arXiv subjects

Ying Lu

Publications and source records attributed to Ying Lu.

At least 19 recordsLinked to original sources

arXiv2026

PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction

Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints. We propose PARA-PV, a Physics-Aware Retrieval-Augmented framework that embeds physical knowledge throughout the forecasting process. The framework first encodes multivariate PV observations into patch-level representations and, through a physics-aware retrieval-augmented learner, retrieves historical patches and analog trajectories that are consistent with the current window in temporal shape, power level, PV operating state, and intra-day period; this yields a physically grounded base forecast. To supplement local memory with broader temporal knowledge, the base forecast is then calibrated against a frozen Chronos time-series foundation-model prior through a lightweight residual adapter, so that general temporal regularities are adapted to PV-specific dynamics without overriding the physically grounded prediction. Because residual conditional distribution shifts persist when weather and diurnal regimes change, a physics-aware distribution shift correction module subsequently adjusts the preliminary forecast using power, weather, timestamp, and day/night conditions, applying gated mean-shift and scale corrections selectively. Finally, a physics-constrained loss function partitions the samples into peak, ramping, night-time, and regular regimes and adaptively reweights their error contributions, preventing the dominant regular regime from suppressing learning of operationally critical states. Our code is available at https://github.com/weican1103/PARA-PV.

cs.AI↗

arXiv2026

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models

Dense linear maps carry much of the parameter and computational burden of modern neural networks, yet their dense form leaves the organization of learned couplings implicit. Quantum many-body physics organizes exponentially large operators by writing a global Hamiltonian as a sum of local terms, \(\hat H=\sum_k\hat h_k\). Whether the same structural principle can carry learned neural maps is unknown. We introduce Tensor Mixture (MixT), which represents a dense map as a natively executable sum of overlapping local tensor operators without imposing an explicit matrix-rank constraint. The local-term count \(N_T\) sets the effective nonlocality and operator complexity, while the number of replaced Transformer blocks \(N_B\) extends this structural coordinate across network depth. Tests on Qwen3-8B and LLaMA2-7B reveal a broad recoverable regime followed by an abrupt, model-specific boundary that is remarkably stable against changes in \(N_T\). Accuracy and output-distribution statistics reorganize together across the boundary; in LLaMA2-7B, the same depth separates two scaling regimes of inter-layer geometry drift. The directly executed structure also reduces parameters, arithmetic, storage, and memory. These results establish the local-sum structure as a viable organizing principle for learned linear maps at billion-parameter scale and expose a sharp boundary in their tolerance to structural simplification.

cs.CL↗

arXiv2026

TWICEBEE: A Two-stage Intra-patient Curve-free Bayesian Decision-Theoretic Dose Escalation Design

We propose a novel Phase I intra-patient dose-escalation design tailored for multi-cycle immunotherapy settings, in which toxicity at a fixed dose level is clinically expected to decrease over successive treatment cycles. This design was motivated by a phase I trial of CAR T cell therapy, an emerging cellular immunotherapy with established applications in cancer and growing investigation in autoimmune disease. The design is intended for settings in which nonincreasing cycle-specific toxicity assumption is clinically justified. Specifically, we build on the extrapolation property of the modified curve-free Bayesian decision-theoretic (c-CFBD) design for two-agent trials (Xu, et al. 2025), treating treatment cycle as a second dimension. By redefining the partial order, the c-CFBD framework can accommodate the reduction in toxicity across cycles. The proposed design adopts a two-stage structure: an initial accelerated titration stage to rapidly explore dose levels, followed by a c-CFBD stage to improve safety and estimate the cycle-specific maximum tolerated dose sequence. Simulation studies across a range of scenarios demonstrate favorable operating characteristics.

stat.ME↗

arXiv2026

An End-to-end Building Load Forecasting Framework with Patch-based Information Fusion Network and Error-weighted Adaptive Loss

