arXiv subjects

Lin Wang

Publications and source records attributed to Lin Wang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction

Open world Machine Learning (OWML) aims to develop intelligent systems capable of recognizing known categories, rejecting unknown samples, and continually learning from novel information. Despite significant progress in open set recognition, novelty detection, and continual learning, the field still lacks a unified theoretical foundation that can quantify uncertainty, characterize information transfer, and explain learning adaptability in dynamic, nonstationary environments. This paper presents a comprehensive review of information theoretic approaches in open world machine learning, emphasizing how core concepts such as entropy, mutual information, and Kullback Leibler divergence provide a mathematical language for describing knowledge acquisition, uncertainty suppression, and risk control under open world conditions. We synthesize recent studies into three major research axes: information theoretic open set recognition enabling safe rejection of unknowns, information driven novelty discovery guiding new concept formation, and information retentive continual learning ensuring stable long term adaptation. Furthermore, we discuss theoretical connections between information theory and provable learning frameworks, including PAC Bayes bounds, open-space risk theory, and causal information flow, to establish a pathway toward provable and trustworthy open world intelligence. Finally, the review identifies key open problems and future research directions, such as the quantification of information risk, development of dynamic mutual information bounds, multimodal information fusion, and integration of information theory with causal reasoning and world model learning.

stat.ML↗

arXiv2025

Efficient Graph Condensation via Gaussian Process

Graph condensation reduces the size of large graphs while preserving performance, addressing the scalability challenges of Graph Neural Networks caused by computational inefficiencies on large datasets. Existing methods often rely on bi-level optimization, requiring extensive GNN training and limiting their scalability. To address these issues, this paper proposes Graph Condensation via Gaussian Process (GCGP), a novel and computationally efficient approach to graph condensation. GCGP utilizes a Gaussian Process (GP), with the condensed graph serving as observations, to estimate the posterior distribution of predictions. This approach eliminates the need for the iterative and resource-intensive training typically required by GNNs. To enhance the capability of the GCGP in capturing dependencies between function values, we derive a specialized covariance function that incorporates structural information. This covariance function broadens the receptive field of input nodes by local neighborhood aggregation, thereby facilitating the representation of intricate dependencies within the nodes. To address the challenge of optimizing binary structural information in condensed graphs, Concrete random variables are utilized to approximate the binary adjacency matrix in a continuous counterpart. This relaxation process allows the adjacency matrix to be represented in a differentiable form, enabling the application of gradient-based optimization techniques to discrete graph structures. Experimental results show that the proposed GCGP method efficiently condenses large-scale graph data while preserving predictive performance, addressing the scalability and efficiency challenges. The implementation of our method is publicly available at https://github.com/WANGLin0126/GCGP.

cs.LG↗

arXiv2025

Contrastive Learning Augmented Social Recommendations

Recommender systems are essential for modern content platforms, yet traditional behavior-based models often struggle with cold users who have limited interaction data. Engaging these users is crucial for platform growth. To bridge this gap, we propose leveraging the social-relation graph to enrich interest representations from behavior-based models. However, extracting value from social graphs is challenging due to relation noise and cross-domain inconsistency. To address the noise propagation and obtain accurate social interest, we employ a dual-view denoising strategy, employing low-rank SVD to the user-item interaction matrix for a denoised social graph and contrastive learning to align the original and reconstructed social graphs. Addressing the interest inconsistency between social and behavioral interests, we adopt a "mutual distillation" technique to isolate the original interests into aligned social/behavior interests and social/behavior specific interests, maximizing the utility of both. Experimental results on widely adopted industry datasets verify the method's effectiveness, particularly for cold users, offering a fresh perspective for future research. The implementation can be accessed at https://github.com/WANGLin0126/CLSRec.

cs.IR↗

arXiv2025

Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios.

cs.RO↗

arXiv2025

Automated and Scalable SEM Image Analysis of Perovskite Solar Cell Materials via a Deep Segmentation Framework

