arXiv subjects

Hang Li

Publications and source records attributed to Hang Li.

At least 271 records · Page 15Linked to original sources

arXiv2020

Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach

With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which often deliver higher education, i.e., graduate level courses at top institutions. However, few studies have focused on developing a machine learning approach for students in K-12 online courses. In this paper, we develop a machine learning framework to conduct accurate at-risk student identification specialized in K-12 multimodal online environments. Our approach considers both online and offline factors around K-12 students and aims at solving the challenges of (1) multiple modalities, i.e., K-12 online environments involve interactions from different modalities such as video, voice, etc; (2) length variability, i.e., students with different lengths of learning history; (3) time sensitivity, i.e., the dropout likelihood is changing with time; and (4) data imbalance, i.e., only less than 20\% of K-12 students will choose to drop out the class. We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach. In our offline experiments, we show that our method improves the dropout prediction performance when compared to state-of-the-art baselines on a real-world educational dataset. In our online experiments, we test our approach on a third-party K-12 online tutoring platform for two months and the results show that more than 70\% of dropout students are detected by the system.

cs.CY↗

arXiv2020

Feature Statistics Guided Efficient Filter Pruning

Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications. As a common approach to reduce the size of CNNs, pruning methods delete part of the CNN filters according to some metrics such as $l1$-norm. However, previous methods hardly leverage the information variance in a single feature map and the similarity characteristics among feature maps. In this paper, we propose a novel filter pruning method, which incorporates two kinds of feature map selections: diversity-aware selection (DFS) and similarity-aware selection (SFS). DFS aims to discover features with low information diversity while SFS removes features that have high similarities with others. We conduct extensive empirical experiments with various CNN architectures on publicly available datasets. The experimental results demonstrate that our model obtains up to 91.6% parameter decrease and 83.7% FLOPs reduction with almost no accuracy loss.

cs.LG↗

arXiv2020

Spelling Error Correction with Soft-Masked BERT

Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using `Soft-Masked BERT' is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.

cs.CL↗

arXiv2020

Siamese Neural Networks for Class Activity Detection

Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).

eess.AS↗

arXiv2020

Large anisotropic topological Hall effect in a hexagonal non-collinear magnet Fe5Sn3

We report the observation of a large anisotropic topological Hall effect (THE) in the hexagonal non-collinear magnet Fe5Sn3 single crystals. It is found that the sign of the topological Hall resistivity is negative when a magnetic field H perpendicular to the bc-plane (H\perp bc-plane), however, it changes form negative to positive when H parallel to the c-axis (H\parallel c-axis). The value of topological Hall resistivity increased with the increasing temperature and reached approximately -2.12 μΩcm (H\perp bc-plane) and 0.5 μΩcm (H\parallel c-axis) at 350 K, respectively. Quantitative analyses of the measured data suggest that the observed anisotropic THE may originate from the opposite scalar spin chirality induced by the magnetic fields perpendicular and parallel to the c-axis, respectively.

cond-mat.mtrl-sci↗

arXiv2020

Angular momentum conservation in counter-propagating vectorially structured light

It is well-known that electric spin angular momentum and electric orbital angular momentum are conserved under paraxial propagation of travelling waves in free-space. Here we study the electric and magnetic angular momentum in counter-propagating waves and show both theoretically and experimentally that neither component alone is conserved except in special cases. We attribute this non-conservation to spin-spin and orbit-orbit coupling between the electric and magnetic fields. This work generalises previous findings based on travelling waves, explains the apparent spin-orbit coupling in counter-propagating paraxial light, and broadens our understanding of angular momentum conservation in arbitrary structured light waves.

physics.optics↗

arXiv2020

Multifunctional Metasurface Design with a Generative Adversarial Network

Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta-atom structures based on specific requirements (commonly formulated in terms of electromagnetic responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, we present a Generative Adversarial Networks (GAN) that can tackle this problem and generate meta-atom/metasurface designs to meet multifunctional design goals. Unlike conventional trial-and-error or iterative optimization design methods, this new methodology produces on-demand free-form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar electromagnetic responses but different physical features could provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. We demonstrate the network's ability to produce a variety of multifunctional metasurface designs by presenting a bifocal metalens, a polarization-multiplexed beam deflector, a polarization-multiplexed metalens and a polarization-independent metalens.

physics.optics↗

arXiv2020

Large anomalous Hall effect in a hexagonal ferromagnetic Fe5Sn3 single crystal

In this paper, we report an experimental observation of the large anomalous Hall effect (AHE) in a hexagonal ferromagnetic Fe5Sn3 single crystal with current along the b axis and a magnetic field normal to the bc plane. The intrinsic contribution of the anomalous Hall conductance sigma_AH^int was approximately 613 Ω-1 cm-1, which was more than 3 times the maximum value in the frustrated kagome magnet Fe3Sn2 and nearly independent of the temperature over a wide range between 5 and 350 K. The analysis results revealed that the large AHE was dominated by a common, intrinsic term, while the extrinsic contribution, i.e., the skew scattering and side jump, turned out to be small. In addition to the large AHE, it was found the types of majority carriers changed at approximately 275 and 30 K, consistent with the critical temperatures of the spin reorientation. These findings suggest that the hexagonal ferromagnetic Fe5Sn3 single crystal is an excellent candidate to use for the study of the topological features in ferromagnets.

