arXiv subjects

Hang Li

Publications and source records attributed to Hang Li.

At least 235 records · Page 13Linked to original sources

arXiv2022

A special cross-tie domain wall in helimagnet

A special cross-tie (SCT) domain wall was discovered in the helimagnet MnCoSi alloy via the magnetic vector field tomography in Lorentz transmission electron microscopy (LTEM). Different to the traditional cross-tie (TCT) domain wall where the convergent/divergent magnetic moment configuration line up one by one, the relative large Bloch type sub-walls emerge in this brand-new SCT domain wall and two mutually perpendicular rotation axes coexist in this special feature. The straight magnetic stripes accompanied with the unraveled domain walls hint the complex mechanism to form this SCT structure. Interestingly, different orientation of this domain wall in LTEM can easily exhibit various magnetic features, including meron/antimeron chains or bimeron strings.

cond-mat.mtrl-sci↗

arXiv2021

Generating Haar-uniform Randomness using Stochastic Quantum Walks on a Photonic Chip

As random operations for quantum systems are intensively used in various quantum information tasks, a trustworthy measure of the randomness in quantum operations is highly demanded. The Haar measure of randomness is a useful tool with wide applications such as boson sampling. Recently, a theoretical protocol was proposed to combine quantum control theory and driven stochastic quantum walks to generate Haar-uniform random operations. This opens up a promising route to converting classical randomness to quantum randomness. Here, we implement a two-dimensional stochastic quantum walk on the integrated photonic chip and demonstrate that the average of all distribution profiles converges to the even distribution when the evolution length increases, suggesting the 1-pad Haar-uniform randomness. We further show that our two-dimensional array outperforms the one-dimensional array of the same number of waveguide for the speed of convergence. Our work demonstrates a scalable and robust way to generate Haar-uniform randomness that can provide useful building blocks to boost future quantum information techniques.

quant-ph↗

arXiv2021

Self-Supervised Learning by Estimating Twin Class Distributions

We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we enforce the class distributions of different augmentations to be consistent. However, simply minimizing the divergence between augmentations will cause collapsed solutions, i.e., outputting the same class probability distribution for all images. In this case, no information about the input image is left. To solve this problem, we propose to maximize the mutual information between the input and the class predictions. Specifically, we minimize the entropy of the distribution for each sample to make the class prediction for each sample assertive and maximize the entropy of the mean distribution to make the predictions of different samples diverse. In this way, TWIST can naturally avoid the collapsed solutions without specific designs such as asymmetric network, stop-gradient operation, or momentum encoder. As a result, TWIST outperforms state-of-the-art methods on a wide range of tasks. Especially, TWIST performs surprisingly well on semi-supervised learning, achieving 61.2% top-1 accuracy with 1% ImageNet labels using a ResNet-50 as backbone, surpassing previous best results by an absolute improvement of 6.2%. Codes and pre-trained models are given on: https://github.com/bytedance/TWIST

cs.CV↗

arXiv2021

MeSH Term Suggestion for Systematic Review Literature Search

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., MeSH), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This paper investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We perform an extensive analysis of the retrieval, ranking, and refinement of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

cs.IT↗

arXiv2021

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

cs.IT↗

arXiv2021

The dynamic chirality flips of Skyrmion bubbles

Magnetic skyrmion, a topological magnetic domain with complex non-coplanar spin texture, appears a disk-like structure in two dimensions. Exploring three-dimensional spin texture and related chirality switching has drawn enormous interests from the perspective of fundamental research. Here, the three-dimensional magnetic moment of the skyrmion bubbles in centrosymmetric Mn-Ni-Ga were reconstructed with the vector field tomography approach via Lorentz transmission electron microscopy. The type of the bubbles was determined from investigating the magnetic vectors in entire space. We found that the bubbles switched their chirality easily but still keep the polarity to remain the singularity of the bubbles within the material. Our results offer valuable insights into the fundamental mechanisms underlying the spin chirality flips dynamics of skyrmion bubbles.

cond-mat.mtrl-sci↗

arXiv2021

Probe Skyrmion phases and dynamics in MnSi via the magnetoelectric effect in a composite configuration

We have developed a sensitive technique to probe the magnetic skyrmion phases and dynamics by employing the interfacial coupling effect in a magnetoelectric composite configuration. The study on a MnSi single crystal sample using this technique provides clear evidences for the skyrmion lattice phase and coexistence of skyrmion and conical phase. Above the Curie temperature TC, a region with strong spin fluctuation is revealed as well. By tuning the density of Skyrmion or disorder, a transition from the skyrmion lattice to skyrmion-conical coexisting phase is observed. The observation is in good agreement with a theoretical model which predicts the dissipation behavior in the coexistence phase.

cond-mat.mtrl-sci↗

arXiv2021

Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic. To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations. The proposed model (1) learns both representations from edge-enhanced Levi graphs of symbolic equations and commonsense knowledge; (2) automatically fuses equation and commonsense knowledge information via a self-planning module when generating the MWPs. Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the SOTA models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e., equation relevance, topic relevance, and language coherence. To encourage reproducible results, we make our code and MWP dataset public available at \url{https://github.com/tal-ai/MaKE_EMNLP2021}.

cs.CL↗

arXiv2021

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results.

cs.SD↗

arXiv2021

Covert Beamforming Design for Intelligent Reflecting Surface Assisted IoT Networks

