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Zhenwei Yang

Publications and source records attributed to Zhenwei Yang.

17 recordsLinked to original sources
arXiv2026

Absolute scintillator light yield correction for SiPIN readout via Transfer Matrix Method and Geant4 optical simulation

Precise measurement of the absolute light yield (LY) of scintillators has long been limited by systematic effects inherent in realistic readout geometries. Large-angle incidence, multiple reflections inside the optical housing, and refractive-index mismatch at the coupling interface all introduce biases that cannot be removed by a simple conversion based on the detector's nominal quantum efficiency. To address this problem, we present a correction method that combines the Transfer Matrix Method (TMM) with Geant4 optical Monte Carlo simulation. A wave-optics model of the SiPIN surface thin-film stack is used to extract the angle- and wavelength-dependent single-hit detection probability $p_{\mathrm{det}}(\lambda,\theta)$, which is then dynamically coupled into the macroscopic photon transport simulation, achieving a full-chain integration of the microscopic interface optical response with macroscopic geometric light collection. We demonstrate the method using a GAGG:Ce crystal as the test sample. Two types of optical housings -- a high-absorption Absorber and a high-reflection Reflector -- are each combined with air and optical-grease coupling, forming four independent configurations whose overall photon-to-signal conversion efficiencies $\alpha_{\mathrm{SiPIN}}$ span more than a factor of three. Despite the very different optical boundaries, the intrinsic light yields derived from the four configurations show excellent mutual consistency (coefficient of variation $= 1.8\%$). The measured intrinsic light yield of GAGG:Ce is $LY_{\mathrm{int}} = (5.63 \pm 0.10_{\mathrm{spread}} \pm 0.16_{\mathrm{syst}}) \times 10^{4}~\mathrm{ph/MeV}$. The correction framework effectively decouples the systematic influence of complex geometry and interface optics from photon detection, providing a general-purpose scheme for high-precision, traceable scintillator characterization.

hep-ex↗
arXiv2025

End-to-End 3-D Spatiotemporal Perception with Multimodal Fusion and V2X Collaboration

Multiview cooperative perception and multimodal fusion are essential for reliable 3-D spatiotemporal understanding in autonomous driving, especially in cases with occlusions, limited viewpoints, and communication delays in vehicle-to-everything (V2X) scenarios. In this paper, Cross-modal End-to-End Tracking for V2X (XET-V2X), a multimodal fused end-to-end tracking framework for V2X collaboration that unifies multiview multimodal sensing within a shared spatiotemporal representation, is proposed. To efficiently align heterogeneous viewpoints and modalities, XET-V2X introduces a dual-layer spatial cross-attention module based on multiscale deformable attention. Multiview image features are aggregated to enhance semantic consistency, followed by point cloud fusion guided by the updated spatial queries, enabling effective cross-modal interaction while reducing computational overhead. Experiments based on the real-world V2X Sequential Perception Dataset (V2X-Seq-SPD) dataset and two simulated V2X-Sim-derived subsets, namely the vehicle-to-vehicle (V2X-Sim-V2V) and vehicle-to-infrastructure (V2X-Sim-V2I) subsets, demonstrate consistent improvements in detection and tracking performance under varying communication delays, with XET-V2X achieving up to 15-20% relative gains in mean average precision (mAP) and average multi-object tracking accuracy (AMOTA) over single-view or single-modal baselines, while also outperforming representative tracking-by-detection cooperative perception methods.

cs.CV↗
arXiv2025

Time-dependent Predictive Accuracy Metrics in the Context of Interval Censoring and Competing Risks

