arXiv subjects

Feng Qiu

Publications and source records attributed to Feng Qiu.

At least 19 recordsLinked to original sources

arXiv2026

OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation

Power-outage planning requires scenarios before an event occurs. These scenarios must represent uncertainty in magnitude, timing, and duration while preserving temporal dependence. However, severe events are rare, and data from any single region contain few examples of extreme outage and restoration patterns. To address this challenge, we introduce OutageDiT, a foundation model for generating seven-day outage trajectories at quarter-hour resolution, trained on outage and weather records across the United States. Specifically, a condition encoder processes the historical context and known future covariates once per forecast, and a shallow flow decoder reuses the resulting horizon-aligned states to generate complete trajectories. The resulting samples support point forecasting, uncertainty quantification, and conditional event simulation within one deep generative model. Across outage forecasting benchmarks, OutageDiT improves forecast accuracy and scenario quality over strong baselines and supports zero-shot transfer to held-out regions. Together, these results position conditional outage simulation as a bridge from outage forecasting to operational planning under uncertainty.

cs.LG↗

arXiv2026

Unveiling Orbital-mediated Ultrafast Demagnetization in Rare Earth-Transition-Metal Ferrimagnets

The ultimate speed limit of magnetic recording and spintronic devices is set by the efficiency of angular-momentum transfer during ultrafast demagnetization, yet its microscopic pathway in Rare-Earth-Transition-Metal (RE-TM) ferrimagnets remains debated. Here, we establish an orbital-mediated framework in which 3d spin-orbit coupling (SOC) governs angular momentum (AM) dissipation. Strong 3d-SOC in RE-Co enables sub-picosecond, single-step demagnetization via direct orbital-to-lattice transfer, whereas weak 3d-SOC in RE-Fe redirects AM into 4f orbitals, producing slower two-step dynamics. The second-stage rate scales with 4f-SOC strength, revealing a distinct orbital-mediated dissipation channel. Using time-resolved magneto-optical Kerr measurements, supported by an extended four-temperature model, corroborate this picture across diverse RE-TM systems (RE = Sm, Gd, Tb, Dy, Ho and TM = Fe, Co, CoNi). Our results identify the SOC-driven competition between 3d and 4f orbital channels as the universal mechanism governing ultrafast demagnetization in RE-TM ferrimagnets, enabling rational design of the switching speed for next-generation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Ultrafast and long-term stability Integrated Pockels laser with thin-film PZT

Integrated tunable lasers are central to coherent communications, wavelength-routed optical interconnects, spectroscopy and frequency-modulated continuous-wave LiDAR, yet chip-scale sources rarely combine broad wavelength coverage, nanosecond switching, high spectral purity and stable high-power operation. Here we demonstrate a frequency-agile hybrid external-cavity laser enabled by the Pockels effect in thin-film lead zirconate titanate (PZT). The strong linear electro-optic response of PZT provides direct, non-thermal tuning of compact microring resonators with a wavelength-tuning efficiency of 17 pm/V. In contrast to conventional anisotropic Pockels materials, the near-isotropic in-plane electro-optic behaviour of thin-film PZT relaxes crystal-axis layout constraints, allowing efficient Vernier wavelength selection in compact ring cavities. The PZT resonators also show no measurable photorefractive resonance distortion and no resolvable DC-bias drift during operation, preserving stable wavelength-selective feedback. The demonstrated laser achieves an 82 nm tuning range, a 5 mW fiber-coupled output power, a side-mode suppression ratio (SMSR) exceeding 56.7 dB, and a wavelength-switching time of 5.5 ns. These results establish thin-film PZT photonics as a powerful electro-optic platform for compact, high-power, and frequency-agile integrated laser sources.

physics.optics↗

arXiv2026

Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm

Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network optimization. Most current methods assume an independently and identically distributed (i.i.d.) training and testing setting based on random sampling. However, practical UAV measurements are collected sequentially along feasible trajectories, resulting in highly structured and spatially correlated patterns. This mismatch introduces a sampling distribution shift that increases the intrinsic difficulty of spatial field recovery and compromises the generalization of models trained under i.i.d. assumptions. To mitigate this issue, we propose a trajectory-aware training paradigm based on Stochastic-Triggered Trajectory-Based Sampling (ST-TBS), which preserves trajectory continuity while introducing sampling variability. Moreover, from a statistical perspective, we show that trajectory-based sampling reduces spatial diversity and increases information redundancy compared to random sampling. Extensive experiments on the RadioMapSeer and SpectrumNet datasets demonstrate that models trained with random sampling suffer significant performance degradation under trajectory-based observations, with RMSE increasing from 0.0391 to 0.2632 on SpectrumNet. Conversely, our proposed ST-TBS method effectively reduces the RMSE to 0.0571. These results highlight the necessity of aligning training and deployment sampling distributions for reliable RME.

