arXiv subjects

Ming Shao

Publications and source records attributed to Ming Shao.

At least 19 recordsLinked to original sources

arXiv2025

A Low Background Beta Detection System using a Time Projection Chamber

In this paper, we present a Time Projection Chamber (TPC) system for low-background beta radiation measurements. The system consists of a TPC with two-dimensional-strip readout Micromegas and an anti-coincidence detector with readout pads for cosmic ray veto. The detector system utilize an AGET-based waveform sampling system for data acquisition. The beta detection capability of the system was verified through experimental test using $^{90}$Sr beta source. Additionally, a dedicated simulation program based on Geant4 was developed to model the entire detection process, including responses to both the beta source and background radiation. Simulation results were compared with experimental data for both beta and background samples, showing good agreements. The simulation samples were utilized to optimize and train classification models for beta and background discrimination. By applying the selected model into test data, the system achieved a background rate of 0.49 $\rm cpm/cm^2$ while retaining more than 55% of $^{90}$Sr beta signals within a 7 cm diameter detection region. Further analysis revealed that approximately 70% of the background originates from environmental gamma radiation, while the remaining contribution mainly comes from intrinsic radioactivity of detector materials, particularly the FR-4 based field cage and readout plane. Based on the knowledge gained from the experiments and simulations, an optimization of the TPC system has been proposed, with simulation predicting a potential reduction of the background rate to 0.0012 $\rm cpm/cm^2$.

physics.ins-det↗

arXiv2025

Optimization of gain uniformity in thermal bonding Micromegas for the PandaX-III experiment

Micro-pattern gas detectors (MPGDs) are widely utilized in physics experiments owing to their excellent spatial resolution and high-rate capabilities. Within the PandaX-III experiment, which aims to investigate neutrinoless double beta decay, Micromegas detectors serve as charge readout devices. High energy resolution is a critical requirement for the readout plane in this context, and gain uniformity significantly impacts the achievable resolution, primarily because of the extended tracks of primary ionization electrons. However, scaling up MPGDs to larger active areas exacerbates the challenge of maintaining gain uniformity, and effectively controlling the uniformity of the avalanche gap is a key factor in the detector manufacturing process via the thermal bonding method. This study demonstrates that optimizing the thermal bonding films specifically at the detector edges can effectively improve the gain uniformity, achieving a gain uniformity of < 5% over the entire 200*200 mm2 active area in a 1 bar Ar/isobutane (96.5/3.5) gas mixture. Additionally, the gain uniformity of approximately 14% was characterized at high pressures of up to 10 bar, revealing promising potential for high resolution measurements in the PandaX-III experiment and other high-pressure applications.

physics.ins-det↗

arXiv2025

Robust Defense Strategies for Multimodal Contrastive Learning: Efficient Fine-tuning Against Backdoor Attacks

The advent of multimodal deep learning models, such as CLIP, has unlocked new frontiers in a wide range of applications, from image-text understanding to classification tasks. However, these models are not safe for adversarial attacks, particularly backdoor attacks, which can subtly manipulate model behavior. Moreover, existing defense methods typically involve training from scratch or fine-tuning using a large dataset without pinpointing the specific labels that are affected. In this study, we introduce an innovative strategy to enhance the robustness of multimodal contrastive learning models against such attacks. In particular, given a poisoned CLIP model, our approach can identify the backdoor trigger and pinpoint the victim samples and labels in an efficient manner. To that end, an image segmentation ``oracle'' is introduced as the supervisor for the output of the poisoned CLIP. We develop two algorithms to rectify the poisoned model: (1) differentiating between CLIP and Oracle's knowledge to identify potential triggers; (2) pinpointing affected labels and victim samples, and curating a compact fine-tuning dataset. With this knowledge, we are allowed to rectify the poisoned CLIP model to negate backdoor effects. Extensive experiments on visual recognition benchmarks demonstrate our strategy is effective in CLIP-based backdoor defense.

cs.CV↗

arXiv2025

Toward Improving fNIRS Classification: A Study on Activation Functions in Deep Neural Architectures

