arXiv subjects

Yihang Zhou

Publications and source records attributed to Yihang Zhou.

At least 19 recordsLinked to original sources

arXiv2026

ElastiCo: Elastic Configuration and Interference-Aware Orchestration for GPU Clusters

Modern GPU clusters must simultaneously serve deep learning training and offline large language model inference workloads, yet existing schedulers treat these as isolated resource consumers with rigid, static allocations. This leaves substantial GPU capacity underutilized: training jobs reserve entire devices despite periodic idle phases, while offline inference tasks over-provision GPUs despite bursty demand patterns. We present ElastiCo, an elastic co-location framework that enables training and inference workloads to safely share GPUs through three integrated mechanisms. First, Resource Shape Transformation exposes each job as a family of feasible resource-performance configurations. Second, Elastic Shadow Pricing decomposes the resulting multi-resource allocation problem into per-job configuration selection subproblems via dynamic per-resource shadow prices. Third, Interference-Aware Co-location uses a predictor trained on hardware-counter and task-level features to estimate pairwise performance degradation under GPU sharing. Implemented as native Kubernetes middleware requiring no user-code modifications, ElastiCo is evaluated on a 64-GPU testbed and through large-scale trace-driven simulations (up to 512 GPUs), reducing the average JCT by up to 2.94x, increasing the cluster throughput by 2.02x, and increasing the GPU utilization from approximately 25% to 46%.

cs.DC↗

arXiv2026

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \textbf{ProtLiD$^2$}, a \textbf{Prot}ein \textbf{L}igand-conditioned \textbf{D}iscrete \textbf{D}iffusion model for protein sequence-structure co-design. ProtLiD$^2$ jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, ProtLiD$^2$ extends masked discrete diffusion to ligand-aware functional protein design. We further propose maximum confidence-margin guided ReMask decoding, an inference-time self-correction strategy that retains confident predictions and remasks uncertain tokens. ProtLiD$^2$ improves global fold confidence over Complexa in whole-protein design, increasing TM-score from 0.672 to 0.802 and pLDDT from 64.55 to 73.00. In pocket co-design, ProtLiD$^2$ reduces active-site BB-RMSD from 3.46/3.40{\AA} for FAIR/PocketGen to 1.97{\AA}, and improves ligand-aware pass rates over PocketGen from 14.86% to 59.73% and from 6.08% to 23.49% under stricter docking thresholds. These results support ligand-conditioned discrete diffusion as an effective token-space framework for functional protein co-design. Code will be available at https://github.com/auroua/ProtLiD.

q-bio.BM↗

arXiv2026

Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods

Real-time rooftop wind-speed distribution is important for the safe operation of drones and urban air mobility systems, wind control systems, and rooftop utilization. However, rooftop flows show strong nonlinearity, separation, and cross-direction variability, which make flow field reconstruction from sparse sensors difficult. This study develops a learning-from-observation framework using wind-tunnel experimental data obtained by Particle Image Velocimetry (PIV) and compares Kriging interpolation with three deep learning models: UNet, Vision Transformer Autoencoder (ViTAE), and Conditional Wasserstein GAN (CWGAN). We evaluate two training strategies, single wind-direction training (SDT) and mixed wind-direction training (MDT), across sensor densities from 5 to 30, test robustness under sensor position perturbations of plus or minus 1 grid, and optimize sensor placement via Proper Orthogonal Decomposition with QR decomposition. Results show that deep learning methods can reconstruct rooftop wind fields from sparse sensor data effectively. Compared with Kriging interpolation, the deep learning models improved SSIM by up to 32.7%, FAC2 by 24.2%, and NMSE by 27.8%. Mixed wind-direction training further improved performance, with gains of up to 173.7% in SSIM, 16.7% in FAC2, and 98.3% in MG compared with single-direction training. The results also show that sensor configuration, optimization, and training strategy should be considered jointly for reliable deployment. QR-based optimization improved robustness by up to 27.8% under sensor perturbations, although with metric-dependent trade-offs. Training on experimental rather than simulated data also provides practical guidance for method selection and sensor placement in different scenarios.

