Owing to advancements in deep learning technology, vision transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. Nonetheless, ViTs still face some challenges, such as high computational complexity and the absence of desirable inductive biases. To alleviate these issues, the potential advantages of combining eagle vision with ViTs are explored. A bi-fovea visual interaction (BFVI) structure inspired by the unique physiological and visual characteristics of eagle eyes is introduced. Based on this structural design approach, a novel bi-fovea self-attention (BFSA) mechanism and bi-fovea feedforward network (BFFN) are proposed. These components are employed to mimic the hierarchical and parallel information processing scheme of the biological visual cortex, thereby enabling networks to learn the feature representations of the targets in a coarse-to-fine manner. Furthermore, a bionic eagle vision (BEV) block is designed as the basic building unit based on the BFSA mechanism and the BFFN. By stacking the BEV blocks, a unified and efficient family of pyramid backbone networks called eagle ViTs (EViTs) is developed. Experimental results indicate that the EViTs exhibit highly competitive performance in various computer vision tasks, demonstrating their potential as backbone networks. In terms of computational efficiency and scalability, EViTs show significant advantages compared with other counterparts. The developed code is available at https://github.com/nkusyl/EViT.

In large-scale cloud data centers and distributed storage systems, erasure coding is usually employed to enhance data availability and storage efficiency. However, with the explosive growth of data volume and the continuous expansion of storage system scale, traditional erasure coding techniques face significant challenges in handling single-disk failures. These challenges are primarily reflected in low data recovery efficiency and imbalanced system load distribution, which ultimately result in excessive I/O load and network bandwidth consumption, severely limiting the overall performance of the system. To address these issues, this article proposes a load-balanced data repair algorithm for single disk failures in erasure coded storage systems, called MNCR (Multi-Node Cooperative Repair). This algorithm improves data recovery efficiency in single-disk failure scenarios by minimizing data reading and inter-disk data transmission, using a cooperative repair strategy among disks. In addition, the algorithm designs a dynamic load balancing mechanism, which effectively resolves the issue of imbalanced data load distribution among disks during the repair process, thus avoiding performance bottlenecks caused by overloaded disks. Experimental results show that the MNCR algorithm significantly outperforms traditional methods in terms of repair efficiency and load balancing, providing an effective solution for single disk failure recoveries in erasure coding based large-scale storage systems.

Node failures in decentralized distributed storage systems are common. To ensure data availability, these systems employ data redundancy mechanisms, typically relying on repli-cas. This paper proposes a decentralized data redundancy scheme based on erasure coding, with Reed-Solomon and IPFS as exam-ples. Compared with replication, the proposed scheme reduces storage space and enhances fault tolerance. Files are sharded and encoded across multiple nodes, avoiding the high redundancy of replicas. Users can retrieve any K shards from N nodes to reconstruct the original files. This erasure coding optimization combines efficient data exchange among decentralized nodes with erasure coding technology, significantly reducing storage space compared with the replica mechanism. The implementation involves truncating and sharding the blocks within the Merkle DAG generated by files, enabling flexible adjustments to the code rate of erasure codes and the allocation of storage nodes based on user needs and available resources. This method achieves a balance between storage efficiency and data availability.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.

Although the current coronavirus disease 2019 (COVID-19) vaccines have been used worldwide to halt spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the emergence of new SARS-CoV-2 variants with E484K mutation shows significant resistance to the neutralization of vaccine sera. To better understand the resistant mechanism, we calculated the binding affinities of 26 antibodies to wild-type (WT) spike protein and to the protein harboring E484K mutation, respectively. The results showed that most antibodies (~85%) have weaker binding affinities to the E484K mutated spike protein than to the WT, indicating the high risk of immune evasion of the mutated virus from most of current antibodies. Binding free energy decomposition revealed that the residue E484 forms attraction with most antibodies, while the K484 has repulsion from most antibodies, which should be the main reason of the weaker binding affinities of E484K mutant to most antibodies. Impressively, a monoclonal antibody (mAb) combination was found to have much stronger binding affinity with E484K mutant than WT, which may work well against the mutated virus. Based on binding free energy decomposition, we predicted that the mutation of four more residues on receptor-binding domain (RBD) of spike protein, viz., F490, V483, G485 and S494, may have high risk of immune evasion, which we should pay close attention on during the development of new mAb therapeutics.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

