arXiv subjects

Long Chen

Publications and source records attributed to Long Chen.

At least 559 records · Page 31Linked to original sources

arXiv2020

Revisiting the Bottom Quark Forward-Backward Asymmetry $A_{\rm {FB}}$ in Electron-Positron Collisions

The bottom quark forward-backward asymmetry $A_{\rm{FB}}$ is a key observable in electron-positron collisions at the $Z^{0}$ peak. In this paper, we employ the Principle of Maximum Conformality (PMC) to fix the $α_s$-running behavior of the next-to-next-to-leading order QCD corrections to $A_{\rm{FB}}$. The resulting PMC scale for this $A_{\rm{FB}}$ is an order of magnitude smaller than the conventional choice $μ_r=M_Z$. This scale has the physically reasonable behavior and reflects the virtuality of its QCD dynamics, which is independent to the choice of renormalization scale. Our analyses show that the effective momentum flow for the bottom quark forward-backward asymmetry should be $μ_r\ll M_Z$ other than the conventionally suggested $μ_r=M_Z$. Moreover, the convergence of perturbative QCD series for $A_{\rm{FB}}$ is greatly improved using the PMC. Our prediction for the bare bottom quark forward-backward asymmetry is refined to be $A^{0,b}_{\rm FB}=0.1004\pm0.0016$, which diminishes the well known tension between the experimental determination for this (pseudo) observable and the respective Standard Model fit to $2.1σ$.

hep-ph↗

arXiv2020

The Adaptability and Challenges of Autonomous Vehicles to Pedestrians in Urban China

China is the world's largest automotive market and is ambitious for autonomous vehicles (AVs) development. As one of the key goals of AVs, pedestrian safety is an important issue in China. Despite the rapid development of driverless technologies in recent years, there is a lack of researches on the adaptability of AVs to pedestrians. To fill the gap, this study would discuss the adaptability of current driverless technologies to China urban pedestrians by reviewing the latest researches. The paper firstly analyzed typical Chinese pedestrian behaviors and summarized the safety demands of pedestrians for AVs through articles and open database data, which are worked as the evaluation criteria. Then, corresponding driverless technologies are carefully reviewed. Finally, the adaptability would be given combining the above analyses. Our review found that autonomous vehicles have trouble in the occluded pedestrian environment and Chinese pedestrians do not accept AVs well. And more explorations should be conducted on standard human-machine interaction, interaction information overload avoidance, occluded pedestrians detection and nation-based receptivity research. The conclusions are very useful for motor corporations and driverless car researchers to place more attention on the complexity of the Chinese pedestrian environment, for transportation experts to protect pedestrian safety in the context of AVs, and for governors to think about making new pedestrians policies to welcome the upcoming driverless cars.

cs.HC↗

arXiv2020

Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving

Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber-physical-social systems (CPSS), based on the ACP method, the names of the digital quadruplets are first given, which are descriptive, predictive, prescriptive and real vehicles. The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles. Then, the three virtual components of the digital quadruplets are introduced in detail and their applications are also illustrated. Finally, the real vehicles in the parallel driving system and the research process of the digital quadruplets are depicted. The presented digital quadruplets in parallel driving are expected to make the future connected automated driving safety, efficiently and synergistically.

cs.AI↗

arXiv2020

Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications

Digital quadruplets aiming to improve road safety, traffic efficiency, and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP based parallel driving. The ACP method denotes Artificial societies, Computational experiments, and Parallel execution modules for cyber-physical-social systems. Four agents are designed in the framework of digital quadruplets: descriptive vehicles, predictive vehicles, prescriptive vehicles, and real vehicles. The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle. The details of the three virtual vehicles in the digital quadruplets are described. Then, the interactions between the virtual and real vehicles are presented. The experimental results of the digital quadruplets demonstrate the effectiveness of the proposed framework.

cs.RO↗

arXiv2020

Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles

As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control. Since there is no human driver to handle the emergency situation, future transportation information is significant for automated vehicles. This paper proposes different methods to forecast the time series for autonomous vehicles, which are the nearest neighborhood (NN), fuzzy coding (FC), and long short term memory (LSTM). First, the formulation and operational process for these three approaches are introduced. Then, the vehicle velocity is regarded as a case study and the real-world dataset is utilized to predict future information via these techniques. Finally, the performance, merits, and drawbacks of the presented methods are analyzed and discussed.

cs.LG↗

arXiv2020

Polarized $q \bar{q} \rightarrow Z +$Higgs amplitudes at two loops in QCD: the interplay between vector and axial vector form factors and a pitfall in applying a non-anticommuting $γ_5$

We consider QCD corrections to two loops for the polarized amplitudes of $q{\bar q}\to Z +$ Higgs boson. First we show how the polarized amplitudes of $b \bar{b} \rightarrow Z h$ associated with a non-vanishing $b$-quark Yukawa coupling and a scalar or pseudoscalar Higgs boson $h$ can be built up solely from vector form factors (FF) of properly grouped classes of diagrams, bypassing completely the need of explicitly manipulating $γ_5$ in dimensional regularization (up to a few "anomalous", i.e., triangle diagrams). We determine the contributions of the triangle diagrams in the heavy top limit. We present the analytic results of the vector FF and the triangle-diagram contributions to the axial vector FF, which are sufficient for deriving the two-loop QCD amplitudes for $b \bar{b} \rightarrow Z h$ with a CP-even and CP-odd Higgs boson $h$. We derive the respective Ward identity for these amplitudes, which are subsequently verified to two-loop order in QCD using these FF. In addition, the FF of a class of corrections to $q \bar{q} \rightarrow ZH$ proportional to the top-Yukawa coupling are obtained analytically to two-loop order in QCD in the heavy-top limit using the Higgs-gluon effective Lagrangian where the top quark is integrated out. We address a pitfall that occurs when applying the non-anticommutating $γ_5$ prescription to this class of contributions that has been overlooked so far in the literature. We attribute this issue to the fact that the absence of certain heavy-mass expanded diagrams in the infinite-mass limit of a scattering amplitude with an axial vector current depends on the particular $γ_5$ prescription in use.