cs.CV↗

arXiv2026

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Cyber-attacks are becoming increasingly sophisticated and frequent, highlighting the importance of network intrusion detection systems. This paper explores the potential and challenges of using deep reinforcement learning (DRL) in network intrusion detection. It begins by introducing key DRL concepts and frameworks, such as deep Q-networks and actor-critic algorithms, and reviews recent research utilizing DRL for intrusion detection. The study evaluates challenges related to model training efficiency, detection of minority and unknown class attacks, feature selection, and handling unbalanced datasets. The performance of DRL models is comprehensively analyzed, showing that while DRL holds promise, many recent technologies remain underexplored. Some DRL models achieve state-of-the-art results on public datasets, occasionally outperforming traditional deep learning methods. The paper concludes with recommendations for enhancing DRL deployment and testing in real-world network scenarios, with a focus on Internet of Things intrusion detection. It discusses recent DRL architectures and suggests future policy functions for DRL-based intrusion detection. Finally, the paper proposes integrating DRL with generative methods to further improve performance, addressing current gaps and supporting more robust and adaptive network intrusion detection systems.

cs.CR↗

arXiv2026

Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models

The prevalent paradigm in robot learning attempts to generalize across environments, embodiments, and tasks with language prompts at runtime. A fundamental tension limits this approach: language is often too abstract to guide the concrete physical understanding required for robust manipulation. In this work, we introduce Contact-Anchored Policies (CAP), which replace language conditioning with points of physical contact in space. Simultaneously, we structure CAP as a library of modular utility models rather than a monolithic generalist policy. This factorization allows us to implement a real-to-sim iteration cycle: we build EgoGym, a lightweight simulation benchmark, to rapidly identify failure modes and refine our models and datasets prior to real-world deployment. We show that by conditioning on contact and iterating via simulation, CAP generalizes to novel environments and embodiments out of the box on three fundamental manipulation skills while using only 23 hours of demonstration data, and outperforms large, state-of-the-art VLAs in zero-shot evaluations by 56%. All model checkpoints, codebase, hardware, simulation, and datasets will be open-sourced. Project page: https://cap-policy.github.io/

cs.RO↗

arXiv2026

Extreme Value Policy Optimization for Safe Reinforcement Learning

Ensuring safety is a critical challenge in applying Reinforcement Learning (RL) to real-world scenarios. Constrained Reinforcement Learning (CRL) addresses this by maximizing returns under predefined constraints, typically formulated as the expected cumulative cost. However, expectation-based constraints overlook rare but high-impact extreme value events in the tail distribution, such as black swan incidents, which can lead to severe constraint violations. To address this issue, we propose the Extreme Value policy Optimization (EVO) algorithm, leveraging Extreme Value Theory (EVT) to model and exploit extreme reward and cost samples, reducing constraint violations. EVO introduces an extreme quantile optimization objective to explicitly capture extreme samples in the cost tail distribution. Additionally, we propose an extreme prioritization mechanism during replay, amplifying the learning signal from rare but high-impact extreme samples. Theoretically, we establish upper bounds on expected constraint violations during policy updates, guaranteeing strict constraint satisfaction at a zero-violation quantile level. Further, we demonstrate that EVO achieves a lower probability of constraint violations than expectation-based methods and exhibits lower variance than quantile regression methods. Extensive experiments show that EVO significantly reduces constraint violations during training while maintaining competitive policy performance compared to baselines.

cs.LG↗

arXiv2025

Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction

Dynamic magnetic resonance imaging (dMRI) captures temporally-resolved anatomy but is often challenged by limited sampling and motion-induced artifacts. Conventional motion-compensated reconstructions typically rely on pre-estimated optical flow, which is inaccurate under undersampling and degrades reconstruction quality. In this work, we propose a novel implicit neural representation (INR) framework that jointly models both the dynamic image sequence and its underlying motion field. Specifically, one INR is employed to parameterize the spatiotemporal image content, while another INR represents the optical flow. The two are coupled via the optical flow equation, which serves as a physics-inspired regularization, in addition to a data consistency loss that enforces agreement with k-space measurements. This joint optimization enables simultaneous recovery of temporally coherent images and motion fields without requiring prior flow estimation. Experiments on dynamic cardiac MRI datasets demonstrate that the proposed method outperforms state-of-the-art motion-compensated and deep learning approaches, achieving superior reconstruction quality, accurate motion estimation, and improved temporal fidelity. These results highlight the potential of implicit joint modeling with flow-regularized constraints for advancing dMRI reconstruction.

