Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection

Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) applications. However, GNSS are highly vulnerable to spoofing attacks, where adversaries transmit counterfeit signals to mislead receivers. Such attacks can lead to severe consequences, including misdirected navigation, compromised data integrity, and operational disruptions. Most existing spoofing detection methods depend on supervised learning techniques and struggle to detect novel, evolved, and unseen attacks. To overcome this limitation, we develop a zero-day spoofing detection method using a Hybrid Quantum-Classical Autoencoder (HQC-AE), trained solely on authentic GNSS signals without exposure to spoofed data. By leveraging features extracted during the tracking stage, our method enables proactive detection before PNT solutions are computed. We focus on spoofing detection in static GNSS receivers, which are particularly susceptible to time-push spoofing attacks, where attackers manipulate timing information to induce incorrect time computations at the receiver. We evaluate our model against different unseen time-push spoofing attack scenarios: simplistic, intermediate, and sophisticated. Our analysis demonstrates that the HQC-AE consistently outperforms its classical counterpart, traditional supervised learning-based models, and existing unsupervised learning-based methods in detecting zero-day, unseen GNSS time-push spoofing attacks, achieving an average detection accuracy of 97.71% with an average false negative rate of 0.62% (when an attack occurs but is not detected). For sophisticated spoofing attacks, the HQC-AE attains an accuracy of 98.23% with a false negative rate of 1.85%. These findings highlight the effectiveness of our method in proactively detecting zero-day GNSS time-push spoofing attacks across various stationary GNSS receiver platforms.

GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction

In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra noise, leading to interference with the model's judgment of the long-distance dependencies between events. To address these issues, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. We first present a distance-aware graph structure utilizing Graph Attention Network(GAT) to assist the LLMs in capturing long-distance dependency features. Additionally, we design a temporal feature learning paradigm based on soft inference to augment the identification of relations with a short-distance proximity band, which supplements the probabilistic information generated by LLMs into the multi-head attention mechanism. Since the global feature can be captured effectively, our framework substantially enhances the performance of minority relation classes and improves the overall learning ability. Experiments on two publicly available datasets, TB-Dense and MATRES, demonstrate that our approach achieves state-of-the-art (SOTA) performance.

Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks

Accurate air quality prediction is becoming increasingly important in the environmental field. To address issues such as low prediction accuracy and slow real-time updates in existing models, which lead to lagging prediction results, we propose a Transformer-based spatiotemporal data prediction method (Ada-TransGNN) that integrates global spatial semantics and temporal behavior. The model constructs an efficient and collaborative spatiotemporal block set comprising a multi-head attention mechanism and a graph convolutional network to extract dynamically changing spatiotemporal dependency features from complex air quality monitoring data. Considering the interaction relationships between different monitoring points, we propose an adaptive graph structure learning module, which combines spatiotemporal dependency features in a data-driven manner to learn the optimal graph structure, thereby more accurately capturing the spatial relationships between monitoring points. Additionally, we design an auxiliary task learning module that enhances the decoding capability of temporal relationships by integrating spatial context information into the optimal graph structure representation, effectively improving the accuracy of prediction results. We conducted comprehensive evaluations on a benchmark dataset and a novel dataset (Mete-air). The results demonstrate that our model outperforms existing state-of-the-art prediction models in short-term and long-term predictions.

Multi-Level LVLM Guidance for Untrimmed Video Action Recognition

Action recognition and localization in complex, untrimmed videos remain a formidable challenge in computer vision, largely due to the limitations of existing methods in capturing fine-grained actions, long-term temporal dependencies, and high-level semantic information from low-level visual features. This paper introduces the Event-Contextualized Video Transformer (ECVT), a novel architecture that leverages the advanced semantic understanding capabilities of Large Vision-Language Models (LVLMs) to bridge this gap. ECVT employs a dual-branch design, comprising a Video Encoding Branch for spatio-temporal feature extraction and a Cross-Modal Guidance Branch. The latter utilizes an LVLM to generate multi-granularity semantic descriptions, including Global Event Prompting for macro-level narrative and Temporal Sub-event Prompting for fine-grained action details. These multi-level textual cues are integrated into the video encoder's learning process through sophisticated mechanisms such as adaptive gating for high-level semantic fusion, cross-modal attention for fine-grained feature refinement, and an event graph module for temporal context calibration. Trained end-to-end with a comprehensive loss function incorporating semantic consistency and temporal calibration terms, ECVT significantly enhances the model's ability to understand video temporal structures and event logic. Extensive experiments on ActivityNet v1.3 and THUMOS14 datasets demonstrate that ECVT achieves state-of-the-art performance, with an average mAP of 40.5% on ActivityNet v1.3 and mAP@0.5 of 67.1% on THUMOS14, outperforming leading baselines.

Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology

Immunohistochemical (IHC) biomarker prediction benefits from multi-modal data fusion analysis. However, the simultaneous acquisition of multi-modal data, such as genomic and pathological information, is often challenging due to cost or technical limitations. To address this challenge, we propose an online distillation approach based on Multi-modal Knowledge Decomposition (MKD) to enhance IHC biomarker prediction in haematoxylin and eosin (H\&E) stained histopathology images. This method leverages paired genomic-pathology data during training while enabling inference using either pathology slides alone or both modalities. Two teacher and one student models are developed to extract modality-specific and modality-general features by minimizing the MKD loss. To maintain the internal structural relationships between samples, Similarity-preserving Knowledge Distillation (SKD) is applied. Additionally, Collaborative Learning for Online Distillation (CLOD) facilitates mutual learning between teacher and student models, encouraging diverse and complementary learning dynamics. Experiments on the TCGA-BRCA and in-house QHSU datasets demonstrate that our approach achieves superior performance in IHC biomarker prediction using uni-modal data. Our code is available at https://github.com/qiyuanzz/MICCAI2025_MKD.

Defending Deepfake via Texture Feature Perturbation

The rapid development of Deepfake technology poses severe challenges to social trust and information security. While most existing detection methods primarily rely on passive analyses, due to unresolvable high-quality Deepfake contents, proactive defense has recently emerged by inserting invisible signals in advance of image editing. In this paper, we introduce a proactive Deepfake detection approach based on facial texture features. Since human eyes are more sensitive to perturbations in smooth regions, we invisibly insert perturbations within texture regions that have low perceptual saliency, applying localized perturbations to key texture regions while minimizing unwanted noise in non-textured areas. Our texture-guided perturbation framework first extracts preliminary texture features via Local Binary Patterns (LBP), and then introduces a dual-model attention strategy to generate and optimize texture perturbations. Experiments on CelebA-HQ and LFW datasets demonstrate the promising performance of our method in distorting Deepfake generation and producing obvious visual defects under multiple attack models, providing an efficient and scalable solution for proactive Deepfake detection.

HyperTASR: Hypernetwork-Driven Task-Aware Scene Representations for Robust Manipulation

Effective policy learning for robotic manipulation requires scene representations that selectively capture task-relevant environmental features. Current approaches typically employ task-agnostic representation extraction, failing to emulate the dynamic perceptual adaptation observed in human cognition. We present HyperTASR, a hypernetwork-driven framework that modulates scene representations based on both task objectives and the execution phase. Our architecture dynamically generates representation transformation parameters conditioned on task specifications and progression state, enabling representations to evolve contextually throughout task execution. This approach maintains architectural compatibility with existing policy learning frameworks while fundamentally reconfiguring how visual features are processed. Unlike methods that simply concatenate or fuse task embeddings with task-agnostic representations, HyperTASR establishes computational separation between task-contextual and state-dependent processing paths, enhancing learning efficiency and representational quality. Comprehensive evaluations in both simulation and real-world environments demonstrate substantial performance improvements across different representation paradigms. Through ablation studies and attention visualization, we confirm that our approach selectively prioritizes task-relevant scene information, closely mirroring human adaptive perception during manipulation tasks. The project website is at \href{https://lisunphil.github.io/HyperTASR_projectpage/}{lisunphil.github.io/HyperTASR\_projectpage}.

