arXiv subjects

Yuxuan Zhu

Publications and source records attributed to Yuxuan Zhu.

48 records · Page 3Linked to original sources

arXiv2024

Ground calibration and network of the first CATCH pathfinder

The Chasing All Transients Constellation Hunters (CATCH) space mission is focused on exploring the dynamic universe via X-ray follow-up observations of various transients. The first pathfinder of the CATCH mission, CATCH-1, was launched on June 22, 2024, alongside the Space-based multiband astronomical Variable Objects Monitor (SVOM) mission. CATCH-1 is equipped with narrow-field optimized Micro Pore Optics (MPOs) featuring a large effective area and incorporates four Silicon Drift Detectors (SDDs) in its focal plane. This paper presents the system calibration results conducted before the satellite integration. Utilizing the data on the performance of the mirror and detectors obtained through the system calibration, combined with simulated data, the ground calibration database can be established. Measuring the relative positions of the mirror and detector system, which were adjusted during system calibration, allows for accurate installation of the entire satellite. Furthermore, the paper outlines the operational workflow of the ground network post-satellite launch.

astro-ph.IM↗

arXiv2024

On the Robustness of Graph Reduction Against GNN Backdoor

Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be susceptible to backdoor poisoning attacks, which pose serious threats to real-world applications. Meanwhile, graph reduction techniques, including coarsening and sparsification, which have long been employed to improve the scalability of large graph computational tasks, have recently emerged as effective methods for accelerating GNN training on large-scale graphs. However, the current development and deployment of graph reduction techniques for large graphs overlook the potential risks of data poisoning attacks against GNNs. It is not yet clear how graph reduction interacts with existing backdoor attacks. This paper conducts a thorough examination of the robustness of graph reduction methods in scalable GNN training in the presence of state-of-the-art backdoor attacks. We performed a comprehensive robustness analysis across six coarsening methods and six sparsification methods for graph reduction, under three GNN backdoor attacks against three GNN architectures. Our findings indicate that the effectiveness of graph reduction methods in mitigating attack success rates varies significantly, with some methods even exacerbating the attacks. Through detailed analyses of triggers and poisoned nodes, we interpret our findings and enhance our understanding of how graph reduction influences robustness against backdoor attacks. These results highlight the critical need for incorporating robustness considerations in graph reduction for GNN training, ensuring that enhancements in computational efficiency do not compromise the security of GNN systems.

cs.LG↗

arXiv2024

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation

We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contributions of different features, when their prediction changes are similar after perturbation. To enhance FAMs' discriminative power, we introduce Feature Attribution with Necessity and Sufficiency (FANS), which find a neighborhood of the input such that perturbing samples within this neighborhood have a high Probability of being Necessity and Sufficiency (PNS) cause for the change in predictions, and use this PNS as the importance of the feature. Specifically, FANS compute this PNS via a heuristic strategy for estimating the neighborhood and a perturbation test involving two stages (factual and interventional) for counterfactual reasoning. To generate counterfactual samples, we use a resampling-based approach on the observed samples to approximate the required conditional distribution. We demonstrate that FANS outperforms existing attribution methods on six benchmarks. Please refer to the source code via \url{https://github.com/DMIRLAB-Group/FANS}.

cs.LG↗

arXiv2024

FedTrans: Efficient Federated Learning via Multi-Model Transformation

Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notoriously challenging due to the heterogeneity of client data, device capabilities, and the massive scale of clients, making individualized model exploration prohibitively expensive. State-of-the-art FL solutions personalize a globally trained model or concurrently train multiple models, but they often incur suboptimal model accuracy and huge training costs. In this paper, we introduce FedTrans, a multi-model FL training framework that automatically produces and trains high-accuracy, hardware-compatible models for individual clients at scale. FedTrans begins with a basic global model, identifies accuracy bottlenecks in model architectures during training, and then employs model transformation to derive new models for heterogeneous clients on the fly. It judiciously assigns models to individual clients while performing soft aggregation on multi-model updates to minimize total training costs. Our evaluations using realistic settings show that FedTrans improves individual client model accuracy by 14% - 72% while slashing training costs by 1.6X - 20X over state-of-the-art solutions.

