A $(k+r,k,l)$ binary array code of length $k+r$, dimension $k$, and sub-packetization $l$ is composed of $l\times(k+r)$ matrices over $\mathbb{F}_2$, with every column of the matrix stored on a separate node in the distributed storage system and viewed as a coordinate of the codeword. It is said to be maximum distance separable (MDS) if any $k$ out of $k+r$ coordinates suffice to reconstruct the whole codeword. The repair problem of binary MDS array codes has drawn much attention, particularly for single-node failures. In this paper, given an arbitrary binary MDS array code with sub-packetization $m$ as the base code, we propose two generic approaches (Generic Construction I and II) for constructing binary MDS array codes with optimal access (or repair) bandwidth for single-node failures. For every $s\leq r$, a $(k+r,k,ms^{\lceil \frac{k+r}{s}\rceil})$ code $\mathcal{C}_1$ with optimal access bandwidth can be constructed by Generic Construction I. Repairing a failed node of $\mathcal{C}_1$ requires connecting to $d = k+s-1$ helper nodes, in which $s-1$ helper nodes are designated and $k$ are free to select. $\mathcal{C}_1$ generally achieves smaller sub-packetization and provides greater flexibility in the selection of its coefficient matrices. For even $r\geq4$ and $s=\frac{r}{2}$ such that $s+1$ divides $k+r$, a $(k+r, k,ms^{\frac{k+r}{s+1}})$ code $\mathcal{C}_2$ with optimal repair bandwidth can be constructed by Generic Construction II, with $\frac{s}{s+1}(k+r)$ out of $k+r$ nodes having the optimal access property. To the best of our knowledge, $\mathcal{C}_2$ possesses the smallest sub-packetization among existing binary MDS array codes with optimal repair bandwidth known to date.

3D Gaussian Splatting (3DGS) has recently attracted wide attentions in various areas such as 3D navigation, Virtual Reality (VR) and 3D simulation, due to its photorealistic and efficient rendering performance. High-quality reconstrution of 3DGS relies on sufficient splats and a reasonable distribution of these splats to fit real geometric surface and texture details, which turns out to be a challenging problem. We present GeoTexDensifier, a novel geometry-texture-aware densification strategy to reconstruct high-quality Gaussian splats which better comply with the geometric structure and texture richness of the scene. Specifically, our GeoTexDensifier framework carries out an auxiliary texture-aware densification method to produce a denser distribution of splats in fully textured areas, while keeping sparsity in low-texture regions to maintain the quality of Gaussian point cloud. Meanwhile, a geometry-aware splitting strategy takes depth and normal priors to guide the splitting sampling and filter out the noisy splats whose initial positions are far from the actual geometric surfaces they aim to fit, under a Validation of Depth Ratio Change checking. With the help of relative monocular depth prior, such geometry-aware validation can effectively reduce the influence of scattered Gaussians to the final rendering quality, especially in regions with weak textures or without sufficient training views. The texture-aware densification and geometry-aware splitting strategies are fully combined to obtain a set of high-quality Gaussian splats. We experiment our GeoTexDensifier framework on various datasets and compare our Novel View Synthesis results to other state-of-the-art 3DGS approaches, with detailed quantitative and qualitative evaluations to demonstrate the effectiveness of our method in producing more photorealistic 3DGS models.

Reed–Solomon (RS) codes are widely used in modern communication and storage systems. However, conventional iteration-based RS decoder architectures are struggling to meet the stringent demands of emerging latency-critical applications. Although noniterative architectures have been proposed for$t \leq 4$RS codes with low latency and low complexity, the design of such architectures remains largely unexplored for five-error-correcting RS codes. Motivated by this gap, we extend the Peterson–Gorenstein–Zierler (PGZ) algorithm to directly compute the error locator polynomial for$t=5$RS codes, thereby eliminating the need for iterative computation in the circuit. Then, a systematic three-step optimization method is further proposed to mitigate the hardware complexity increase caused by PGZ, which significantly decreases the required number of multipliers. Besides, given the prohibitive complexity of noniterative root-finding algorithms for$t = 5$RS codes, we instead adopt a hybrid PGZ-Chien search (PGZ-CS) architecture for the whole decoder. Based on the proposed methods, a low-latency and area-efficient$t=5$RS decoder is finally developed and implemented under the example RS(255, 245, and 5)code. Synthesized under 28-nm CMOS technology, the proposed decoder reduces decoding latency by 65.6%, overall area by 29.7%, and power consumption by 32.6% compared to the compensated simplified reformulated inversionless Berlekamp–Massey (CS-RiBM) decoder.

