arXiv subjects

Mo Zhang

Publications and source records attributed to Mo Zhang.

16 recordsLinked to original sources

arXiv2026

SVOM Science User Support Services at Chinese Science Center

The Chinese-French SVOM (Space-based Multi-band Astronomical Variable Objects Monitor) mission is dedicated to the study of gamma-ray bursts (GRBs) from the distant universe. A key component of the SVOM Chinese Ground Segment, the Science User Support Services (SUSS) provides comprehensive support for the mission's scientific operations. SUSS consists of two integral pillars: a suite of specialized software tools that automate key workflows, and a dedicated User Support Team that delivers expert-led, human services. These human-delivered services include operational coordination across telescope networks, direct technical assistance to astronomers, user training, and proactive problem-solving throughout the observation lifecycle. This paper focuses on the organization of SVOM scientific operations and the role of SUSS in facilitating these tasks. We provide a detailed description of the SUSS software architecture and its functionalities, encompassing the General Platform, the Burst Advocate (BA) support tools for GRB counterpart identification, the Target of Opportunity (ToO) support tools, and the General Program (GP) support tools. The structure and services provided by the user support team at the Chinese Science Center (CSC) are also elaborated. Furthermore, we evaluate the performance of SUSS during its first operational year, assessing its effectiveness in fulfilling user requirements. The evaluation offers valuable insights to guide future user support strategies and software enhancements, ultimately enabling better service for the SVOM scientific community.

astro-ph.IM↗

arXiv2026

SVOM/VT: Overview of data processing and GRB identifications with X-band data

VT (the Visible Telescope) is an optical telescope onboard the SVOM (Space-based Multi-band Astronomical Variable Objects Monitor) mission, specifically designed to detect optical counterparts of gamma-ray bursts (GRBs), study their afterglows, and select high-redshift candidates. It performs rapid follow-up observations simultaneously in two channels either via autonomous platform slewing or Target of Opportunity (ToO) observations. The science images acquired by VT and transmitted via the X-band downlink system are designated as VT X-band data. This paper provides an overview of GRB optical afterglow identifications with VT and describes the ground-based processing pipeline for VT X-band data, including preprocessing, astrometric calibration, and photometry. Up to 2025 December 3, VT has followed up 111 GRBs triggered by SVOM or external missions. The overall detection rate of optical counterparts is approximately 75%. Specifically, for bursts detected by SVOM/ECLAIRs, the detection rate is 77% when observed by VT within 30 minutes after the burst. A slightly higher detection rate of 81% is achieved for GRBs triggered by external missions through rapid ToO observations with a mid-time of less than 3 hours.

astro-ph.HE↗

arXiv2026

SVOM/VT: On-ground processing of VT-VHF data

The VT--VHF data comprise three types of onboard-processed data results generated from four sequential observational sequences and transmitted to the ground via a Very High Frequency (VHF) downlink. On the ground, these data are processed by three successive pipelines: the pre-processing pipeline, the VT--VHF data processing pipeline (VVPP), and the VT afterglow candidate pipeline (VTAC). These pipelines perform packet decoding, astrometric and photometric calibration, and afterglow candidate identification, respectively. This paper describes the architecture and operational implementation of the VT--VHF ground processing system and assesses its end-to-end performance using the first year of SVOM operations. These data enable rapid identification of GRB optical counterparts. Early detections, while the source is still optically bright, facilitate spectroscopic redshift measurements. Dual-band colors provide preliminary redshift constraints and help identify high-redshift candidates, whereas non-detections in both bands may indicate very high redshift, significant extinction, or intrinsically dark bursts. In-orbit operations show that the VT--VHF ground processing system successfully identifies optical afterglow candidates for a significant fraction of ECLAIRs triggers with available VT--VHF data, demonstrating its robustness and readiness.

astro-ph.IM↗

arXiv2026

SVOM/C-GFT: Instrumentation and Performances on the SVOM Alerts

The Chinese Ground Follow-up Telescope (C-GFT) is an optical facility upgraded to support the Space Variable Objects Monitor mission (\textit{SVOM}). Located at the Jilin Observation Station, it is capable of rapidly identifying and monitoring the optical counterparts of Gamma-Ray Bursts (GRBs). The 1.2-m telescope is equipped with two switchable focal-plane instruments: the prime-focus wide-field LATIOS camera and the Cassegrain-focus three-channel CATCH camera. In this paper, we present a system overview, including the observatory, the telescope, the instrumentation, the automated operational framework managed by the Operations Center, and the data processing pipelines. We also report the performance results obtained during over one year of \textit{SVOM}'s post-launch operations. The results demonstrate that the system meets its design specifications and delivers robust observational and operational performance.