cs.LG↗

arXiv2024

Noiseless linear amplification-based quantum Ziv-Zakai bound for phase estimation and its Heisenberg error limits in noisy scenarios

In this work, we address the central problem about how to effectively find the available precision limit of unknown parameters. In the framework of the quantum Ziv-Zakai bound (QZZB), we employ noiseless linear amplification (NLA)techniques to an initial coherent state (CS) as the probe state, and focus on whether the phase estimation performance is improved significantly in noisy scenarios, involving the photon-loss and phase-diffusion cases. More importantly, we also obtain two kinds of Heisenberg error limits of the QZZB with the NLA-based CS in these noisy scenarios, making comparisons with both the Margolus-Levitin (ML) type bound and the Mandelstam-Tamm (MT) type bound. Our analytical results show that in cases of photon loss and phase diffusion, the phase estimation performance of the QZZB can be improved remarkably by increasing the NLA gain factor. Particularly, the improvement is more pronounced with severe photon losses. Furthermore in minimal photon losses, our Heisenberg error limit shows better compactness than the cases of the ML-type and MT-type bounds. Our findings will provide an useful guidance for accomplishing more complex quantum information processing tasks.

quant-ph↗

arXiv2024

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted \emph{adversarial examples}, which are generated through either well-conceived $\mathcal{L}_p$-norm restricted or unrestricted attacks. Nevertheless, the majority of those approaches assume that adversaries can modify any features as they wish, and neglect the causal generating process of the data, which is unreasonable and unpractical. For instance, a modification in income would inevitably impact features like the debt-to-income ratio within a banking system. By considering the underappreciated causal generating process, first, we pinpoint the source of the vulnerability of DNNs via the lens of causality, then give theoretical results to answer \emph{where to attack}. Second, considering the consequences of the attack interventions on the current state of the examples to generate more realistic adversarial examples, we propose CADE, a framework that can generate \textbf{C}ounterfactual \textbf{AD}versarial \textbf{E}xamples to answer \emph{how to attack}. The empirical results demonstrate CADE's effectiveness, as evidenced by its competitive performance across diverse attack scenarios, including white-box, transfer-based, and random intervention attacks.

cs.LG↗

arXiv2023

A Survey on Explainable Anomaly Detection

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.

cs.LG↗

arXiv2023

In-orbit performance of LE onboard Insight-HXMT in the first 5 years

Purpose: The Low-Energy X-ray telescope (LE) is a main instrument of the Insight-HXMT mission and consists of 96 Swept Charge Devices (SCD) covering the 1-10 keV energy band. The energy gain and resolution are continuously calibrated by analysing Cassiopeia A (Cas A) and blank sky data, while the effective areas are also calibrated with the observations of the Crab Nebula. In this paper, we present the evolution of the in-orbit performances of LE in the first 5 years since launch. Methods: The Insight-HXMT Data Analysis Software package (HXMTDAS) is utilized to extract the spectra of Cas A, blank sky, and Crab Nebula using different Good Time Interval (GTI) selections. We fit a model with a power-law continuum and several Gaussian lines to different ranges of Cas A and blank sky spectra to get peak energies of their lines through xspec. After updating the energy gain calibration in CALibration DataBase (CALDB), we rerun the Cas A data to obtain the energy resolution. An empirical function is used to modify the simulated effective areas so that the background-subtracted spectrum of the Crab Nebula can best match the standard model of the Crab Nebula. Results: The energy gain, resolution, and effective areas are calibrated every month. The corresponding calibration results are duly updated in CALDB, which can be downloaded and used for the analysis of Insight-HXMT data. Simultaneous observations with NuSTAR and NICER can also be used to verify our derived results. Conclusion: LE is a well calibrated X-ray telescope working in 1-10 keV band. The uncertainty of LE gain is less than 20 eV in 2-9 keV band and the uncertainty of LE resolution is less than 15eV. The systematic errors of LE, compared to the model of the Crab Nebula, are lower than 1.5% in 1-10 keV.

