arXiv subjects

Xia Li

Publications and source records attributed to Xia Li.

At least 91 records · Page 5Linked to original sources

arXiv2023

Quasi-periodic Variations of Coronal Mass Ejections with Different Angular Widths

Coronal mass ejections (CMEs) are energetic expulsions of organized magnetic features from the Sun. The study of CME quasi-periodicity helps establish a possible relationship between CMEs, solar flares, and geomagnetic disturbances. We used the angular width of CMEs as a criterion for classifying the CMEs in the study. Based on 25 years of observational data, we systematically analyzed the quasi-periodic variations corresponding to the CME occurrence rate of different angular widths in the northern and southern hemispheres, using frequency and time-frequency analysis methods. There are various periods for CMEs of different angular widths: 9 months, 1.7 years, and 3.3-4.3 years. Compared with previous studies based on the occurrence rate of CMEs, we obtained the same periods of 1.2(+-0.01) months, 3.1(+-0.04) months, ~6.1(+-0.4) months, 1.2(+-0.1) years, and 2.4(+-0.4) years. We also found additional periods of all CMEs that appear only in one hemisphere or during a specific solar cycle. For example, 7.1(+-0.2) months and 4.1(+-0.2) years in the northern hemisphere, 1(+-0.004) months, 5.9(+-0.2) months, 1(+-0.1) years, 1.4(+-0.1) years, and 2.4(+-0.4) years in the southern hemisphere, 6.1(+-0.4) months in solar cycle 23 (SC23) and 6.1(+-0.4) months, 1.2(+-0.1) years, and 3.7(+-0.2) years in solar cycle 24 (SC24). The analysis shows that quasi-periodic variations of the CMEs are a link among oscillations in coronal magnetic activity, solar flare eruptions, and interplanetary space.

astro-ph.SR↗

arXiv2022

Software Engineering Process and Methodology in Blockchain-Oriented Software Development: A Systematic Study

Software Engineering is the process of a systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software product development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of the software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented Software Engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current software engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futuristic technology would be challenging and promising research is needed extensively towards addressing and improving state-of-the-art software engineering processes and methodology for novel technologies.

cs.SE↗

arXiv2022

Distributed randomized Kaczmarz for the adversarial workers

Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. Here, we propose an iterative approach that is adversary-tolerant for least-squares problems. The algorithm utilizes simple statistics to guarantee convergence and is capable of learning the adversarial distributions. Additionally, the efficiency of the proposed method is shown in simulations in the presence of adversaries. The results demonstrate the great capability of such methods to tolerate different levels of adversary rates and to identify the erroneous workers with high accuracy.

cs.LG↗

arXiv2022

On the regular 2-connected 2-path Hamiltonian graphs

A graph $G$ is $l$-path Hamiltonian if every path of length not exceeding $l$ is contained in a Hamiltonian cycle. It is well known that a 2-connected, $k$-regular graph $G$ on at most $3k-1$ vertices is edge-Hamiltonian if for every edge $uv$ of $G$, $\{u,v\}$ is not a cut-set. Thus $G$ is 1-path Hamiltonian if $G\setminus \{u,v\}$ is connected for every edge $uv$ of $G$. Let $P=uvz$ be a 2-path of a 2-connected, $k$-regular graph $G$ on at most $2k$ vertices. In this paper, we show that there is a Hamiltonian cycle containing the 2-path $P$ if $G\setminus V(P)$ is connected. Therefore, the work implies a condition for a 2-connected, $k$-regular graph to be 2-path Hamiltonian. An example shows that the $2k$ is almost sharp, i.e., the number is at most $2k+1$.

math.CO↗

arXiv2021

Is Attention Better Than Matrix Decomposition?

