arXiv subjects

Jiaxin Liu

Publications and source records attributed to Jiaxin Liu.

79 records · Page 5Linked to original sources

arXiv2023

Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection

User-generated reviews of products are vital assets of online commerce, such as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN is the state-of-the-art method that detects suspicious reviewers by exploiting the topologies of the graph connecting reviewers, reviews, and target products. However, the discrepancy in the detection accuracy over different groups of reviewers can degrade reviewer engagement and customer trust in the review websites. Unlike the previous belief that the difference between the groups causes unfairness, we study the subgroup structures within the groups that can also cause discrepancies in treating different groups. This paper addresses the challenges of defining, approximating, and utilizing a new subgroup structure for fair spam detection. We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups. The complex dependencies over the review graph create difficulties in teasing out subgroups hidden within larger groups. We design a model that can be trained to jointly infer the hidden subgroup memberships and exploits the membership for calibrating the detection accuracy across groups. Comprehensive comparisons against baselines on three large Yelp review datasets demonstrate that the subgroup membership can be identified and exploited for group fairness.

cs.LG↗

arXiv2023

On the generation of ultra-bright and low energy spread electron beams in laser wakefield acceleration in a uniform plasma

The quality of electron beams produced from plasma-based accelerators, i.e., normalized brightness and energy spread, has made transformative progress in the past several decades in both simulation and experiment. Recently, full-scale particle-in-cell (PIC) simulations have shown that electron beams with unprecedented brightness ($10^{20}\sim10^{21}~\mathrm{A}/\mathrm{m}^2/\mathrm{rad}^2$) and $0.1\sim 1$ MeV energy spread can be produced through controlled injection in a slowly expanding bubble that arises when a particle beam or laser pulse propagates in density gradient, or when a particle beam self-focuses in uniform plasma or has a superluminal flying focus. However, in previous simulations of work on self-injection triggered by an evolving laser driver in a uniform plasma, the resulting beams did not exhibit comparable brightnesses and energy spreads. Here, we demonstrate through the use of large-scale high-fidelity PIC simulations that a slowly expanding bubble driven by a laser pulse in a uniform plasma can indeed produce self-injected electron beams with similar brightnesses and energy spreads as for an evolving bubble driven by an electron beam driver. We consider laser spot sizes roughly equal to the matched spot sizes in a uniform plasma and find that the evolution of the bubble occurs naturally through the evolution of the laser. The effects of the electron beam quality on the choice of physical as well as numerical parameters, e.g. grid sizes and field solvers used in the PIC simulations are presented. It is found that this original and simplest injection scheme can produce electron beams with beam quality exceeding that of the more recent concepts.

physics.acc-ph↗

arXiv2023

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving.

cs.RO↗

arXiv2023

Ultra-compact attosecond X-ray free-electron lasers utilizing unique beams from plasma-based acceleration and an optical undulator

Accelerator-based X-ray free-electron lasers (XFELs) are the latest addition to the revolutionary tools of discovery for the 21st century. The two major components of an XFEL are an accelerator-produced electron beam and a magnetic undulator which tend to be kilometer-scale long and expensive. Here, we present an ultra-compact scheme to produce 10s of attosecond X-ray pulses with several GW peak power utilizing a novel aspect of the FEL instability using a highly chirped, pre-bunched and ultra-bright electron beam from a plasma-based accelerator interacting with an optical undulator. The self-selection of electrons from the combination of a highly chirped and pre-bunched beam leads to the stable generation of attosecond X-ray pulses. Furthermore, two-color attosecond pulses with sub-femtosecond separation can be produced by adjusting the energy distribution of the electron beam so that multiple FEL resonances occur at different locations within the beam. Such a tunable coherent attosecond X-ray sources may open up a new area of attosecond science enabled by X-ray attosecond pump/probe techniques.

physics.plasm-ph↗

arXiv2022

Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection

Social networks are frequently polluted by rumors, which can be detected by advanced models such as graph neural networks. However, the models are vulnerable to attacks and understanding the vulnerabilities is critical to rumor detection in practice. To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors. The environment has exponentially large state spaces, high-order graph dependencies, and delayed noisy rewards, making the state-of-the-art end-to-end approaches difficult to learn features as large learning costs and expressive limitation of graph deep models. Instead, we design domain-specific features to avoid learning features and produce interpretable attack policies. To further speed up policy optimization, we devise: (i) a credit assignment method that decomposes delayed rewards to atomic attacking actions proportional to the their camouflage effects on target rumors; (ii) a time-dependent control variate to reduce reward variance due to large graphs and many attacking steps, supported by the reward variance analysis and a Bayesian analysis of the prediction distribution. On three real world datasets of rumor detection tasks, we demonstrate: (i) the effectiveness of the learned attacking policy compared to rule-based attacks and current end-to-end approaches; (ii) the usefulness of the proposed credit assignment strategy and variance reduction components; (iii) the interpretability of the policy when generating strong attacks via the case study.

cs.LG↗

arXiv2019

Deep Anomaly Detection in Packet Payload

With the widespread adoption of cloud services, especially the extensive deployment of plenty of Web applications, it is important and challenging to detect anomalies from the packet payload. For example, the anomalies in the packet payload can be expressed as a number of specific strings which may cause attacks. Although some approaches have achieved remarkable progress, they are with limited applications since they are dependent on in-depth expert knowledge, e.g., signatures describing anomalies or communication protocol at the application level. Moreover, they might fail to detect the payload anomalies that have long-term dependency relationships. To overcome these limitations and adaptively detect anomalies from the packet payload, we propose a deep learning based framework which consists of two steps. First, a novel feature engineering method is proposed to obtain the block-based features via block sequence extraction and block embedding. The block-based features could encapsulate both the high-dimension information and the underlying sequential information which facilitate the anomaly detection. Second, a neural network is designed to learn the representation of packet payload based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Furthermore, we cast the anomaly detection as a classification problem and stack a Multi-Layer Perception (MLP) on the above representation learning network to detect anomalies. Extensive experimental results on three public datasets indicate that our model could achieve a higher detection rate, while keeping a lower false positive rate compared with five state-of-the-art methods.

eess.SP↗

arXiv2015

Collisional-Radiative Model for the visible spectrum of $W^{26+}$ ions

Plasma diagnostics in magnetic confinement fusion plasmas by using visible spectrum strongly depends on the knowledge of fundamental atomic properties. A detailed collisional-radiative model of W$^{26+}$ ions has been constructed by considering radiative and electron excitation processes, in which the necessary atomic data had been calculated by relativistic configuration interaction method with the implementation of Flexible Atomic Code. The visible spectrum observed at an electron beam ion trap (EBIT) in Shanghai in the range of 332 nm to 392 nm was reproduced by present calculations. Some transition pairs of which the intensity ratio are sensitive to the electron density were selected as potential candidate of plasma diagnostics. Their electron density dependence are theoretically evaluated for the cases of EBIT plasmas and magnetic confinement fusion plasmas.

physics.atom-ph↗