In this paper, we consider covert beamforming design for intelligent reflecting surface (IRS) assisted Internet of Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie's channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the later case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of Kullback-Leibler divergence to develop the robust beamformer based on a relaxation, S-Lemma and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.

cs.IT↗

arXiv2021

Secoco: Self-Correcting Encoding for Neural Machine Translation

This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.

cs.CL↗

arXiv2021

Magnetic anisotropy and critical behavior of the quaternary van der Waals ferromagnetic material $\bf CrGe_δSi_{1-δ}Te_3$

Recently, two-dimensional ferromagnetism in the family of Chromium compounds $\rm CrXTe_3 (X=Si, Ge)$ has attracted a broad research interest. Despite the structural similarity in $\rm CrTe_6$ octahedra, the size effect of inserted Ge or Si dimer contributes to significant differences in magnetism. Here, we report a new quaternary van der Waals ferromagnetic material $\rm CrGe_δSi_{1-δ}Te_3$ synthesized by flux method. Ge substitution in Si site results in the lattice expansion, further increasing the Curie temperature and reducing the magnetic anisotropy. The critical behavior of $\rm Cr_{0.96}Ge_{0.17}Si_{0.82}Te_3$ has been studied by specific heat as well as magnetization measurements. And the extracted critical exponents are self-consistent and well-obeying the scaling laws, which are closer to the 2D Ising model with interaction decaying as $J(r)\approx r^{-3.44}$.

physics.app-ph↗

arXiv2021

Learning Fine-Grained Cross Modality Excitement for Speech Emotion Recognition

Speech emotion recognition is a challenging task because the emotion expression is complex, multimodal and fine-grained. In this paper, we propose a novel multimodal deep learning approach to perform fine-grained emotion recognition from real-life speeches. We design a temporal alignment mean-max pooling mechanism to capture the subtle and fine-grained emotions implied in every utterance. In addition, we propose a cross modality excitement module to conduct sample-specific adjustment on cross modality embeddings and adaptively recalibrate the corresponding values by its aligned latent features from the other modality. Our proposed model is evaluated on two well-known real-world speech emotion recognition datasets. The results demonstrate that our approach is superior on the prediction tasks for multimodal speech utterances, and it outperforms a wide range of baselines in terms of prediction accuracy. Further more, we conduct detailed ablation studies to show that our temporal alignment mean-max pooling mechanism and cross modality excitement significantly contribute to the promising results. In order to encourage the research reproducibility, we make the code publicly available at \url{https://github.com/tal-ai/FG_CME.git}.

cs.SD↗

arXiv2021

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at \url{https://github.com/AIED2021/multitask-dialogic-instruction}.

cs.CL↗

arXiv2021

An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms

In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes. Our system consists of (1) a pseudo matching score module that provides reliable training labels; (2) a ranking model that scores every candidate teacher; (3) a novelty boosting module that gives additional opportunities to new teachers; and (4) a diversity metric that guardrails the recommended results to reduce the chance of collision. Offline experimental results show that our approach outperforms a wide range of baselines. Furthermore, we show that our approach is able to reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a five-month observation on a third-party online education platform.

cs.AI↗

arXiv2021

A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation

The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities. However, existing evaluation for vocal delivery is mainly conducted with manual ratings, which faces two core challenges: subjectivity and time-consuming. In this paper, we present a novel machine learning approach that utilizes pairwise comparisons and a multimodal orthogonal fusing algorithm to generate large-scale objective evaluation results of the teacher vocal delivery in terms of fluency and passion. We collect two datasets from real-world education scenarios and the experiment results demonstrate the effectiveness of our algorithm. To encourage reproducible results, we make our code public available at \url{https://github.com/tal-ai/ML4VocalDelivery.git}.

cs.SD↗

arXiv2021

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering

Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. Concretely, our method is based on performing context-driven, sequential reasoning based on the scene entities and their semantic and spatial relationships. As a first step, we derive a scene graph that describes the objects in the image, as well as their attributes and their mutual relationships. Subsequently, a reinforcement learning agent is trained to autonomously navigate in a multi-hop manner over the extracted scene graph to generate reasoning paths, which are the basis for deriving answers. We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs. Our results show that we keep up with a human performance on manually curated scene graphs. Moreover, we find that Graphhopper outperforms another state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.

cs.CV↗

arXiv2021

Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification

The amount of medical images for training deep classification models is typically very scarce, making these deep models prone to overfit the training data. Studies showed that knowledge distillation (KD), especially the mean-teacher framework which is more robust to perturbations, can help mitigate the over-fitting effect. However, directly transferring KD from computer vision to medical image classification yields inferior performance as medical images suffer from higher intra-class variance and class imbalance. To address these issues, we propose a novel Categorical Relation-preserving Contrastive Knowledge Distillation (CRCKD) algorithm, which takes the commonly used mean-teacher model as the supervisor. Specifically, we propose a novel Class-guided Contrastive Distillation (CCD) module to pull closer positive image pairs from the same class in the teacher and student models, while pushing apart negative image pairs from different classes. With this regularization, the feature distribution of the student model shows higher intra-class similarity and inter-class variance. Besides, we propose a Categorical Relation Preserving (CRP) loss to distill the teacher's relational knowledge in a robust and class-balanced manner. With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively. Extensive experiments on the HAM10000 and APTOS datasets demonstrate the superiority of the proposed CRCKD method.

cs.CV↗