Protein design is revolutionizing biotechnology, yet existing approaches struggle to balance structural foldability with functional performance. Structure-based models excel at generating stable protein backbones but often overlook critical functional properties, while protein language models capture evolutionary and functional signals but frequently predict sequences lacking structural stability. Integrating these complementary approaches remains challenging due to their inherently conflicting objectives. We present MAProt, a multi-agent framework that synergistically combines structure-based and protein language model-based methods for protein design. Each agent specializes in a distinct aspect of the design objective: the structure-based agent (e.g., ProteinMPNN) ensures compatibility with the target backbone, while protein language model-based agents (e.g., ESM, SaProt) capture evolutionary plausibility and functional potential. To reconcile conflicts and achieve optimal trade-offs, we introduce a Pareto-based negotiation module that enables effective multi-objective coordination and consensus among agents. Extensive experiments on benchmark datasets demonstrate that MAProt achieves a remarkable improvement over state-of-the-art baselines, and generalizes robustly across a range of tasks, including thermodynamic folding stability design, functional protein design, and high-affinity antibody design. These results highlight the power of collaborative optimization for advancing rational protein engineering.

Predicting changes in binding free energy ($\Delta\Delta G$) is essential for understanding protein-protein interactions, which are critical in drug design and protein engineering. However, existing methods often rely on pre-trained knowledge and heuristic features, limiting their ability to accurately model complex mutation effects, particularly higher-order and many-body interactions. To address these challenges, we propose H3-DDG, a Hypergraph-driven Hierarchical network to capture Higher-order many-body interactions across multiple scales. By introducing a hierarchical communication mechanism, H3-DDG effectively models both local and global mutational effects. Experimental results demonstrate state-of-the-art performance on multiple benchmarks. On the SKEMPI v2 dataset, H3-DDG achieves a Pearson correlation of 0.75, improving multi-point mutations prediction by 12.10%. On the challenging BindingGYM dataset, it outperforms Prompt-DDG and BA-DDG by 62.61% and 34.26%, respectively. Ablation and efficiency analyses demonstrate its robustness and scalability, while a case study on SARS-CoV-2 antibodies highlights its practical value in improving binding affinity for therapeutic design.

With the development of synthetic aperture radar (SAR)-based imaging technology, SAR ship detection has achieved notable breakthroughs. Due to the scale diversity and class imbalance of ships, ship detection in SAR images is still a substantial challenge. To solve these problems, this paper proposes a multiscale ship detection method for SAR images based on information compensation and feature enhancement. To improve the feature representations of multiscale ships, an information compensation module (ICM) and a feature enhancement module (FEM) are embedded into a feature pyramid network (FPN). Specifically, the ICM is constructed to extract and aggregate diverse spatial contextual information. At the same time, the FEM is introduced to solve the feature-level imbalance of the FPN by integrating multilevel ship features. In addition, a gradient density parameter is introduced to solve class imbalance problems. Experiments on a high-resolution SAR image dataset (HRSID) show that the proposed method achieves a comprehensive detection performance, i.e., 60.6% average precision (AP) and 67.1 frames per second (FPS) and outperforms other state-of-the-art methods.

Rapid intelligent detection of airports from remote sensing images is required to accomplish autonomous intelligent landing of unmanned aerial vehicles (UAVs) and other tasks. To address the insufficiency of traditional models in detecting airports under complicated backgrounds from remote sensing images, we propose an end-to-end remote sensing airport hierarchical expression and detection model based on deep transferable convolutional neural networks. Based on transfer learning, we solve the fundamental problem of overfitting due to the inadequate number of labeled remote sensing images by transferring the network model from natural image source domain to remote sensing image target domain. In addition, we introduce a cascade region proposal network with soft-decision nonmaximal suppression to improve the network structure and the performance of our method under complex backgrounds. Moreover, we use skip-layer feature fusion and hard example mining methods to improve the object expression ability and the training efficiency. Finally, the experimental results demonstrate that the method established in this letter can quickly and effectively detect different types of airports over complex backgrounds and obtain better detection performance than the other detection methods.