Scanning Electron Microscopy (SEM) is indispensable for characterizing the microstructure of thin films during perovskite solar cell fabrication. Accurate identification and quantification of lead iodide and perovskite phases are critical because residual lead iodide strongly influences crystallization pathways and defect formation, while the morphology of perovskite grains governs carrier transport and device stability. Yet current SEM image analysis is still largely manual, limiting throughput and consistency. Here, we present an automated deep learning-based framework for SEM image segmentation that enables precise and efficient identification of lead iodide, perovskite and defect domains across diverse morphologies. Built upon an improved YOLOv8x architecture, our model named PerovSegNet incorporates two novel modules: (i) Adaptive Shuffle Dilated Convolution Block, which enhances multi-scale and fine-grained feature extraction through group convolutions and channel mixing; and (ii) Separable Adaptive Downsampling module, which jointly preserves fine-scale textures and large-scale structures for more robust boundary recognition. Trained on an augmented dataset of 10,994 SEM images, PerovSegNet achieves a mean Average Precision of 87.25% with 265.4 Giga Floating Point Operations, outperforming the baseline YOLOv8x-seg by 4.08%, while reducing model size and computational load by 24.43% and 25.22%, respectively. Beyond segmentation, the framework provides quantitative grain-level metrics, such as lead iodide/perovskite area and count, which can serve as reliable indicators of crystallization efficiency and microstructural quality. These capabilities establish PerovSegNet as a scalable tool for real-time process monitoring and data-driven optimization of perovskite thin-film fabrication.The source code is available at:https://github.com/wlyyj/PerovSegNet/tree/master.

cond-mat.mtrl-sci↗

arXiv2025

Superconductivity at 22.3 K in Compressed Sodium-intercalated Graphite

Graphite intercalation compounds (GICs) have long been recognized as promising candidates for high-temperature superconductivity by intercalation or charge doping, yet experimental progress has stalled with transition temperatures (Tc) limited to 11.5 K at ambient pressure and 15.1 K at 7.5 GPa in calcium-intercalated graphite over decades. Here, we report robust superconductivity in sodium-intercalated graphite with Tc of 22.3 K, as demonstrated by clear zero-resistance behavior. Our approach involves simply room-temperature grinding of graphite with sodium, followed by slight compression up to 7.1 GPa, circumventing complex synthesis procedures. Through synchrotron X-ray diffraction combined with first-principles calculations, we identify the major superconducting phase as an orthorhombic stage-2 GIC structure with slightly over-stoichiometric composition (Na1+xC8). Electron-phonon coupling calculations reveal that superconductivity primarily emerges from the interactions between out-of-plane carbon electrons and low-frequency Na/C vibrations.The enhancement in Tc establishes sodium as superior for achieving higher-Tc in GICs and illustrates promising pathway for further optimization through compositional and structural tuning.

cond-mat.supr-con↗

arXiv2025

Nanoimprinted topological laser in the visible

Nanoimprint lithography (NIL) is a widely used high-throughput fabrication technique for photonic devices, yet its reliability is often compromised by the inevitable imperfections that arise during the demolding process. Topological photonics, which harnesses topologically nontrivial structures to support defect-robust photonic states, offers a promising solution to this challenge. Here, we demonstrate a topological laser that is one-step nanoimprinted upon colloidal perovskite nanocrystals. This laser features multiple higher-order topological corner states, with topological protection provided by the structure effectively mitigating imperfections caused by the nanoimprinting process. This property enables the reliable detection of these states, which is particularly challenging to achieve in the visible spectrum. Our work establishes topological photonics as a viable pathway to enhance the reliability of NIL-based manufacturing, providing a scalable and practical route for mass-producing topological lasers with low-index materials.

physics.optics↗

arXiv2025

Evolution of ultra-flat band in van der Waals kagome semiconductor Pd3P2(S1-xSex)8