In some special circumstances, e.g. tsunamis, floods, battlefields, earthquakes, etc., communication infrastructures are damaged or non-existent, as well as unmanned aerial vehicle (UAV) cluster. For the communication between people or UAVs, UAVs or mobile smart devices (MSDs) can be used to construct Mobile Ad Hoc Networks (MANETs), and Multipath TCP (MPTCP) can be used to simultaneously transmit in one TCP connection via multiple interfaces of MSDs. However the original MPTCP subpaths creating algorithm can establish multiple subpaths between two adjacent nodes, thus cannot achieve true concurrent data transmission. To solve this issue, we research and improve both the algorithm of adding routing table entries and the algorithm of establishing subpaths to offer more efficient use of multiple subpaths and better network traffic load balancing. The main works are as follows: (1) improve multi-hop routing protocol; (2) run MPTCP on UAVs or MSDs; (3) improve MPTCP subpaths establishment algorithm. The results show that our algorithms have better performance than the original MPTCP in achieving higher data throughput.

In some special circumstances, e.g. tsunamis, floods, battlefields, earthquakes, etc., communication infrastructures are damaged or non-existent, as well as unmanned aerial vehicle (UAV) cluster. For the communication between people or UAVs, UAVs or mobile smart devices (MSDs) can be used to construct Mobile Ad Hoc Networks (MANETs), and Multipath TCP (MPTCP) can be used to simultaneously transmit in one TCP connection via multiple interfaces of MSDs. However the original MPTCP subpaths creating algorithm can establish multiple subpaths between two adjacent nodes, thus cannot achieve true concurrent data transmission. To solve this issue, we research and improve both the algorithm of adding routing table entries and the algorithm of establishing subpaths to offer more efficient use of multiple subpaths and better network traffic load balancing. The main works are as follows: (1) improve multi-hop routing protocol; (2) run MPTCP on UAVs or MSDs; (3) improve MPTCP subpaths establishment algorithm. The results show that our algorithms have better performance than the original MPTCP in achieving higher data throughput.

Energy efficiency is becoming an increasingly important issue in wireless networks. With the increasing diversity of types of wireless services, a user has the requirement to transmit various hybrid services simultaneously. In this paper, we propose a Cross-layer Cooperative delay-energy Tradeoff (CCT) scheme to achieve total energy consumption minimization for hybrid types of services while satisfying their quality of service (QoS) requirements in cellular networks. We exploit the enormous difference of delays and the diversity of the amounts of data between delay-sensitive services and delay-tolerant ones to conduct dynamic resource allocation. Experimental simulation results can verify that our CCT scheme can save total energy consumption of hybrid services significantly outperforming existing energy efficient schemes.

In cooperative localization, distributed algorithm is more attractive than its centralized counterpart due to the low complexity and robustness. However, the position ambiguity of cooperative mobile terminal (MT) is usually neglected by related works, causing the loss of accuracy in distributed algorithms. In this paper, we establish an element-wise-weighted total least-squares (EW-TLS) model for distributed cooperative localization. Considering the impact of position ambiguity and distance measurement error, this localization model yields an optimal consistent estimator. Meanwhile, a general geometric dilution of precision (G-GDOP) metric is derived to estimate MT's coordinate error. Then a distributed cooperative localization algorithm is proposed by integrating the EW-TLS model with the G-GDOP metric. Simulation results indicate that the proposed algorithm outperforms traditional methods in high accuracy and strong robustness.

