arXiv subjects

Lin Dong

Publications and source records attributed to Lin Dong.

At least 19 recordsLinked to original sources

arXiv2026

Flexibility-Aware Framework for Efficient Planner-Initiated Siting of Data Center

Explosive growth in energy-intensive AI data centers is outstripping the pace of power grid interconnection and transmission expansion. While operational flexibility has been proposed to mitigate this stress, existing processes are often reactive and evaluate projects only after they enter a multi-year interconnection queue. To address this, we introduce a planner-initiated siting framework that integrates (i) reliability-gated screening, (ii) system-wide market-impact assessment under standardized flexibility envelopes (firm, pause, and shift), and (iii) entropy-weighted multi-criteria scoring to produce ranked, pre-certified catalogues of interconnection-ready locations. Applied to a synthetic 2,000-bus Texas power system, the framework demonstrates that operational flexibility expands the siting frontier by 9-17% at 1 GW and 19-21% at 2 GW compared to firm operation. Median all-hour average prices remain essentially unchanged (USD 24.32/MWh for the 2 GW cases), and the shift envelope attenuates peak-hour price dispersion by approximately 3.4% with minimal side effects during off-peak hours. Utilizing pre-certified envelopes to bypass major transmission reinforcements, this workflow enables first energization in 12-18 months, a conservative reduction of 3.5-4 years versus the conventional 5-8 year project-led process. This technology-agnostic framework provides a proactive decision-making tool for system operators and regulators to fast-track large flexible loads while preserving grid reliability and market stability.

eess.SY↗

arXiv2025

Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model

Accurate forecasting of tropical cyclone (TC) intensity - particularly during periods of rapid intensification and rapid weakening - remains a challenge for operational meteorology, with high-stakes implications for disaster preparedness and infrastructure resilience. Recent advances in machine learning have yielded notable progress in TC prediction; however, most existing systems provide forecasts that degrade rapidly in extreme regimes and lack long-range consistency. Here we introduce TIFNet, a transformer-based forecasting model that generates non-iterative, 5-day intensity trajectories by integrating high-resolution global forecasts with a historical-evolution fusion mechanism. Trained on reanalysis data and fine-tuned with operational data, TIFNet consistently outperforms operational numerical models across all forecast horizons, delivering robust improvements across weak, strong, and super typhoon categories. In rapid intensity change regimes - long regarded as the most difficult to forecast - TIFNet reduces forecast error by 29-43% relative to current operational baselines. These results represent a substantial advance in artificial-intelligence-based TC intensity forecasting, especially under extreme conditions where traditional models consistently underperform.

physics.ao-ph↗

arXiv2025

Unlocking Multi-Task Electric Energy System Intelligence: Data Scaling Laws and Performance with Limited Fine-Tuning

Data scaling has revolutionized research fields like natural language processing, computer vision, and robotics control, providing foundation models with remarkable multi-task and generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in developing foundation models for power systems, and whether appropriate data scaling can yield multi-task, cross-timescales capabilities that can be deployed in \textit{unseen} operational scenarios. To this end, we conducted a comprehensive empirical study on data scaling by fine-tuning open-source foundation models using labeled data collected from diverse operational tasks and scenarios. We study how a foundation model's scenario generalization performance evolves with the number of training tasks, scenarios, and demonstrations. Our study involved collecting more than 450k demonstrations and implementing independent tests under a rigorous evaluation framework. Our findings reveal several key insights: First, the generalization performance of a fine-tuned foundation model follows an approximate power-law relationship with the number of demonstrations and scenarios. Second, the fine-tuned model also demonstrates impressive multi-task capabilities, where multi-task training shares similar performance improvements with single-task training as the number of demonstrations increases, without interference among tasks. Lastly, models with small parameter sizes could have strong performance as well. Model performance does not scale significantly with parameter size. These findings underscore the feasibility of developing multi-task foundation models tailored for power systems, demonstrating that while larger datasets and models generally improve performance, extreme scaling is unnecessary to achieve satisfactory outcomes.

eess.SY↗

arXiv2024

Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this article identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.