Accurate building load forecasting plays a critical role in facilitating demand response aggregation and optimizing energy management. However, the complex temporal dependencies and high volatility of building loads limit the improvement of prediction accuracy. To this end, we propose a novel end-to-end building load forecasting framework. Specifically, the framework can be divided into two main stages. In the two-stage data preprocessing module enhanced by interpretable feature selection, we utilize the Local Outlier Factor (LOF) algorithm to accurately detect and correct anomalies in the original building load series. Furthermore, we employ SVM-SHAP feature analysis to quantify the impact of environmental variables, filtering out critical feature combinations to mitigate redundancy. In the building load forecasting module, we propose the patch-based information fusion network (PIF-Net). This model applies patching technology to process input series into local blocks, extracting temporal features through a shared Gated Recurrent Unit (GRU) network with residual connections. Subsequently, an information fusion module based on a customized gating mechanism integrates the ensemble hidden states to weight the importance of different temporal patches dynamically. Additionally, the framework is trained using a novel Error-weighted Adaptive Loss (EWAL) function. By combining a rational quadratic function and logarithmic loss to dynamically adjust penalty weights based on real-time prediction error distributions, EWAL significantly enhances the model's robustness under extreme load conditions. Finally, extensive experiments demonstrate the effectiveness and superiority of our proposed framework.

cs.CE↗

arXiv2025

IDS-Net: A novel framework for few-shot photovoltaic power prediction with interpretable dynamic selection and feature information fusion

With the growing demand for renewable energy, countries are accelerating the construction of photovoltaic (PV) power stations. However, accurately forecasting power data for newly constructed PV stations is extremely challenging due to limited data availability. To this end, we propose a novel interpretable dynamic selection network (IDS-Net) based on feature information fusion to achieve accurate few-shot prediction. This transfer learning framework primarily consists of two parts. In the first stage, we pre-train on the large dataset, utilizing Maximum Mean Discrepancy (MMD) to select the source domain dataset most similar to the target domain data distribution. Subsequently, the ReliefF algorithm is utilized for feature selection, reducing the influence of feature redundancy. Then, the Hampel Identifier (HI) is used for training dataset outlier correction. In the IDS-Net model, we first obtain the initial extracted features from a pool of predictive models. Following this, two separate weighting channels are utilized to determine the interpretable weights for each sub-model and the adaptive selection outcomes, respectively. Subsequently, the extracted feature results from each sub-model are multiplied by their corresponding weights and then summed to obtain the weighted extracted features. Then, we perform cross-embedding on the additional features and fuse them with the extracted weighted features. This fused information is then passed through the MLP (Multi-Layer Perceptron) layer to obtain predictions. In the second stage, we design an end-to-end adaptive transfer learning strategy to obtain the final prediction results on the target dataset. We validate the transfer learning process using two PV power datasets from Hebei province, China, to demonstrate the effectiveness and generalization of our framework and transfer learning strategy.

cs.CE↗

arXiv2025

Conditional Inference for Secondary Outcomes Based on the Testing Result for the Primary Outcome in Clinical Trials

In clinical trials, inferences on clinical outcomes are often made conditional on specific selective processes. For instance, only when a treatment demonstrates a significant effect on the primary outcome, further analysis is conducted to investigate its efficacy on selected secondary outcomes. Similarly, inferences may also depend on whether a trial is terminated early at interim stage. While conventional approaches primarily aim to control the family-wise error rate through multiplicity adjustments, they do not necessarily ensure the desired statistical property of the inference result, when the analysis is conducted according to a selective process. For example, the conditional coverage level of a regular confidence interval under a selective processes can be very different from its nominal level even after adjustment for multiple testing. In this paper, we argue that the validity of the inference procedure conditional on selective process is important in many applications. In addition, we propose to construct confidence intervals with correct conditional coverage probability by accounting for related selective process. Specifically, our approach involves a pivotal quantity constructed by inversing the cumulative distribution function of a truncated normal distribution induced by the selective process. Theoretical justification and comprehensive simulations illustrate the effectiveness of this method in realistic settings. We also apply our method to analyze data from the SPRINT, resulting in more conservative but conditionally valid confidence intervals for the average treatment effect than those originally published.