Cloud storage systems often face the issues of failure and straggler nodes. Failure is characterized as a fail-stop scenario, which refers to disk failures that can result in significant data unavailability. Straggler nodes are typically those with heavy workloads or poor performance. Usually, both failure and straggler nodes coexist, posing a significant challenge to data availability in storage systems. In such failure scenarios, parallel recovery and straggler recovery methods are commonly used as separate approaches for data recovery. However, parallel recovery methods encounter bottlenecks on the recovery path due to the presence of straggler nodes. Meanwhile, straggler recovery methods face the challenge of lacking available recovery paths in cases of multiple node failures. Scenarios involving both multiple failures and stragglers are common, yet there is a lack of efficient recovery methods for these situations. In this paper, we focus on scenarios involving video data, which occupies a significant portion of cloud storage systems, to address the above issues. We propose a Hybrid Global Graph-based Recovery (HGR) method that integrates parallel and straggler recovery approaches into a single global graph. The key idea of HGR is to construct a global graph that includes global node parameter information, enabling comprehensive coordination. We partition the global graph into two subgraphs: one containing straggler nodes and the other containing failure nodes. Resources are efficiently allocated to each subgraph to schedule recovery tasks in parallel. For data that presents significant recovery challenges, exhibits poor parallelism, has substantial tail latency, or exceeds fault tolerance limits, we employ approximate recovery methods. To demonstrate HGR's effectiveness, we conducted several experiments. The results indicate that HGR can reduce recovery time by up to 45.06% and improve I/O throughput by as much as 1.79× compared to state-of-the-art recovery methods.

Recently, remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Previous LLM-based multi-agent systems can already solve simple dialogue tasks. More complex tasks, however, face challenges through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems.

Consumer-level RGB-D cameras have been widely used for dense 3D reconstruction of scenes. Especially for textureless or non-lambertian surfaces, consumer RGB-D cameras can ensure completeness of the reconstructed models at a low cost. However, the reconstruction quality relies heavily on the accuracy of the depth sensors. Digital cameras are also used popularly for capturing high-resolution pictures to achieve high-quality dense reconstruction of the scenes, but cannot handle textureless or non-lambertian regions well due to the visual ambiguity problem. To ensure both completeness and accuracy of the reconstructed 3D models, we propose a hybrid multi-view reconstruction pipeline named Hybrid-MVS, which combines the high-resolution images taken by a digital camera and the low-resolution RGB-D frames captured by a consumer RGB-D camera for robust reconstruction of complicated scenes with challenging textureless and non-lambertian surfaces. Unlike most existing multi-sensor systems which require explicit hardware calibration and synchronization of various sensors, the calibration and synchronization problems between the digital camera and RGB-D camera are implicitly solved for compositing reliable depth prior of the digital images in our pipeline. Especially, we propose a hybrid MVS framework for robust PatchMatch stereo and Delaunay meshing, which tightly couples both visual cues given by the digital images and depth cues from the RGB-D frames to maximize the complementary advantages. The experiments with quantitative and qualitative evaluations demonstrate the effectiveness of the proposed Hybrid-MVS framework, which can successfully achieve high-quality 3D reconstruction of complicated natural scenes with robustness to weakly textured and non-lambertian areas.

Rack-aware regenerating codes (RRCs) achieve the optimal repair bandwidth for single node failures in the hierarchical data center where nodes are organized into racks and the intra-rack communication is cost-free. In this work, a vertical-horizontal framework is proposed for building RRCs from MDS array codes and regenerating codes (RCs). Particularly for RRCs with the minimum storage (i.e., MSRR codes), the framework is further improved to achieve lower sub-packetization. As a key for building MSRR codes, MSR codes (i.e., RCs with the minimum storage) with improved sub-packetization level are also developed. Applying the newly derived MSR codes into the vertical-horizontal framework, MSRR codes with an improved sub-packetization level are explicitly constructed, and those achieving the lowest sub-packetization by far are proved to exist over sufficiently large fields.