Evaluating the performance of a prediction model is a common task in medical statistics. Standard accuracy metrics require the observation of the true outcomes. This is typically not possible in the setting with time-to-event outcomes due to censoring. Interval censoring, the presence of time-varying covariates, and competing risks present additional challenges in obtaining those accuracy metrics. In this study, we propose two methods to deal with interval censoring in a time-varying competing risk setting: a model-based approach and the inverse probability of censoring weighting (IPCW) approach, focusing on three key time-dependent metrics: area under the receiver-operating characteristic curve (AUC), Brier score, and expected predictive cross-entropy (EPCE). The evaluation is conducted over a medically relevant time interval of interest, $[t, \Delta t)$. The model-based approach includes all subjects in the risk set, using their predicted risks to contribute to the accuracy metrics. In contrast, the IPCW approach only considers the subset of subjects who are known to be event-free or experience the event within the interval of interest. we performed a simulation study to compare the performance of the two approaches with regard to the three metrics.

stat.ME↗
arXiv2024

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation

Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-vehicle perception systems encounter limitations, stemming from incomplete perception due to object occlusion and inherent blind spots, cooperative perception systems present their own challenges in terms of sensor calibration precision and positioning accuracy. To address these issues, we introduce LET-VIC, a LiDAR-based End-to-End Tracking framework for Vehicle-Infrastructure Cooperation (VIC). First, we employ Temporal Self-Attention and VIC Cross-Attention modules to effectively integrate temporal and spatial information from both vehicle and infrastructure perspectives. Then, we develop a novel Calibration Error Compensation (CEC) module to mitigate sensor misalignment issues and facilitate accurate feature alignment. Experiments on the V2X-Seq-SPD dataset demonstrate that LET-VIC significantly outperforms baseline models. Compared to LET-V, LET-VIC achieves +15.0% improvement in mAP and a +17.3% improvement in AMOTA. Furthermore, LET-VIC surpasses representative Tracking by Detection models, including V2VNet, FFNet, and PointPillars, with at least a +13.7% improvement in mAP and a +13.1% improvement in AMOTA without considering communication delays, showcasing its robust detection and tracking performance. The experiments demonstrate that the integration of multi-view perspectives, temporal sequences, or CEC in end-to-end training significantly improves both detection and tracking performance. All code will be open-sourced.

cs.CV↗
arXiv2024

A Bayesian Joint Modelling for Misclassified Interval-censoring and Competing Risks

In active surveillance of prostate cancer, cancer progression is interval-censored and the examination to detect progression is subject to misclassification, usually false negatives. Meanwhile, patients may initiate early treatment before progression detection, constituting a competing risk. We developed the Misclassification-Corrected Interval-censored Cause-specific Joint Model (MCICJM) to estimate the association between longitudinal biomarkers and cancer progression in this setting. The sensitivity of the examination is considered in the likelihood of this model via a parameter that may be set to a specific value if the sensitivity is known, or for which a prior distribution can be specified if the sensitivity is unknown. Our simulation results show that misspecification of the sensitivity parameter or ignoring it entirely impacts the model parameters, especially the parameter uncertainty and the baseline hazards. Moreover, specification of a prior distribution for the sensitivity parameter may reduce the risk of misspecification in settings where the exact sensitivity is unknown, but may cause identifiability issues. Thus, imposing restrictions on the baseline hazards is recommended. A trade-off between modelling with a sensitivity constant at the risk of misspecification and a sensitivity prior at the cost of flexibility needs to be decided.

stat.ME↗
arXiv2024

End-to-End Autonomous Driving through V2X Cooperation

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. In this paper, we introduce UniV2X, a pioneering cooperative autonomous driving framework that seamlessly integrates all key driving modules across diverse views into a unified network. We propose a sparse-dense hybrid data transmission and fusion mechanism for effective vehicle-infrastructure cooperation, offering three advantages: 1) Effective for simultaneously enhancing agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. 2) Transmission-friendly for practical and limited communication conditions. 3) Reliable data fusion with interpretability of this hybrid data. We implement UniV2X, as well as reproducing several benchmark methods, on the challenging DAIR-V2X, the real-world cooperative driving dataset. Experimental results demonstrate the effectiveness of UniV2X in significantly enhancing planning performance, as well as all intermediate output performance. The project is available at \href{https://github.com/AIR-THU/UniV2X}{https://github.com/AIR-THU/UniV2X}.