cs.CV↗

arXiv2026

Hybrid-plasticity Photonic Synapses Enabling Hardware-Level Neural Reuse

Biological intelligence is distinguished by neural reuse, the capacity to preserve established learning memory while repurposing it for new tasks and dynamic environments. Bringing this capability to photonic hardware requires hybrid plasticity, namely the coexistence of long-term synaptic plasticity for persistent weight storage and short-term synaptic plasticity for rapid, reversible adaptation within a single synaptic element; however, current photonic architectures lack such a unified mechanism. Here, we demonstrate a hybrid-plasticity photonic synapse on thin-film lead zirconate titanate (PZT) that couples non-volatile and volatile modes to enable hardware-level neural reuse. Crucially, high-speed refresh operations can be superimposed without perturbing the stored weight. Such a neural-reuse framework yields a convergence speedup of over 20-fold and reduces the weight updates by approximately 30-fold compared with random initialization. These results establish hybrid-plasticity photonic synapses as a pathway toward on-chip learning systems that are both memory-preserving and rapidly adaptable.

physics.optics↗

arXiv2026

Scheduling Electricity Production Units to Mitigate Severe Weather Impact: An Efficient Computational Implementation

Extreme weather events in electric power systems can cause line trips or physical damage to transmission infrastructure, potentially leading to large-scale load shedding. To mitigate this risk, we propose a framework that strategically pre-positions the commitment of generation units--particularly slow-start units--to adapt to transmission topologies that may arise following such events. The objective is to minimize load shedding under worst-case conditions. This paper makes two main contributions. First, we provide a more accurate representation of the underlying physical laws than those used in prior studies. Second, we develop a highly efficient solution algorithm that outperforms state-of-the-art, off-the-shelf solvers. The proposed framework is formulated as a two-stage robust optimization model. In the first stage, generation units are scheduled in anticipation of disruptions. In the second stage, power dispatch decisions are optimized to minimize load shedding under the worst-case transmission topology. To ensure system reliability and security, we incorporate convexified AC power flow constraints. The resulting model is a tri-level mixed-integer nonlinear optimization problem. To address its computational complexity, we design a problem-specific outer approximation algorithm embedded within a column-and-constraint generation framework. Computational results show that the proposed model and solution approach can achieve solutions within a standard optimality gap in a reasonable time for moderately large instances.

math.OC↗

arXiv2026

From Prediction to Diagnosis: Reasoning-Aware AI for Photovoltaic Defect Inspection

Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93\% classification accuracy while producing interpretable diagnostic rationales and maintaining strong robustness under realistic image corruptions. A blind concordance study with a certified solar inspection expert shows strong semantic alignment between model explanations and expert assessments across defect identification, root-cause attribution, and visual descriptions. These results demonstrate that reasoning-aware multimodal learning establishes a general paradigm for trustworthy AI-assisted inspection of photovoltaic energy infrastructure.

cs.CV↗

arXiv2026

Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution

In this report, we introduce Xiaomi-Robotics-0, an advanced vision-language-action (VLA) model optimized for high performance and fast and smooth real-time execution. The key to our method lies in a carefully designed training recipe and deployment strategy. Xiaomi-Robotics-0 is first pre-trained on large-scale cross-embodiment robot trajectories and vision-language data, endowing it with broad and generalizable action-generation capabilities while avoiding catastrophic forgetting of the visual-semantic knowledge of the underlying pre-trained VLM. During post-training, we propose several techniques for training the VLA model for asynchronous execution to address the inference latency during real-robot rollouts. During deployment, we carefully align the timesteps of consecutive predicted action chunks to ensure continuous and seamless real-time rollouts. We evaluate Xiaomi-Robotics-0 extensively in simulation benchmarks and on two challenging real-robot tasks that require precise and dexterous bimanual manipulation. Results show that our method achieves state-of-the-art performance across all simulation benchmarks. Moreover, Xiaomi-Robotics-0 can roll out fast and smoothly on real robots using a consumer-grade GPU, achieving high success rates and throughput on both real-robot tasks. To facilitate future research, code and model checkpoints are open-sourced at https://xiaomi-robotics-0.github.io

cs.RO↗

arXiv2025

Ultrafast Reconfigurable Topological Photonic Processing Accelerator

The rise of artificial intelligence has triggered exponential growth in data volume, demanding rapid and efficient processing. High-speed, energy-efficient, and parallel-scalable computing hardware is thus increasingly critical. We demonstrate a wafer-scale non-volatile topological photonic computing chip using topological modulators. Leveraging the GHz-speed electro-optic response and nonvolatility of ferroelectric lead zirconate titanate (PZT) thin films via topological photonic confinement, Our chip enables thousand-fold faster reconfiguration, zero-static-power operation, and a computational density of 266 trillion operations per second per square millimeter . This density surpasses that of silicon photonic reconfigurable computing chips by two orders of magnitude and thin-film lithium niobate platforms by four orders of magnitude. A 16-channel wavelength-space multiplexed chip delivers 1.92 TOPS throughput with 95.64% digit-recognition accuracy and 94.5% precision for solving time-varying partial differential equations. Additionally, the chip supports functional reconfiguration for high bandwidth density optical I/O. This work establishes ferroelectric topological photonics for efficient high-speed photonic tensor processing.