eess.SY↗

arXiv2023

GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning

Gait recognition aims at identifying the pedestrians at a long distance by their biometric gait patterns. It is inherently challenging due to the various covariates and the properties of silhouettes (textureless and colorless), which result in two kinds of pair-wise hard samples: the same pedestrian could have distinct silhouettes (intra-class diversity) and different pedestrians could have similar silhouettes (inter-class similarity). In this work, we propose to solve the hard sample issue with a Memory-augmented Progressive Learning network (GaitMPL), including Dynamic Reweighting Progressive Learning module (DRPL) and Global Structure-Aligned Memory bank (GSAM). Specifically, DRPL reduces the learning difficulty of hard samples by easy-to-hard progressive learning. GSAM further augments DRPL with a structure-aligned memory mechanism, which maintains and models the feature distribution of each ID. Experiments on two commonly used datasets, CASIA-B and OU-MVLP, demonstrate the effectiveness of GaitMPL. On CASIA-B, we achieve the state-of-the-art performance, i.e., 88.0% on the most challenging condition (Clothing) and 93.3% on the average condition, which outperforms the other methods by at least 3.8% and 1.4%, respectively.

cs.CV↗

arXiv2022

MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing

The interpolation and reconstruction of missing traces is a crucial step in seismic data processing, moreover it is also a highly ill-posed problem, especially for complex cases such as high-ratio random discrete missing, continuous missing and missing in fault-rich or salt body surveys. These complex cases are rarely mentioned in current works. To cope with complex missing cases, we propose Multi-Dimensional Adversarial GAN (MDA GAN), a novel 3-D GAN framework. It keeps anisotropy and spatial continuity of the data after 3D complex missing reconstruction using three discriminators. The feature stitching module is designed and embedded in the generator to retain more information of the input data. The Tanh cross entropy (TCE) loss is derived, which provides the generator with the optimal reconstruction gradient to make the generated data smoother and continuous. We experimentally verified the effectiveness of the individual components of the study and then tested the method on multiple publicly available data. The method achieves reasonable reconstructions for up to 95% of random discrete missing and 100 traces of continuous missing. In fault and salt body enriched surveys, MDA GAN still yields promising results for complex cases. Experimentally it has been demonstrated that our method achieves better performance than other methods in both simple and complex cases.https://github.com/douyimin/MDA_GAN

physics.geo-ph↗

arXiv2021

VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios

With the motivation of practical gait recognition applications, we propose to automatically create a large-scale synthetic gait dataset (called VersatileGait) by a game engine, which consists of around one million silhouette sequences of 11,000 subjects with fine-grained attributes in various complicated scenarios. Compared with existing real gait datasets with limited samples and simple scenarios, the proposed VersatileGait dataset possesses several nice properties, including huge dataset size, high sample diversity, high-quality annotations, multi-pitch angles, small domain gap with the real one, etc. Furthermore, we investigate the effectiveness of our dataset (e.g., domain transfer after pretraining). Then, we use the fine-grained attributes from VersatileGait to promote gait recognition in both accuracy and speed, and meanwhile justify the gait recognition performance under multi-pitch angle settings. Additionally, we explore a variety of potential applications for research.Extensive experiments demonstrate the value and effective-ness of the proposed VersatileGait in gait recognition along with its associated applications. We will release both VersatileGait and its corresponding data generation toolkit for further studies.

cs.CV↗

arXiv2020

Improving trial generalizability using observational studies

Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early-stage non-small cell lung patients after surgery.

stat.ME↗

arXiv2019

Assessing Biosimilarity using Functional Metrics

In recent years there have been a lot of interest to test for similarity between biological drug products, commonly known as biologics. Biologics are large and complex molecule drugs that are produced by living cells and hence these are sensitive to the environmental changes. In addition, biologics usually induce antibodies which raises the safety and efficacy issues. The manufacturing process is also much more complicated and often costlier than the small-molecule generic drugs. Because of these complexities and inherent variability of the biologics, the testing paradigm of the traditional generic drugs cannot be directly used to test for biosimilarity. Taking into account some of these concerns we propose a functional distance based methodology that takes into consideration the entire time course of the study and is based on a class of flexible semi-parametric models. The empirical results show that the proposed approach is more sensitive than the classical equivalence tests approach which are usually based on arbitrarily chosen time point. Bootstrap based methodologies are also presented for statistical inference.