cs.RO↗
arXiv2023

V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequential V2X dataset, which includes data frames, trajectories, vector maps, and traffic lights captured from natural scenery. V2X-Seq comprises two parts: the sequential perception dataset, which includes more than 15,000 frames captured from 95 scenarios, and the trajectory forecasting dataset, which contains about 80,000 infrastructure-view scenarios, 80,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios captured from 28 intersections' areas, covering 672 hours of data. Based on V2X-Seq, we introduce three new tasks for vehicle-infrastructure cooperative (VIC) autonomous driving: VIC3D Tracking, Online-VIC Forecasting, and Offline-VIC Forecasting. We also provide benchmarks for the introduced tasks. Find data, code, and more up-to-date information at \href{https://github.com/AIR-THU/DAIR-V2X-Seq}{https://github.com/AIR-THU/DAIR-V2X-Seq}.

cs.CV↗
arXiv2023

A scheme to fix multiple solutions in amplitude analyses

Decays of unstable heavy particles usually involve the coherent sum of several amplitudes, like in a multiple slit experiment. Dedicated amplitude analysis techniques have been widely used to resolve these amplitudes for better understanding of the underlying dynamics. For special cases, where two spin-1/2 particles and two (pseudo-)scalar particles are present in the process, multiple equivalent solutions are found due to intrinsic symmetries in the summed probability density function. In this paper, the problem of multiple solutions is discussed and a scheme to overcome this problem is proposed by fixing some free parameters. Toys are generated to validate the strategy. A new approach to align helicities of initial- and final-state particles in different decay chains is also introduced.

hep-ph↗
arXiv2022

Personalized Biopsy Schedules Using an Interval-censored Cause-specific Joint Model

Active surveillance (AS), where biopsies are conducted to detect cancer progression, has been acknowledged as an efficient way to reduce the overtreatment of prostate cancer. Most AS cohorts use fixed biopsy schedules for all patients. However, the ideal test frequency remains unknown, and the routine use of such invasive tests burdens the patients. An emerging idea is to generate personalized biopsy schedules based on each patient's progression-specific risk. To achieve that, we propose the interval-censored cause-specific joint model (ICJM), which models the impact of longitudinal biomarkers on cancer progression while considering the competing event of early treatment initiation. The underlying likelihood function incorporates the interval-censoring of cancer progression, the competing risk of treatment, and the uncertainty about whether cancer progression occurred since the last biopsy in patients that are right-censored or experience the competing event. The model can produce patient-specific risk profiles until a horizon time. If the risk exceeds a certain threshold, a biopsy is conducted. The optimal threshold can be chosen by balancing two indicators of the biopsy schedules: the expected number of biopsies and expected delay in detection of cancer progression. A simulation study showed that our personalized schedules could considerably reduce the number of biopsies per patient by 34%-54% compared to the fixed schedules, though at the cost of a slightly longer detection delay.

stat.ME↗
arXiv2021

Heavy Flavour Physics and CP Violation at LHCb: a Ten-Year Review

Heavy flavour physics provides excellent opportunities to indirectly search for new physics at very high energy scales and to study hadron properties for deep understanding of the strong interaction. The LHCb experiment has been playing a leading role in the study of heavy flavour physics since the start of the LHC operations about ten years ago, and made a range of high-precision measurements and unexpected discoveries, which may have far-reaching implications on the field of particle physics. This review highlights a selection of the most influential physics results on CP violation, rare decays, and heavy flavour production and spectroscopy obtained by LHCb using the data collected during the first two operation periods of the LHC. The upgrade plan of LHCb and the physics prospects are also briefly discussed.

