Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local light changes. In this paper, we propose DAVIGS, a method that decouples appearance variations in a plug-and-play and efficient manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. Furthermore, our method gathers appearance-related information in 3D space to transform the rendered images, thus building 3D consistency across views implicitly. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds. Additionally, it provides performance improvements for different Gaussian Splatting baselines in a plug-and-play manner.

3D Gaussian Splatting (3DGS), a recently emerged multi-view 3D reconstruction technique, has shown significant advantages in real-time rendering and explicit editing. However, 3DGS encounters challenges in the accurate modeling of both high-frequency view-dependent appearances and global illumination effects, including inter-reflection. This paper introduces SpecTRe-GS, which addresses these challenges and models highly Specular surfaces that reflect nearby objects through Tracing Rays in 3D Gaussian Splatting. SpecTRe-GS separately models reflections from highly specular and rough surfaces to leverage the distinctions between their reflective properties and integrates an efficient ray tracer within the 3DGS framework for querying secondary rays, thus achieving fast and accurate rendering. Also, it incorporates normal prior guidance and joint geometry optimization at various stages of the training process to enhance geometry reconstruction for undistorted reflections. Experiments on both synthetic and real-world scenes demonstrate the superiority of SpecTRe-GS compared to existing 3DGS-based methods in capturing highly specular inter-reflections and also showcase its editing applications.

In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras within those regions are less correlated, leading to a low average contribution to the overall reconstruction. In this paper, we propose an occlusion-aware scene division strategy that clusters training cameras based on their positions and co-visibilities to acquire multiple regions. Cameras in such regions exhibit stronger correlations and a higher average contribution, facilitating high-quality scene reconstruction. We further propose a region-based rendering technique to accelerate large scene rendering, which culls Gaussians invisible to the region where the viewpoint is located. Such a technique significantly speeds up the rendering without compromising quality. Extensive experiments on multiple large scenes show that our method achieves superior reconstruction results with faster rendering speed compared to existing state-of-the-art approaches. Project page: https://occlugaussian.github.io.

3D Gaussian splatting (3DGS) has demonstrated exceptional performance in image-based 3D reconstruction and real-time rendering. However, regions with complex textures require numerous Gaussians to capture significant color variations accurately, leading to inefficiencies in rendering speed. To address this challenge, we introduce a hybrid representation for indoor scenes that combines 3DGS with textured meshes. Our approach uses textured meshes to handle texture-rich flat areas, while retaining Gaussians to model intricate geometries. The proposed method begins by pruning and refining the extracted mesh to eliminate geometrically complex regions. We then employ a joint optimization for 3DGS and mesh, incorporating a warm-up strategy and transmittance-aware supervision to balance their contributions seamlessly.Extensive experiments demonstrate that the hybrid representation maintains comparable rendering quality and achieves superior frames per second FPS with fewer Gaussian primitives.

Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering. Project page: https://vastgaussian.github.io.

Kubernetes (k8s) is a system for managing containerized applications across multiple hosts. It offers automatic deployment, maintenance, scaling, and resource management for applications. Applications in k8s usually have different storage requirements in the form of service-level objectives (SLOs). However, the current k8s storage management has several limitations which cause explicit performance and cost overhead. K8s administrators have to configure storage in advance manually, and users must know configurations and capabilities of provided storage. Users' storage SLOs can be easily violated in k8s.In this paper, we design and implement k8s Enhanced Storage (k8sES) which efficiently supports applications with various storage SLOs along with all other requirements in the Kubernetes environment. We design and incorporate storage scheduling as part of the node scheduling process in k8s. Applications will be scheduled onto the correct nodes and storage without intervention from either users or administrators. Proper storage resources will be dynamically carved based on users' storage SLOs. In addition, we provide a tool to monitor the I/O activities of both applications and storage devices in k8sES. The evaluation shows that k8sES can better meet users' storage SLOs along with other requirements. Also, k8sES can achieve higher resource utilization efficiency with overhead similar to that of the current k8s.