Accurately predicting for network traffic is significant for network operation and maintenance in software-defined networks (SDN). In this paper, Multi-frequency characteristic of complex network traffic is considered, and a new algorithm named EMD-based multi-model Prediction (EMD-MMP) for network prediction is proposed. The main idea in this algorithm is to decompose the network traffic series into different modes with different frequency by Empirical Mode Decomposition (EMD). According to the characteristics and the cross correlation coefficient of the modes, we reconstruct new components for de-noising by summing up parts of the high frequency modes. Then the new components and the remaining old modes are predicted by ARMA and SVR methods. Finally, the historical traffic data of Internet2 is employed for our experiments to demonstrate the precision of our new prediction algorithm compared with the Auto-Regressive and Moving Average (ARMA) and Support Vector Regression (SVR) models. On average, the EMD-MMP method improves ARMA and SVR by 0.62% and 10.6% at the Mean Absolute Percentage Error (MAPE) statistic indicator, and the Mean Square Error (MSE) of EMD-MMP is 12060.92 while the ARMA and SVR are 13968.8 and 47588.3. Besides, the EMD-MMP algorithm gives a better understanding of the nature of the network traffic.

Software-Defined Networking (SDN), enabled by OpenFlow, represents a paradigm shift from traditional network to the future Internet. Compared with traditional networks, its controllers and network applications have higher requirements on the availability and scalability. This paper proposes OFBGP, a new scalable, highly available architecture of BGP totally based on distributed design, without any centralized component. OFBGP is an application of SDN controller. In the design of OFBGP, BGP functionalities are divided according to their characteristics and requirements into two modules: BGP Protocol and BGP Decision. By doing so, the application can easily scale out when the number of BGP neighbors increases, and the process of BGP routing decisions can be parallelized. The paper also presents a new solution to implement BGP NSR through snapshots and backup event flow so that from a historical snapshot, replaying real-time backup event flow can rebuild the state that has happened behind at any moment. When a fault occurs, we can easily restore a state to ensure BGP neighbors do not perceive failure. We have also built a prototype to demonstrate our architecture. After the test of the performance of some core modules, the result shows that our new architecture can help improve the availability and scalability of BGP.

Software-Defined Networking (SDN), enabled by Open Flow, represents a paradigm shift from traditional network to the future Internet. Replicate or distributed controllers have been proposed to address the issues of availability and scalability that a centralized controller suffers from. However, it lacks a flexible mechanism to balance load among distributed controllers. To address this problem, this paper presents DALB, a dynamic and adaptive algorithm for controller load balancing totally based on distributed architecture, without any centralized component. This algorithm is running as a module of SDN controller. On one hand, it adopts an adjustable load collection threshold so as to reduce the overhead of exchanging messages for load collection, and on the other hand it can make policy and election locality in order to reduce the decision delay caused by network transmission. In this paper, we build the prototype system on floodlight to demonstrate our design and test the performance of our algorithm.

Network controller is the core of the OpenFlow-Based Software-Defined Networks (SDN). High availability of network controller is an urgent need objectively. The appearance of controller instance fault should not be perceived by data plane. During the fault recovery, OpenFlow messages, especially asynchronous message from data plane, should not be discarded. Otherwise the control platform would hold the outdated network status. This is an interesting problem which we call Asynchronous Message Discard (ASMD) trouble. This paper proposes a Non-stop network controller (NSNC). It can troubleshoot the ASMD problem based on highly available TCP (HA-TCP) technology. During the fault recovery on distributed control platform, we want to elect the optimal controller to take over the network devices managed by the failed controller instance. We analyses the minimum metrics set for controller election. The view of this paper is verified on Floodlight controller. Experiments show that when the controller instance fails, asynchronous messages from switch will not be discarded, and using HA-TCP only adds negligible overhead to throughput and latency. We test the performance of election algorithm based on Mininet. Currently, there is only a theoretical analysis of metrics related to the election. We will test the validity of the election strategy in a real scenario as part of our future work.

The feature of centralized network control logic in Software-Defined Networks (SDNs) paves a way for green energy saving. Router power consumption attracts a wide spread attention in terms of energy saving. Most research on it is at component level or link level, i.e. each router is independent of energy saving. In this paper, we study global power management at network level by rerouting traffic through different paths to adjust the workload of links when the network is relatively idle. We construct the expand network topology according to routers' connection. A 0-1 integer linear programming model is formulated to minimize the power of integrated chassis and linecards that are used while putting idle ones to sleep under constraints of link utilization and packet delay. We proposed two algorithms to solve this problem: alternative greedy algorithm and global greedy algorithm. For comparison, we also use Cplex method just as GreenTe does since GreenTe is state of the art global traffic engineering mechanism. Simulation results on synthetic topologies and real topology of CERNET show the effectiveness of our algorithms.