cs.CV↗

arXiv2025

Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstruction

Magnetic resonance imaging (MRI) is a vital clinical diagnostic tool, yet its application is limited by prolonged scan times. Accelerating MRI reconstruction addresses this issue by reconstructing high-fidelity MR images from undersampled k-space measurements. In recent years, deep learning-based methods have demonstrated remarkable progress. However, most methods rely on supervised learning, which requires large amounts of fully-sampled training data that are difficult to obtain. This paper proposes a novel zero-shot self-supervised reconstruction method named UnrollINR, which enables scan-specific MRI reconstruction without external training data. UnrollINR adopts a physics-guided unrolled reconstruction architecture and introduces implicit neural representation (INR) as a regularization prior to effectively constrain the solution space. This method overcomes the local bias limitation of CNNs in traditional deep unrolled methods and avoids the instability associated with relying solely on INR's implicit regularization in highly ill-posed scenarios. Consequently, UnrollINR significantly improves MRI reconstruction performance under high acceleration rates. Experimental results show that even at a high acceleration rate of 10, UnrollINR achieves superior reconstruction performance compared to supervised and self-supervised learning methods, validating its effectiveness and superiority.

cs.CV↗

arXiv2025

EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation

Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce EnzyBind, a dataset with 11,100 experimentally validated enzyme-substrate pairs specifically curated from PDBbind. Building on this, we propose EnzyControl, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSA-annotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme-substrate data. At the core of EnzyControl is EnzyAdapter, a lightweight, modular component integrated into a pretrained motif-scaffolding model, allowing it to become substrate-aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyControl achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements of 13\% in designability and 13\% in catalytic efficiency compared to the baseline models. The code is released at https://github.com/Vecteur-libre/EnzyControl.

q-bio.BM↗

arXiv2025

Accurate myocardial T1 mapping at 5T using an improved MOLLI method: A validation study

Background: Accurate myocardial T1 mapping at 5T remains a technical challenge due to field inhomogeneity and prolonged T1 values. The aim of this study is to develop an accurate and clinically applicable myocardial T1 mapping technique for 5T magnetic resonance imaging (MRI) systems and validate its performance in a multicenter study. Methods: The proposed method is based on a 5-(3)-3 MOLLI sequence, dubbed combined-correction MOLLI (coMOLLI), which corrects for both inversion efficiency and readout-induced signal disturbances. Specifically, coMOLLI employs a gradient recalled echo (GRE) readout rather than the commonly used bSSFP readout. Its signal evolution is modeled to estimate T1 values, which incorporates both inversion efficiency and readout disturbances to improve fitting accuracy. To further enhance accuracy, the inversion pulse was redesigned under hardware constraints and the observed B0 and B1 variations over the heart at 5T, using adiabatic hyperbolic secant (HSn) and tangent/hyperbolic tangent (Tan/Tanh) pulses. The method was validated in phantom experiments, as well as in 21 healthy volunteers and 9 patients. Results: The optimized inversion pulse at 5T is the Tan/Tanh pulse with A = 10 kHz, K_s = 4, k = 22, and T_p = 8 ms. In phantom studies, coMOLLI showed high accuracy versus reference IR-FSE, yielding relative errors within 5% for all nine vials. In vivo studies, the average native myocardial T1 values across 21 healthy volunteers were 1468 +/- 48 ms, 1514 +/- 39 ms, and 1545 +/- 50 ms, and blood T1 values were 2182 +/- 132 ms, 2124 +/- 153 ms, and 2131 +/- 158 ms for apical, middle, and base slices, respectively. Conclusion: The coMOLLI method demonstrated high accuracy in phantom studies and feasibility in vivo studies. By adopting the widely used 5-(3)-3 MOLLI acquisition scheme, it shows potential for clinical cardiac imaging at 5T.