As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decomposition (MD) model developed 20 years ago regarding the performance and computational cost for encoding the long-distance dependencies. We model the global context issue as a low-rank recovery problem and show that its optimization algorithms can help design global information blocks. This paper then proposes a series of Hamburgers, in which we employ the optimization algorithms for solving MDs to factorize the input representations into sub-matrices and reconstruct a low-rank embedding. Hamburgers with different MDs can perform favorably against the popular global context module self-attention when carefully coping with gradients back-propagated through MDs. Comprehensive experiments are conducted in the vision tasks where it is crucial to learn the global context, including semantic segmentation and image generation, demonstrating significant improvements over self-attention and its variants.

cs.CV↗

arXiv2021

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. Most approaches only exploit the temporal dimension to address the association problem, while relying on single frame predictions for the segmentation mask itself. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation. PCAN first distills a space-time memory into a set of prototypes and then employs cross-attention to retrieve rich information from the past frames. To segment each object, PCAN adopts a prototypical appearance module to learn a set of contrastive foreground and background prototypes, which are then propagated over time. Extensive experiments demonstrate that PCAN outperforms current video instance tracking and segmentation competition winners on both Youtube-VIS and BDD100K datasets, and shows efficacy to both one-stage and two-stage segmentation frameworks. Code and video resources are available at http://vis.xyz/pub/pcan.

cs.CV↗

arXiv2021

Mask Transfiner for High-Quality Instance Segmentation

Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Transfiner for high-quality and efficient instance segmentation. Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree. Our transformer-based approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. While these sparse pixels only constitute a small proportion of the total number, they are critical to the final mask quality. This allows Mask Transfiner to predict highly accurate instance masks, at a low computational cost. Extensive experiments demonstrate that Mask Transfiner outperforms current instance segmentation methods on three popular benchmarks, significantly improving both two-stage and query-based frameworks by a large margin of +3.0 mask AP on COCO and BDD100K, and +6.6 boundary AP on Cityscapes. Our code and trained models will be available at http://vis.xyz/pub/transfiner.

cs.CV↗

arXiv2021

Covering functionals of convex polytopes with few vertices

By using elementary yet interesting observations and refining techniques used in a recent work by Fei Xue et al., we present new upper bounds for covering functionals of convex polytopes in $\mathbb{R}^n$ with few vertices. In these estimations, no information other than the number of vertices of the convex polytope is used.

math.MG↗

arXiv2021

Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep convolution network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.

cs.CV↗

arXiv2021

Quasi-Dense Similarity Learning for Multiple Object Tracking

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets. Our code and trained models are available at http://vis.xyz/pub/qdtrack.

cs.CV↗

arXiv2021

Binary irreducible quasi-cyclic parity-check subcodes of Goppa codes and extended Goppa codes

Goppa codes are particularly appealing for cryptographic applications. Every improvement of our knowledge of Goppa codes is of particular interest. In this paper, we present a sufficient and necessary condition for an irreducible monic polynomial $g(x)$ of degree $r$ over $\mathbb{F}_{q}$ satisfying $γg(x)=(x+d)^rg({A}(x))$, where $q=2^n$, $A=\left(\begin{array}{cc} a&b\\1&d\end{array}\right)\in PGL_2(\Bbb F_{q})$, $\mathrm{ord}(A)$ is a prime, $g(a)\ne 0$, and $0\ne γ\in \Bbb F_q$. And we give a complete characterization of irreducible polynomials $g(x)$ of degree $2s$ or $3s$ as above, where $s$ is a positive integer. Moreover, we construct some binary irreducible quasi-cyclic parity-check subcodes of Goppa codes and extended Goppa codes.

cs.IT↗

arXiv2021

Towards Efficient Scene Understanding via Squeeze Reasoning

Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, its pixel-wise computational overhead is prohibitive which renders it unsuitable for high resolution imagery. In this paper, we explore the efficiency of context graph reasoning and propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced. Specifically, we build the node graph in the vector where each node represents an abstract semantic concept. The refined feature within the same semantic category results to be consistent, which is thus beneficial for downstream tasks. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks. {Despite its simplicity and being lightweight, the proposed strategy allows us to establish the considerable results on different semantic segmentation datasets and shows significant improvements with respect to strong baselines on various other scene understanding tasks including object detection, instance segmentation and panoptic segmentation.} Code is available at \url{https://github.com/lxtGH/SFSegNets}.

