arXiv subjects

Yuqing Wang

Publications and source records attributed to Yuqing Wang.

At least 127 records · Page 7Linked to original sources

arXiv2022

Enhancing Transformer Efficiency for Multivariate Time Series Classification

Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequential) time series (TS) datasets, there is an additional consideration: to design an efficient network architecture to reduce computational costs such as training time and memory footprint. In this work we propose a methodology based on module-wise pruning and Pareto analysis to investigate the relationship between model efficiency and accuracy, as well as its complexity. Comprehensive experiments on benchmark MTS datasets illustrate the effectiveness of our method.

cs.LG↗

arXiv2022

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect

Recent empirical advances show that training deep models with large learning rate often improves generalization performance. However, theoretical justifications on the benefits of large learning rate are highly limited, due to challenges in analysis. In this paper, we consider using Gradient Descent (GD) with a large learning rate on a homogeneous matrix factorization problem, i.e., $\min_{X, Y} \|A - XY^\top\|_{\sf F}^2$. We prove a convergence theory for constant large learning rates well beyond $2/L$, where $L$ is the largest eigenvalue of Hessian at the initialization. Moreover, we rigorously establish an implicit bias of GD induced by such a large learning rate, termed 'balancing', meaning that magnitudes of $X$ and $Y$ at the limit of GD iterations will be close even if their initialization is significantly unbalanced. Numerical experiments are provided to support our theory.

cs.LG↗

arXiv2022

Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification

Incremental learning methods can learn new classes continually by distilling knowledge from the last model (as a teacher model) to the current model (as a student model) in the sequentially learning process. However, these methods cannot work for Incremental Implicitly-Refined Classification (IIRC), an incremental learning extension where the incoming classes could have two granularity levels, a superclass label and a subclass label. This is because the previously learned superclass knowledge may be occupied by the subclass knowledge learned sequentially. To solve this problem, we propose a novel Multi-Teacher Knowledge Distillation (MTKD) strategy. To preserve the subclass knowledge, we use the last model as a general teacher to distill the previous knowledge for the student model. To preserve the superclass knowledge, we use the initial model as a superclass teacher to distill the superclass knowledge as the initial model contains abundant superclass knowledge. However, distilling knowledge from two teacher models could result in the student model making some redundant predictions. We further propose a post-processing mechanism, called as Top-k prediction restriction to reduce the redundant predictions. Our experimental results on IIRC-ImageNet120 and IIRC-CIFAR100 show that the proposed method can achieve better classification accuracy compared with existing state-of-the-art methods.

cs.CV↗

arXiv2022

Improving Test Automation Maturity: a Multivocal Literature Review

Mature test automation is key for achieving software quality at speed. In this paper, we present a multivocal literature review with the objective to survey and synthesize the guidelines given in the literature for improving test automation maturity. We selected and reviewed 81 primary studies, consisting of 26 academic literature and 55 grey literature sources. From primary studies, we extracted 26 test automation best practices (e.g., Define an effective test automation strategy, Set up good test environments, Develop high-quality test scripts) and collected many pieces of advice (e.g., in forms of implementation/improvement approaches, technical techniques, concepts, experience-based heuristics) on how to conduct these best practices. We made main observations: (1) There are only 6 best practices whose positive effect on maturity improvement have been evaluated by academic studies using formal empirical methods; (2) Several technical related best practices in this MLR were not presented in test maturity models; (3) Some best practices can be linked to success factors and maturity impediments proposed by other scholars; (4) Most pieces of advice on how to conduct proposed best practices were identified from experience studies and their effectiveness need to be further evaluated with cross-site empirical evidence using formal empirical methods; (5) In the literature, some advice on how to conduct certain best practices are conflicting, and some advice on how to conduct certain best practices still need further qualitative analysis.

cs.SE↗

arXiv2022

Test Automation Maturity Improves Product Quality -- Quantitative Study of Open Source Projects Using Continuous Integration

The popularity of continuous integration (CI) is increasing as a result of market pressure to release product features or updates frequently. The ability of CI to deliver quality at speed depends on reliable test automation. In this paper, we present an empirical study to observe the effect of test automation maturity (assessed by standard best practices in the literature) on product quality, test automation effort, and release cycle in the CI context of open source projects. We run our test automation maturity survey and got responses from 37 open source java projects. We also mined software repositories of the same projects. The main results of regression analysis reveal that, higher levels of test automation maturity are positively associated with higher product quality (p-value=0.000624) and shorter release cycle (p-value=0.01891); There is no statistically significant evidence of increased test automation effort due to higher levels of test automation maturity and product quality. Thus, we conclude that, a potential benefit of improving test automation maturity (using standard best practices) is product quality improvement and release cycle acceleration in the CI context of open source projects. We encourage future research to extend our findings by adding more datasets with different programming languages and CI tools, closed source projects, and large-scale industrial projects. Our recommendation to practitioners (in the similar CI context) is to utilize standard best practices to improve test automation maturity.

