VisionNVS: Self-Supervised Inpainting for Novel View Synthesis under the Virtual-Shift Paradigm

A fundamental bottleneck in Novel View Synthesis (NVS) for autonomous driving is the inherent supervision gap on novel trajectories: models are tasked with synthesizing unseen views during inference, yet lack ground truth images for these shifted poses during training. In this paper, we propose VisionNVS, a camera-only framework that fundamentally reformulates view synthesis from an ill-posed extrapolation problem into a self-supervised inpainting task. By introducing a ``Virtual-Shift'' strategy, we use monocular depth proxies to simulate occlusion patterns and map them onto the original view. This paradigm shift allows the use of raw, recorded images as pixel-perfect supervision, effectively eliminating the domain gap inherent in previous approaches. Furthermore, we address spatial consistency through a Pseudo-3D Seam Synthesis strategy, which integrates visual data from adjacent cameras during training to explicitly model real-world photometric discrepancies and calibration errors. Experiments demonstrate that VisionNVS achieves superior geometric fidelity and visual quality compared to LiDAR-dependent baselines, offering a robust solution for scalable driving simulation.

WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.

Variational Rectification Inference for Learning with Noisy Labels

Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re-weighting, or loss rectification) have been broadly applied in prevailing approaches, which have been generally learned under the meta-learning scenario. Despite the robustness of noise achieved by the probabilistic meta-learning models, they usually suffer from model collapse that degenerates generalization performance. In this paper, we propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions as an amortized variational inference problem and derive the evidence lower bound under the meta-learning framework. Specifically, VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable, which can rectify the loss of the noisy sample with the extra randomness regularization and is, therefore, more robust to label noise. To achieve the inference of the rectifying vector, we approximate its conditional posterior with an amortization meta-network. By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function, which can significantly improve the generalization performance. The elaborated meta-network and prior network adhere to the smoothness assumption, enabling the generation of reliable rectification vectors. Given a set of clean meta-data, VRI can be efficiently meta-learned within the bi-level optimization programming. Besides, theoretical analysis guarantees that the meta-network can be efficiently learned with our algorithm. Comprehensive comparison experiments and analyses validate its effectiveness for robust learning with noisy labels, particularly in the presence of open-set noise.

Clutter-Resilient ISAC for Low-Altitude Wireless Networks: A 5G Base Station-Compatible Protocol, Waveform, and Prototype

Integrated sensing and communications (ISAC) has been envisioned as a promising solution to support emerging services in low-altitude wireless networks (LAWNs), where upgrading 5G ground base stations (GBS) toward new active sensing systems with wide coverage, low cost, high accuracy, and favorable spectrum compatibility, is strongly desired. However, such an evolution faces several critical challenges, particularly in the detection and tracking of weak and slow unmanned aerial vehicles (UAVs). These challenges include ISAC waveform design, clutter cancellation resilient to high clutter-to-noise ratios (CNRs), and efficient Doppler separation between UAVs and clutter. To that end, we summarize potential solutions and raise a comprehensive framework on implementing the 5Gadvanced (5G-A) GBS. Outfield experiments demonstrate that the developed 5G-A GBS can effectively track weak and slow targets at distances exceeding 1 kilometer, while incurring only a 1.2% downlink rate loss relative to commercial 5G-A GBS.

Constellation Selection and Power Control for OFDM-based ISAC: From Theory to Prototype

Integrated sensing and communication (ISAC) techniques can leverage existing, wide-coverage communication networks to perform sensing tasks, enabling large-scale and low-cost target sensing. However, the inherent randomness of communication data payloads introduces undesired sidelobes in the ambiguity function that may degrade target detection and parameter estimation performance. This paper develops a communication-centric ISAC framework that is standards-compliant and compatible with existing devices. Specifically, we propose a low-complexity constellation selection scheme over a finite, off-the-shelf alphabet, achieving an efficient sensing-communication trade-off without custom waveforms or frame-structure changes. To this end, we analyze two classical sensing receivers including matched filtering (MF) and reciprocal filtering (RF) for ranging measurements, and derive closed-form sensing laws that link constellation statistics to sensing performance. Under any finite-alphabet constellation combination, MF sidelobes depend on the weighted sum of the kurtosis values of the per-subcarrier constellations, while RF noise enhancement depends on the inverse second moment of the transmit symbol, providing a tractable expression for tuning the sensing-communication trade-off. The analysis extends to multi-symbol coherent integration and achieves the expected processing gain. We prove that in flat-fading channels, any Pareto-optimal solution activates no more than three constellations. For frequency-selective channels, a bilevel algorithm with closed-form inner updates attains near-optimal performance while sharply reducing computational complexity. We validate the entire theoretical pipeline with numerical simulations as well as experimental results.