cs.CV↗

arXiv2021

Optimization Induced Equilibrium Networks

Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an emerging question: can an implicit equilibrium model's equilibrium point be regarded as the solution of an optimization problem? To this end, we first decompose DNNs into a new class of unit layer that is the proximal operator of an implicit convex function while keeping its output unchanged. Then, the equilibrium model of the unit layer can be derived, named Optimization Induced Equilibrium Networks (OptEq), which can be easily extended to deep layers. The equilibrium point of OptEq can be theoretically connected to the solution of its corresponding convex optimization problem with explicit objectives. Based on this, we can flexibly introduce prior properties to the equilibrium points: 1) modifying the underlying convex problems explicitly so as to change the architectures of OptEq; and 2) merging the information into the fixed point iteration, which guarantees to choose the desired equilibrium point when the fixed point set is non-singleton. We show that deep OptEq outperforms previous implicit models even with fewer parameters. This work establishes the first step towards the optimization-guided design of deep models.

cs.LG↗

arXiv2021

Quality Assessment of DIBR-synthesized views: An Overview

The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several efforts have been made towards this topic, but there {is a lack of} detailed survey in {the} literature. In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views. The current accessible datasets of DIBR-synthesized views are firstly reviewed{, followed} by a summary analysis of the representative state-of-the-art objective metrics. Then, the performances of different objective metrics are evaluated and discussed on all available datasets. Finally, we discuss the potential challenges and suggest possible directions for future research.

eess.IV↗

arXiv2021

Self-Boosted Automated Program Repair

Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, researchers have proposed various automated program repair (APR) techniques to reduce efforts of manual debugging. However, given a real-world buggy program, a typical APR technique usually generates a large number of patches, each of which needs to be validated against the original test suite which incurs extremely high computation costs. Although existing APR techniques have already leveraged various static and/or dynamic information to find the desired patches faster, they are still rather costly. In a recent work, researchers proposed unified debugging to leverage the patch execution information during APR to help boost fault localization; in this way,the application scope of APR techniques can be extended to all possible bugs, e.g., the patch execution information during APR can help with manual repair of the bugs that cannot be automatically fixed. Inspired by unified debugging, this work proposes SeAPR (Self-Boosted Automated Program Repair), the first technique to leverage the earlier patch execution information during APR to help boost automated repair itself on-the-fly. Our basic intuition is that patches similar to earlier high-quality/low-quality patches should be promoted/degraded to speed up the detection of the desired patches. This experimental study on 12 state-of-the-art APR systems demonstrates that, overall, SeAPR can substantially reduce the number of patch executions with negligible overhead. Our study also investigates the impact of various configurations on SeAPR. Lastly, our study demonstrates that SeAPR can even leverage the patch execution information from other APR tools from the same buggy program to further boost APR.

cs.SE↗

arXiv2021

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).

cs.CV↗

arXiv2021

Charge-transfer effect on local lattice distortion in a HfNbTiZr high entropy alloy

It is often assumed that atoms are hard spheres in the estimation of local lattice distortion (LLD) in high-entropy alloys (HEAs). However, our study demonstrates that the hard sphere model misses the key effect, charge transfer among atoms with different electronegativities, in the understanding of the stabilization of severely-distorted HEAs. Through the characterization and simulations of the local structure of the HfNbTiZr HEA, we found that the charge transfer effect competes with LLD to significantly reduce the average atomic-size mismatch. Our finding may form the basis for the design of severely distorted, but stable HEAs.

cond-mat.mtrl-sci↗

arXiv2020

Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification

RGB-Infrared person re-identification (RGB-IR Re- ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset. Code: https://github.com/prismformore/expAT

cs.CV↗