cond-mat.mtrl-sci↗

arXiv2020

Thermally induced generation and annihilation of magnetic chiral skyrmion bubbles and achiral bubbles in Mn-Ni-Ga Magnets

Magnetic chiral skyrmion bubbles and achiral bubbles are two independent magnetic domain structures, in which the former with equivalent winding number to skyrmions offers great promise as information carriers for further spintronic devices. Here, in this work, we experimentally investigate the generation and annihilation of magnetic chiral skyrmion bubbles and achiral bubbles in the Mn-Ni-Ga thin plate by using the Lorentz transmission electron microscopy (L-TEM). The two independent magnetic domain structures can be directly controlled after the field cooling manipulation by varying the titled angles of external magnetic fields. By imaging the magnetization reversal with increasing temperature, we found an extraordinary annihilation mode of magnetic chiral skyrmion bubbles and a non-linear frequency for the winding number reversal. Quantitative analysis of such dynamics was performed by using L-TEM to directly determine the barrier energy for the magnetization reversal of magnetic chiral skyrmion bubbles.

cond-mat.mtrl-sci↗

arXiv2020

Hacking Quantum Key Distribution via Injection Locking

Unconditionally secure communication, being pursued for thousands of years, however, hasn't been reached yet due to continuous competitions between encryption and hacking. Quantum key distribution (QKD), harnessing the quantum mechanical nature of superposition and non-cloning, may promise unconditional security by incorporating the one-time pad algorithm rigorously proved by Claude Shannon. Massive efforts have been made in building practical and commercial QKD systems, in particular, decoy states are employed to detect photon-number splitting attack against single-photon source loophole, and measurement-device-independent (MDI) QKD has further closed all loopholes in detection side, which leads to a seemingly real-life application. Here, we propose and experimentally demonstrate an MDI-QKD hacking strategy on the trusted source assumption by using injection locking technique. Eve injects near off-resonance photons in randomly chosen polarization into sender's laser, where injection locking in a shifted frequency can happen only when Eve's choice matches with sender's state. By setting a shifted window and switching the frequency of photons back afterwards, Eve in principle can obtain all the keys without terminating the real-time QKD. We observe the dynamics of a semiconductor laser with injected photons, and obtain a hacking success rate reaching 60.0% of raw keys. Our results suggest that the spear-and-shield competitions on unconditional security may continue until all potential loopholes are discovered and closed ultimately.

quant-ph↗

arXiv2020

Multipartite Entanglement of Billions of Motional Atoms Heralded by Single Photon

Quantum entanglement is of central importance to quantum computing, quantum metrology, quantum information as well as the nature of quantum physics. Quantum theory does not prevent entanglement from being created and observed in macroscopic physical systems, in reality however, the accessible scale of entanglement is still very limited due to decoherence effects. Recently, entanglement has been observed among atoms from thousands to millions level in extremely low-temperature and well-isolated systems. Here, we create multipartite entanglement of billions of motional atoms in a quantum memory at room temperature, and certify the genuine entanglement via $M$-separability witness associated with photon statistics. The information contained in a single photon is found strongly correlated with the excitation shared by the motional atoms, which intrinsically address the large system and therefore stimulate the multipartite entanglement. Remarkably, our heralded and quantum memory built-in entanglement generation allows us to directly observe the dynamic evolution of entanglement depth and further to reveal the effects of decoherence. Our results verify the existence of genuine multipartite entanglement among billions of motional atoms at ambient condition, significantly extending the boundary of the accessible scale of entanglement. Besides probing the quantum-to-classical transition in an entirely new realm, the developed abilities of manipulating such a large-scale entanglement may enhance a wide spectrum of applications for emerging quantum technologies.

quant-ph↗

arXiv2020

A Hybrid Quantum Memory Enabled Network at Room Temperature

Quantum memory capable of storage and retrieval of flying photons on demand is crucial for developing quantum information technologies. However, the devices needed for long-distance links are quite different from those envisioned for local processing. Here, we present the first hybrid quantum memory enabled network by demonstrating the interconnection and simultaneous operation of two types of quantum memory: an atomic-ensemble-based memory and an all-optical loop memory. The former generates and stores single atomic excitations that can then be converted to single photons; and the latter maps incoming photons in and out on demand, at room-temperature and with a broad acceptance bandwidth. Interfacing these two types of quantum memories, we observe a well-preserved quantum cross-correlation, reaching a value of 22, and a violation of the Cauchy-Schwarz inequality up to 549 standard deviations. Furthermore, we demonstrate the creation and storage of a fully operable heralded photon chain state that can achieve memory-built-in combining, swapping, splitting, tuning and chopping single photons in a chain temporally. Such a quantum network allows atomic excitations to be generated, stored, and converted to broadband photons, which are then transferred to the next node, stored, and faithfully retrieved, all at high speed and in a programmable fashion.