Distributed cooperative localisation, owing to its low computational burden, is more attractive in wireless networks than the centralised paradigm. However, the distributed algorithms tend to have worse location accuracy among existing works. In this study, the authors focus on the performance dissimilarity between distributed and centralised localisation algorithms, and analyse the Cramér–Rao lower bound. Then, their performance relationship is investigated, and verified by theoretical proof and extensive simulations. Furthermore, it is observed that the position ambiguity of cooperative mobile terminal is the primary cause of performance loss in the distributed algorithm, in contrast with the centralised one. To suppress the impact of position ambiguity, the authors establish an element‐wise‐weighted total least‐squares model and devise the corresponding distributed localisation algorithm. Numerical simulations indicate that the proposed algorithm outperforms traditional methods.

It is hard to efficiently model the light transport in scenes with translucent objects for interactive applications. The inter-reflection between objects and their environments and the subsurface scattering through the materials intertwine to produce visual effects like color bleeding, light glows, and soft shading. Monte-Carlo based approaches have demonstrated impressive results but are computationally expensive, and faster approaches model either only inter-reflection or only subsurface scattering. In this paper, we present a simple analytic model that combines diffuse inter-reflection and isotropic subsurface scattering. Our approach extends the classical work in radiosity by including a subsurface scattering matrix that operates in conjunction with the traditional form factor matrix. This subsurface scattering matrix can be constructed using analytic, measurement-based or simulation-based models and can capture both homogeneous and heterogeneous translucencies. Using a fast iterative solution to radiosity, we demonstrate scene relighting and dynamically varying object translucencies at near interactive rates.

Light propagation in scenes with translucent objects is hard to model efficiently for interactive applications. The inter-reflections between objects and their environments and the subsurface scattering through the materials intertwine to produce visual effects like color bleeding, light glows and soft shading. Monte-Carlo based approaches have demonstrated impressive results but are computationally expensive, and faster approaches model either only inter-reflections or only subsurface scattering. In this paper, we present a simple analytic model that combines diffuse inter-reflections and isotropic subsurface scattering. Our approach extends the classical work in radiosity by including a subsurface scattering matrix that operates in conjunction with the traditional form-factor matrix. This subsurface scattering matrix can be constructed using analytic, measurement-based or simulation-based models and can capture both homogeneous and heterogeneous translucencies. Using a fast iterative solution to radiosity, we demonstrate scene relighting and dynamically varying object translucencies at near interactive rates.

Abstract Texture bombing is a texture synthesis approach that saves memory by stopping short of assembling the output texture from the arrangement of input texture patches; instead, the arrangement is used directly at run time to texture surfaces. However, several problems remain in need of better solutions. One problem is improving texture diversification. A second problem is that mipmapping cannot be used because texel data is not stored explicitly. The lack of an appropriate level‐of‐detail (LoD) scheme results in severe minification artefacts. We present a just‐in‐time texturing method that addresses these two problems. Texture diversification is achieved by modelling a texture patch as an umbrella, a versatile hybrid 3‐D geometry and texture structure with parameterized appearance. The LoD is adapted continuously with a hierarchical algorithm that acts directly on the arrangement map. Results show that our method can model and render the diversity present in nature with only small texture memory requirements.

In restricted communication environment, multiple-target localization is of important practical significance. So the cooperative group localization (CGL) was firstly put forward, which has been verified the effectiveness on localization performance gain and simultaneous multiple-target localization in ill conditions. However, it exists two inherent difficulties: the strict demand for CGL topology and the high complexity. By the rational use of information to relax restrictions on topology, and by dividing the complex problem into some simple local ones, the factor graph (FG) together with the sum-product algorithm is a perfect candidate for the problems above. This paper firstly proposes a novel FG-based CGL algorithm. Numerical results indicate that compared with the existing CGL algorithm, the proposed algorithm not only performs better in relaxing CGL topology requirement, but also enjoys high localization accuracy under low complexity.

This paper proposes an multi-objective evolutionary algorithm. The algorithm is based on OMOEA-II. A new linear breeding operator with lower-dimensional crossover and copy operation is used. By using the lower-dimensional crossover, the complexity of searching is decreased so the algorithm converges faster. The orthogonal crossover increase probability of producing potential superior solutions, which helps the algorithm get better results. Ten unconstrained problems are used to test the algorithm. For three problems, the obtained solutions are very close to the true Pareto front, and for one problem, the obtained solutions distribute on part of the true Pareto front.