In software-defined satellite information networks (SD-SINs), each requested service can be characterized by a predetermined sequence of virtual network functions (VNFs), referred to as a service function chain (SFC). However, VNFs shared by multiple requested services are prone to failures, causing service interruptions. Furthermore, the rapid movement of satellites results in an intermittent yet predictable network topology. Moreover, efficient use of multi-dimensional heterogeneous resources can enhance reliability and network performance. Therefore, in this paper, we investigate reliability-enhanced network slicing by jointly exploiting communication, storage, and computation resources in time-varying SD-SINs. Specifically, we use the time-expanded graph (TEG) to model time-varying SD-SINs with multi-dimensional heterogeneous resources. Based on TEG, we propose a joint reliability-enhanced VNF deployment and flow routing strategy, formulated as an integer nonlinear programming (INLP) problem, to maximize the number of completed services with reliability requirements. To effectively solve the INLP problem, we propose two novel algorithms: the integer linear programming reformulation (ILPR) algorithm, which achieves optimal solutions but with high complexity, and the LP relaxation-based VNF deployment and routing (LPR-VDR) algorithm, which provides near-optimal solutions with significantly lower complexity. Simulation results demonstrate that the LPR-VDR algorithm performs very closely to the ILPR algorithm.

The challenge of mitigating interference in Space-Air-Ground Integrated Networks (SAGINs) is exacerbated by the inherent channel uncertainty, which arises due to dynamic weather conditions, heterogeneous user deployment, and different altitude of transmitters. To tackle this problem, Rate-Splitting Multiple Access (RSMA) has been seen as a promising solution due to its robustness. However, conventional beamforming designs for RSMA often suffer from two major limitations: high processing delays and overfitting to specific channel conditions. When the channel conditions change, the performance of these predictors degrades significantly, limiting their effectiveness in dynamic environments. To address these challenges, we propose a novel Fast-Adaptive Predictive Beamforming (FA-PB) framework for RSMA in SAGINs. Unlike traditional predictive beamforming approaches that rely on fixed predictive models, FA-PB integrates a transfer-learning-based online learning mechanism. This innovative approach allows the predictor to dynamically adapt to new channel conditions with minimal computational overhead. FA-PB achieves this by leveraging few-shot Channel State Information at the Transmitter (CSIT) samples, enabling real-time updates and adjustments to the predictor. Consequently, FA-PB ensures that the beamforming process can rapidly adapt to fluctuating channel conditions, maintaining high levels of performance even in highly dynamic SAGIN environments. Extensive simulation results validate the superiority of the FA-PB framework, demonstrating its enhanced adaptability and improved beamforming performance in SAGINs.

This study presents anchor-regularized generative adversarial network (GAN) priors to delicately explore the inherent knowledge of a pretrained generative model. Previous research leveraged the latent space of a pretrained GAN model to provide a variety of image-editing operations. However, the semantically meaningful regions within latent space are distinctly bounded; therefore, the manipulation of the latent code can easily land out of the domain. To address this problem, we introduce an anchoring mechanism that enables novel and robust image editing. The key insights driving the method are that latent space is structurally organized, and that natural coherence allows semantically correlated latent code to be located in the areas surrounding a meaningful anchor. By using different input anchors, the proposed method forms the basis for a variety of robust and flexible editing operations, including misaligned domain translation, interactive editing, and few-shot interpretable direction exploration. Extensive experiments demonstrated the superior performance of the proposed method compared with state-of-the-art editing methods.

Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.

Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the generation process. Instead of predicting the bounding box values, our approach autoregressively outputs the intermediate action sequence, which can then be deterministically converted to the final layout. We achieve state-of-the-art performances on three datasets. Both automatic and human evaluations show that our approach generates high-quality and diverse layouts. Furthermore, we revisit the commonly used evaluation metric FID adapted in this task, and observe that previous works use different settings to train the feature extractor for obtaining real/generated data distribution, which leads to inconsistent conclusions. We conduct an in-depth analysis on this metric and settle for a more robust and reliable evaluation setting. Code is available at this website.

Space-air-ground integrated network (SAGIN) aided multi-tier computing network can be modelled as a dynamic and predictable network. For the SAGIN aided multi-tier computing network, the traditional time expanded graph (TEG) can only jointly model communication and storage capability, as well as one computing function for one mission flow within one same node. However, for multiple computing functions for one mission flow in one same node, TEG is not applicable. In this paper, for SAGIN aided multi-tier computing networks, we propose an multi-functional time expanded graph (MF-TEG) to jointly model the communication, storage, and computation capability of nodes where multiple computing functions for one mission flow in one same node can be characterized. Specifically, based on TEG, for each node having computation functions, we adopt the virtual network graph (VNG) to virtually decompose it into three virtual components: sub-virtual node, virtual computing nodes, and virtual transmission links, where the virtual computing node provides the computing function. We characterize the amount of data flow on each link and also present four kinds of fundamental constraints for the data flow in the MF-TEG for joint communication, storage, and computing function: computation capacity constraints, communication capacity constraints, storage capacity constraints, and flow conservation constraints. We provide one example of using MF-TEG to model the SAGIN aided multi-tier computing network with a service function chain (SFC), where satellite nodes could provide communication, storage, and multiple computing functions for one mission flow in one same node, where TEG is not valid. Furthermore, simulation results show that for SAGIN aided multi-tier computing network, the proposed MF-TEG model significantly outperforms the snapshot graph-aided VNG (SSG-aided VNG) model. The reason for that is only communication and computation capability is considered by the SSG-aided VNG model, while storage capability is not exploited.

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

In this paper, we investigate the maximum flow routing strategy with the service function chain (SFC) constraints in the space information networks (SINs), where a SFC consists of a specific ordered sequence of service functions, and the mission flow must go through these functions in a predefined order. The time-varying SIN is modeled by the time-expanded graph (TEG). We formulate the maximum flow routing strategy problem with the SFC constraints as a linear programming (LP) problem. Furthermore, for a large-scale SIN, as the complexity of solving the LP problem is still very high, we propose a novel low-complexity SFC-constrained graph theory based (SFC-GT) algorithm. Specifically, we formulate this problem as one special single commodity maximum flow problem, where this flow must satisfy the SFC constraints. We first define the SFC-constrained residual network and the SFC-constrained augmenting path. Afterwards, we iteratively search the SFC-constrained augmenting path and update the SFC-constrained residual network. Simulation results demonstrate our proposed SFC-GT algorithm can achieve near-optimal performance with much less complexity.

Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free-form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions in the absence of well-labeled scores or binary attributes. In particular, the proposed adversarial method simultaneously optimizes the discovered directions and the attribute assessor using the target attribute data as positive samples, while the generated ones being negative. In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics. Moreover, the proposed learning strategy attenuates the entanglement between attributes, such that multi-attribute manipulation can be easily achieved without any additional constraint. Furthermore, we reveal several interesting semantics with the involuntarily learned negative directions. Extensive experiments on 9 anime attributes and 7 human attributes demonstrate the effectiveness of our adversarial approach qualitatively and quantitatively. Code is available at https://github.com/BERYLSHEEP/AdvStyle.