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In this paper, we combine the multiscale flnite element method to propose an algorithm for solving the non-stationary Stokes-Darcy model, where the permeability coefflcient in the Darcy region exhibits multiscale characteristics. Our algorithm involves two steps: first, conducting the parallel computation of multiscale basis functions in the Darcy region. Second, based on these multiscale basis functions, we employ an implicitexplicit scheme to solve the Stokes-Darcy equations. One signiflcant feature of the algorithm is that it solves problems on relatively coarse grids, thus signiflcantly reducing computational costs. Moreover, under the same coarse grid size, it exhibits higher accuracy compared to standard flnite element method. Under the assumption that the permeability coefflcient is periodic and independent of time, this paper demonstrates the stability and convergence of the algorithm. Finally, the rationality and effectiveness of the algorithm are verifled through three numerical experiments, with experimental results consistent with theoretical analysis.

This paper explores the application of the multiscale finite element method (MsFEM) to address steady-state Stokes-Darcy problems with BJS interface conditions in highly heterogeneous porous media. We assume the existence of multiscale features in the Darcy region and propose an algorithm for the multiscale Stokes-Darcy model. During the offline phase, we employ MsFEM to construct permeability-dependent offline bases for efficient coarse-grid simulation, with this process conducted in parallel to enhance its efficiency. In the online phase, we use the Robin-Robin algorithm to derive the model's solution. Subsequently, we conduct error analysis based on $L^2$ and $H^1$ norms, assuming certain periodic coefficients in the Darcy region. To validate our approach, we present extensive numerical tests on highly heterogeneous media, illustrating the results of the error analysis.

The two functional forms, D~1/tau and D~T/tau, are usually adopted as the variants of the Stokes-Einstein relation; where D is the diffusion constant, tau the relaxation time and T the temperature. The self-consistent generalized Langevin equation (SCGLE) theory is presented as an analytical tool to predict the long time dynamics of colloids and molecular liquids. In this work, taking truncated Lennard-Jones-like liquids as an example, the rationality of the two variants were tested in the framework of the SCGLE theory. Our results indicate that D~1/tau is a good variant of the Stokes-Einstein relation in the framework of SCGLE theory; however, D~T/tau is not a good one but taking a fractional from as D~(T/tau)^zeta with an exponent zeta is not equal to 1.0 even the Stokes-Einstein relation is established in SCGLE theory.

The detection of small and medium-sized objects in three dimensions has always been a frontier exploration problem. This technology has a very wide application in sports analysis, games, virtual reality, human animation and other fields. The traditional three-dimensional small target detection technology has the disadvantages of high cost, low precision and inconvenience, so it is difficult to apply in practice. With the development of machine learning and deep learning, the technology of computer vision algorithms is becoming more mature. Creating an immersive media experience is considered to be a very important research work in sports. The main work is to explore and solve the problem of football detection under the multiple cameras, aiming at the research and implementation of the live broadcast system of football matches. Using multi cameras detects a target ball and determines its position in three dimension with the occlusion, motion, low illumination of the target object. This paper designed and implemented football detection system under multiple cameras for the detection and capture of targets in real-time matches. The main work mainly consists of three parts, football detector, single camera detection, and multi-cameras detection. The system used bundle adjustment to obtain the three-dimensional position of the target, and the GPU to accelerates data pre-processing and achieve accurate real-time capture of the target. By testing the system, it shows that the system can accurately detect and capture the moving targets in 3D. In addition, the solution in this paper is reusable for large-scale competitions, like basketball and soccer. The system framework can be well transplanted into other similar engineering project systems. It has been put into the market., Comment: 89 pages

Topological insulators are bulk semiconductors that manifest in-gap massless Dirac surface states due to the topological bulk-boundary correspondence principle [1-3]. These surface states have been a subject of tremendous ongoing interest, due both to their intrinsic properties and to higher order emergence phenomena that can be achieved by manipulating the interface environment [4-11]. Here, angle resolved photoemission (ARPES) spectromicroscopy and supplementary scanning tunneling microscopy (STM) are performed on the model topological insulator Bi2Se3 to investigate the interplay of crystallographic inhomogeneity with the topologically ordered bulk and surface band structure. Quantitative analysis methods are developed to obtain key spectroscopic information in spite of a limited dwell time on each measured point. Band energies are found to vary on the scale of 50 meV across the sample surface, enabling single-sample measurements that are analogous to a multi-sample doping series (termed a "binning series"). Focusing separately on the surface and bulk electrons reveals a nontrivial hybridization-like interplay between fluctuations in the surface and bulk state energetics., Comment: 4 figures and 6 supplementary figures