quant-ph↗

arXiv2020

A Full Quantum Eigensolver for Quantum Chemistry Simulations

Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing. It is of particular importance in areas ranging from materials science, biochemistry and condensed matter physics. Here, we propose a full quantum eigensolver (FQE) algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent. Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver (VQE), our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence. The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision. Moreover, the FQE can be further simplified by exploiting perturbation theory for the calculations of intermediate matrix elements, and obtain results with a precision that satisfies the requirement of chemistry application. The full quantum eigensolver can be implemented on a near-term quantum computer. With the rapid development of quantum computing hardware, FQE provides an efficient and powerful tool to solve quantum chemistry problems.

quant-ph↗

arXiv2020

Multimodal Learning For Classroom Activity Detection

Classroom activity detection (CAD) focuses on accurately classifying whether the teacher or student is speaking and recording both the length of individual utterances during a class. A CAD solution helps teachers get instant feedback on their pedagogical instructions. This greatly improves educators' teaching skills and hence leads to students' achievement. However, CAD is very challenging because (1) the CAD model needs to be generalized well enough for different teachers and students; (2) data from both vocal and language modalities has to be wisely fused so that they can be complementary; and (3) the solution shouldn't heavily rely on additional recording device. In this paper, we address the above challenges by using a novel attention based neural framework. Our framework not only extracts both speech and language information, but utilizes attention mechanism to capture long-term semantic dependence. Our framework is device-free and is able to take any classroom recording as input. The proposed CAD learning framework is evaluated in two real-world education applications. The experimental results demonstrate the benefits of our approach on learning attention based neural network from classroom data with different modalities, and show our approach is able to outperform state-of-the-art baselines in terms of various evaluation metrics.

eess.AS↗

arXiv2020

Conversational Contextual Bandit: Algorithm and Application

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses a critical issue in applications like recommender systems, since users may need to provide feedbacks on a lot of uninterested items. To accelerate the learning speed, we generalize contextual bandit to conversational contextual bandit. Conversational contextual bandit leverages not only behavioral feedbacks on arms (e.g., articles in news recommendation), but also occasional conversational feedbacks on key-terms from the user. Here, a key-term can relate to a subset of arms, for example, a category of articles in news recommendation. We then design the Conversational UCB algorithm (ConUCB) to address two challenges in conversational contextual bandit: (1) which key-terms to select to conduct conversation, (2) how to leverage conversational feedbacks to accelerate the speed of bandit learning. We theoretically prove that ConUCB can achieve a smaller regret upper bound than the traditional contextual bandit algorithm LinUCB, which implies a faster learning speed. Experiments on synthetic data, as well as real datasets from Yelp and Toutiao, demonstrate the efficacy of the ConUCB algorithm.

cs.LG↗

arXiv2020

A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations.

physics.optics↗

arXiv2019

Observation of Magnetic Skyrmion Bubbles in a van der Waals ferromagnet Fe3GeTe2

Two-dimensional (2D) van der Waals (vdW) magnetic materials have recently been introduced as a new horizon in materials science and enable the potential applications for next-generation spintronic devices. Here, in this communication, the observations of stable Bloch-type magnetic skyrmions in single crystals of 2D vdW Fe3GeTe2 (FGT) are reported by using in-situ Lorentz transmission electron microscopy (TEM). We find the ground-state magnetic stripe domains in FGT transform into skyrmion bubbles when an external magnetic field is applied perpendicularly to the (001) thin plate with temperatures below the Curie-temperature TC. Most interestingly, a hexagonal lattice of skyrmion bubbles is obtained via field cooling manipulation with magnetic field applied along the [001] direction. Owing to their topological stability, the skyrmion bubble lattices are stable to large field-cooling tilted angles and further reproduced by utilizing the micromagnetic simulations. These observations directly demonstrate that the 2D vdW FGT possesses a rich variety of topological spin textures, being of a great promise candidate for future applications in the field of spintronics.

cond-mat.mtrl-sci↗

arXiv2019

Frequency tunable topological edge states of two-dimensional honeycomb lattice photonic crystals

In this paper, the photonic quantum spin Hall effect (PQSHE) is realized in dielectric two-dimensional (2D) honeycomb lattice photonic crystal (PC) by stretching and shrinking the honeycomb unit cell. Combining two honeycomb lattice PCs with a common photonic band gap (PBG) but different band topologies can generate a topologically protected edge state at the combined junction. The topological edge states and their unidirectional transmission as the scatterers with triangular, pentagonal, and heptagonal shapes are researched. Meanwhile, the unidirectional transmission in an inverted Ω-shaped waveguide with large bending angle is realized, and verifies the characteristics of the topological protection by adding different kind of defects. Moreover, the frequency varies significantly when changing the scatterers shape, which shows that the PC with various scatterers shape can tune the frequency range of the topological edge state significantly. In other words, it can adjust the frequency of unidirectional transmission and increase the adjustability of the topological edge state.

physics.optics↗