stat.ME↗

arXiv2016

Effects of Spin-Orbit Coupling on Jaynes-Cummings and Tavis-Cummings Models

We consider ultracold atoms inside a ring optical cavity that supports a single plane-wave mode. The cavity field, together with an external coherent laser field, drives a two-photon Raman transition between two internal pseudo-spin states of the atom. This gives rise to an effective coupling between atom's pseudo-spin and external center-of-mass (COM) motion. For the case of a single atom inside the cavity, We show how the spin-orbit coupling modifies the static and dynamic properties of the Jaynes-Cummings (JC) model. In the case of many atoms in thermodynamic limit, we show that the spin-orbit coupling modifies the Dicke superradiance phase transition boundary and the non-superradiant normal phase may become reentrant in some regimes.

cond-mat.quant-gas↗

arXiv2016

Harmonically Trapped Atoms with Spin-Orbit Coupling

We study harmonically trapped one-dimensional atoms subjected to an equal combination of Rashba and Dresselhaus spin-orbit coupling induced by Raman transition. We first examine the wave function and the degeneracy of the single-particle ground state, followed by a study of two weakly interacting bosons or fermions. For the two-particle ground state, we focus on the effects of the interaction on the degeneracy, the spin density profiles, and the density-density correlation functions. Finally we show how these studies help us to understand the many-body properties of the system.

cond-mat.quant-gas↗

arXiv2015

Photon-Induced Spin-Orbit Coupling in Ultracold Atoms inside Optical Cavity

We consider an atom inside a ring cavity, where a plane-wave cavity field together with an external coherent laser beam induces a two-photon Raman transition between two hyperfine ground states of the atom. This cavity-assisted Raman transition induces effective coupling between atom's internal degrees of freedom and its center-of-mass motion. In~the meantime, atomic dynamics exerts a back-action to cavity photons. We investigate the properties of this system by adopting a mean-field and a full quantum approach, and show that the interplay between the atomic dynamics and the cavity field gives rise to intriguing nonlinear phenomena.

cond-mat.quant-gas↗

arXiv2014

Dynamical topological phases in quenched spin-orbit coupled degenerate Fermi gas

The spin-orbit coupled degenerate Fermi gas provides a totally new platform to realize topological superfluids and related topological excitations. Previous studies have mainly focused on the properties of the ground state. Here we consider a two-dimensional Fermi gas with Rashba spin-orbit coupling subject to a perpendicular Zeeman field. For this system, we have found that its ground state topological structure is captured by the spin texture, which is readily measurable in experiments. We show that, when the Zeeman field is suddenly quenched, dynamical topological phases can be realized. More specifically, three post-quench dynamical phases can be identified according to the asymptotic behavior of the order parameter. In the undamped phase, a persistent oscillation of the order parameter may support a topological Floquet state with multiple edge states. In the Landau damped phase, the magnitude of the order parameter approaches a constant via a power-law decay, and this system can support a dynamical topological phase with a pair of edge states at the boundary. In the over-damped phase, the order parameter decays to zero exponentially although the condensate fraction remains finite. These predictions can be observed in the strong coupling regime of ultracold Fermi gas.