astro-ph.IM↗

arXiv2022

The 100-m X-ray Test Facility at IHEP

The 100-m X-ray Test Facility of the Institute of High Energy Physics (IHEP) was initially proposed in 2012 for the test and calibration of the X-ray detectors of the Hard X-ray Modulation Telescope (HXMT) with the capability to support future X-ray missions. The large instrument chamber connected with a long vacuum tube can accommodate the X-ray mirror, focal plane detector and other instruments. The X-ray sources are installed at the other end of the vacuum tube with a distance of 105 m, which can provide an almost parallel X-ray beam covering 0.2$\sim$60 keV energy band. The X-ray mirror modules of the Einstein Probe (EP) and the enhanced X-ray Timing and Polarimetry mission (eXTP) and payload of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) have been tested and calibrated with this facility. It has been also used to characterize the focal plane camera and aluminum filter used on the Einstein Probe. In this paper, we will introduce the overall configuration and capability of the facility, and give a brief introduction of some calibration results performed with this facility.

astro-ph.IM↗

arXiv2021

Performance of a focal plane detector for soft X-ray imaging spectroscopy based on back-illuminated sCMOS

Spectroscopy focusing array (SFA) and Polarimetry focusing array (PFA) are the two major payloads of enhanced X-ray Timing and Polarimetry mission (eXTP). Nested Wolter-\RNum{1} X-ray mirror module is implemented in SFA and PFA to achive high effective area. When evaluating the properties of the mirror module, the alignment of the optical axis of the X-ray mirror module and a quasi-parallel X-ray beam is a prerequisite to ensure the accuracy of the results. Hence, to assist the alignment of the X-ray mirror module, an X-ray focal plane detector is designed based on the back-illuminated scientific Complementary Metal-Oxide-Semiconductor Transistor (sCMOS) sensor GSENSE6060BSI, one of the largest detection areas, is produced by \textit{Gpixel Inc}. Then the characteristics of readout noise, dark current, and split-pixel event properties of the detector are studied with the self-developed multi-target fluorescence X-ray source in a 100 m long X-ray test facility. The energy calibration is carried out with the single-pixel event and the energy non-linearity of the detector is also obtained. Eventually, the simulation of the eXTP mirror module based on the optical model is conducted and the alignment test of the Wolter-\RNum{1} X-ray mirror module designed for \textit{EP/FXT} (Einstein Probe/Follow-up X-ray Telescope) with "Burkert test" method is shown.

physics.ins-det↗

arXiv2021

Design and test of a portable Gamma-Ray Burst simulator for GECAM

The main scientific goal of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is to monitor various types of Gamma-Ray Bursts (GRB) originated from merger of binary compact stars, which could also produce gravitational wave, and collapse of massive stars. In order to study the response of GECAM Gamma-Ray Detectors (GRDs) to high-energy bursts and test the in-flight trigger and localization software of GECAM before the launch, a portable GRB simulator device is designed and implemented based on grid controlled X-ray tube (GCXT) and direct digital synthesis (DDS) technologies. The design of this GRB simulator which modulates X-ray flux powered by high voltage up to 20 kV is demonstrated, and the time jitter (FWHM) of the device is about 0.9 $μ$s. Before the launch in December, 2020, both two GECAM satellites were irradiated by different types of GRBs (including short and long bursts in duration) generated by this GRB simulator. The light curves detected with GECAM/GRDs are consistent with the programmed input functions within statistical uncertainties, indicating the good performance of both the GRDs and the GRB simulator.

physics.ins-det↗

arXiv2014

Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks

Determination of boiling range of xylene mixed in PX device is currently a crucial topic in the practical applications because of the recent disputes of PX project in China. In our study, instead of determining the boiling range of xylene mixed by traditional approach in laboratory or industry, we successfully established two Artificial Neural Networks (ANNs) models to determine the initial boiling point and final boiling point respectively. Results show that the Multilayer Feedforward Neural Networks (MLFN) model with 7 nodes (MLFN-7) is the best model to determine the initial boiling point of xylene mixed, with the RMS error 0.18; while the MLFN model with 4 nodes (MLFN-4) is the best model to determine the final boiling point of xylene mixed, with the RMS error 0.75. The training and testing processes both indicate that the models we developed are robust and precise. Our research can effectively avoid the damage of the PX device to human body and environment.

cs.CE↗