We investigate the evolutions of structural parameters, optical properties, and electronic structures of van der Waals kagome semiconductor Pd3P2S8 with Se doping. When the doping level of Se increases, the bandgaps of Pd3P2(S1-xSex)8 single crystals decrease gradually, accompanying with the expanded unit cells. The first-principles calculations show that there is a flat band (FB) near the Fermi level in bulk Pd3P2S8. This FB mainly originates from the dz2-like orbitals of Pd atoms in the Pd kagome lattice, which has a finite interlayer electron hopping perpendicular to the PdS4 square plane. The interlayer hopping can be reinforced with the Se doping, inducing a stronger interlayer coupling via the chalcogen atoms at apical sites, which reduces the bandgap and enhances the cleavage energy. In contrast, the vanishing interlayer hopping in the two-dimensional limit results in the formation of ultra-FB in the monolayers of these compounds. The easy exfoliation and the existence of unique ultra-FB near EF make Pd3P2(S1-xSex)8 a model system to explore the exotic physics of FB in two-dimensional kagome lattice.

cond-mat.mtrl-sci↗

arXiv2025

Event-guided 3D Gaussian Splatting for Dynamic Human and Scene Reconstruction

Reconstructing dynamic humans together with static scenes from monocular videos remains difficult, especially under fast motion, where RGB frames suffer from motion blur. Event cameras exhibit distinct advantages, e.g., microsecond temporal resolution, making them a superior sensing choice for dynamic human reconstruction. Accordingly, we present a novel event-guided human-scene reconstruction framework that jointly models human and scene from a single monocular event camera via 3D Gaussian Splatting. Specifically, a unified set of 3D Gaussians carries a learnable semantic attribute; only Gaussians classified as human undergo deformation for animation, while scene Gaussians stay static. To combat blur, we propose an event-guided loss that matches simulated brightness changes between consecutive renderings with the event stream, improving local fidelity in fast-moving regions. Our approach removes the need for external human masks and simplifies managing separate Gaussian sets. On two benchmark datasets, ZJU-MoCap-Blur and MMHPSD-Blur, it delivers state-of-the-art human-scene reconstruction, with notable gains over strong baselines in PSNR/SSIM and reduced LPIPS, especially for high-speed subjects.

cs.CV↗

arXiv2025

Seeing Through Reflections: Advancing 3D Scene Reconstruction in Mirror-Containing Environments with Gaussian Splatting

Mirror-containing environments pose unique challenges for 3D reconstruction and novel view synthesis (NVS), as reflective surfaces introduce view-dependent distortions and inconsistencies. While cutting-edge methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) excel in typical scenes, their performance deteriorates in the presence of mirrors. Existing solutions mainly focus on handling mirror surfaces through symmetry mapping but often overlook the rich information carried by mirror reflections. These reflections offer complementary perspectives that can fill in absent details and significantly enhance reconstruction quality. To advance 3D reconstruction in mirror-rich environments, we present MirrorScene3D, a comprehensive dataset featuring diverse indoor scenes, 1256 high-quality images, and annotated mirror masks, providing a benchmark for evaluating reconstruction methods in reflective settings. Building on this, we propose ReflectiveGS, an extension of 3D Gaussian Splatting that utilizes mirror reflections as complementary viewpoints rather than simple symmetry artifacts, enhancing scene geometry and recovering absent details. Experiments on MirrorScene3D show that ReflectiveGaussian outperforms existing methods in SSIM, PSNR, LPIPS, and training speed, setting a new benchmark for 3D reconstruction in mirror-rich environments.

cs.CV↗

arXiv2025

PASS: Path-selective State Space Model for Event-based Recognition

Event cameras are bio-inspired sensors that capture intensity changes asynchronously with distinct advantages, such as high temporal resolution. Existing methods for event-based object/action recognition predominantly sample and convert event representation at every fixed temporal interval (or frequency). However, they are constrained to processing a limited number of event lengths and show poor frequency generalization, thus not fully leveraging the event's high temporal resolution. In this paper, we present our PASS framework, exhibiting superior capacity for spatiotemporal event modeling towards a larger number of event lengths and generalization across varying inference temporal frequencies. Our key insight is to learn adaptively encoded event features via the state space models (SSMs), whose linear complexity and generalization on input frequency make them ideal for processing high temporal resolution events. Specifically, we propose a Path-selective Event Aggregation and Scan (PEAS) module to encode events into features with fixed dimensions by adaptively scanning and selecting aggregated event presentations. On top of it, we introduce a novel Multi-faceted Selection Guiding (MSG) loss to minimize the randomness and redundancy of the encoded features during the PEAS selection process. Our method outperforms prior methods on five public datasets and shows strong generalization across varying inference frequencies with less accuracy drop (ours -8.62% vs. -20.69% for the baseline). Overall, PASS exhibits strong long spatiotemporal modeling for a broader distribution of event length (1-10^9), precise temporal perception, and generalization for real-world