hep-ex↗
arXiv2021

Neural Networks for Latent Budget Analysis of Compositional Data

Compositional data are non-negative data collected in a rectangular matrix with a constant row sum. Due to the non-negativity the focus is on conditional proportions that add up to 1 for each row. A row of conditional proportions is called an observed budget. Latent budget analysis (LBA) assumes a mixture of latent budgets that explains the observed budgets. LBA is usually fitted to a contingency table, where the rows are levels of one or more explanatory variables and the columns the levels of a response variable. In prospective studies, there is only knowledge about the explanatory variables of individuals and interest goes out to predicting the response variable. Thus, a form of LBA is needed that has the functionality of prediction. Previous studies proposed a constrained neural network (NN) extension of LBA that was hampered by an unsatisfying prediction ability. Here we propose LBA-NN, a feed forward NN model that yields a similar interpretation to LBA but equips LBA with a better ability of prediction. A stable and plausible interpretation of LBA-NN is obtained through the use of importance plots and table, that show the relative importance of all explanatory variables on the response variable. An LBA-NN-K- means approach that applies K-means clustering on the importance table is used to produce K clusters that are comparable to K latent budgets in LBA. Here we provide different experiments where LBA-NN is implemented and compared with LBA. In our analysis, LBA-NN outperforms LBA in prediction in terms of accuracy, specificity, recall and mean square error. We provide open-source software at GitHub.

stat.ML↗
arXiv2020

Entanglement-based quantum deep learning

Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger-Horne-Zeilinger entanglement states. An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed

quant-ph↗
arXiv2016

Letter of Intent: Jinping Neutrino Experiment

Jinping Neutrino Experiment (Jinping) is proposed to significantly improve measurements on solar neutrinos and geoneutrinos in China Jinping Laboratory - a lab with a number of unparalleled features, thickest overburden, lowest reactor neutrino background, etc., which identify it as the world-best low-energy neutrino laboratory. The proposed experiment will have target mass of 4 kilotons of liquid scintillator or water-based liquid scintillator, with a fiducial mass of 2 kilotons for neutrino-electron scattering events and 3 kilotons for inverse-beta interaction events. A number of initial sensitivities studies have been carried out, including on the transition phase for the solar neutrinos oscillation from the vacuum to the matter effect, the discovery of solar neutrinos from the carbon-nitrogen-oxygen (CNO) cycle, the resolution of the high and low metallicity hypotheses, and the unambiguous separation on U and Th cascade decays from the dominant crustal anti-electron neutrinos in China.

physics.ins-det↗
arXiv2016

A Time Projection Chamber with GEM-Based Readout

For the International Large Detector concept at the planned International Linear Collider, the use of time projection chambers (TPC) with micro-pattern gas detector readout as the main tracking detector is investigated. In this paper, results from a prototype TPC, placed in a 1 T solenoidal field and read out with three independent GEM-based readout modules, are reported. The TPC was exposed to a 6 GeV electron beam at the DESY II synchrotron. The efficiency for reconstructing hits, the measurement of the drift velocity, the space point resolution and the control of field inhomogeneities are presented.

physics.ins-det↗
arXiv2011

Simulation Study on neutrino nucleus cross section measurement in Segmented Detector at Spallation Neutron Source

Knowledge of $ν_e$-$\mathrm{Fe}/\mathrm{Pb}$ differential cross sections for $ν_e$ energy below several tens of MeV scale is believed to be crucial in understanding Supernova physics. In a segmented detector at Spallation Neutrino Source, $ν_e$ energy reconstructed from the electron range measurement is strongly affected because of both multiple scattering and electromagnetic showers occurring along the electron passage in target materials. In order to estimate the effect, a simulation study has been performed with a cube block model assuming a perfect tracking precision. The distortion of energy spectrum is observed to be proportional to the atomic number of target material. Feasibility of unfolding the distorted $ν_e$ energy spectrum is studied for both Fe and Pb cases. Evaluation of statistical accuracy attainable is therefore provided for a segmented detector.

nucl-ex↗
arXiv2003

Deconfinement Phase Transition in an Expanding Quark system in Relaxation Time Approximation

We investigated the effects of nonequilibrium and collision terms on the deconfinement phase transition of an expanding quark system in Friedberg-Lee model in relaxation time approximation. By calculating the effective quark potential, the critical temperature of the phase transition is dominated by the mean field, while the collisions among quarks and mesons change the time structure of the phase transition significantly.

hep-ph↗