Activation functions are critical to the performance of deep neural networks, particularly in domains such as functional near-infrared spectroscopy (fNIRS), where nonlinearity, low signal-to-noise ratio (SNR), and signal variability poses significant challenges to model accuracy. However, the impact of activation functions on deep learning (DL) performance in the fNIRS domain remains underexplored and lacks systematic investigation in the current literature. This study evaluates a range of conventional and field-specific activation functions for fNIRS classification tasks using multiple deep learning architectures, including the domain-specific fNIRSNet, AbsoluteNet, MDNN, and shallowConvNet (as the baseline), all tested on a single dataset recorded during an auditory task. To ensure fair a comparison, all networks were trained and tested using standardized preprocessing and consistent training parameters. The results show that symmetrical activation functions such as Tanh and the Absolute value function Abs(x) can outperform commonly used functions like the Rectified Linear Unit (ReLU), depending on the architecture. Additionally, a focused analysis of the role of symmetry was conducted using a Modified Absolute Function (MAF), with results further supporting the effectiveness of symmetrical activation functions on performance gains. These findings underscore the importance of selecting proper activation functions that align with the signal characteristics of fNIRS data.

cs.LG↗

arXiv2025

AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing

In recent years, deep learning (DL) approaches have demonstrated promising results in decoding hemodynamic responses captured by functional near-infrared spectroscopy (fNIRS), particularly in the context of brain-computer interface (BCI) applications. This work introduces AbsoluteNet, a novel deep learning architecture designed to classify auditory event-related responses recorded using fNIRS. The proposed network is built upon principles of spatio-temporal convolution and customized activation functions. Our model was compared against several models, namely fNIRSNET, MDNN, DeepConvNet, and ShallowConvNet. The results showed that AbsoluteNet outperforms existing models, reaching 87.0% accuracy, 84.8% sensitivity, and 89.2% specificity in binary classification, surpassing fNIRSNET, the second-best model, by 3.8% in accuracy. These findings underscore the effectiveness of our proposed deep learning model in decoding hemodynamic responses related to auditory processing and highlight the importance of spatio-temporal feature aggregation and customized activation functions to better fit fNIRS dynamics.

cs.LG↗

arXiv2024

Optimizing Neon-based Gas Mixtures for Two-stage Amplification Fast-timing Micromegas Detectors

Working gas components significantly impact the performance of gaseous detectors. A fast-timing Micromegas detector with two-stage amplification is prone to notable deterioration of uniformity when scaled up. This paper presents a simulation study based on Garfield++ that aims to enhance the performance of such detectors by exploring different gas mixtures. The properties of various gas compositions and their impact on detector performance including gain uniformity and time resolution were investigated in the simulation study. The gain uniformity and single-photon time resolution of the detector were evaluated in tests using a multi-channel PICOSEC Micromegas (MM) prototype with different gas mixtures. The experimental results are consistent with the findings of the simulation. Both simulation and experimental results indicate that a higher concentration of neon improves the detector's gain uniformity, while the impact of gas mixtures on time resolution should also be considered as a critical performance indicator. The study presented in this paper offers valuable insights for improving uniformity in large-area PICOSEC MM detectors and optimizing overall performance.

physics.ins-det↗

arXiv2024

Properties of the QCD Matter -- An Experimental Review of Selected Results from RHIC BES Program

In the paper, we discuss the development of the multi-gap resistive plate chamber Time-of-Flight (TOF) technology and the production of the STAR TOF detector in China at the beginning of the 21st century. Then we review recent experimental results from the first beam energy scan program (BES-I) at the Relativistic Heavy Ion Collider (RHIC). Topics cover measurements of collectivity, chirality, criticality, global polarization, strangeness, heavy-flavor, di-lepton and light nuclei productions.

nucl-ex↗

arXiv2024

Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition

Human action video recognition has recently attracted more attention in applications such as video security and sports posture correction. Popular solutions, including graph convolutional networks (GCNs) that model the human skeleton as a spatiotemporal graph, have proven very effective. GCNs-based methods with stacked blocks usually utilize top-layer semantics for classification/annotation purposes. Although the global features learned through the procedure are suitable for the general classification, they have difficulty capturing fine-grained action change across adjacent frames -- decisive factors in sports actions. In this paper, we propose a novel ``Cross-block Fine-grained Semantic Cascade (CFSC)'' module to overcome this challenge. In summary, the proposed CFSC progressively integrates shallow visual knowledge into high-level blocks to allow networks to focus on action details. In particular, the CFSC module utilizes the GCN feature maps produced at different levels, as well as aggregated features from proceeding levels to consolidate fine-grained features. In addition, a dedicated temporal convolution is applied at each level to learn short-term temporal features, which will be carried over from shallow to deep layers to maximize the leverage of low-level details. This cross-block feature aggregation methodology, capable of mitigating the loss of fine-grained information, has resulted in improved performance. Last, FD-7, a new action recognition dataset for fencing sports, was collected and will be made publicly available. Experimental results and empirical analysis on public benchmarks (FSD-10) and self-collected (FD-7) demonstrate the advantage of our CFSC module on learning discriminative patterns for action classification over others.

