DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors

High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors arising from systematic flaws in data preparation pipelines, including label errors, feature errors, and spurious correlations. Effective debugging of training data requires both detecting erroneous samples and identifying their specific error types to enable targeted repair, yet existing data cleaning and attribution methods fail to adequately address this dual requirement. In this paper, we propose DeMix, a novel framework that simultaneously diagnoses erroneous samples and their error types. Our key insight is that different error types produce distinct patterns on model behavior. DeMix captures such error-specific patterns by influence vectors that characterize how each training sample affects model predictions across all validation samples. We formulate training data debugging as a multi-label classification problem where a classifier is developed to predict error types directly from influence vectors. We further introduce an intervention-based learning strategy that guides the classifier to capture invariant rationales specific to each error type, ensuring the learned classifier generalizes effectively. Empirical evaluations on 11 tasks across tabular data prediction, recommendation systems, and LLM alignment demonstrate that DeMix significantly outperforms state-of-the-art approaches, achieving a 22.61% improvement in data debugging F1-score and a 9.32% gain in task model performance after data repair. Code is available at: https://github.com/SJTU-DMTai/DeMix.

6DMA-Enabled ISAC for Low-Altitude Economy

In this paper, we investigate a six dimensional movable antenna (6DMA) enable integrated sensing and communications (ISAC) network in low-altitude economy. The studied 6DMA can move in a three-dimensional space and rotate around its surface center, making full use of spatial freedom to adapt to the different location distributions of uncrewed aerial vehicles (UAVs) adjust channel conditions in time. However, since the rotation and location change of 6DMA requires the assistance of a physical device, it is unreasonable for 6DMA to change locations too frequently. Therefore, we propose a hierarchical deep reinforcement learning algorithm based on twin delayed deep deterministic policy gradient. The first layer optimizes the rotation and location of 6DMA with infrequent updates, and the second layer optimizes the UAV flight direction and base station transmit beamforming matrix in each time slot. Under the condition of satisfying the sensing intensity of the sensing target, the total data transmission rate to the UAVs is maximized. The numerical results show that the proposed 6DMA-enable ISAC algorithm through joint optimization of multiple variables performs significantly better than the partially movable scheme and the fixed antenna position scheme.

Movable-Antenna Enabled Robust Vehicular Consumer Networks Under Imperfect CSI

The accelerating advancement of intelligent transportation systems has established consumer-oriented vehicular networks (CVNs) as a critical infrastructure for next-generation connected mobility. However, the high mobility of vehicular users (VUs) introduces significant channel state information (CSI) uncertainty, which severely undermines the performance of conventional fixed-position antenna systems. To address this, this paper explores the deployment of movable-antennas (MAs) to enhance communication robustness in CVNs under imperfect CSI conditions. We develop a joint optimization framework that dynamically coordinates the spatial positioning of MAs and transmit beamforming at the base station, with the objective of maximizing the worst-case sum rate across all VUs. The problem is formulated as a non-convex max-min optimization problem, subject to bounded CSI estimation errors, transmit power limits, and physical constraints on antenna displacement. By adopting an alternating optimization strategy, the original problem is decomposed into tractable subproblems, solved via techniques including the S-Procedure, Schur complement, and successive convex approximation. Numerical evaluations confirm that the proposed approach achieves substantial gains over existing benchmarks in terms of worst-case throughput.

Architecture-Agnostic Feature Synergy for Universal Defense Against Heterogeneous Generative Threats

Generative AI deployment poses unprecedented challenges to content safety and privacy. However, existing defense mechanisms are often tailored to specific architectures (e.g., Diffusion Models or GANs), creating fragile "defense silos" that fail against heterogeneous generative threats. This paper identifies a fundamental optimization barrier in naive pixel-space ensemble strategies: due to divergent objective functions, pixel-level gradients from heterogeneous generators become statistically orthogonal, causing destructive interference. To overcome this, we observe that despite disparate low-level mechanisms, high-level feature representations of generated content exhibit alignment across architectures. Based on this, we propose the Architecture-Agnostic Targeted Feature Synergy (ATFS) framework. By introducing a target guidance image, ATFS reformulates multi-model defense as a unified feature space alignment task, enabling intrinsic gradient alignment without complex rectification. Extensive experiments show ATFS achieves SOTA protection in heterogeneous scenarios (e.g., Diffusion+GAN). It converges rapidly, reaching over 90% performance within 40 iterations, and maintains strong attack potency even under tight perturbation budgets. The framework seamlessly extends to unseen architectures (e.g., VQ-VAE) by switching the feature extractor, and demonstrates robust resistance to JPEG compression and scaling. Being computationally efficient and lightweight, ATFS offers a viable pathway to dismantle defense silos and enable universal generative security. Code and models are open-sourced for reproducibility.