physics.optics↗

arXiv2025

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series

Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate systems. We present DoFlow, a flow-based generative model defined over a causal Directed Acyclic Graph (DAG) that delivers coherent observational and interventional predictions, as well as counterfactuals through the natural encoding-decoding mechanism of continuous normalizing flows (CNFs). We also provide a supporting counterfactual recovery theory under certain assumptions. Beyond forecasting, DoFlow provides explicit likelihoods of future trajectories, enabling principled anomaly detection. Experiments on synthetic datasets with various causal DAG structures and real-world hydropower and cancer-treatment time series show that DoFlow achieves accurate system-wide observational forecasting, enables causal forecasting over interventional and counterfactual queries, and effectively detects anomalies. This work contributes to the broader goal of unifying causal reasoning and generative modeling for complex dynamical systems.

stat.ML↗

arXiv2025

Integrating Object Interaction Self-Attention and GAN-Based Debiasing for Visual Question Answering

Visual Question Answering (VQA) presents a unique challenge by requiring models to understand and reason about visual content to answer questions accurately. Existing VQA models often struggle with biases introduced by the training data, leading to over-reliance on superficial patterns and inadequate generalization to diverse questions and images. This paper presents a novel model, IOG-VQA, which integrates Object Interaction Self-Attention and GAN-Based Debiasing to enhance VQA model performance. The self-attention mechanism allows our model to capture complex interactions between objects within an image, providing a more comprehensive understanding of the visual context. Meanwhile, the GAN-based debiasing framework generates unbiased data distributions, helping the model to learn more robust and generalizable features. By leveraging these two components, IOG-VQA effectively combines visual and textual information to address the inherent biases in VQA datasets. Extensive experiments on the VQA-CP v1 and VQA-CP v2 datasets demonstrate that our model shows excellent performance compared with the existing methods, particularly in handling biased and imbalanced data distributions highlighting the importance of addressing both object interactions and dataset biases in advancing VQA tasks. Our code is available at https://github.com/HubuKG/IOG-VQA.

cs.CV↗

arXiv2025

From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problem

The exponential growth of computational workloads is surpassing the capabilities of conventional architectures, which are constrained by fundamental limits. In-memory computing (IMC) with RRAM provides a promising alternative by providing analog computations with significant gains in latency and energy use. However, existing algorithms developed for conventional architectures do not translate to IMC, particularly for constrained optimization problems where frequent matrix reprogramming remains cost-prohibitive for IMC applications. Here we present a distributed in-memory primal-dual hybrid gradient (PDHG) method, specifically co-designed for arrays of RRAM devices. Our approach minimizes costly write cycles, incorporates robustness against device non-idealities, and leverages a symmetric block-matrix formulation to unify operations across distributed crossbars. We integrate a physics-based simulation framework called MELISO+ to evaluate performance under realistic device conditions. Benchmarking against GPU-accelerated solvers on large-scale linear programs demonstrates that our RRAM-based solver achieves comparable accuracy with up to three orders of magnitude reductions in energy consumption and latency. These results demonstrate the first PDHG-based LP solver implemented on RRAMs, showcasing the transformative potential of algorithm-hardware co-design for solving large-scale optimization through distributed in-memory computing.

cs.DC↗

arXiv2025

Retrieval Augmented Comic Image Generation

We present RaCig, a novel system for generating comic-style image sequences with consistent characters and expressive gestures. RaCig addresses two key challenges: (1) maintaining character identity and costume consistency across frames, and (2) producing diverse and vivid character gestures. Our approach integrates a retrieval-based character assignment module, which aligns characters in textual prompts with reference images, and a regional character injection mechanism that embeds character features into specified image regions. Experimental results demonstrate that RaCig effectively generates engaging comic narratives with coherent characters and dynamic interactions. The source code will be publicly available to support further research in this area.

cs.CV↗

arXiv2025

A Sensor-Driven Optimization Framework for Asset Management in Energy Systems: Implications for Full and Partial Digital Transformation in Hydro Fleets