astro-ph.IM↗

arXiv2025

Multiwavelength Observations of the Apparently Non-repeating FRB 20250316A

The physical origin of fast radio bursts (FRBs) remains uncertain. Although multiwavelength observations have been widely conducted, only Galactic FRB~20200428D is associated with an X-ray burst from the magnetar SGR J1935+2154. Here, we present multiwavelength follow-up observations of the nearby bright FRB~20250316A, including the Five-hundred-meter Aperture Spherical radio Telescope (FAST), Einstein Probe (EP) X-ray mission, Chandra X-ray Observatory, Wide Field Survey Telescope (WFST) and Space Variable Object Monitor/Visible Telescope (SVOM/VT). The 13.08-hour FAST follow-up campaign without pulse detection requires an energy distribution flatter than those of well-known repeating FRBs, suggesting that this burst is likely a one-off event. A prompt EP follow-up and multi-epoch observational campaign totaling $>$ 100 ks led to the detection of an X-ray source within the angular resolution of its Follow-up X-ray Telescope (FXT, $10^{\prime\prime}$). A subsequent Chandra observation revealed this source to be offset by $7^{\prime\prime}$ from the FRB position, and established a 0.5-10 keV flux upper limit of $7.6\times 10^{-15}$ $\rm erg\,cm^{-2}\,s^{-1}$ at the FRB position, corresponding to $\sim 10^{39}$ $\rm erg\,s^{-1}$ at the 40 Mpc distance of the host galaxy NGC~4141. These results set one of the most stringent limits on X-ray emission from a non-repeating FRB, disfavoring ultra-luminous X-ray sources (ULXs) as counterparts of apparently one-off FRBs and offering critical insights into afterglow models. Our study suggests that an arcsecond localization of both the FRB and its potential X-ray counterpart is essential for exploring the X-ray counterpart of an FRB.

astro-ph.HE↗

arXiv2025

Channel-Independent Federated Traffic Prediction

In recent years, traffic prediction has achieved remarkable success and has become an integral component of intelligent transportation systems. However, traffic data is typically distributed among multiple data owners, and privacy constraints prevent the direct utilization of these isolated datasets for traffic prediction. Most existing federated traffic prediction methods focus on designing communication mechanisms that allow models to leverage information from other clients in order to improve prediction accuracy. Unfortunately, such approaches often incur substantial communication overhead, and the resulting transmission delays significantly slow down the training process. As the volume of traffic data continues to grow, this issue becomes increasingly critical, making the resource consumption of current methods unsustainable. To address this challenge, we propose a novel variable relationship modeling paradigm for federated traffic prediction, termed the Channel-Independent Paradigm(CIP). Unlike traditional approaches, CIP eliminates the need for inter-client communication by enabling each node to perform efficient and accurate predictions using only local information. Based on the CIP, we further develop Fed-CI, an efficient federated learning framework, allowing each client to process its own data independently while effectively mitigating the information loss caused by the lack of direct data sharing among clients. Fed-CI significantly reduces communication overhead, accelerates the training process, and achieves state-of-the-art performance while complying with privacy regulations. Extensive experiments on multiple real-world datasets demonstrate that Fed-CI consistently outperforms existing methods across all datasets and federated settings. It achieves improvements of 8%, 14%, and 16% in RMSE, MAE, and MAPE, respectively, while also substantially reducing communication costs.

cs.LG↗

arXiv2023

Safeguarding Crowdsourcing Surveys from ChatGPT with Prompt Injection

ChatGPT and other large language models (LLMs) have proven useful in crowdsourcing tasks, where they can effectively annotate machine learning training data. However, this means that they also have the potential for misuse, specifically to automatically answer surveys. LLMs can potentially circumvent quality assurance measures, thereby threatening the integrity of methodologies that rely on crowdsourcing surveys. In this paper, we propose a mechanism to detect LLM-generated responses to surveys. The mechanism uses "prompt injection", such as directions that can mislead LLMs into giving predictable responses. We evaluate our technique against a range of question scenarios, types, and positions, and find that it can reliably detect LLM-generated responses with more than 93% effectiveness. We also provide an open-source software to help survey designers use our technique to detect LLM responses. Our work is a step in ensuring that survey methodologies remain rigorous vis-a-vis LLMs.

cs.HC↗

arXiv2021

BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation

Blood vessel segmentation is crucial for many diagnostic and research applications. In recent years, CNN-based models have leaded to breakthroughs in the task of segmentation, however, such methods usually lose high-frequency information like object boundaries and subtle structures, which are vital to vessel segmentation. To tackle this issue, we propose Boundary Enhancement and Feature Denoising (BEFD) module to facilitate the network ability of extracting boundary information in semantic segmentation, which can be integrated into arbitrary encoder-decoder architecture in an end-to-end way. By introducing Sobel edge detector, the network is able to acquire additional edge prior, thus enhancing boundary in an unsupervised manner for medical image segmentation. In addition, we also utilize a denoising block to reduce the noise hidden in the low-level features. Experimental results on retinal vessel dataset and angiocarpy dataset demonstrate the superior performance of the new BEFD module.