cs.CV↗

arXiv2025

Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models

Recently, the diffusion model has gained significant attention as one of the most successful image generation models, which can generate high-quality images by iteratively sampling noise. However, recent studies have shown that diffusion models are vulnerable to backdoor attacks, allowing attackers to enter input data containing triggers to activate the backdoor and generate their desired output. Existing backdoor attack methods primarily focused on target noise-to-image and text-to-image tasks, with limited work on backdoor attacks in image-to-image tasks. Furthermore, traditional backdoor attacks often rely on a single, conspicuous trigger to generate a fixed target image, lacking concealability and flexibility. To address these limitations, we propose a novel backdoor attack method called "Parasite" for image-to-image tasks in diffusion models, which not only is the first to leverage steganography for triggers hiding, but also allows attackers to embed the target content as a backdoor trigger to achieve a more flexible attack. "Parasite" as a novel attack method effectively bypasses existing detection frameworks to execute backdoor attacks. In our experiments, "Parasite" achieved a 0 percent backdoor detection rate against the mainstream defense frameworks. In addition, in the ablation study, we discuss the influence of different hiding coefficients on the attack results. You can find our code at https://anonymous.4open.science/r/Parasite-1715/.

cs.CV↗

arXiv2025

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective

Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advancing Agentic Spatial Intelligence toward better interaction with the physical 3D world. To this end, we first start from scrutinizing the spatial neural models as studied in computational neuroscience, and accordingly introduce a novel computational framework grounded in neuroscience principles. This framework maps core biological functions to six essential computation modules: bio-inspired multimodal sensing, multi-sensory integration, egocentric-allocentric conversion, an artificial cognitive map, spatial memory, and spatial reasoning. Together, these modules form a perspective landscape for agentic spatial reasoning capability across both virtual and physical environments. On top, we conduct a framework-guided analysis of recent methods, evaluating their relevance to each module and identifying critical gaps that hinder the development of more neuroscience-grounded spatial reasoning modules. We further examine emerging benchmarks and datasets and explore potential application domains ranging from virtual to embodied systems, such as robotics. Finally, we outline potential research directions, emphasizing the promising roadmap that can generalize spatial reasoning across dynamic or unstructured environments. We hope this work will benefit the research community with a neuroscience-grounded perspective and a structured pathway. Our project page can be found at Github.

cs.AI↗

arXiv2025

Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks

This paper characterizes the fundamental trade-off between throughput and Age of Information (AoI) in wireless networks where multiple devices transmit status updates to a central base station over unreliable channels. To address the complexity introduced by stochastic transmission successes, we propose the throughput-AoI capacity region, which defines all feasible throughput-AoI pairs achievable under any scheduling policy. Using a second-order approximation that incorporates both mean and temporal variance, we derive an outer bound and a tight inner bound for the throughput-AoI capacity region. Furthermore, we propose a simple and low complexity scheduling policy and prove that it achieves every interior point within the tight inner bound. This establishes a systematic and theoretically grounded framework for the joint optimization of throughput and information freshness in practical wireless communication scenarios. To validate our theoretical framework and demonstrate the utility of the throughput-AoI capacity region, extensive simulations are implemented. Simulation results demonstrate that our proposed policy significantly outperforms conventional methods across various practical network optimization scenarios. The findings highlight our approach's effectiveness in optimizing both throughput and AoI, underscoring its applicability and robustness in practical wireless networks.