Improving Interpretability in Alzheimer's Prediction via Joint Learning of ADAS-Cog Scores

Accurate prediction of clinical scores is critical for early detection and prognosis of Alzheimers disease (AD). While existing approaches primarily focus on forecasting the ADAS-Cog global score, they often overlook the predictive value of its sub-scores (13 items), which capture domain-specific cognitive decline. In this study, we propose a multi task learning (MTL) framework that jointly predicts the global ADAS-Cog score and its sub-scores (13 items) at Month 24 using baseline MRI and longitudinal clinical scores from baseline and Month 6. The main goal is to examine how each sub scores particularly those associated with MRI features contribute to the prediction of the global score, an aspect largely neglected in prior MTL studies. We employ Vision Transformer (ViT) and Swin Transformer architectures to extract imaging features, which are fused with longitudinal clinical inputs to model cognitive progression. Our results show that incorporating sub-score learning improves global score prediction. Subscore level analysis reveals that a small subset especially Q1 (Word Recall), Q4 (Delayed Recall), and Q8 (Word Recognition) consistently dominates the predicted global score. However, some of these influential sub-scores exhibit high prediction errors, pointing to model instability. Further analysis suggests that this is caused by clinical feature dominance, where the model prioritizes easily predictable clinical scores over more complex MRI derived features. These findings emphasize the need for improved multimodal fusion and adaptive loss weighting to achieve more balanced learning. Our study demonstrates the value of sub score informed modeling and provides insights into building more interpretable and clinically robust AD prediction frameworks. (Github repo provided)

Deep Pre-trained Time Series Features for Tree Species Classification in the Dutch Forest Inventory

National Forest Inventory (NFI)s serve as the primary source of forest information, providing crucial tree species distribution data. However, maintaining these inventories requires labor-intensive on-site campaigns. Remote sensing approaches, particularly when combined with machine learning, offer opportunities to update NFIs more frequently and at larger scales. While the use of Satellite Image Time Series has proven effective for distinguishing tree species through seasonal canopy reflectance patterns, current approaches rely primarily on Random Forest classifiers with hand-designed features and phenology-based metrics. Using deep features from an available pre-trained remote sensing foundation models offers a complementary strategy. These pre-trained models leverage unannotated global data and are meant to used for general-purpose applications and can then be efficiently fine-tuned with smaller labeled datasets for specific classification tasks. This work systematically investigates how deep features improve tree species classification accuracy in the Netherlands with few annotated data. Data-wise, we extracted time-series data from Sentinel-1, Sentinel-2 and ERA5 satellites data and SRTM data using Google Earth Engine. Our results demonstrate that fine-tuning a publicly available remote sensing time series foundation model outperforms the current state-of-the-art in NFI classification in the Netherlands by a large margin of up to 10% across all datasets. This demonstrates that classic hand-defined harmonic features are too simple for this task and highlights the potential of using deep AI features for data-limited application like NFI classification. By leveraging openly available satellite data and pre-trained models, this approach significantly improves classification accuracy compared to traditional methods and can effectively complement existing forest inventory processes.

A Novel Local Focusing Mechanism for Deepfake Detection Generalization

The rapid advancement of deepfake generation techniques has intensified the need for robust and generalizable detection methods. Existing approaches based on reconstruction learning typically leverage deep convolutional networks to extract differential features. However, these methods show poor generalization across object categories (e.g., from faces to cars) and generation domains (e.g., from GANs to Stable Diffusion), due to intrinsic limitations of deep CNNs. First, models trained on a specific category tend to overfit to semantic feature distributions, making them less transferable to other categories, especially as network depth increases. Second, Global Average Pooling (GAP) compresses critical local forgery cues into a single vector, thus discarding discriminative patterns vital for real-fake classification. To address these issues, we propose a novel Local Focus Mechanism (LFM) that explicitly attends to discriminative local features for differentiating fake from real images. LFM integrates a Salience Network (SNet) with a task-specific Top-K Pooling (TKP) module to select the K most informative local patterns. To mitigate potential overfitting introduced by Top-K pooling, we introduce two regularization techniques: Rank-Based Linear Dropout (RBLD) and Random-K Sampling (RKS), which enhance the model's robustness. LFM achieves a 3.7 improvement in accuracy and a 2.8 increase in average precision over the state-of-the-art Neighboring Pixel Relationships (NPR) method, while maintaining exceptional efficiency at 1789 FPS on a single NVIDIA A6000 GPU. Our approach sets a new benchmark for cross-domain deepfake detection. The source code are available in https://github.com/lmlpy/LFM.git