eess.IV↗

arXiv2020

MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis

Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. By aggregating features at different scales, MS-GWNN can encode the multi-scale contextual interactions in the whole pathological slide. Experimental results on two public datasets demonstrate the superiority of the proposed method. Moreover, through ablation studies, we find that multi-scale analysis has a significant impact on the accuracy of cancer diagnosis.

cs.CV↗

arXiv2019

Multi-label Detection and Classification of Red Blood Cells in Microscopic Images

Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containing congregating cells) is more challenging, as separation of individual cells can be difficult (e.g. touching cells) or even impossible (e.g. overlapping cells). As multi-instance images are common in analyzing Red Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and implement a multi-instance cell detection and classification framework to address this challenge. The framework firstly trains a region proposal model based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of regions potentially containing single or multiple cells from input microscopic images, which are extracted as image patches. High-level image features are then calculated from image patches through a pre-trained Convolutional Neural Network (CNN) with ResNet-50 structure. Using these image features inputs, six networks are then trained to make multi-label prediction of whether a given patch contains cells belonging to a specific cell type. As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples. Finally, for the purpose of SCD testing, we train another machine learning classifier to predict whether the given image patch contains abnormal cell type based on outputs from the six networks. Testing result of the proposed framework shows that it can achieve good performance in automatic cell detection and classification.

eess.IV↗

arXiv2019

PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation

Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still challenging. The classic U-net has not achieved satisfactory results on this task, because it mixes the information of different scales that affect each other, which limits the segmentation accuracy of the model. To solve this problem, we propose a progressive growing U-net (PGU-net+) model, which uses two paradigms to extract image features at different scales in a more independent way. First, we add residual modules between different scales of U-net, which enforces the model to learn the approximate shape of the annotation in the coarser scale, and to learn the residual between the annotation and the approximate shape in the finer scale. Second, we start to train the model with the coarsest part and then progressively add finer part to the training until the full model is included. When we train a finer part, we will reduce the learning rate of the previous coarser part, which further ensures that the model independently extracts information from different scales. We conduct several comparative experiments on the Herlev dataset. The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.

eess.IV↗

arXiv2019

Prototype VOEvent Network Systems based on VTP and XMPP for the SVOM Chinese Science Center

We present the current progress of design and build of two prototype VOEvent network systems for the SVOM Chinese Science Center. One is based on VTP which is compatible with the global VOEvent network, the other is based on XMPP which enables cross-platform messaging and information sharing among human users. We also present a demonstration of VOEvent controlled follow-up observation, including triggering, observational data transferring, as well as other procedures.

astro-ph.IM↗

arXiv2019

ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning

Extracting multi-scale information is key to semantic segmentation. However, the classic convolutional neural networks (CNNs) encounter difficulties in achieving multi-scale information extraction: expanding convolutional kernel incurs the high computational cost and using maximum pooling sacrifices image information. The recently developed dilated convolution solves these problems, but with the limitation that the dilation rates are fixed and therefore the receptive field cannot fit for all objects with different sizes in the image. We propose an adaptivescale convolutional neural network (ASCNet), which introduces a 3-layer convolution structure in the end-to-end training, to adaptively learn an appropriate dilation rate for each pixel in the image. Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale. We compare the segmentation results using the classic CNN, the dilated CNN and the proposed ASCNet on two types of medical images (The Herlev dataset and SCD RBC dataset). The experimental results show that ASCNet achieves the highest accuracy. Moreover, the automatically generated dilation rates are positively correlated to the sizes of the objects, confirming the effectiveness of the proposed method.

cs.CV↗

arXiv2018

Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification.

cs.CV↗

arXiv2017

Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net

Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the deep U-Net structure with deformable convolution layers. The U-Net architecture for deep learning has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution layer enables the free form deformation of the feature learning process, thus makes the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for segmentation and classification, comparing with original U-Net structure.

q-bio.CB↗

arXiv2014

Simulation of Ultra-Long Wavelength interferometer in the Earth orbit and on the lunar surface

We present simulations for interferometer arrays in Earth orbit and on the lunar surface to guide the design and optimization of space-based Ultra-Long Wavelength missions, such as those of China's Chang'E program. We choose parameters and present simulations using simulated data to identify inter-dependencies and constraints on science and engineering parameters. A regolith model is created for the lunar surface array simulation, the results show that the lunar regolith will have an undesirable effect on the observation. We estimate data transmission requirement, calculate sensitivities for both cases, and discuss the trade-off between brightness temperature sensitivity and angular resolution for the Earth orbit array case.

astro-ph.IM↗