eess.SY↗

arXiv2025

SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification

We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position information and a SAC block to perform full instance correlations. The positional encoding module utilizes the instance coordinates within the slide to encode the spatial relationships instead of the instance index in the input WSI sequence. The positional encoding module can also handle the length extrapolation issue where the training and testing sequences have different lengths. The SAC block is an MLP-based method that performs full instance correlation in linear time complexity with respect to the sequence length. Due to the simple structure of MLP, it is easy to deploy since it does not require custom CUDA kernels, compared to Transformer-based methods for WSI classification. SAC-MIL has achieved state-of-the-art performance on the CAMELYON-16, TCGA-LUNG, and TCGA-BRAC datasets. The code will be released upon acceptance.

cs.CV↗

arXiv2025

Uirapuru: Timely Video Analytics for High-Resolution Steerable Cameras on Edge Devices

Real-time video analytics on high-resolution cameras has become a popular technology for various intelligent services like traffic control and crowd monitoring. While extensive work has been done on improving analytics accuracy with timing guarantees, virtually all of them target static viewpoint cameras. In this paper, we present Uirapuru, a novel framework for real-time, edge-based video analytics on high-resolution steerable cameras. The actuation performed by those cameras brings significant dynamism to the scene, presenting a critical challenge to existing popular approaches such as frame tiling. To address this problem, Uirapuru incorporates a comprehensive understanding of camera actuation into the system design paired with fast adaptive tiling at a per-frame level. We evaluate Uirapuru on a high-resolution video dataset, augmented by pan-tilt-zoom (PTZ) movements typical for steerable cameras and on real-world videos collected from an actual PTZ camera. Our experimental results show that Uirapuru provides up to 1.45x improvement in accuracy while respecting specified latency budgets or reaches up to 4.53x inference speedup with on-par accuracy compared to state-of-the-art static camera approaches.

cs.CV↗

arXiv2025

Coherent Compensation-Based Sensing for Long-Range Targets in Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) is a promising candidate technology for 6G due to its improvement in spectral efficiency and energy efficiency. Orthogonal frequency division multiplexing (OFDM) signal is a mainstream candidate ISAC waveform. However, there are inter-symbol interference (ISI) and inter-carrier interference (ICI) when the round-trip delay exceeds the cyclic prefix (CP) duration for OFDM signals, which limits the maximum sensing range of ISAC system. When detecting a long-range target, the wide beam inevitably covers the close-range target, of which the echo's power is much larger than that of the long-range target. In order to tackle the above problem, a multiple signal classification (MUSIC) and least squares (LS)-based spatial signal separation method is proposed to separate the echo signals reflected from different targets. Moreover, a coherent compensation-based sensing signal processing method at the receiver is proposed to enhance the signal to interference plus noise power ratio (SINR) of the OFDM block for generating the range-Doppler map (RDM) with higher SINR. Simulation results reveal that the proposed method greatly enhances the SINR of RDM by 10 dB for a target at 500 m compared with two-dimensional fast Fourier transform (2D-FFT) method. Besides, the detection probability is also significantly improved compared to the benchmarking method.

eess.SP↗

arXiv2025

A Two-Step Test to Identify Zero-Inflated Biomarkers in Early-Phase Clinical Trials

In early-phase clinical trials, a predictive biomarker may identify subgroups that benefit from an experimental therapy, even when the overall average treatment effect is negligible. Recently proposed nonparametric interaction tests such as the Average Kolmogorov-Smirnov Approach (AKSA) avoid prespecified biomarker cutting points and model assumptions, but their power degrades when the biomarker distribution is zero-inflated. We propose a two-step test that partitions the analysis into a spike test for biomarker-negative patients and a tail test for biomarker-positive patients, then combines the resulting p-values using Fisher's or Brown's method. This design isolates distinct sources of predictive effects, mitigates dilution, and preserves exact type I error control through permutation calibration. We derive theoretical properties showing that the proposed test retains nominal size and improves power over AKSA when predictive effects are concentrated in either the spike or tail subpopulation. Extensive simulations confirm robust type I error control under various zero-inflation rates, sample sizes, and skewed biomarker distributions. We also demonstrate consistent power gains across spike-only, tail-only, and mixed-effect scenarios. Our method provides a practical and flexible tool for early-phase trials with sparse biomarker distributions, enabling more reliable identification of predictive biomarkers to guide later-phase development.

stat.ME↗