cs.SE↗

arXiv2021

End-to-End Video Instance Segmentation with Transformers

Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches. Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.

cs.CV↗

arXiv2021

Empirical Quantitative Analysis of COVID-19 Forecasting Models

COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large number of different forecasting models have been proposed, mainly including statistical models, compartmental models, and deep learning models. However, due to various uncertain factors across different regions such as economics and government policy, no forecasting model appears to be the best for all scenarios. In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training. We find that if a dimension brings about higher performance gains, if not well-tuned, it may also lead to harsher performance penalties. Furthermore, model selection is the dominant factor in determining the predictive performance. It is responsible for both the largest improvement and the largest variation in performance in all prediction tasks across different regions. While practitioners may perform more complicated time series analysis in practice, they should be able to achieve reasonable results if they have adequate insight into key decisions like model selection.

cs.LG↗

arXiv2021

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks, including image level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code is released at https://github.com/Meituan-AutoML/Twins .

cs.CV↗

arXiv2021

Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients

Multiple organ failure (MOF) is a life-threatening condition. Due to its urgency and high mortality rate, early detection is critical for clinicians to provide appropriate treatment. In this paper, we perform quantitative analysis on early MOF prediction with comprehensive machine learning (ML) configurations, including data preprocessing (missing value treatment, label balancing, feature scaling), feature selection, classifier choice, and hyperparameter tuning. Results show that classifier choice impacts both the performance improvement and variation most among all the configurations. In general, complex classifiers including ensemble methods can provide better performance than simple classifiers. However, blindly pursuing complex classifiers is unwise as it also brings the risk of greater performance variation.

cs.LG↗

arXiv2021

BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients

Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, deep learning models have drawn increasing attention in healthcare. However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes. In this paper we introduce BERTSurv, a deep learning survival framework which applies Bidirectional Encoder Representations from Transformers (BERT) as a language representation model on unstructured clinical notes, for mortality prediction and survival analysis. We also incorporate clinical measurements in BERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality as a binary outcome (mortality prediction). With partial log-likelihood (PLL) loss, BERTSurv predicts the probability of mortality as a time-to-event outcome (survival analysis). We apply BERTSurv on Medical Information Mart for Intensive Care III (MIMIC III) trauma patient data. For mortality prediction, BERTSurv obtained an area under the curve of receiver operating characteristic curve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of multilayer perceptron (MLP) without notes. For survival analysis, BERTSurv achieved a concordance index (C-index) of 0.7. In addition, visualizations of BERT's attention heads help to extract patterns in clinical notes and improve model interpretability by showing how the model assigns weights to different inputs.

cs.AI↗

arXiv2020

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective

Deep residual networks (ResNets) have demonstrated better generalization performance than deep feedforward networks (FFNets). However, the theory behind such a phenomenon is still largely unknown. This paper studies this fundamental problem in deep learning from a so-called "neural tangent kernel" perspective. Specifically, we first show that under proper conditions, as the width goes to infinity, training deep ResNets can be viewed as learning reproducing kernel functions with some kernel function. We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity. In contrast, the class of functions induced by the kernel of ResNets does not exhibit such degeneracy. Our discovery partially justifies the advantages of deep ResNets over deep FFNets in generalization abilities. Numerical results are provided to support our claim.

cs.LG↗

arXiv2020

Non-equilibrium relaxation process of complex systems and its statistical physical properties

We constructed a model that evolved from a non-equilibrium state to an equilibrium state. The model only needs two basic coefficients, including self-similar coefficients and non-equilibrium coefficients. The coefficients of the model can be obtained directly from real data. The model can well represent the phenomena of anomalous diffusion, non-Gaussian distribution and so on in non-equilibrium complex systems. It is important to mention that the model is a global iterative model that can exhibit statistical physical properties on multiple time scales, as well as exhibiting long-term memory properties. This model may be used as a basic model for the research of various non-equilibrium relaxation process phenomena.

cond-mat.stat-mech↗

arXiv2020

Software Test Automation Maturity -- A Survey of the State of the Practice

The software industry has seen an increasing interest in test automation. In this paper, we present a test automation maturity survey serving as a self-assessment for practitioners. Based on responses of 151 practitioners coming from above 101 organizations in 25 countries, we make observations regarding the state of the practice of test automation maturity: a) The level of test automation maturity in different organizations is differentiated by the practices they adopt; b) Practitioner reported the quite diverse situation with respect to different practices, e.g., 85\% practitioners agreed that their test teams have enough test automation expertise and skills, while 47\% of practitioners admitted that there is lack of guidelines on designing and executing automated tests; c) Some practices are strongly correlated and/or closely clustered; d) The percentage of automated test cases and the use of Agile and/or DevOps development models are good indicators for a higher test automation maturity level; (e) The roles of practitioners may affect response variation, e.g., QA engineers give the most optimistic answers, consultants give the most pessimistic answers. Our results give an insight into present test automation processes and practices and indicate chances for further improvement in the present industry.

