Large language models (LLMs) typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adaptors. LoRA is also communication-efficient for federated LLMs when multiple users collaboratively fine-tune a global LLM model without sharing their proprietary raw data. However, even the transmission of local adaptors between a server and clients risks serious privacy leakage. Applying differential privacy (DP) to federated LoRA encounters a dilemma: adding noise to both adaptors amplifies synthetic noise on the model, while fixing one adaptor impairs the learnability of fine-tuning. In this paper, we propose FedASK (Differentially Private Federated Low Rank Adaptation with Double SKetching) , a novel federated LoRA framework to enable effective updating of both low-rank adaptor matrices with robust differential privacy. Inspired by randomized SVD, our key idea is a two-stage sketching pipeline. This pipeline first aggregates carefully sketched, privacy-preserving local updates, and then reconstructs the global matrices on the server to facilitate effective updating of both adaptors. We theoretically prove FedASK's differential privacy guarantee and its exact aggregation property. Comprehensive experiments demonstrate that FedASK consistently outperforms baseline methods across a variety of privacy settings and data distributions.

Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiologial signals across individuals and recording modalities. We propose \model{FAPEX}, a novel architecture that introduces a learnable \emph{fractional neural frame operator} (FrNFO) for adaptive time–frequency decomposition. Unlike conventional models that exhibit spectral bias toward low frequencies, our FrNFO employs fractional-order convolutions to capture both high and low-frequency dynamics, achieving approximately $10\%$ improvement in F1-score and sensitivity over state-of-the-art baselines. The FrNFO enables the extraction of \emph{instantaneous phase and amplitude representations} that are particularly informative for preictal biomarker discovery and enhance out-of-distribution generalization. \model{FAPEX} further integrates structural state-space modeling and channelwise attention, allowing it to handle heterogeneous electrode montages. Evaluated across 12 benchmarks spanning species (human, rat, dog, macaque) and modalities (Scalp‑EEG, SEEG, ECoG, LFP), \model{FAPEX} consistently outperforms 23 supervised and 10 self-supervised baselines under nested cross-validation, with gains of up to $15\%$ in sensitivity on complex cross-domain scenarios. It further demonstrates superior performance in several external validation cohorts. To our knowledge, these establish \model{FAPEX} as the first epilepsy model to show consistent superiority in SASP, offering a promising solution for discovering epileptic biomarker evidence supporting the existence of a distinct and identifiable preictal state for and clinical translation.

Traffic flow prediction is crucial for intelligent transportation systems (ITS). Traditional graph convolutional networks (GCNs) have limitations in handling road network data. These GCNs can only handle binary relationships between nodes and cannot effectively capture the dynamic and nonlinear spatial dependencies among multiple nodes in the road network. This paper proposes an innovative spatial-temporal traffic flow prediction model to address this challenge. Firstly, by introducing novel graphs fusion and hypergraph encoding module, a fused graph and its dual hypergraph are constructed to provide richer structural information for the model. Then, the model can learn complex relationships among multiple nodes through the collaborative convolution of GCN and hypergraph convolutional network (HGCN). To comprehensively capture the dynamic nature of traffic flow, we utilize a variant of Transformer combined with time encoding information to capture the periodicity in the data, thereby enhancing the model’s ability to recognize periodic traffic flow patterns. Comprehensive experimental results on two publicly accessible real-world traffic datasets demonstrate the superiority of our proposed model over state-of-the-art traffic prediction models.

Vehicle re-identification (re-ID) has attracted significant attention within the computer vision community due to its wide-ranging applications in intelligent transportation systems and law enforcement. Nevertheless, this field faces considerable challenges owing to the high inter-class similarity and the large intra-class difference among vehicles. To address these challenges, this paper proposes a novel network incorporating a dual self-attention mechanism based on a space-channel grouped compression operation (DSA-SCGC). This innovative approach combines channel and spatial self-attention mechanisms to selectively enhance pivotal channel features and spatial local details while minimizing attention toward backgrounds and occlusions commonly encountered in real-world scenarios. Moreover, to address the issue of spatial information loss in channel attention, we propose a space-channel grouped compression (SCGC) operation that effectively compresses spatial information into channels, thereby significantly preserving spatial information. Comprehensive experiments conducted on the VeRi-776 and VehicleID datasets validate the superiority of our proposed DSA-SCGC model over the existing state-of-the-art vehicle re-identification methods.

In this paper, we focus on vehicle detection algorithms from UAV images in intelligent transportation applications. We propose an improved YOLOX model called CCHYOLOX for the problems of dense distribution and drastic scale changes of vehicle targets from the UAV viewpoint. Firstly, we construct a novel plug-and-play module, called Correlation Extraction and Feature Fusion (CEFF), to process the multi-layer adaptive fusion of pyramidal features. It enables the adaptive fusion of features in adjacent layers by adding spatial-awareness and extracting global channels correlation of adjacent layers features to mitigate the effect of target size diversity. Then, we design a special cascade strategy, including a feature alignment module named DConv, for single-stage and anchor-free detectors by considering the feature offset problem to produce more accurate detection bounding boxes. The cascade strategy allows the model to improve performance with little increase in computational cost. Finally, a high-resolution branch is designed for the small-target detection task, which greatly improves the detection accuracy of the model. Experiments on the challenging Visdrone-vehicle and Drone Vehicle datasets show that the proposed method effectively tackle the detection accuracy decrease caused by the above problems. CCH-YOLOX achieves 43.2% mAP and 57.3% mAP on the above two datasets, respectively, which is about 3%-4% higher than the strong baseline model (YOLOX) and exceeds many current popular models. The code is available at https://github.com/lz06787/CCH-YOLOX