physics.med-ph↗

arXiv2025

HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion

The reconstruction of visual information from brain activity fosters interdisciplinary integration between neuroscience and computer vision. However, existing methods still face challenges in accurately recovering highly complex visual stimuli. This difficulty stems from the characteristics of natural scenes: low-level features exhibit heterogeneity, while high-level features show semantic entanglement due to contextual overlaps. Inspired by the hierarchical representation theory of the visual cortex, we propose the HAVIR model, which separates the visual cortex into two hierarchical regions and extracts distinct features from each. Specifically, the Structural Generator extracts structural information from spatial processing voxels and converts it into latent diffusion priors, while the Semantic Extractor converts semantic processing voxels into CLIP embeddings. These components are integrated via the Versatile Diffusion model to synthesize the final image. Experimental results demonstrate that HAVIR enhances both the structural and semantic quality of reconstructions, even in complex scenes, and outperforms existing models.

cs.CV↗

arXiv2025

NeuroSwift: A Lightweight Cross-Subject Framework for fMRI Visual Reconstruction of Complex Scenes

Reconstructing visual information from brain activity via computer vision technology provides an intuitive understanding of visual neural mechanisms. Despite progress in decoding fMRI data with generative models, achieving accurate cross-subject reconstruction of visual stimuli remains challenging and computationally demanding. This difficulty arises from inter-subject variability in neural representations and the brain's abstract encoding of core semantic features in complex visual inputs. To address these challenges, we propose NeuroSwift, which integrates complementary adapters via diffusion: AutoKL for low-level features and CLIP for semantics. NeuroSwift's CLIP Adapter is trained on Stable Diffusion generated images paired with COCO captions to emulate higher visual cortex encoding. For cross-subject generalization, we pretrain on one subject and then fine-tune only 17 percent of parameters (fully connected layers) for new subjects, while freezing other components. This enables state-of-the-art performance with only one hour of training per subject on lightweight GPUs (three RTX 4090), and it outperforms existing methods.

cs.CV↗

arXiv2025

Myocardial T1 mapping at 5T using multi-inversion recovery real-time spoiled GRE

Objective: To develop an accurate myocardial T1 mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisition. Approach: The proposed T1 mapping technique (mIR-rt) samples the recovery of inverted magnetization using the real-time GRE and the images captured during diastole are selected for T1 fitting. Multiple-inversion recoveries are employed to increase the sample size for accurate fitting. The T1 mapping method was validated using Bloch simulation, phantom studies, and in 16 healthy volunteers at 5T. Main results: In both simulation and phantom studies, the T1 values measured by mIR-rt closely approximate the reference T1 values, with errors less than 3%, while the conventional MOLLI sequence underestimates T1 values. The myocardial T1 values at 5T are 1553 +/- 52 ms, 1531 +/- 53 ms, and 1526 +/- 60 ms (mean +/- standard deviation) at the apex, middle, and base, respectively. The T1 values measured by MOLLI (1350 +/- 48 ms, 1349 +/- 47 ms, and 1354 +/- 45 ms at the apex, middle, and base) were significantly lower than those of mIR-rt with p<0.001 for all three layers. The mIR-rt sequence method used in our study provides high reproducibility, particularly in the middle slices, supporting its practical relevance for myocardial T1 mapping. Significance: The proposed method is feasible for myocardial T1 mapping at 5T and provides better accuracy than the conventional MOLLI sequence.

physics.med-ph↗

arXiv2025

HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion

Reconstructing visual information from brain activity bridges the gap between neuroscience and computer vision. Even though progress has been made in decoding images from fMRI using generative models, a challenge remains in accurately recovering highly complex visual stimuli. This difficulty stems from their elemental density and diversity, sophisticated spatial structures, and multifaceted semantic information. To address these challenges, we propose HAVIR that contains two adapters: (1) The AutoKL Adapter transforms fMRI voxels into a latent diffusion prior, capturing topological structures; (2) The CLIP Adapter converts the voxels to CLIP text and image embeddings, containing semantic information. These complementary representations are fused by Versatile Diffusion to generate the final reconstructed image. To extract the most essential semantic information from complex scenarios, the CLIP Adapter is trained with text captions describing the visual stimuli and their corresponding semantic images synthesized from these captions. The experimental results demonstrate that HAVIR effectively reconstructs both structural features and semantic information of visual stimuli even in complex scenarios, outperforming existing models.

