the initial data are, and also we give the lifespan estimate of solutions for above problems.

preprint2010arXiv

Epitaxial EuO Thin Films on GaAs

We demonstrate the epitaxial growth of EuO on GaAs by reactive molecular beam epitaxy. Thin films are grown in an adsorption-controlled regime with the aid of an MgO diffusion barrier. Despite the large lattice mismatch, it is shown that EuO grows well on MgO(001) with excellent magnetic properties. Epitaxy on GaAs is cube-on-cube and longitudinal magneto-optic Kerr effect measurements demonstrate a large Kerr rotation of 0.57°, a significant remanent magnetization, and a Curie temperature of 69 K.

preprint2010arXiv

Manipulation of Spin Transport in Graphene by Surface Chemical Doping

The effects of surface chemical doping on spin transport in graphene are investigated by performing non-local measurements in ultrahigh vacuum while depositing gold adsorbates. We demonstrate manipulation of the gate-dependent non-local spin signal as a function of gold coverage. We discover that charged impurity scattering is not the dominant mechanism for spin relaxation in graphene, despite its importance for momentum scattering. Finally, unexpected enhancements of the spin lifetime illustrate the complex nature of spin relaxation in graphene.

preprint2010arXiv

Oscillatory Spin Polarization and Magneto-Optic Kerr Effect in Fe3O4 Thin Films on GaAs(001)

The spin dependent properties of epitaxial Fe3O4 thin films on GaAs(001) are studied by the ferromagnetic proximity polarization (FPP) effect and magneto-optic Kerr effect (MOKE). Both FPP and MOKE show oscillations with respect to Fe3O4 film thickness, and the oscillations are large enough to induce repeated sign reversals. We attribute the oscillatory behavior to spin-polarized quantum well states forming in the Fe3O4 film. Quantum confinement of the t2g states near the Fermi level provides an explanation for the similar thickness dependences of the FPP and MOKE oscillations.

preprint2010arXiv

Tailoring Interlayer Exchange Coupling of Ferromagnetic Films Across MgO with Fe Nanoclusters

We investigate the interlayer exchange coupling in Fe/MgO/Fe and Fe/MgO/Co systems with magnetic Fe nanoclusters embedded in the MgO spacer. Samples are grown by molecular beam epitaxy (MBE) and utilize wedged MgO films to independently vary the film thickness and the position of the Fe nanoclusters. Depending on the position of the Fe nanoclusters, the bilinear coupling (J1) exhibits strong variations in magnitude and can even switch between antiferromagnetic and ferromagnetic. This effect is explained by the magnetic coupling between the ferromagnetic films and the magnetic nanoclusters. Interestingly, the coupling of Fe nanoclusters to a Co film is 160% stronger than their coupling to a Fe film (at MgO spacing of 0.56 nm). This is much greater than the coupling difference of 20% observed in the analogous thin film systems (i.e. Fe/MgO/Co vs. Fe/MgO/Fe), identifying an interesting nano-scaling effect related to the coupling between films and nanoclusters.

preprint2010arXiv

The Effect of Cluster Formation on Graphene Mobility

We investigate the effect of gold (Au) atoms in the form of both point-like charged impurities and clusters on the transport properties of graphene. Cryogenic deposition (18 K) of Au decreases the mobility and shifts the Dirac point in a manner that is consistent with scattering from point-like charged impurities. Increasing the temperature to room temperature promotes the formation of clusters, which is verified with atomic force microscopy. We find that for a fixed amount of Au impurities, the formation of clusters enhances the mobility and causes the Dirac point to shift back towards zero.

preprint2010arXiv

Tunneling Spin Injection into Single Layer Graphene

We achieve tunneling spin injection from Co into single layer graphene (SLG) using TiO2 seeded MgO barriers. A non-local magnetoresistance (ΔRNL) of 130 Ω is observed at room temperature, which is the largest value observed in any material. Investigating ΔRNL vs. SLG conductivity from the transparent to the tunneling contact regimes demonstrates the contrasting behaviors predicted by the drift-diffusion theory of spin transport. Furthermore, tunnel barriers reduce the contact-induced spin relaxation and are therefore important for future investigations of spin relaxation in graphene.

