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Yong Wang

Publications and source records attributed to Yong Wang.

At least 505 records · Page 28Linked to original sources
arXiv2020

Skyrmion-based Magnetic Traps for Ultracold Atoms

We show that the stray field generated by isolated magnetic skyrmions can be used to trap ultracold atoms. Specially, ring-shaped and double-well trapping potentials for ultracold atoms can be created by combining the field from two isolated skyrmions. The geometry size, potential barrier, trapping frequency and Majorana loss rate of these magnetic traps can be tuned by the external magnetic field or device configuration. The results here could be useful to develop atomtronics devices by manipulating the magnetic skyrmions with modern spintronics techniques.

cond-mat.mes-hall↗
arXiv2020

Predicting Student Performance in Interactive Online Question Pools Using Mouse Interaction Features

Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models.

cs.HC↗
arXiv2019

Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks

The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model. Previous work shows that the standard neural machine translation (NMT) model, trained on mixed-domain data, generally captures the general knowledge, but misses the domain-specific knowledge. In response to this problem, we augment NMT model with additional domain transformation networks to transform the general representations to domain-specific representations, which are subsequently fed to the NMT decoder. To guarantee the knowledge transformation, we also propose two complementary supervision signals by leveraging the power of knowledge distillation and adversarial learning. Experimental results on several language pairs, covering both balanced and unbalanced multi-domain translation, demonstrate the effectiveness and universality of the proposed approach. Encouragingly, the proposed unified model achieves comparable results with the fine-tuning approach that requires multiple models to preserve the particular knowledge. Further analyses reveal that the domain transformation networks successfully capture the domain-specific knowledge as expected.

cs.CL↗
arXiv2019

Real-time Trajectory Generation for Quadrotors using B-spline based Non-uniform Kinodynamic Search

In this paper, we propose a time-efficient approach to generate safe, smooth and dynamically feasible trajectories for quadrotors in obstacle-cluttered environment. By using the uniform B-spline to represent trajectories, we transform the trajectory planning to a graph-search problem of B-spline control points in discretized space. Highly strict convex hull property of B-spline is derived to guarantee the dynamical feasibility of the entire trajectory. A novel non-uniform kinodynamic search strategy is adopted, and the step length is dynamically adjusted during the search process according to the Euclidean signed distance field (ESDF), making the trajectory achieve reasonable time-allocation and be away from obstacles. Non-static initial and goal states are allowed, therefore it can be used for online local replanning as well as global planning. Extensive simulation and hardware experiments show that our method achieves higher performance comparing with the state-of-the-art method.

cs.RO↗
arXiv2019

Correlations in the chain melting of host-guest Calcium

In this paper, we study chain melting state in the commensurate host-guest structure of calcium. We trained a NN (neural network) force field with DeepMD-kit, and by implementing classical simulations, we found that the convergence of z-proejcted MSD of guest atoms needs at least 8192-atom supercell. We ascribe the origination of chain melting to the phase transition at 600 K, in which the arrangement of guest chains transform from a checkerboard pattern to a stripe pattern, and the energy barrier of sliding chains against each other is obviously reduced. We also found that the chain melting state of calcium could be separated into two 2D (two-dimensional) and 3D (three-dimensional) disorder, with distinct chain dynamics.

cond-mat.mtrl-sci↗
arXiv2019

Dispersion independent long-haul photon counting OTDR

Photon counting optical time-domain reflectometry (PC-OTDR) based on the single photon detection is an effective scheme to attain the high spatial resolution for optical fiber fault monitoring. Currently, due to the spatial resolution of PC-OTDR is proportional to the pulse width of a laser beam, short laser pulses are essential for the high spatial resolution. However, short laser pulses have a large bandwidth, which would be widened by the dispersion of fiber, thereby causing inevitable deterioration in spatial resolution, especially for long-haul fiber links. In this letter, we propose a scheme of dispersion independent PC-OTDR based on an infinite backscatter technique. Our experimental results -with more than 50 km long fiber - show that the spatial resolution of the PC-OTDR system is independent with the total dispersion of the fiber under test. Our method provides an avenue for developing the long-haul PC-OTDR with high performance.

quant-ph↗
arXiv2019

Hydrodynamic limit of the Boltzmann equation to the planar rarefaction wave in three dimensional space