cond-mat.quant-gas↗

arXiv2014

Gapless topological Fulde-Ferrell superfluidity induced by in-plane Zeeman field

Topological superfluids are recently discovered quantum matters that host topologically protected gapless edge states known as Majorana fermions - exotic quantum particles that act as their own anti-particles and obey non-Abelian statistics. Their realizations are believed to lie at the heart of future technologies such as fault-tolerant quantum computation. To date, the most efficient scheme to create topological superfluids and Majorana fermions is based on the Sau-Lutchyn-Tewari-Das Sarma model with a Rashba-type spin-orbit coupling on the }\textbf{\textit{x-y}}\textbf{ plane and a large out-of-plane (perpendicular) Zeeman field along the }\textbf{\textit{z}}\textbf{-direction. Here we propose an alternative setup, where the topological superfluid phase is driven by applying an in-plane Zeeman field. This scheme offers a number of new features, notably Cooper pairings at finite centre-of-mass momentum (i.e., Fulde-Ferrell pairing) and gapless excitations in the bulk. As a result, a novel gapless topological quantum matter with inhomogeneous pairing order parameter appears. It features unidirected Majorana surface states at boundaries, which propagate in the same direction and connect two Weyl nodes in the bulk. We demonstrate the emergence of such an exotic topological matter and the associated Majorana fermions in spin-orbit coupled atomic Fermi gases and determine its parameter space. The implementation of our scheme in semiconductor/superconductor heterostructures is briefly discussed.

cond-mat.quant-gas↗

arXiv2013

Cavity-Assisted Dynamical Spin-Orbit Coupling in Cold Atoms

We consider ultracold atoms subjected to a cavity-assisted two-photon Raman transition. The Raman coupling gives rise to effective spin-orbit interaction which couples atom's center-of-mass motion to its pseudospin degrees of freedom. Meanwhile, the cavity photon is dynamically affected by the atom. This feedback between atom and photon leads to a dramatic modification of the atomic dispersion relation, and further leads to dynamical instability of the system. We propose to detect the change of cavity photon number as a direct way to demonstrate dynamical instability.

cond-mat.quant-gas↗

arXiv2013

Fulde-Ferrell Pairing instability in Spin-Orbit Coupled Fermi Gas

We consider finite-momentum pairing of superfluid phase in ultracold Fermi gas with spin-orbit coupling when subjected to an effective Zeeman field. Based on our two body and mean field many body calculations, we show that the Fulde-Ferrell type superfluid dominates in zero and finite temperature phase diagram. We examine the origin and properties of this novel phase systematically.

cond-mat.quant-gas↗

arXiv2012

Finite-Momentum Dimer Bound State in Spin-Orbit Coupled Fermi Gas

We investigate the two-body properties of a spin-1/2 Fermi gas subject to a spin-orbit coupling induced by laser fields. When an attractive s-wave interaction between unlike spins is present, the system may form a dimer bound state. Surprisingly, in the presence of a Zeeman field along the direction of the spin-orbit coupling, the bound state obtains finite center-of-mass mechanical momentum, whereas under the same condition but in the absence of the two-body interaction, the system has zero total momentum. This unusual result can be regarded as a consequence of the broken Galilean invariance by the spin-orbit coupling. Such a finite-momentum bound state will have profound effects on the many-body properties of the system.

cond-mat.quant-gas↗

arXiv2012

Optimal Contrast Greyscale Visual Cryptography Schemes with Reversing

Visual cryptography scheme (VCS) is an encryption technique that utilizes human visual system in recovering secret image and it does not require any complex calculation. However, the contrast of the reconstructed image could be quite low. A number of reversing-based VCSs (or VCSs with reversing) (RVCS) have been proposed for binary secret images, allowing participants to perform a reversing operation on shares (or shadows). This reversing operation can be easily implemented by current copy machines. Some existing traditional VCS schemes without reversing (nRVCS) can be extended to RVCS with the same pixel expansion for binary image, and the RVCS can achieve ideal contrast, significantly higher than that of the corresponding nRVCS. In the application of greyscale VCS, the contrast is much lower than that of the binary cases. Therefore, it is more desirable to improve the contrast in the greyscale image reconstruction. However, when greyscale images are involved, one cannot take advantage of this reversing operation so easily. Many existing greyscale nRVCS cannot be directly extended to RVCS. In this paper, we first give a new greyscale nRVCS with minimum pixel expansion and propose an optimal-contrast greyscale RVCS (GRVCS) by using basis matrices of perfect black nRVCS. Also, we propose an optimal GRVCS even though the basis matrices are not perfect black. Finally, we design an optimal-contrast GRVCS with minimum number of shares held by each participant. The proposed schemes can satisfy different user requirement, previous RVCSs for binary images can be viewed as special cases in the schemes proposed here.

cs.CR↗