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Jia Xu

Publications and source records attributed to Jia Xu.

At least 91 records · Page 5Linked to original sources
arXiv2021

EdgeWorkflowReal: An Edge Computing based Workflow Execution Engine for Smart Systems

Current cloud-based smart systems suffer from weaknesses such as high response latency, limited network bandwidth and the restricted computing power of smart end devices which seriously affect the system's QoS (Quality of Service). Recently, given its advantages of low latency, high bandwidth and location awareness, edge computing has become a promising solution for smart systems. However, the development of edge computing based smart systems is a very challenging job for software developers who do not have the skills for the creation of edge computing environments. The management of edge computing resources and computing tasks is also very challenging. Workflow technology has been widely used in smart systems to automate task and resource management, but there does not yet exist a real-world deployable edge computing based workflow execution engine. To fill this gap, we present EdgeWorkflowReal, an edge computing based workflow execution engine for smart systems. EdgeWorkflowReal supports: 1) automatic creation of a real edge computing environment according to user settings; 2) visualized modelling of edge workflow applications; and 3) automatic deployment, monitoring and performance evaluation of edge workflow applications in a smart system.

cs.SE↗
arXiv2020

Adversarial Defense via Local Flatness Regularization

Adversarial defense is a popular and important research area. Due to its intrinsic mechanism, one of the most straightforward and effective ways of defending attacks is to analyze the property of loss surface in the input space. In this paper, we define the local flatness of the loss surface as the maximum value of the chosen norm of the gradient regarding to the input within a neighborhood centered on the benign sample, and discuss the relationship between the local flatness and adversarial vulnerability. Based on the analysis, we propose a novel defense approach via regularizing the local flatness, dubbed local flatness regularization (LFR). We also demonstrate the effectiveness of the proposed method from other perspectives, such as human visual mechanism, and analyze the relationship between LFR and other related methods theoretically. Experiments are conducted to verify our theory and demonstrate the superiority of the proposed method.

cs.CV↗
arXiv2020

Learning 3D Face Reconstruction with a Pose Guidance Network

We present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN). First, we unveil the bottleneck of pose estimation in prior parametric 3D face learning methods, and propose to utilize 3D face landmarks for estimating pose parameters. With our specially designed PGN, our model can learn from both faces with fully labeled 3D landmarks and unlimited unlabeled in-the-wild face images. Our network is further augmented with a self-supervised learning scheme, which exploits face geometry information embedded in multiple frames of the same person, to alleviate the ill-posed nature of regressing 3D face geometry from a single image. These three insights yield a single approach that combines the complementary strengths of parametric model learning and data-driven learning techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D, Florence and FaceWarehouse datasets, and show that our method outperforms the state-of-the-art for all metrics.

cs.CV↗
arXiv2020

Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching

In this paper, we propose a unified method to jointly learn optical flow and stereo matching. Our first intuition is stereo matching can be modeled as a special case of optical flow, and we can leverage 3D geometry behind stereoscopic videos to guide the learning of these two forms of correspondences. We then enroll this knowledge into the state-of-the-art self-supervised learning framework, and train one single network to estimate both flow and stereo. Second, we unveil the bottlenecks in prior self-supervised learning approaches, and propose to create a new set of challenging proxy tasks to boost performance. These two insights yield a single model that achieves the highest accuracy among all existing unsupervised flow and stereo methods on KITTI 2012 and 2015 benchmarks. More remarkably, our self-supervised method even outperforms several state-of-the-art fully supervised methods, including PWC-Net and FlowNet2 on KITTI 2012.

cs.CV↗
arXiv2020

A Clustering Framework for Lexical Normalization of Roman Urdu

Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence poses several normalization challenges during automatic language processing. In this article, we present a feature-based clustering framework for the lexical normalization of Roman Urdu corpora, which includes a phonetic algorithm UrduPhone, a string matching component, a feature-based similarity function, and a clustering algorithm Lex-Var. UrduPhone encodes Roman Urdu strings to their pronunciation-based representations. The string matching component handles character-level variations that occur when writing Urdu using Roman script.

cs.CL↗
arXiv2020

Optical imaging of antiferromagnetic domains in ultrathin CoO(001) films

Antiferromagnetic (AFM) domains in ultrathin CoO(001) films are imaged by a wide-field optical microscopy using magneto-optical birefringence effect. The magnetic origin of observed optical contrast is confirmed by the spin orientation manipulation through exchange coupling in Fe/CoO(001) bilayer. The finite size effect of ordering temperature for ultrathin single crystal CoO film is revealed by the thickness and temperature dependent measurement of birefringence contrast. The magneto-optical birefringence effect is found to strongly depend on the photon energy of incident light, and a surprising large polarization rotation angle up to 168.5 mdeg is obtained from a 4.6 nm CoO film with a blue light source, making it possible to further investigate the evolution of AFM domains in AFM ultrathin film under external field.

cond-mat.mtrl-sci↗
arXiv2019

Diversity by Phonetics and its Application in Neural Machine Translation

We introduce a powerful approach for Neural Machine Translation (NMT), whereby, during training and testing, together with the input we provide its phonetic encoding and the variants of such an encoding. This way we obtain very significant improvements up to 4 BLEU points over the state-of-the-art large-scale system. The phonetic encoding is the first part of our contribution, with a second being a theory that aims to understand the reason for this improvement. Our hypothesis states that the phonetic encoding helps NMT because it encodes a procedure to emphasize the difference between semantically diverse sentences. We conduct an empirical geometric validation of our hypothesis in support of which we obtain overwhelming evidence. Subsequently, as our third contribution and based on our theory, we develop artificial mechanisms that leverage during learning the hypothesized (and verified) effect phonetics. We achieve significant and consistent improvements overall language pairs and datasets: French-English, German-English, and Chinese-English in medium task IWSLT'17 and French-English in large task WMT'18 Bio, with up to 4 BLEU points over the state-of-the-art. Moreover, our approaches are more robust than baselines when evaluated on unknown out-of-domain test sets with up to a 5 BLEU point increase.