stat.ME↗

arXiv2025

Balancing the effective sample size in prior across different doses in the curve-free Bayesian decision-theoretic design for dose-finding trials

The primary goal of dose allocation in phase I trials is to minimize patient exposure to subtherapeutic or excessively toxic doses, while accurately recommending a phase II dose that is as close as possible to the maximum tolerated dose (MTD). Fan et al. (2012) introduced a curve-free Bayesian decision-theoretic design (CFBD), which leverages the assumption of a monotonic dose-toxicity relationship without directly modeling dose-toxicity curves. This approach has also been extended to drug combinations for determining the MTD (Lee et al., 2017). Although CFBD has demonstrated improved trial efficiency by using fewer patients while maintaining high accuracy in identifying the MTD, it may artificially inflate the effective sample sizes for the updated prior distributions, particularly at the lowest and highest dose levels. This can lead to either overshooting or undershooting the target dose. In this paper, we propose a modification to CFBD's prior distribution updates that balances effective sample sizes across different doses. Simulation results show that with the modified prior specification, CFBD achieves a more focused dose allocation at the MTD and offers more precise dose recommendations with fewer patients on average. It also demonstrates robustness to other well-known dose finding designs in literature.

stat.ME↗

arXiv2025

On the two-step hybrid design for augmenting randomized trials using real-world data

Hybrid clinical trials, that borrow real-world data (RWD), are gaining interest, especially for rare diseases. They assume RWD and randomized control arm be exchangeable, but violations can bias results, inflate type I error, or reduce power. A two-step hybrid design first tests exchangeability, reducing inappropriate borrowing but potentially inflating type I error (Yuan et al., 2019). We propose four methods to better control type I error. Approach 1 estimates the variance of test statistics, rejecting the null hypothesis based on large sample normal approximation. Approach 2 uses a numerical approach for exact critical value determination. Approach 3 splits type I error rates by equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. Simulation studies using a hypothetical ALS scenario, evaluate type I error and power under various conditions, compared to the Bayesian power prior approach (Ibrahim et al., 2015). Our methods and the Bayesian power prior control type I error, whereas Yuan et al. (2019) increases it under exchangeability. If exchangeability doesn't hold, all methods fail to control type I error. Our methods show type I error inflation of 6%-8%, compared to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior.

stat.ME↗

arXiv2024

Stain-aware Domain Alignment for Imbalance Blood Cell Classification

Blood cell identification is critical for hematological analysis as it aids physicians in diagnosing various blood-related diseases. In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification. To address these issues, we propose a novel blood cell classification method termed SADA via stain-aware domain alignment. The primary objective of this work is to mine domain-invariant features in the presence of domain shifts and data imbalances. To accomplish this objective, we propose a stain-based augmentation approach and a local alignment constraint to learn domain-invariant features. Furthermore, we propose a domain-invariant supervised contrastive learning strategy to capture discriminative features. We decouple the training process into two stages of domain-invariant feature learning and classification training, alleviating the problem of data imbalance. Experiment results on four public blood cell datasets and a private real dataset collected from the Third Affiliated Hospital of Sun Yat-sen University demonstrate that SADA can achieve a new state-of-the-art baseline, which is superior to the existing cutting-edge methods with a big margin. The source code can be available at the URL (\url{https://github.com/AnoK3111/SADA}).

cs.CV↗

arXiv2024

Lead-free Hybrid Perovskite: An Efficient Room Temperature Spin Generator via Large Interfacial Rashba effect

Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BRE in 2D HOIP faces huge challenges, particularly due to the toxicity of lead, which is crucial for achieving large spin-orbit coupling, and the restrictions in 2D HOIP candidates to meet specific symmetry-breaking requirements. To overcome these obstacles, we design a strategy to exploit the interfacial Rashba effect (IRE) of lead-free 2D HOIP (C6H5CH2CH2NH3)2CuCl4 (PEA-CuCl), manifesting as an efficient spin generator at room temperature. IRE of PEA-CuCl originates from the large orbital hybridization at the interface between PEA-CuCl and adjacent ferromagnetic layers. Spin-torque ferromagnetic resonance measurements further quantify a large Rashba effective field of 14.04 Oe per 10^11 A m-2, surpassing those of lead-based HOIP and traditional all-inorganic heterojunctions with noble metals. Our lead-free 2D HOIP PEA-CuCl, which harnesses large IRE for spin generation, is efficient, nontoxic, and economic, offering huge promise for future flexible and wearable spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective

There has been a growing trend that activities relating to clinical trials take place at locations other than traditional trial sites (hence decentralized clinical trials or DCTs), some of which are at settings of real-world clinical practice. Although there are numerous benefits of DCTs, this also brings some implications on a number of issues relating to the design, conduct, and analysis of DCTs. The Real-World Evidence Scientific Working Group of the American Statistical Association Biopharmaceutical Section has been reviewing the field of DCTs and provides in this paper considerations for decentralized trials from a statistical perspective. This paper first discusses selected critical decentralized elements that may have statistical implications on the trial and then summarizes regulatory guidance, framework, and initiatives on DCTs. More discussions are presented by focusing on the design (including construction of estimand), implementation, statistical analysis plan (including missing data handling), and reporting of safety events. Some additional considerations (e.g., ethical considerations, technology infrastructure, study oversight, data security and privacy, and regulatory compliance) are also briefly discussed. This paper is intended to provide statistical considerations for decentralized trials of medical products to support regulatory decision-making.

stat.AP↗

arXiv2024

Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification

Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variability in laboratory procedures and settings, result in a rapid deterioration of the model's generalization performance. To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification. The DoRL comprises two main components: a LoRA-based SAM (LoRA-SAM) and a cross-domain autoencoder (CAE). The advantage of DoRL is that it can extract domain-invariant representations from various blood cell datasets in an unsupervised manner. Specifically, we first leverage the large-scale foundation model of SAM, fine-tuned with LoRA, to learn general image embeddings and segment blood cells. Additionally, we introduce CAE to learn domain-invariant representations across different-domain datasets while mitigating images' artifacts. To validate the effectiveness of domain-invariant representations, we employ five widely used machine learning classifiers to construct blood cell classification models. Experimental results on two public blood cell datasets and a private real dataset demonstrate that our proposed DoRL achieves a new state-of-the-art cross-domain performance, surpassing existing methods by a significant margin. The source code can be available at the URL (https://github.com/AnoK3111/DoRL).

cs.CV↗

arXiv2024

Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model

Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a novel approach for classifying blood cell images known as BC-SAM. BC-SAM leverages the large-scale foundation model of Segment Anything Model (SAM) and incorporates a fine-tuning technique using LoRA, allowing it to extract general image embeddings from blood cell images. To enhance the applicability of BC-SAM across different blood cell image datasets, we introduce an unsupervised cross-domain autoencoder that focuses on learning intrinsic features while suppressing artifacts in the images. To assess the performance of BC-SAM, we employ four widely used machine learning classifiers (Random Forest, Support Vector Machine, Artificial Neural Network, and XGBoost) to construct blood cell classification models and compare them against existing state-of-the-art methods. Experimental results conducted on two publicly available blood cell datasets (Matek-19 and Acevedo-20) demonstrate that our proposed BC-SAM achieves a new state-of-the-art result, surpassing the baseline methods with a significant improvement. The source code of this paper is available at https://github.com/AnoK3111/BC-SAM.