preprint2010arXiv

Tunneling Spin Injection into Single Layer Graphene (Supplementary Information)

preprint2009arXiv

Electrical Detection of Spin Precession in Single Layer Graphene Spin Valves with Transparent Contacts

Spin accumulation and spin precession in single-layer graphene are studied by non-local spin valve measurements at room temperature. The dependence of the non-local magnetoresistance on electrode spacing is investigated and the results indicate a spin diffusion length of ~1.6 microns and a spin injection/detection efficiency of 0.013. Electrical detection of the spin precession confirms that the non-local signal originates from spin injection and transport. Fitting of the Hanle spin precession data yields a spin relaxation time of ~84 ps and a spin diffusion length of ~1.5 microns, which is consistent with the value obtained through the spacing dependence.

preprint2009arXiv

Electron-Hole Asymmetry of Spin Injection and Transport in Single-Layer Graphene

Spin-dependent properties of single-layer graphene (SLG) have been studied by non-local spin valve measurements at room temperature. Gate voltage dependence shows that the non-local magnetoresistance (MR) is proportional to the conductivity of the SLG, which is the predicted behavior for transparent ferromagnetic/nonmagnetic contacts. While the electron and hole bands in SLG are symmetric, gate voltage and bias dependence of the non-local MR reveal an electron-hole asymmetry in which the non-local MR is roughly independent of bias for electrons, but varies significantly with bias for holes.

preprint2009arXiv

Electronic Doping and Scattering by Transition Metals on Graphene

We investigate the effects of transition metals (TM) on the electronic doping and scattering in graphene using molecular beam epitaxy combined with in situ transport measurements. The room temperature deposition of TM onto graphene produces clusters that dope n-type for all TM investigated (Ti, Fe, Pt). We also find that the scattering by TM clusters exhibits different behavior compared to 1/r Coulomb scattering. At high coverage, Pt films are able to produce doping that is either n-type or weakly p-type, which provides experimental evidence for a strong interfacial dipole favoring n-type doping as predicted theoretically.

preprint2006arXiv

Remarks on CLEO New Measurements for Upsilon(1S) Decays to Charmonium Final States and Investigations on Associate Strange Particle Enhancement in Upsilon to J/Psi +X

The recent measurements by CLEO Collaboration for the inclusive J/Psi and Psi(2S) production in Upsilon(1S) decay and our previous calculation are analyzed. The J/Psi momentum spectrum and the production ratio of Psi(2S) versus J/Psi favour Upsilon to J/Psi(Psi(2S)) + ccbar g as the dominant contribution. We point out that the differences between the experimental data and our previous results are mainly originated from the setting of the parameter charm quark mass. We further suggest the associate strange particle enhancement as a probe for the open charm particles in Upsilon to J/Psi (Psi(2S)) + ccbar g.

Wei Han

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85 published item(s)

LithoBench: Benchmarking Large Multimodal Models for Remote-Sensing Lithology Interpretation

Electromagnetic-Compliant Channel Modeling and Performance Evaluation for Holographic MIMO

Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data

An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

Lossy Computing with Side Information via Multi-Hypergraphs

Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Semantic Compression with Side Information: A Rate-Distortion Perspective

Simulation and detection of Weyl fermions in ultracold Fermi gases with Raman-assisted spin-orbit coupling

Spin Seebeck effect in quantum magnet Pb2V3O9

Unsupervised Data Selection via Discrete Speech Representation for ASR

A Better and Faster End-to-End Model for Streaming ASR

Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling

FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data

Structural Entropy of the Stochastic Block Models

TAPL: Dynamic Part-based Visual Tracking via Attention-guided Part Localization

Conformer: Convolution-augmented Transformer for Speech Recognition

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides

Rate-Splitting Multiple Access for Downlink Multi-Antenna Communications: Physical Layer Design and Link-level Simulations

Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Streaming Object Detection for 3-D Point Clouds

Comparison of OFDM and Single-Carrier for Large-Scale Antenna Systems

Crystal Structure Manipulation of the Exchange Bias in an Antiferromagnetic Film

Device-to-device Cooperation in Massive MIMO Systems with Cascaded Precoding

Double-Quantum Spin Vortices in SU(3) Spin-Orbit Coupled Bose Gases

Equilibrium vortex lattices of a binary rotating atomic Bose-Einstein condensate with unequal atomic masses

Experimental Investigation of Temperature-Dependent Gilbert Damping in Permalloy Thin Films

Facet-dependent giant spin orbit torque in single crystalline antiferromagnetic Ir-Mn / ferromagnetic permalloy bilayers

Localized spatially nonlinear matter waves in atomic-molecular Bose-Einstein condensates with space-modulated nonlinearity

Low-Complexity Recursive Convolutional Precoding for OFDM-based Large-Scale Antenna System

Magnetic anisotropy of the single-crystalline ferromagnetic insulator Cr2Ge2Te6

Perspectives for Spintronics in 2D Materials

Positive Exchange Bias between Permalloy and Twined (10-10)-Cr2O3 Films

Quantization and Feedback of Spatial Covariance Matrix for Massive MIMO Systems with Cascaded Precoding

Relative contravariantly finite subcategories and relative tilting modules

Seq-NMS for Video Object Detection

Spin Injection and Inverse Edelstein Effect in the Surface States of Topological Kondo Insulator SmB6

An Analysis of Unsupervised Pre-training in Light of Recent Advances

Deep Networks for Image Super-Resolution with Sparse Prior

Enhancing entanglement trapping by weak measurement and quantum measurement reversal

Epitaxial growth and properties of La0.7Sr0.3MnO3 thin films with micrometer wide atomic terraces

Graphene Spintronics

Role of initial system-bath correlation on coherence trapping

Role of transparency of platinum-ferromagnet interface in determining intrinsic magnitude of spin Hall effect

Self-Tuned Deep Super Resolution

Supersolid with nontrivial topological spin textures in spin-orbit-coupled Bose gases

Classical-driving-assisted quantum speed-up

Quantum speed limits for Bell-diagonal states

Competing With Strategies

Effect of in-situ deposition of Mg adatoms on spin relaxation in graphene

Integrating MBE materials with graphene to induce novel spin-based phenomena

Quantum speed limit for arbitrary initial states

Spin injection and detection in lanthanum- and niobium-doped SrTiO3 using the Hanle technique

Spin-orbit Coupled Bose-Einstein Condensates in Spin-dependent Optical Lattices

The synthesis and characterization of 1111-type diluted magnetic semiconductors (La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co)

Blow up for some semilinear wave equations in multi-space dimensions

Electric Field Control of the Verwey Transition and Induced Magnetoelectric Effect in Magnetite

Magnetic Moment Formation in Graphene Detected by Scattering of Pure Spin Currents

Spin Relaxation in Single Layer Graphene with Tunable Mobility

Electrical spin injection and transport in Germanium

Enhancement of Spin Injection into Graphene by Water Dipping

Half-vortex sheets and domain-wall trains of rotating two-component Bose-Einstein condensates in spin-dependent optical lattices

Life-Span of Solutions to Critical Semilinear Wave Equations

Quantum correlations dynamics under different non-markovian environmental models

Spin Relaxation in Single Layer and Bilayer Graphene

Spin Transport and Relaxation in Graphene

Suppression of FM-to-AM conversion in third-harmonic generation at the retracing point of a crystal

Suppression of FM-to-AM conversion in third-harmonic generation by tuning the ratio of modulation depth

Blow up of Solutions to Semilinear Wave Equations with variable coefficients and boundary