Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models (VLMs) have demonstrated remarkable OOD detection performance by integrating both visual and textual modalities. In this context, negative prompts are introduced to emphasize the dissimilarity between image features and prompt content. However, these prompts often include a broad range of non-ID features, which may result in suboptimal outcomes due to the capture of overlapping or misleading information. To address this issue, we propose Positive and Negative Prompt Supervision, which encourages negative prompts to capture inter-class features and transfers this semantic knowledge to the visual modality to enhance OOD detection performance. Our method begins with class-specific positive and negative prompts initialized by large language models (LLMs). These prompts are subsequently optimized, with positive prompts focusing on features within each class, while negative prompts highlight features around category boundaries. Additionally, a graph-based architecture is employed to aggregate semantic-aware supervision from the optimized prompt representations and propagate it to the visual branch, thereby enhancing the performance of the energy-based OOD detector. Extensive experiments on two benchmarks, CIFAR-100 and ImageNet-1K, across eight OOD datasets and five different LLMs, demonstrate that our method outperforms state-of-the-art baselines.

Out-of-distribution (OOD) detection poses a signifi-cant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most existing OOD detection methods on graphs primarily focus on identifying instances in test data domains caused by either semantic shifts (changes in data classes) or covariate shifts (changes in data features), while leaving the simultaneous occurrence of both distribution shifts under-explored. In this work, we address both types of shifts simultaneously and introduce a novel challenge for OOD detection on graphs: graph-level semantic OOD detection under covariate shift. In this scenario, variations between the training and test domains result from the concurrent presence of both covariate and semantic shifts, where only graphs associated with unknown classes are identified as OOD samples (OODs). To tackle this challenge, we propose a novel two-phase framework called Graph Disentangled Diffusion Augmentation (GDDA). The first phase focuses on disentangling graph representations into domain-invariant semantic factors and domain-specific style factors. In the second phase, we introduce a novel distribution-shift-controlled score-based generative diffusion model that generates latent factors outside the training semantic and style spaces. Additionally, auxiliary pseudo-in-distribution (InD) and pseudo-OOD graph representations are employed to enhance the effectiveness of the energy-based semantic OOD detector. Extensive empirical studies on three benchmark datasets demonstrate that our approach outperforms state-of-the-art baselines.

Time-triggered (TT) flows are usually periodic in time-sensitive networks. However, nondeterministic end systems can generate TT flow frames with significant jitter (i.e., jittery TT flows). Jitter can cause frames to miss the TT windows scheduled for the current period, resulting in excessive access delays, which in turn affect the end-to-end deterministic transmission of the TT flows. In our previous study, we proposed the use of a dynamic multiwindow approach to achieve deterministic access to jittery TT flows; however, its window schedule computation is too slow, and this method is only suitable for small networks with a few TT flows. We therefore propose a group-based, fast scheduling method for accessing and transmitting the windows of jittery TT flows based on multiple windows. A combination of heuristic algorithms and solvers, including the establishment of TT window groups, division of the solution region, and integrated parallel and serial incremental coarse- and fine-grained computations, significantly improves the efficiency of TT window scheduling. For coarse-grained scheduling, by establishing large window clusters and central alignment, the complexity of scheduling is considerably reduced while keeping success rates high. Furthermore, the integer linear programming constraints and objective functions for this method are provided. Compared with the conventional dynamic multiwindow approach, the proposed approach reduces the scheduling time for TT windows by two orders of magnitude for a small star network with a small number of jittery TT flows. Moreover, the reduction in scheduling time becomes more pronounced as the network topology complexity and number of jittery TT flows increase. Finally, the scheduling time performance of the proposed method is verified in commonly used star, tree, and bus networks. Evaluations demonstrate that the access and transmission windows for 500 jittery TT flows can be scheduled in these networks, enabling deterministic access and significantly improving scheduling efficiency.

Network function virtualization (NFV) offers a flexible and effective means to utilize heterogeneous resources for space-air–ground integrated networks (SAGINs), enabling seamless connectivity for data transmission over large spans. Converging the cloud and edge computing capabilities, SAGINs have the potential to further provision service function chains (SFCs) with various Internet applications. This is driving the need for efficient schemes of the cloud- or edge-based services in SAGINs. In this article, we propose a novel SAGIN architecture based on the cloud-serving and edge-processing collaboration. The cloud-based services are provisioned by the selected ground data centers (DCs), in which the traffic is processed by the virtual network function (VNF) hosted in the edge nodes and DCs. In such an architecture, the edge nodes enable flexible SFC provisioning solutions while DCs offer a variety of cloud-oriented network services. In addition, we apply anycast to further improve the agility of SFC provisioning. From these perspectives, we investigate the cloud-serving and edge-processing SFC provisioning problem leveraging anycast, concerning DC assignment, edge and VNF placement, SFC mapping, and delay constraints simultaneously. The joint problem is formulated by a mixed integer linear program (MILP) model to jointly minimize the communication and computation costs subject to their tradeoff. A decomposition approach is further developed for the sake of scalability. Results from numerical simulations show that the proposed approach can reduce overall costs by up to 32.99%.

This paper presents 256-element transmitter (TX) and receiver (RX) phased arrays for satellite fixed communication at the Q and V bands, which integrate the phased-array antennas with eight-channel beamforming ICs. Wideband vector-modulated phase shifters (VGPS) and combinations of variable gain amplifier (VGA) and attenuators (ATT) are applied in the TX/RX beamforming ICs to achieve phase and gain tunings with large range and high precision. Based on the proposed beamforming ICs, the TX/RX phased arrays are realized with stacked aperture-coupled microstrip antennas on a cost-effective multi-layer PCB. Each array contains 32 TX/RX beamforming ICs, 256 antennas, and a 1-to-32 Wilkinson power divider/combiner networks. Fabricated in 65-nm CMOS technology, the packaged TX IC achieves an RMS gain error of 0.58 dB and an RMS phase error of 4.5° with 78.5-mW dc power per channel, while the$\text {OP}_{\text {1dB}}$is 9.6 dBm at 50.5 GHz. The packaged RX IC realizes a 5.3-dB NF, 0.47-dB RMS gain error, and 1.7° RMS phase error with 24.2-mW dc power per channel. The Q/V-band phased arrays are capable of scanning ±60°, while the 256-element TX phased array achieves an EIRP of 63.5 dBm. Modulated signal measurements with 200- and 400-MHz QPSK, 16-QAM and 64-QAM are also provided.