cs.CV↗

arXiv2024

Considerations for Single-Arm Trials to Support Accelerated Approval of Oncology Drugs

In the last two decades, single-arm trials (SATs) have been effectively used to study anticancer therapies in well-defined patient populations using durable response rates as an objective and interpretable clinical endpoints. With a growing trend of regulatory accelerated approval (AA) requiring randomized controlled trials (RCTs), some confusions have arisen about the roles of SATs in AA. This paper is intended to elucidate conditions under which an SAT may be considered reasonable for AA. Specifically, the paper describes (1) two necessary conditions for designing an SAT, (2) three sufficient conditions that help either optimize the study design or interpret the study results, (3) four conditions that demonstrate substantial evidence of clinical benefits of the drug, and (4) a plan of a confirmatory RCT to verify the clinical benefits. Some further considerations are discussed to help design a scientifically sound SAT and communicate with regulatory agencies. Conditions presented in this paper may serve as a set of references for sponsors using SATs for regulatory decision.

stat.AP↗

arXiv2024

Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis

4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this work, we investigate radar-based ITS for scalable traffic analysis. Localization of these radar sensors at city scale is a fundamental task in ITS. For flexible sensor setups, it requires even more effort. To address this task, we propose a self-localization approach that matches two descriptions of the "road": the one from the geometry of the motion trajectories of cumulatively observed vehicles, and the other one from the aerial laser scan. An Iterative Closest Point (ICP) algorithm is used to register the motion trajectory in the road section of the laser scan. The resulting estimate of the transformation matrix represents the sensor pose in a global reference frame. We evaluate the results and show that it outperforms other map-based radar localization methods, especially for the orientation estimation. Beyond the localization result, we project radar sensor data onto a city-scale laser scan and generate a scalable occupancy heat map as a traffic analysis tool. This is demonstrated using two radar sensors monitoring an urban area in the real world.

cs.RO↗

arXiv2024

Opportunities and challenges in the application of large artificial intelligence models in radiology

Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography.

cs.CV↗

arXiv2024

Distribution Properties of the 6.7 GHz Methanol Masers and Their Surrounding Gases in the Milky Way

An updated catalog consisting of 1092 6.7-GHz methanol maser sources was reported in this work. Additionally, the NH3 (1, 1), NH3 (2, 2), and NH3 (3, 3) transitions were observed towards 214 star forming regions using the Shanghai Tianma radio telescope (TMRT) in order to examine the differences in physical environments, such as excitation temperature and column density of molecular clouds associated with methanol masers on the Galactic scale. Statistical results reveal that the number of 6.7 GHz methanol masers in the Perseus arm is significantly lower than that in the other three main spiral arms. In addition, the Perseus arm also has the lowest gas column density among the main spiral arms traced by the NH3 observations. Both of these findings suggest that the Perseus arm has the lowest rate of high-mass star formation compared to the other three main spiral arms. We also observed a trend in which both the luminosity of 6.7 GHz methanol masers and the ammonia gas column density decreased as the galactocentric distances. This finding indicates that the density of material in the inner Milky Way is generally higher than that in the outer Milky Way. It further suggests that high-mass stars are more easily formed at the head of spiral arms. Furthermore, we found that the column density of ammonia gas is higher in the regions on the arms than that in the inter-arm regions, supporting that the former is more likely to be the birthplace of high-mass stars.

astro-ph.GA↗

arXiv2023

Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is therefore crucial to implement effective social risk management strategies at the point of care. Objective: To develop an EHR-based machine learning (ML) analytical pipeline to identify the unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the University of Florida Health Integrated Data Repository, including contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing stability). We developed an electronic health records (EHR)-based machine learning (ML) analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) techniques and fairness assessment and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial-ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk; the actual 1-year hospitalization rate in the top 5% of iPsRS was ~13 times as high as the bottom decile. Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in T2D patients.

cs.LG↗