cs.CV↗

arXiv2025

COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing

Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. While considerable success has been achieved, there still exist urgent limitations. Existing works ignore the locality relationships of representations and attributes, which have effective transferability between seeable classes and unseeable classes. Also, the continuous-value attributes are not fully harnessed. In response, we conduct a COMprehensive Attribute Exploration for ZSH, named COMAE, which depicts the relationships from seen classes to unseen ones through three meticulously designed explorations, i.e., point-wise, pair-wise and class-wise consistency constraints. By regressing attributes from the proposed attribute prototype network, COMAE learns the local features that are relevant to the visual attributes. Then COMAE utilizes contrastive learning to comprehensively depict the context of attributes, rather than instance-independent optimization. Finally, the class-wise constraint is designed to cohesively learn the hash code, image representation, and visual attributes more effectively. Experimental results on the popular ZSH datasets demonstrate that COMAE outperforms state-of-the-art hashing techniques, especially in scenarios with a larger number of unseen label classes.

cs.CV↗

arXiv2025

Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data

Facing the escalating threat of global wildfires, numerous computer vision techniques using remote sensing data have been applied in this area. However, the selection of deep learning methods for wildfire prediction remains uncertain due to the lack of comparative analysis in a quantitative and explainable manner, crucial for improving prevention measures and refining models. This study aims to thoroughly compare the performance, efficiency, and explainability of four prevalent deep learning architectures: Autoencoder, ResNet, UNet, and Transformer-based Swin-UNet. Employing a real-world dataset that includes nearly a decade of remote sensing data from California, U.S., these models predict the spread of wildfires for the following day. Through detailed quantitative comparison analysis, we discovered that Transformer-based Swin-UNet and UNet generally outperform Autoencoder and ResNet, particularly due to the advanced attention mechanisms in Transformer-based Swin-UNet and the efficient use of skip connections in both UNet and Transformer-based Swin-UNet, which contribute to superior predictive accuracy and model interpretability. Then we applied XAI techniques on all four models, this not only enhances the clarity and trustworthiness of models but also promotes focused improvements in wildfire prediction capabilities. The XAI analysis reveals that UNet and Transformer-based Swin-UNet are able to focus on critical features such as 'Previous Fire Mask', 'Drought', and 'Vegetation' more effectively than the other two models, while also maintaining balanced attention to the remaining features, leading to their superior performance. The insights from our thorough comparative analysis offer substantial implications for future model design and also provide guidance for model selection in different scenarios.

cs.CV↗

arXiv2025

Origin of $α$-satellite repeat arrays from mitochondrial molecular fossils -- sequential insertion, expansion, and evolution in the nuclear genome

Alpha satellite DNA is large tandem arrays of 150-400 bp units, and its origin remains an evolutionary mystery. In this research, we identified 1,545 alpha-satellite-like (SatL) repeat units in the nuclear genome of jewel wasp Nasonia vitripennis. Among them, thirty-nine copies of SatL were organized in two palindromic arrays in mitochondria, resulting in a 50% increase in the genome size. Strikingly, genomic neighborhood analyses of 1,516 nuclear SatL repeats revealed that they are located in NuMT (nuclear mitochondrial DNA) regions, and SatL phylogeny matched perfectly with mitochondrial genes and NuMT pseudogenes. These results support that SatL arrays originated from ten independent mitochondria insertion events into the nuclear genome within the last 500,000 years, after divergence from its sister species N. giraulti. Dramatic repeat GC-percent elevation (from 33.9% to 50.4%) is a hallmark of rapid SatL sequence evolution in mitochondria due to GC-biased gene conversion facilitated by the palindromic sequence pairing of the two mitochondrial SatL arrays. The nuclear SatL repeat arrays underwent substantial copy number expansion, from 12-15 (SatL1) to over 400 copies (SatL4). The oldest SatL4B array consists of four types of repeat units derived from deletions in the AT-rich region of ancestral repeats, and complex high-order structures have evolved through duplications. We also discovered similar repeat insertions into the nuclear genome of Muscidifurax, suggesting this mechanism can be common in insects. This is the first report of the mitochondrial origin of nuclear satellite sequences, and our findings shed new light on the origin and evolution of satellite DNA.

q-bio.GN↗

arXiv2025

K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This study proposed a novel k-space diffusion model of SMS reconstruction that does not utilize SMS data for training. Instead, it incorporates Slice GRAPPA during the sampling process to reconstruct SMS data from different acquisition modes.Our results demonstrated that this method outperforms traditional SMS reconstruction methods and can achieve higher acceleration factors without in-plane aliasing.

eess.IV↗