UAV-Enabled Fluid Antenna Systems for Multi-Target Wireless Sensing over LAWCNs

Fluid antenna system (FAS) is emerging as a key technology for enhancing spatial flexibility and sensing accuracy in future wireless systems. This paper investigates an unmanned aerial vehicle (UAV)-enabled FAS for multi-target wireless sensing in low-altitude wireless consumer networks (LAWCNs) for achieving the low-altitude economy (LAE) missions. We formulate an optimization problem aimed at minimizing the average Cram\'er-Rao bound (CRB) for multiple target estimations. To tackle this non-convex problem, an efficient alternating optimization (AO) algorithm is proposed, which jointly optimizes the UAV trajectory, the antenna position of the transmit fluid antennas (FAs) and the receive FAs, and the transmit beamforming at the UAV. Simulation results demonstrate significant performance improvements in estimation accuracy and sensing reliability compared to conventional schemes, e.g., the fixed position antenna scheme. The proposed system achieves enhanced sensing performance through adaptive trajectory design and beamforming, alongside effective interference suppression via the flexible FAS antenna repositioning, underscoring its practical potential for precision sensing in the UAV-enabled LAWCNs.

RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems

This paper investigates the data collection enhancement problem in a reconfigurable intelligent surface (RIS)-empowered intelligent consumer transportation system (ICTS). We propose a novel framework where a data center (DC) provides energy to pre-configured roadside unit (RSU) pairs during the downlink stage. While in the uplink stage, these RSU pairs utilize a hybrid rate-splitting multiple access (RSMA) and time-division multiple access (TDMA) protocol to transmit the processed data to the DC, while simultaneously performing local data processing using the harvested energy. Our objective is to maximize the minimal processed data volume of the RSU pairs by jointly optimizing the RIS downlink and uplink phase shifts, the transmit power of the DC and RSUs, the RSU computation resource allocation, and the time slot allocation. To address the formulated non-convex problem, we develop an efficient iterative algorithm integrating alternating optimization and sequential rank-one constraint relaxation methods. Extensive simulations demonstrate that the proposed algorithm significantly outperforms baseline schemes under diverse scenarios, validating its effectiveness in enhancing the data processing performance for intelligent transportation applications.

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna

Integrated sensing, communication, and control (ISCC) has emerged as a key enabler for low-altitude wireless networks with enhanced adaptability through resource allocation co-design and intelligent environment awareness. However, dynamic interference and channel attenuation constrain the potential of the ISCC system. To address this challenge, we propose a novel movable antenna-empowered ISCC system. An achievable data rate maximization problem is formulated while guaranteeing the sensing and control quality-of-service (QoS) by optimizing the positions of the antennas and the beamforming strategy for communication, sensing, and control co-design. An efficient alternating optimization (AO)-based algorithm is proposed to solve the highly coupled non-convex problem. Numerical results demonstrate that the proposed AO-based algorithm achieves substantial gains in the achievable data rate and the control QoS compared with benchmark schemes.

Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna

This paper investigates an autonomous aerial vehicle (AAV)-enabled integrated sensing, communication, and computation system, with a particular focus on integrating movable antennas (MAs) into the system for enhancing overall system performance. Specifically, multiple MA-enabled AVVs perform sensing tasks and simultaneously transmit the generated computational tasks to the base station for processing. To minimize the maximum latency under the sensing and resource constraints, we formulate an optimization problem that jointly coordinates the position of the MAs, the computation resource allocation, and the transmit beamforming. Due to the non-convexity of the objective function and strong coupling among variables, we propose a two-layer iterative algorithm leveraging particle swarm optimization and convex optimization to address it. The simulation results demonstrate that the proposed scheme achieves significant latency improvements compared to the baseline schemes.

Coordinated Beamforming for RIS-Empowered ISAC Systems over Secure Low-Altitude Networks

Emerging as a cornerstone for next-generation wireless networks, integrated sensing and communication (ISAC) systems demand innovative solutions to balance spectral efficiency and sensing accuracy. In this paper, we propose a coordinated beamforming framework for a reconfigurable intelligent surface (RIS)-empowered ISAC system, where the active precoding at the dual-functional base station (DFBS) and the passive beamforming at the RIS are jointly optimized to provide communication services for legitimate unmanned aerial vehicles (UAVs) while sensing the unauthorized UAVs. The sum-rate of all legitimate UAVs are maximized, while satisfying the radar sensing signal-to-noise ratio requirements, the transmit power constraints, and the reflection coefficients of the RIS. To address the inherent non-convexity from coupled variables, we propose a low-complexity algorithm integrating fractional programming with alternating optimization, featuring convergence guarantees. Numerical results demonstrate that the proposed algorithm achieves higher data rate compared to disjoint optimization benchmarks. This underscores RIS's pivotal role in harmonizing communication and target sensing functionalities for low-altitude networks.

Less is More: Unlocking Specialization of Time Series Foundation Models via Structured Pruning

Scaling laws motivate the development of Time Series Foundation Models (TSFMs) that pre-train vast parameters and achieve remarkable zero-shot forecasting performance. Surprisingly, even after fine-tuning, TSFMs cannot consistently outperform smaller, specialized models trained on full-shot downstream data. A key question is how to realize effective adaptation of TSFMs for a target forecasting task. Through empirical studies on various TSFMs, the pre-trained models often exhibit inherent sparsity and redundancy in computation, suggesting that TSFMs have learned to activate task-relevant network substructures to accommodate diverse forecasting tasks. To preserve this valuable prior knowledge, we propose a structured pruning method to regularize the subsequent fine-tuning process by focusing it on a more relevant and compact parameter space. Extensive experiments on seven TSFMs and six benchmarks demonstrate that fine-tuning a smaller, pruned TSFM significantly improves forecasting performance compared to fine-tuning original models. This "prune-then-finetune" paradigm often enables TSFMs to achieve state-of-the-art performance and surpass strong specialized baselines.