cs.CV↗

arXiv2024

Sketch3D: Style-Consistent Guidance for Sketch-to-3D Generation

Recently, image-to-3D approaches have achieved significant results with a natural image as input. However, it is not always possible to access these enriched color input samples in practical applications, where only sketches are available. Existing sketch-to-3D researches suffer from limitations in broad applications due to the challenges of lacking color information and multi-view content. To overcome them, this paper proposes a novel generation paradigm Sketch3D to generate realistic 3D assets with shape aligned with the input sketch and color matching the textual description. Concretely, Sketch3D first instantiates the given sketch in the reference image through the shape-preserving generation process. Second, the reference image is leveraged to deduce a coarse 3D Gaussian prior, and multi-view style-consistent guidance images are generated based on the renderings of the 3D Gaussians. Finally, three strategies are designed to optimize 3D Gaussians, i.e., structural optimization via a distribution transfer mechanism, color optimization with a straightforward MSE loss and sketch similarity optimization with a CLIP-based geometric similarity loss. Extensive visual comparisons and quantitative analysis illustrate the advantage of our Sketch3D in generating realistic 3D assets while preserving consistency with the input.

cs.CV↗

arXiv2023

Few-shot Shape Recognition by Learning Deep Shape-aware Features

Traditional shape descriptors have been gradually replaced by convolutional neural networks due to their superior performance in feature extraction and classification. The state-of-the-art methods recognize object shapes via image reconstruction or pixel classification. However , these methods are biased toward texture information and overlook the essential shape descriptions, thus, they fail to generalize to unseen shapes. We are the first to propose a fewshot shape descriptor (FSSD) to recognize object shapes given only one or a few samples. We employ an embedding module for FSSD to extract transformation-invariant shape features. Secondly, we develop a dual attention mechanism to decompose and reconstruct the shape features via learnable shape primitives. In this way, any shape can be formed through a finite set basis, and the learned representation model is highly interpretable and extendable to unseen shapes. Thirdly, we propose a decoding module to include the supervision of shape masks and edges and align the original and reconstructed shape features, enforcing the learned features to be more shape-aware. Lastly, all the proposed modules are assembled into a few-shot shape recognition scheme. Experiments on five datasets show that our FSSD significantly improves the shape classification compared to the state-of-the-art under the few-shot setting.

cs.CV↗

arXiv2023

Studies of nuclear equation of state with the HIRFL-CSR external-target experiment

The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of state of nuclear matter (nEOS) with heavy ion collisions at a few hundreds MeV/u beam energies. Based on Geant 4 packages, the fast simulations of the detector responses to the collision events generated using transport model are conducted. The overall performance of CEE, including spatial resolution of hits, momentum resolution of tracks and particle identification ability has been investigated. Various observables proposed to probe the nEOS, such as the production of light clusters, $\rm t/^3He$ yield ratio, the radial flow, $\pi^{-}/\pi^{+}$ yield ratio and the neutral kaon yields, have been reconstructed. The feasibility of studying nEOS beyond the saturation density via the aforementioned observables to be measured with CEE has been demonstrated.

nucl-ex↗

arXiv2023

A large area, high counting rate micromegas-based neutron detector for BNCT

Beam monitoring and evaluation are very important to boron neutron capture therapy (BNCT), and a variety of detectors have been developed for these applications. However, most of the detectors used in BNCT only have a small detection area, leading to the inconvenience of the full-scale 2-D measurement of the beam. Based on micromegas technology, we designed a neutron detector with large detection area and high counting rate. This detector has a detection area of 288 mm multiples 288 mm and can measure thermal, epithermal, and fast neutrons with different detector settings. The BNCT experiments demonstrated that this detector has a very good 2-D imaging performance for the thermal, epithermal, fast neutron and gamma components, a highest counting rate of 94 kHz/channel, and a good linearity response to the beam power. Additionally, the flux fraction of each component can be calculated based on the measurement results. The Am-Be neutron source experiment indicates that this detector has a spatial resolution of approximately 1.4 mm, meeting the requirements of applications in BNCT. It is evident that this micromegas-based neutron detector with a large area and high counting rate capability has great development prospects in BNCT beam monitoring and evaluation applications.