Overcoming BS Down-Tilt for Air-Ground ISAC Coverage: Antenna Design, Beamforming and User Scheduling

Integrated sensing and communication holds great promise for low-altitude economy applications. However, conventional downtilted base stations primarily provide sectorized forward lobes for ground services, failing to sense air targets due to backward blind zones. In this paper, a novel antenna structure is proposed to enable air-ground beam steering, facilitating simultaneous full-space sensing and communication (S&C). Specifically, instead of inserting a reflector behind the antenna array for backlobe mitigation, an omni-steering plate is introduced to collaborate with the active array for omnidirectional beamforming. Building on this hardware innovation, sum S&C mutual information (MI) is maximized, jointly optimizing user scheduling, passive coefficients of the omni-steering plate, and beamforming of the active array. The problem is decomposed into two subproblems: one for optimizing passive coefficients via Riemannian gradient on the manifold, and the other for optimizing user scheduling and active array beamforming. Exploiting relationships among S&C MI, data decoding MMSE, and parameter estimation MMSE, the original subproblem is equivalently transformed into a sum weighted MMSE problem, rigorously established via the Lagrangian and first-order optimality conditions. Simulations show that the proposed algorithm outperforms baselines in sum-MI and MSE, while providing 360 sensing coverage. Beampattern analysis further demonstrates effective user scheduling and accurate target alignment.

Disentangle Object and Non-object Infrared Features via Language Guidance

Infrared object detection focuses on identifying and locating objects in complex environments (\eg, dark, snow, and rain) where visible imaging cameras are disabled by poor illumination. However, due to low contrast and weak edge information in infrared images, it is challenging to extract discriminative object features for robust detection. To deal with this issue, we propose a novel vision-language representation learning paradigm for infrared object detection. An additional textual supervision with rich semantic information is explored to guide the disentanglement of object and non-object features. Specifically, we propose a Semantic Feature Alignment (SFA) module to align the object features with the corresponding text features. Furthermore, we develop an Object Feature Disentanglement (OFD) module that disentangles text-aligned object features and non-object features by minimizing their correlation. Finally, the disentangled object features are entered into the detection head. In this manner, the detection performance can be remarkably enhanced via more discriminative and less noisy features. Extensive experimental results demonstrate that our approach achieves superior performance on two benchmarks: M\textsuperscript{3}FD (83.7\% mAP), FLIR (86.1\% mAP). Our code will be publicly available once the paper is accepted.

MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences

While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench Verified by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.

Integrating Low-Altitude SAR Imaging into UAV Data Backhaul

Synthetic aperture radar (SAR) deployed on unmanned aerial vehicles (UAVs) is expected to provide burgeoning imaging services for low-altitude wireless networks (LAWNs), thereby enabling large-scale environmental sensing and timely situational awareness. Conventional SAR systems typically leverages a deterministic radar waveform, while it conflicts with the integrated sensing and communications (ISAC) paradigm by discarding signaling randomness, in whole or in part. In fact, this approach reduces to the uplink pilot sensing in 5G New Radio (NR) with sounding reference signals (SRS), underutilizing data symbols. To explore the potential of data-aided imaging, we develop a low-altitude SAR imaging framework that sufficiently leverages data symbols carried by the native orthogonal frequency division multiplexing (OFDM) communication waveform. The randomness of modulated data in the temporal-frequency (TF) domain, introduced by non-constant modulus constellations such as quadrature amplitude modulation (QAM), may however severely degrade the imaging quality. To mitigate this effect, we incorporate several TF-domain filtering schemes within a rangeDoppler (RD) imaging framework and evaluate their impact. We further propose using the normalized mean square error (NMSE) of a reference point target's profile as an imaging performance metric. Simulation results with 5G NR parameters demonstrate that data-aided imaging substantially outperforms pilot-only counterpart, accordingly validating the effectiveness of the proposed OFDM-SAR imaging approach in LAWNs.

QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management

Due to the significant variations in unmanned aerial vehicle (UAV) altitude and horizontal mobility, it becomes difficult for any single network to ensure continuous and reliable threedimensional coverage. Towards that end, the space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity. To address the pronounced disparities in coverage and signal characteristics across heterogeneous networks, this paper formulates UAV mobility management in SAGIN as a constrained multi-objective joint optimization problem. The formulation couples discrete link selection with continuous trajectory optimization. Building on this, we propose a two-level multi-agent hierarchical deep reinforcement learning (HDRL) framework that decomposes the problem into two alternately solvable subproblems. To map complex link selection decisions into a compact discrete action space, we conceive a double deep Q-network (DDQN) algorithm in the top-level, which achieves stable and high-quality policy learning through double Q-value estimation. To handle the continuous trajectory action space while satisfying quality of service (QoS) constraints, we integrate the maximum-entropy mechanism of the soft actor-critic (SAC) and employ a Lagrangian-based constrained SAC (CSAC) algorithm in the lower-level that dynamically adjusts the Lagrange multipliers to balance constraint satisfaction and policy optimization. Moreover, the proposed algorithm can be extended to multi-UAV scenarios under the centralized training and decentralized execution (CTDE) paradigm, which enables more generalizable policies. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks in throughput, link switching frequency and QoS satisfaction.