Active storage devices and in-storage computing are proposed and developed in recent years to effectively reduce the amount of required data traffic and to improve the overall application performance. They are especially preferred in the compute-storage disaggregated infrastructure. In both techniques, a simple computing module is added to storage devices/servers such that some stored data can be processed in the storage devices/servers before being transmitted to application servers. This can reduce the required network bandwidth and offload certain computing requirements from application servers to storage devices/servers. However, several challenges exist when designing an in-storage computing- based architecture for applications. These include what computing functions need to be offloaded, how to design the protocol between in-storage modules and application servers, and how to deal with the caching issue in application servers. HBase is an important and widely used distributed Key-Value Store. It stores and indexes key-value pairs in large files in a storage system like HDFS. However, its performance especially read performance, is impacted by the heavy traffics between HBase RegionServers and storage servers in the compute-storage disaggregated infrastructure when the available network bandwidth is limited. We propose an I n- S torage-based HBase architecture, called IS-HBase , to improve the overall performance and to address the aforementioned challenges. First, IS-HBase executes a data pre-processing module ( I n- S torage S can N er, called ISSN ) for some read queries and returns the requested key-value pairs to RegionServers instead of returning data blocks in HFile. IS-HBase carries out compactions in storage servers to reduce the large amount of data being transmitted through the network and thus the compaction execution time is effectively reduced. Second, a set of new protocols is proposed to address the communication and coordination between HBase RegionServers at computing nodes and ISSNs at storage nodes. Third, a new self-adaptive caching scheme is proposed to better serve the read queries with fewer I/O operations and less network traffic. According to our experiments, the IS-HBase can reduce up to 97% network traffic for read queries and the throughput (queries per second) is significantly less affected by the fluctuation of available network bandwidth. The execution time of compaction in IS-HBase is only about 6.31% – 41.84% of the execution time of legacy HBase. In general, IS-HBase demonstrates the potential of adopting in-storage computing for other data-intensive distributed applications to significantly improve performance in compute-storage disaggregated infrastructure.

MOTIVATION: The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS: In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION: www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design. Results: In this study, we present the RBPPred (an RNA-binding protein predictor), a new method based on the support vector machine, to predict whether a protein binds RNAs, based on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, the new approach RBPPred performed much better than state-of-the-art methods. The results show that RBPPred correctly predicted 83% of 2780 RBPs and 96% out of 7093 non-RBPs with MCC of 0.808 using the 10-fold cross validation. Furthermore, we achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. In addition we tested the capability of RBPPred to identify new RBPs, which further confirmed the practicability and predictability of the method. Availability and Implementation: RBPPred program can be accessed at: http://rnabinding.com/RBPPred.html . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

BACKGROUND: Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock). RESULTS: The ASPDock is first tested on the 21 complexes whose binding free energies have been determined experimentally. The results show that the calculated ASP scores have stronger correlation (r ≈ 0.69) with the binding free energies than the pure shape complementarity scores (r ≈ 0.48). The ASPDock is further tested on a large dataset, the benchmark 3.0, which contain 124 complexes and also shows better performance than pure shape complementarity method in docking prediction. Comparisons with other state-of-the-art docking algorithms showed that ASP score indeed gives higher success rate than the pure shape complementarity score of FTDock but lower success rate than Zdock3.0. We also developed a softly restricting method to add the information of predicted binding sites into our docking algorithm. The ASP-based docking method performed well in CAPRI rounds 18 and 19. CONCLUSIONS: ASP may be more accurate and physical than the pure shape complementarity in describing the feature of protein docking.

BACKGROUND: Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state. RESULTS: The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results. CONCLUSIONS: A novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.

UNLABELLED: A protein-protein docking decoy set is built for the Dockground unbound benchmark set. The GRAMM-X docking scan was used to generate 100 non-native and at least one near-native match per complex for 61 complexes. The set is a publicly available resource for the development of scoring functions and knowledge-based potentials for protein docking methodologies. AVAILABILITY: The decoys are freely available for download at http://dockground.bioinformatics.ku.edu/UNBOUND/decoy/decoy.php