We investigated the electronic superstructure of graphite surface in the vicinity to monoatomic armchair step edges with scanning tunneling microscopy and spectroscopy. Only the ($\sqrt{3}\times\sqrt{3}$)$\textit{R}$30$^{\circ}$ superstructure is visualized near atomically clean armchair edges, while the honeycomb superstructure is absent. The spectroscopic mapping near the clean armchair edge clearly reveals the ($\sqrt{3}\times\sqrt{3}$)$\textit{R}$30$^{\circ}$ superstructure on both sides of the step edge. We have also visualized a mixture of ($\sqrt{3}\times\sqrt{3}$)$\textit{R}$30$^{\circ}$ and honeycomb superstructures near structurally defective armchair edges. Our results suggest that the honeycomb superstructure pattern results from superposition of two sets of ($\sqrt{3}\times\sqrt{3}$)$\textit{R}$30$^{\circ}$ superstructure with different phases. Our observation solves the mystery of the coexistence of two types of superstructures reported by prior studies., Comment: 7 pages, 7 figures

Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative., Comment: Four pages, four figures, one table. Accepted by IJCAI2019 Tusion Workshop

Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop., Comment: 3 pages, 2 figures, modified typo. Accepted by CVPR2019 VQA workshop

Image retrieval utilizes image descriptors to retrieve the most similar images to a given query image. Convolutional neural network (CNN) is becoming the dominant approach to extract image descriptors for image retrieval. For low-power hardware implementation of image retrieval, the drawback of CNN-based feature descriptor is that it requires hundreds of megabytes of storage. To address this problem, this paper applies deep model quantization and compression to CNN in ASIC chip for image retrieval. It is demonstrated that the CNN-based features descriptor can be extracted using as few as 2-bit weights quantization to deliver a similar performance as floating-point model for image retrieval. In addition, to implement CNN in ASIC, especially for large scale images, the limited buffer size of chips should be considered. To retrieve large scale images, we propose an improved pooling strategy, region nested invariance pooling (RNIP), which uses cropped sub-images for CNN. Testing results on chip show that integrating RNIP with the proposed 2-bit CNN model compression approach is capable of retrieving large scale images.

The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues. The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses. An interactive demo is ready to show at the workshop., Comment: 5 pages, 2 figures, 1 table. Accepted by CVPR2019 Language and Vision Workshop

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. Experimental results have shown that the proposed SuperTML method had achieved state-of-the-art results on both large and small datasets., Comment: 9 pages, 5 figures, 3 tables. Accepted by CVPR2019 Precognition Workshop

The Super Characters method addresses sentiment analysis problems by first converting the input text into images and then applying 2D-CNN models to classify the sentiment. It achieves state of the art performance on many benchmark datasets. However, it is not as straightforward to apply in Latin languages as in Asian languages. Because the 2D-CNN model is designed to recognize two-dimensional images, it is better if the inputs are in the form of glyphs. In this paper, we propose SEW (Squared English Word) method generating a squared glyph for each English word by drawing Super Characters images of each English word at the alphabet level, combining the squared glyph together into a whole Super Characters image at the sentence level, and then applying the CNN model to classify the sentiment within the sentence. We applied the SEW method to Wikipedia dataset and obtained a 2.1% accuracy gain compared to the original Super Characters method. For multi-modal data with both structured tabular data and unstructured natural language text, the modified SEW method integrates the data into a single image and classifies sentiment with one unified CNN model.

We designed a device for Convolution Neural Network applications with non-volatile MRAM memory and computing-in-memory co-designed architecture. It has been successfully fabricated using 22nm technology node CMOS Si process. More than 40MB MRAM density with 9.9TOPS/W are provided. It enables multiple models within one single chip for mobile and IoT device applications., Comment: 4 pages, 4 figures, 1 table. Accepted by NIPS 2018 MLPCD workshop

We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English., Comment: 7 pages, 1 figure, 5 tables. Accepted by EMNLP2018 workshop WASSA2018

By combining scanning tunneling microscopy/spectroscopy and first-principles calculations, we systematically study the local electronic states of magnetic dopants V and Cr in the topological insulator (TI) Sb2Te3. Spectroscopic imaging shows diverse local defect states between Cr and V, which agree with our first-principle calculations. The unique spectroscopic features of V and Cr dopants provide electronic fingerprints for the co-doped magnetic TI samples with the enhanced quantum anomalous Hall effect. Our results also facilitate the exploration of the underlying mechanism of the enhanced quantum anomalous Hall temperature in Cr/V co-doped TIs., Comment: 21 pages, 7 figures

Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size should all be taken into consideration for mobile and embedded applications. Domain Specific Architecture (DSA) for CNN is the efficient and practical solution for CNN deployment and implementation. We designed and produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra power-efficient performance of 9.3TOPS/Watt and with all processing done in the internal memory instead of outside DRAM. It classifies 224x224 RGB image inputs at more than 140fps with peak power consumption at less than 300mW and an accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is reconfigurable to support CNN model coefficients of various layer sizes and layer types, including convolution, depth-wise convolution, short-cut connections, max pooling, and ReLU. Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator. Live demos with our CNN-DSA accelerator on mobile and embedded systems show its capabilities to be widely and practically applied in the real world., Comment: 9 pages, 10 Figures. Accepted by CVPR 2018 Efficient Deep Learning for Computer Vision workshop

The massless Dirac electrons found at topological insulator surfaces are thought to be influenced very little by weak, non-magnetic disorder. However, a resonance effect of strongly perturbing non-magnetic impurities has been theoretically predicted to change the dispersion and physical nature of low-energy quasiparticles, resulting in unique particle-like states that lack microscopic translational symmetry. Here we report the direct observation of impurities reshaping the surface Dirac cone of the model 3D topological insulator Bi2Se3. For the first time, a pronounced kink-like dispersion feature is observed in disorder-enriched samples, and found to be closely associated with the anomaly caused by impurity resonance in the surface state density of states, as observed by dichroic angle resolved photoemission spectroscopy (ARPES). The experimental observation of these features, which closely resemble theoretical predictions, has significant implications for the properties of topological Dirac cones in applied scenarios that commonly feature point defect disorder at surfaces or interfaces., Comment: Includes main text (4 figures), Methods, and Supplementary Material

Topological insulators are a class of band insulators with non-trivial topology, a result of band inversion due to the strong spin-orbit coupling. The transition between topological and normal insulator can be realized by tuning the spin-orbit coupling strength, and has been observed experimentally. However, the impact of chemical disorders on the topological phase transition was not addressed in previous studies. Herein, we report a systematic scanning tunneling microscopy/spectroscopy and first-principles study of the topological phase transition in single crystals of In doped Bi$_2$Se$_3$. Surprisingly, no band gap closure was observed across the transition. Furthermore, our spectroscopic-imaging results reveal that In defects are extremely effective "suppressors" of the band inversion, which leads to microscopic phase separation of topological-insulator-like and normal-insulator-like nano regions across the "transition". The observed topological electronic inhomogeneity demonstrates the significant impact of chemical disorders in topological materials, shedding new light on the fundamental understanding of topological phase transition., Comment: 16 pages, 5 figures

Bi$_{2}$Se$_{3}$, one of the most widely studied topological insulators (TIs), is naturally electron-doped due to n-type native defects. However, many years of efforts to achieve p-type Bi$_{2}$Se$_{3}$ thin films have failed so far. Here, we provide a solution to this long-standing problem, showing that the main culprit has been the high density of interfacial defects. By suppressing these defects through an interfacial engineering scheme, we have successfully implemented p-type Bi$_{2}$Se$_{3}$ thin films down to the thinnest topological regime. On this platform, we present the first tunable quantum Hall effect (QHE) study in Bi$_{2}$Se$_{3}$ thin films, and reveal not only significantly asymmetric QHE signatures across the Dirac point but also the presence of competing anomalous states near the zeroth Landau level. The availability of doping tunable Bi$_{2}$Se$_{3}$ thin films will now make it possible to implement various topological quantum devices, previously inaccessible., Comment: 40 Pages, 8 Figures, 2 Tables, Accepted to Nano Letters

Topological Weyl semimetal (TWS) is a metal, where low energy excitations behave like Weyl fermions of high-energy physics. It was recently shown that due to the lower symmetry of condensed matter systems, they can realize two distinct types of Weyl fermions. The type-I Weyl fermion in a metal is formed by a linear crossing of two bands at a point in the crystalline momentum space - Brillouin zone (BZ). The second type TWSs host type-II Weyl points appearing at the touching points of electron and hole pockets, which is a result of tilted linear dispersion. The type-II TWS was predicted to exist in several compounds, including WTe$_2$. Several ARPES studies of WTe$_2$ were reported so far, having contradictory conclusions on the topological nature of observed Fermi arcs. In this work, we report the results of spectroscopic imaging with a scanning tunneling microscope and first principle calculations, establishing a clear quasiparticle interference features of the surface states of WTe$_2$. Our work provides a strong evidence for surface state scattering. Although the surface Fermi arcs are clearly observed, it is still difficult to prove the existence of predicted Type-II Weyl points in the bulk., Comment: 11 pages, 3 figures