In this paper, we establish the global in time hydrodynamic limit of Boltzmann equation to the planar rarefaction wave of compressible Euler system in three dimensional space $x\in\mathbb{R}^3$ for general collision kernels. Our approch is based on a generalized Hilbert expansion, and a recent $L^2-L^\infty$ framework. In particular, we improve the $L^2$-estimate to be a localized version because the planar rarefaction wave is indeed a one-dimensional wave which makes the source terms to be not integrable in the $L^2$ energy estimate of three dimensional problem. We also point out that the wave strength of rarefaction may be large.

math.AP↗
arXiv2019

Affine connections of non integrable distributions

In this paper, we study non integrable distributions in a Riemannian manifold with a semi-symmetric metric connection, a semi-symmetric non-metric connection and a statistical connection. We obtain the Gauss, Codazzi, and Ricci equations for non integrable distributions with respect to the semi-symmetric metric connection, the semi-symmetric non-metric connection and the statistical connection. As applications, we obtain Chen's inequalities for non integrable distributions of real space forms endowed with a semi-symmetric metric connection and a semi-symmetric non-metric connection. We give some examples of non integrable distributions in a Riemannian manifold with affine connections. We find some new examples of Einstein distributions and distributions with constant scalar curvature.

math.DG↗
arXiv2019

Analytical calculation of the inverse nabla Laplace transform

The inversion of nabla Laplace transform, corresponding to a causal sequence, is considered. Two classical methods, i.e., residual calculation method and partial fraction method are developed to perform the inverse nabla Laplace transform. For the first method, two alternative formulae are proposed when adopting the poles inside or outside of the contour, respectively. For the second method, a table on the transform pairs of those popular functions is carefully established. Besides illustrating the effectiveness of the developed methods with two illustrative examples, the applicability are further discussed in the fractional order case.

math.GM↗
arXiv2019

DeepDrawing: A Deep Learning Approach to Graph Drawing

Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.

cs.HC↗
arXiv2019

EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos

Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo, an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution. In addition, a detail view and word view enable detailed exploration and comparison from the sentence level and word level, respectively. We thoroughly evaluate the proposed system and visualization techniques through two usage scenarios based on TED Talk videos and interviews with two domain experts. The results demonstrate the effectiveness of our system in gaining insights into emotion coherence in presentations.

cs.CV↗
arXiv2019

PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.

cs.HC↗
arXiv2019

Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms

With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded. However, it remains unclear how to make use of these detailed learning behavior data to improve the design of learning materials and gain deeper insight into students' thinking and learning styles. In this work, we propose a visual analytics system to analyze student learning behaviors on a K-12 mathematics E-learning platform. It supports both correlation analysis between different attributes and a detailed visualization of user mouse-movement logs. Our case studies on a real dataset show that our system can better guide the design of learning resources (e.g., math questions) and facilitate quick interpretation of students' problem-solving and learning styles.

cs.HC↗
arXiv2019

Convolutional neural networks with fractional order gradient method

This paper proposes a fractional order gradient method for the backward propagation of convolutional neural networks. To overcome the problem that fractional order gradient method cannot converge to real extreme point, a simplified fractional order gradient method is designed based on Caputo's definition. The parameters within layers are updated by the designed gradient method, but the propagations between layers still use integer order gradients, and thus the complicated derivatives of composite functions are avoided and the chain rule will be kept. By connecting every layers in series and adding loss functions, the proposed convolutional neural networks can be trained smoothly according to various tasks. Some practical experiments are carried out in order to demonstrate fast convergence, high accuracy and ability to escape local optimal point at last.

math.OC↗
arXiv2019

Algebra of Concurrent Games

We introduce parallelism into the basic algebra of games to model concurrent game algebraically. Parallelism is treated as a new kind of game operation. The resulted algebra of concurrent games can be used widely to reason the parallel systems.

cs.LO↗
arXiv2019

Operational Semantics of Games

We introduce operational semantics into games. And based on the operational semantics, we establish a full algebra of games, including basic algebra of games, algebra of concurrent games, recursion and abstraction. The algebra can be used widely to reason on the behaviors of systems (not only computational systems) with game theory supported.

cs.LO↗
arXiv2019

Plug-and-Play Priors for Reconstruction-based Placental Image Registration

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.

eess.IV↗