arXiv subjects

Yue Gu

Publications and source records attributed to Yue Gu.

26 records · Page 2Linked to original sources

arXiv2021

Layer-controlled Ferromagnetism in Atomically Thin CrSiTe$_3$ Flakes

The research on two-dimensional (2D) van der Waals (vdW) ferromagnets has promoted the development of ultrahigh-density and nanoscale data storage. However, intrinsic ferromagnetism in layered magnets is always subject to many factors, such as stacking orders, interlayer couplings, and the number of layers. Here, we report a magnetic transition from soft to hard ferromagnetic behaviors as the thickness of CrSiTe$_3$ flakes decreases down to several nanometers. Phenomenally, in contrast to the negligible hysteresis loop in the bulk counterparts, atomically thin CrSiTe$_3$ shows a rectangular loop with finite magnetization and coercivity as thickness decreases down to ~8 nm, indicative of a single-domain and out-of-plane ferromagnetic order. We find that the stray field is weakened with decreasing thickness, which suppresses the formation of the domain wall. In addition, thickness-dependent ferromagnetic properties also reveal a crossover from 3 dimensional to 2 dimensional Ising ferromagnets at a ~7 nm thickness of CrSiTe$_3$, accompanied by a drop of the Curie temperature from 33 K for bulk to ~17 K for 4 nm sample.

cond-mat.mtrl-sci↗

arXiv2021

Pressure-enhanced ferromagnetism in layered CrSiTe3 flakes

The research on van der Waals (vdW) layered ferromagnets have promoted the development of nanoscale spintronics and applications. However, low-temperature ferromagnetic properties of these materials greatly hinder their applications. Here, we report pressure-enhanced ferromagnetic behaviours in layered CrSiTe3 flakes revealed by high-pressure magnetic circular dichroism (MCD) measurement. At ambient pressure, CrSiTe3 undergoes a paramagnetic-to-ferromagnetic phase transition at 32.8 K, with a negligible hysteresis loop, indicating a soft ferromagnetic behaviour. Under 4.6 GPa pressure, the soft ferromagnet changes into hard one, signalled by a rectangular hysteretic loop with remnant magnetization at zero field. Interestingly, with further increasing pressure, the coercive field (H_c) dramatically increases from 0.02 T at 4.6 GPa to 0.17 T at 7.8 GPa, and the Curie temperature (T_c^h: the temperature for closing the hysteresis loop) also increases from ~36 K at 4.6 GPa to ~138 K at 7.8 GPa. The influences of pressure on exchange interactions are further investigated by density functional theory calculations, which reveal that the in-plane nearest-neighbor exchange interaction and magneto-crystalline anisotropy increase simultaneously as pressure increases, leading to increased H_c and T_c^h in experiments. The effective interaction between magnetic couplings and external pressure offers new opportunities for both searching room-temperature layered ferromagnets and designing pressure-sensitive magnetic functional devices.

cond-mat.mtrl-sci↗

arXiv2020

Recurrent Distillation based Crowd Counting

In recent years, with the progress of deep learning technologies, crowd counting has been rapidly developed. In this work, we propose a simple yet effective crowd counting framework that is able to achieve the state-of-the-art performance on various crowded scenes. In particular, we first introduce a perspective-aware density map generation method that is able to produce ground-truth density maps from point annotations to train crowd counting model to accomplish superior performance than prior density map generation techniques. Besides, leveraging our density map generation method, we propose an iterative distillation algorithm to progressively enhance our model with identical network structures, without significantly sacrificing the dimension of the output density maps. In experiments, we demonstrate that, with our simple convolutional neural network architecture strengthened by our proposed training algorithm, our model is able to outperform or be comparable with the state-of-the-art methods. Furthermore, we also evaluate our density map generation approach and distillation algorithm in ablation studies.

cs.CV↗

arXiv2019

A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting

Robustness against noise is critical for keyword spotting (KWS) in real-world environments. To improve the robustness, a speech enhancement front-end is involved. Instead of treating the speech enhancement as a separated preprocessing before the KWS system, in this study, a pre-trained speech enhancement front-end and a convolutional neural networks (CNNs) based KWS system are concatenated, where a feature transformation block is used to transform the output from the enhancement front-end into the KWS system's input. The whole model is trained jointly, thus the linguistic and other useful information from the KWS system can be back-propagated to the enhancement front-end to improve its performance. To fit the small-footprint device, a novel convolution recurrent network is proposed, which needs fewer parameters and computation and does not degrade performance. Furthermore, by changing the input features from the power spectrogram to Mel-spectrogram, less computation and better performance are obtained. our experimental results demonstrate that the proposed method significantly improves the KWS system with respect to noise robustness.

cs.SD↗

arXiv2019

RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement

Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn long-range temporal correlations across high-resolution waveforms. These models, however, are limited by memory-intensive dilated convolution and aliasing artifacts from upsampling. We introduce an end-to-end fully-recurrent hourglass-shaped neural network architecture with residual connections for waveform-based single-channel speech enhancement. Our model can efficiently capture long-range temporal dependencies by reducing the features resolution without information loss. Experimental results show that our model outperforms state-of-the-art approaches in six evaluation metrics.

eess.AS↗

arXiv2018

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment

Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utter-ance-level sentiment and emotion from text and audio data. Our introduced model outperforms the state-of-the-art approaches on published datasets and we demonstrated that our model is able to visualize and interpret the synchronized attention over modalities.

cs.CL↗

arXiv2018

Deep Multimodal Learning for Emotion Recognition in Spoken Language

In this paper, we present a novel deep multimodal framework to predict human emotions based on sentence-level spoken language. Our architecture has two distinctive characteristics. First, it extracts the high-level features from both text and audio via a hybrid deep multimodal structure, which considers the spatial information from text, temporal information from audio, and high-level associations from low-level handcrafted features. Second, we fuse all features by using a three-layer deep neural network to learn the correlations across modalities and train the feature extraction and fusion modules together, allowing optimal global fine-tuning of the entire structure. We evaluated the proposed framework on the IEMOCAP dataset. Our result shows promising performance, achieving 60.4% in weighted accuracy for five emotion categories.

cs.CL↗

arXiv2017

Progress Estimation and Phase Detection for Sequential Processes

Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.

cs.LG↗