physics.ins-det↗

arXiv2023

A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis

Deep learning approaches require collection of data on many different input features or variables for accurate model training and prediction. Since data collection on input features could be costly, it is crucial to reduce the cost by selecting a subset of features and developing a budget-constrained model (BCM). In this paper, we introduce an approach to eliminating less important features for big data analysis using Deep Neural Networks (DNNs). Once a DNN model has been developed, we identify the weak links and weak neurons, and remove some input features to bring the model cost within a given budget. The experimental results show our approach is feasible and supports user selection of a suitable BCM within a given budget.

cs.LG↗

arXiv2023

A novel fast response and radiation-resistant scintillator detector for beam loss monitor

At high luminosity areas, beam loss monitor with fast response and high radiation resistance is crucial for accelerator operation. In this article, we report the design and test results of a fast response and radiation-resistant scintillator detector as the beam loss monitor for high luminosity colliders, especially at low energy regions such as RFQ. The detector consists of a 2 cm*2 cm 0.5 cm LYSO crystal readout by a 6 mm*6 mm Silicon photomultiplier. Test results from various radioactive sources show that the detector has good sensitivity to photons from tens of keV to several MeV with good linearity and energy resolution (23% for 60 keV {\gamma}-ray). For the field test, two such detectors are installed outside of the vacuum chamber shell of an 800 MeV electron storage ring. The details of the test and results are introduced.

physics.ins-det↗

arXiv2022

Unsupervised Visual Defect Detection with Score-Based Generative Model

Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs). Our work is inspired by the fact that with noise injected into the original image, the defects may be changed into normal cases in the denoising process (i.e., reconstruction). First, based on the assumption that the anomalous data lie in the low probability density region of the normal data distribution, we explain a common phenomenon that occurs when reconstruction-based approaches are applied to VDD: normal pixels also change during the reconstruction process. Second, due to the differences in normal pixels between the reconstructed and original images, a time-dependent gradient value (i.e., score) of normal data distribution is utilized as a metric, rather than reconstruction loss, to gauge the defects. Third, a novel $T$ scales approach is developed to dramatically reduce the required number of iterations, accelerating the inference process. These practices allow our model to generalize VDD in an unsupervised manner while maintaining reasonably good performance. We evaluate our method on several datasets to demonstrate its effectiveness.

cs.CV↗

arXiv2022

Study of Hybrid MicroPattern Gas Detector with CsI Photocathode For Super Tau-Charm Factory RICH

Super tau-Charm facility(STCF) is a future electron-position collider operating at tau-Charm energy region aimed to study hadron structure and spectroscopy. The baseline design of the STCF barrel particle identification(PID) detector in the momentum range up to 2 GeV/c is provided by a Ring Imaging Cherenkov Counter(RICH). The architecture of the RICH is an approximately focusing design with liquid perfluorohexane sealed in the quartz container as radiator and a hybrid combination of a CsI coated layers of THGEMs and a Micromegas as the photo-electron detector. A 16*16 cm^2 prototype with quartz radiator has been built and tested at DESY. It was stably operated with 10^5 effective gain. In this paper, the design, the performance, the reconstruction algorithm and the systematic error for single photon electron angular resolution in the aspect of RICH detectors are discusses.

physics.ins-det↗

arXiv2021

Cost-sensitive Selection of Variables by Ensemble of Model Sequences

Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been made to incorporate costs in combining and selecting measures. However, existing studies either do not strictly enforce a budget constraint, or are not the `most' cost effective. With a focus on classification problem, we propose a computationally efficient approach that could find a near optimal model under a given budget by exploring the most `promising' part of the solution space. Instead of outputting a single model, we produce a model schedule -- a list of models, sorted by model costs and expected predictive accuracy. This could be used to choose the model with the best predictive accuracy under a given budget, or to trade off between the budget and the predictive accuracy. Experiments on some benchmark datasets show that our approach compares favorably to competing methods.

stat.ME↗

arXiv2021

The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces

Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a large-scale, multi-track visual kinship recognition evaluation. For the fifth edition of RFIW, we continue to attract scholars, bring together professionals, publish new work, and discuss prospects. In this paper, we summarize submissions for the three tasks of this year's RFIW: specifically, we review the results for kinship verification, tri-subject verification, and family member search and retrieval. We look at the RFIW problem, share current efforts, and make recommendations for promising future directions.

cs.CV↗