This paper proposes a novel prognostics-driven approach to optimize operations and maintenance (O&M) decisions in hydropower systems. Our approach harnesses the insights from sensor data to accurately predict the remaining lifetime distribution of critical generation assets in hydropower systems, i.e., thrust bearings, and use these predictions to optimally schedule O&M actions for a fleet of hydro generators. We consider complex interdependencies across hydro generator failure risks, reservoir, production, and demand management decisions. We propose a stochastic joint O&M scheduling model to tackle the unique challenges of hydropower O&M including the interdependency of generation capacities, the nonlinear nature of power production, operational requirements, and uncertainties. We develop a two-level decomposition-based solution algorithm to effectively handle large-scale cases. The algorithm incorporates a combination of Benders optimality cuts and integer cuts to solve the problem in an efficient manner. We design an experimental framework to evaluate the proposed prognostics-driven O&M scheduling framework, using real-world condition monitoring data from hydropower systems, historical market prices, and water inflow data. The developed framework can be partially implemented for a phased-in approach. Our experiments demonstrate the significant benefits of the sensor-driven O&M framework in improving reliability, availability, effective usage of resources, and system profitability, especially when gradually shifting from traditional time-based maintenance policies to condition-based prognostics-driven maintenance policies.

math.OC↗

arXiv2025

PI-Controlled Variable Time-Step Power System Simulation Using an Adaptive Order Differential Transformation Method

Dynamic simulation plays a crucial role in power system transient stability analysis, but traditional numerical integration-based methods are time-consuming due to the small time step sizes. Other semi-analytical solution methods, such as the Differential Transformation method, often struggle to select proper orders and steps, leading to slow performance and numerical instability. To address these challenges, this paper proposes a novel adaptive dynamic simulation approach for power system transient stability analysis. The approach adds feedback control and optimization to selecting the step and order, utilizing the Differential Transformation method and a proportional-integral control strategy to control truncation errors. Order selection is formulated as an optimization problem resulting in a variable-step-optimal-order method that achieves significantly larger time step sizes without violating numerical stability. It is applied to three systems: the IEEE 9-bus, 3-generator system, IEEE 39-bus, 10-generator system, and a Polish 2383-bus, 327-generator system, promising computational efficiency and numerical robustness for large-scale power system is demonstrated in comprehensive case studies.

eess.SY↗

arXiv2025

7ABAW-Compound Expression Recognition via Curriculum Learning

With the advent of deep learning, expression recognition has made significant advancements. However, due to the limited availability of annotated compound expression datasets and the subtle variations of compound expressions, Compound Emotion Recognition (CE) still holds considerable potential for exploration. To advance this task, the 7th Affective Behavior Analysis in-the-wild (ABAW) competition introduces the Compound Expression Challenge based on C-EXPR-DB, a limited dataset without labels. In this paper, we present a curriculum learning-based framework that initially trains the model on single-expression tasks and subsequently incorporates multi-expression data. This design ensures that our model first masters the fundamental features of basic expressions before being exposed to the complexities of compound emotions. Specifically, our designs can be summarized as follows: 1) Single-Expression Pre-training: The model is first trained on datasets containing single expressions to learn the foundational facial features associated with basic emotions. 2) Dynamic Compound Expression Generation: Given the scarcity of annotated compound expression datasets, we employ CutMix and Mixup techniques on the original single-expression images to create hybrid images exhibiting characteristics of multiple basic emotions. 3) Incremental Multi-Expression Integration: After performing well on single-expression tasks, the model is progressively exposed to multi-expression data, allowing the model to adapt to the complexity and variability of compound expressions. The official results indicate that our method achieves the \textbf{best} performance in this competition track with an F-score of 0.6063. Our code is released at https://github.com/YenanLiu/ABAW7th.

cs.CV↗

arXiv2025

Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management

Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-optimize approaches have gained traction in grid operations, where system functionality forecasts are first generated and then used as inputs for downstream decision-making. However, this two-stage method often results in a misalignment between prediction and optimization objectives, leading to suboptimal resource allocation. To address this, we propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions. At its core, our global-decision-focused (GDF) neural ODE model captures outage dynamics while optimizing resilience strategies in a decision-aware manner. Unlike conventional methods, our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency. Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.

cs.LG↗

arXiv2024

Spatio-Temporal Conformal Prediction for Power Outage Data

In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the power industry. To enable rapid recovery, accurately predicting future outage numbers is essential. Rather than relying on simple point estimates, we analyze extensive quarter-hourly outage data and develop a graph conformal prediction method that delivers accurate prediction regions for outage numbers across the states for a time period. We demonstrate the effectiveness of this method through extensive numerical experiments in several states affected by extreme weather events that led to widespread outages.

stat.ML↗