cs.SE↗

arXiv2020

Test Automation Process Improvement in a DevOpsTeam: Experience Report

How to successfully conduct test automation process improvement (TAPI) for continuous development, consisting of iterative software development, continuous testing, and delivery, is the challenge faced by many software organizations. In this paper, we present an experience report on TAPI in one DevOps team in F-Secure (a Finnish software company). The team builds Windows application software and exists in F-Secure's TAPI culture. The team self-reports high satisfaction and maturity in test automation for continuous development. To study their TAPI, we reviewed a collection of experience notes, team reflection reports and telemetry result reports. Then several meetings were held to discuss the details. We found that based on the understanding of the team, test automation maturity for continuous development is defined as a set of indicators, e.g., the increasing speed to release, improving the productivity of the team, high test efficiency. Second, the team indicated that a set of critical success factors have a major impact on successfully carrying out its TAPI, e.g., incremental approach, the whole team effort, test tool choice and architecture, telemetry. Third, we compare the TAPI practices in the observed team with the practices described in prior literature. The team believes that the existing test automation maturity approaches should include the identified practices like the whole team effort to build a more comprehensive test automation improvement model for the software industry.

cs.SE↗

arXiv2020

CenterMask: single shot instance segmentation with point representation

In this paper, we propose a single-shot instance segmentation method, which is simple, fast and accurate. There are two main challenges for one-stage instance segmentation: object instances differentiation and pixel-wise feature alignment. Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner. The outputs of the two branches are assembled to form the final instance masks. To realize that, the local shape information is adopted from the representation of object center points. Totally trained from scratch and without any bells and whistles, the proposed CenterMask achieves 34.5 mask AP with a speed of 12.3 fps, using a single-model with single-scale training/testing on the challenging COCO dataset. The accuracy is higher than all other one-stage instance segmentation methods except the 5 times slower TensorMask, which shows the effectiveness of CenterMask. Besides, our method can be easily embedded to other one-stage object detectors such as FCOS and performs well, showing the generalization of CenterMask.

cs.CV↗

arXiv2019

The Optimal Deterrence of Crime: A Focus on the Time Preference of DWI Offenders

We develop a general model for finding the optimal penal strategy based on the behavioral traits of the offenders. We focus on how the discount rate (level of time discounting) affects criminal propensity on the individual level, and how the aggregation of these effects influences criminal activities on the population level. The effects are aggregated based on the distribution of discount rate among the population. We study this distribution empirically through a survey with 207 participants, and we show that it follows zero-inflated exponential distribution. We quantify the effectiveness of the penal strategy as its net utility for the population, and show how this quantity can be maximized. When we apply the maximization procedure on the offense of impaired driving (DWI), we discover that the effectiveness of DWI deterrence depends critically on the amount of fine and prison condition.

econ.GN↗

arXiv2019

A self-assessment Instrument for assessing test automation maturity

Test automation is important in software industry but self-assessment instruments for assessing its maturity are not sufficient. The two objectives of this study are to synthesize what an organization should focus to assess its test automation; develop a self-assessment instrument (a survey) for assessing test automation maturity and scientifically evaluate it. We carried out the study in four stages. First, a literature review of 25 sources was conducted. Second, the initial instrument was developed. Third, seven experts from five companies evaluated the initial instrument. Content Validity Index and Cognitive Interview methods were used. Fourth, we revised the developed instrument. Our contributions are as follows: (a) we collected practices mapped into 15 KAs that indicate where an organization should focus to assess its test automation; (b) we developed and evaluated a self-assessment instrument for assessing test automation maturity; (c) we discuss important topics such as response bias that threatens self-assessment instruments. Our results help companies and researchers to understand and improve test automation practices and processes.

cs.SE↗

arXiv2018

Anisotropic diffraction induced by orbital angular momentum during propagations of optical beams

It is demonstrated that the orbital angular momentum (OAM) carried by the elliptic beam without the phase-singularity can induce the anisotropic diffraction (AD). The quantitative relation between the OAM and its induced AD is analytically obtained by a comparison of two different kinds of (1+2)-dimensional beam propagations: the linear propagations of the elliptic beam without the OAM in an anisotropic medium and that with the OAM in an isotropic one. In the former case, the optical beam evolves as the fundamental mode of the eigenmodes when its ellipticity is the square root of the anisotropic parameter defined in the paper; while in the latter case, the fundamental mode exists only when the OAM carried by the optical beam equals a specific one called a critical OAM. The OAM always enhances the beam-expanding in the major-axis direction and weakens that in the minor-axis direction no matter the sign of the OAM, and the larger the OAM, the stronger the AD induced by it. Besides, the OAM can also make the elliptic beam rotate, and the absolute value of the rotation angle is no larger than $π/2$ during the propagation.

physics.optics↗