hep-ph↗

arXiv2020

Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation

As a proposal-free approach, instance segmentation through pixel embedding learning and clustering is gaining more emphasis. Compared with bounding box refinement approaches, such as Mask R-CNN, it has potential advantages in handling complex shapes and dense objects. In this work, we propose a simple, yet highly effective, architecture for object-aware embedding learning. A distance regression module is incorporated into our architecture to generate seeds for fast clustering. At the same time, we show that the features learned by the distance regression module are able to promote the accuracy of learned object-aware embeddings significantly. By simply concatenating features of the distance regression module to the images as inputs of the embedding module, the mSBD scores on the CVPPP Leaf Segmentation Challenge can be further improved by more than 8% compared to the identical set-up without concatenation, yielding the best overall result amongst the leaderboard at CodaLab.

cs.CV↗

arXiv2020

On Connections between Regularizations for Improving DNN Robustness

This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically, we study possible connections between several effective methods, including input-gradient regularization, Jacobian regularization, curvature regularization, and a cross-Lipschitz functional. We investigate them on DNNs with general rectified linear activations, which constitute one of the most prevalent families of models for image classification and a host of other machine learning applications. We shed light on essential ingredients of these regularizations and re-interpret their functionality. Through the lens of our study, more principled and efficient regularizations can possibly be invented in the near future.

cs.LG↗

arXiv2020

A Benchmark dataset for both underwater image enhancement and underwater object detection

Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the detection accuracy of the detectors, no work specially focus on investigating the relationship between these two tasks. This is mainly because existing underwater datasets lack either bounding box annotations or high quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To investigate how the underwater image enhancement methods influence the following underwater object detection tasks, in this paper, we provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images, namely OUC dataset. The OUC dataset provides a platform for researchers to comprehensive study the influence of underwater image enhancement algorithms on the underwater object detection task.

cs.CV↗

arXiv2020

Randomized Fast Subspace Descent Methods

Randomized Fast Subspace Descent (RFASD) Methods are developed and analyzed for smooth and non-constraint convex optimization problems. The efficiency of the method relies on a space decomposition which is stable in $A$-norm, and meanwhile, the condition number $κ_A$ measured in $A$-norm is small. At each iteration, the subspace is chosen randomly either uniformly or by a probability proportional to the local Lipschitz constants. Then in each chosen subspace, a preconditioned gradient descent method is applied. RFASD converges sublinearly for convex functions and linearly for strongly convex functions. Comparing with the randomized block coordinate descent methods, the convergence of RFASD is faster provided $κ_A$ is small and the subspace decomposition is $A$-stable. This improvement is supported by considering a multilevel space decomposition for Nesterov's `worst' problem.

math.OC↗

arXiv2020

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or content sharing) which recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items.Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference. However, present works focus mainly on one of the requirements and only consider either user-outfit or outfit-item relationships, thereby easily leading to suboptimal representations and limiting the performance. In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation. Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously. In particular, we construct a hierarchical structure upon user-outfit interactions and outfit-item mappings. We then get inspirations from recent graph neural networks, and employ the embedding propagation on such hierarchical graph, so as to aggregate item information into an outfit representation, and then refine a user's representation via his/her historical outfits. Furthermore, we jointly train these two tasks to optimize these representations. To demonstrate the effectiveness of HFGN, we conduct extensive experiments on a benchmark dataset, and HFGN achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN.

cs.IR↗

arXiv2020

Underwater object detection using Invert Multi-Class Adaboost with deep learning

In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semantic rich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 and URPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.

cs.CV↗

arXiv2020

Mercury-related health benefits from retrofitting coal-fired power plants in China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+.

econ.GN↗

arXiv2020

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

cs.CV↗

arXiv2020

Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19

With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term - "Chinese Virus" is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like "Chinese Virus" or "Wuhan Virus", or using non-controversial terms like "Coronavirus". In this study, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. We find significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

cs.SI↗

arXiv2020

Distinguish Confusing Law Articles for Legal Judgment Prediction

Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.

cs.CL↗

arXiv2020

A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images

Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biological studies involving the nematode C. elegans, a common model organism. Here, we propose a framework for detecting worm-shaped objects in microscopic images that is based on convolutional neural networks (CNNs). We annotate nematodes with curved lines along the body, which is more suitable for worm-shaped objects than bounding boxes. The trained model predicts worm skeletons and body endpoints. The endpoints serve to untangle the skeletons from which segmentation masks are reconstructed by estimating the body width at each location along the skeleton. With light-weight backbone networks, we achieve 75.85 % precision, 73.02 % recall on a potato cyst nematode data set and 84.20 % precision, 85.63 % recall on a public C. elegans data set.

cs.CV↗

arXiv2020

Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available.

cs.CV↗