cs.CL↗
arXiv2019

A sufficient condition for local nonnegativity

A real polynomial $f$ is called local nonnegative at a point $p$, if it is nonnegative in a neighbourhood of $p$. In this paper, a sufficient condition for determining this property is constructed. Newton's principal part of $f$ (denoted as $f_N$) plays a key role in this process. We proved that if every $F$-face, $(f_N)_F$, of $f_N$ is strictly positive over $(\mathbb{R}\setminus 0)^n$, then $f$ is local nonnegative at the origin $O$.

math.AG↗
arXiv2019

Imaging Antiferromagnetic Domains in Nickel-oxide Thin Films by Magneto-optical Voigt Effect

Recent demonstrations of electrical detection and manipulation of antiferromagnets (AFMs) have opened new opportunities towards robust and ultrafast spintronics devices. However, it is difficult to establish the connection between the spin-transport behavior and the microscopic AFM domain states due to the lack of the real-time AFM domain imaging technique under the electric field. Here we report a significant Voigt rotation up to 60 mdeg in thin NiO(001) films at room temperature. Such large Voigt rotation allows us to directly observe AFM domains in thin-film NiO by utilizing a wide-field optical microscope. Further complementary XMLD-PEEM measurement confirms that the Voigt contrast originates from the NiO AFM order. We examine the domain pattern evolution at a wide range of temperature and with the application of external magnetic field. Comparing to large-scale-facility techniques such as the X-ray photoemission electron microscopy, the use with a wide-field, tabletop optical imaging method enables straightforward access to domain configurations of single-layer AFMs.

cond-mat.mtrl-sci↗
arXiv2019

Learning to Solve a Rubik's Cube with a Dexterous Hand

We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand. Despite the promising performance of dexterous in-hand manipulation, solving complex tasks which involve multiple steps and diverse internal object structure has remained an important, yet challenging task. In this paper, we tackle this challenge with a hierarchical deep reinforcement learning method, which separates planning and manipulation. A model-based cube solver finds an optimal move sequence for restoring the cube and a model-free cube operator controls all five fingers to execute each move step by step. To train our models, we build a high-fidelity simulator which manipulates a Rubik's Cube, an object containing high-dimensional state space, with a 24-DoF robot hand. Extensive experiments on 1400 randomly scrambled Rubik's cubes demonstrate the effectiveness of our method, achieving an average success rate of 90.3%.

cs.RO↗
arXiv2019

SelFlow: Self-Supervised Learning of Optical Flow

We present a self-supervised learning approach for optical flow. Our method distills reliable flow estimations from non-occluded pixels, and uses these predictions as ground truth to learn optical flow for hallucinated occlusions. We further design a simple CNN to utilize temporal information from multiple frames for better flow estimation. These two principles lead to an approach that yields the best performance for unsupervised optical flow learning on the challenging benchmarks including MPI Sintel, KITTI 2012 and 2015. More notably, our self-supervised pre-trained model provides an excellent initialization for supervised fine-tuning. Our fine-tuned models achieve state-of-the-art results on all three datasets. At the time of writing, we achieve EPE=4.26 on the Sintel benchmark, outperforming all submitted methods.

cs.CV↗
arXiv2019

DDFlow: Learning Optical Flow with Unlabeled Data Distillation

We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on hand-crafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.

cs.CV↗
arXiv2019

Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers

Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to deal with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.

cs.GT↗
arXiv2018

Learning to See in the Dark

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work. The results are shown in the supplementary video at https://youtu.be/qWKUFK7MWvg

cs.CV↗
arXiv2017

Fast Image Processing with Fully-Convolutional Networks

We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphotorealistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to the most accurate prior approximation scheme, while being the fastest. We show that our models generalize across datasets and across resolutions, and investigate a number of extensions of the presented approach. The results are shown in the supplementary video at https://youtu.be/eQyfHgLx8Dc

cs.CV↗
arXiv2017

Radial Velocity Retrieval for Multichannel SAR Moving Targets with Time-Space Doppler De-ambiguity

In this paper, with respect to multichannel synthetic aperture radars (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in range-compressed domain, range-Doppler domain and image domain, respectively. It is revealed that in these problems, a cascaded time-space Doppler ambiguity (CTSDA) may encounter, i.e., time domain Doppler ambiguity (TDDA) in each channel arises first and then spatial domain Doppler ambiguity (SDDA) among multi-channels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three different cases with diverse Doppler ambiguity properties, and a multi-frequency SAR is then proposed to obtain the RV estimation by solving the ambiguity problem based on Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problems and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied for a multi-channel SAR, a searching based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods.

cs.IT↗
arXiv2017

Accurate Optical Flow via Direct Cost Volume Processing

We present an optical flow estimation approach that operates on the full four-dimensional cost volume. This direct approach shares the structural benefits of leading stereo matching pipelines, which are known to yield high accuracy. To this day, such approaches have been considered impractical due to the size of the cost volume. We show that the full four-dimensional cost volume can be constructed in a fraction of a second due to its regularity. We then exploit this regularity further by adapting semi-global matching to the four-dimensional setting. This yields a pipeline that achieves significantly higher accuracy than state-of-the-art optical flow methods while being faster than most. Our approach outperforms all published general-purpose optical flow methods on both Sintel and KITTI 2015 benchmarks.

cs.CV↗