We study the rack-aware storage system where all storage nodes are organized in racks and within each rack the nodes can communicate freely without taxing the system bandwidth. Rack-aware regenerating codes (RRCs) were proposed for minimizing the repair bandwidth for single erasures. In the initial setting of RRCs, the repair of a single node requires the participation of all the remaining nodes in the rack containing the failed node as well as a large number of helper racks containing no failures. Consequently, the repair may be infeasible in front of multiple node failures. In this work, a relaxed repair model that can tolerate multiple node failures by simultaneously reducing the intra-rack connections and cross-rack connections is proposed. A tradeoff between the storage and repair bandwidth under the relaxed repair model is derived, and parameters of the two extreme points on the tradeoff curve are characterized for the minimum storage and minimum bandwidth respectively. Moreover, two codes corresponding to the extreme points are explicitly constructed over the fields of size comparable to the code length and with the lowest sub-packetization. Finally, for the convenience of practical use, systematic encoding processes for the two codes are also established.

We consider the rack-aware storage system where$n$nodes are organized in$\bar{n}$racks each containing$u$nodes, and any$k$nodes can retrieve the stored file. Moreover, any single node erasure can be recovered by downloading data from$\bar{d}$helper racks as well as the remaining$u-1$nodes in the same rack. Previous work mostly focuses on minimizing the cross-rack repair bandwidth under the condition$\bar{d}\geq\bar{k}$, where$\bar{k}=\lfloor\frac{k}{u}\rfloor$. However,$\bar{d}\geq\bar{k}$is not an intrinsic condition for the rack-aware storage model. Reducing$\bar{d}$can improve the repair efficiency in practice and bring more flexibility into the repair process. We establish a tradeoff between the storage overhead and cross-rack repair bandwidth for the more interesting case$\bar{d} < \bar{k}$, and explicitly construct codes with parameters lying on the tradeoff curve respectively at the minimum storage point and minimum bandwidth point. The codes are scalar or have sub-packetization$\bar{d}$, and operate over finite fields of size comparable to$n$. Moreover, they remove the restriction of MBR codes having rate less than$\frac{1}{2}$and that of high-rate MSR codes having exponential sub-packetization level.

Urban scene modeling is a challenging task for the photogrammetry and computer vision community due to its large scale, structural complexity, and topological delicacy. This paper presents an efficient multistep modeling framework for large-scale urban scenes from aerial images. It takes aerial images and a textured 3D mesh model generated by an image-based modeling system as the input and outputs compact polygon models with semantics at different levels of detail (LODs). Based on the key observation that urban buildings usually have piecewise planar rooftops and vertical walls, we propose a segment-based modeling method, which consists of three major stages: scene segmentation, roof contour extraction, and building modeling. By combining the deep neural network predictions with geometric constraints of the 3D mesh, the scene is first segmented into three classes. Then, for each building mesh, the 2D line segments are detected and used to slice the ground into polygon cells, followed by assigning each cell a roof plane via a MRF optimization. Finally, the LOD model is obtained by extruding cells to their corresponding planes. Compared with direct modeling in 3D space, we transform the mesh into a uniform 2D image grid representation and most of the modeling work is performed in 2D space, which has the advantages of low computational complexity and high robustness. In addition, our method doesn't require any global prior, such as the Manhattan or Atlanta world assumption, making it flexible to model scenes with different characteristics and complexity. Experiments on both single buildings and large-scale urban scenes demonstrate that by combining 2D photometric with 3D geometric information, the proposed algorithm is robust and efficient in urban scene LOD vectorized modeling compared with the state-of-the-art approaches.

We consider the rack-aware storage system where n= n̅u nodes are organized in n̅ racks each containing u nodes, and any k = k̅u+u0(0 ≤ u0n̅to (d̅ - k̅+ 1)⌈n̅/u-u^-0⌉⌉ where d̅ is the number of helper racks that participate in the repair process; (2) The field size is reduced to |F|>n which is almost half of the field used in Chen&Barg's construction. Besides, our code keeps the same access level as Chen&Barg's low-access construction.

We present a real-time monocular 3D reconstruction system on a mobile phone, called Mobile3DRecon. Using an embedded monocular camera, our system provides an online mesh generation capability on back end together with real-time 6DoF pose tracking on front end for users to achieve realistic AR effects and interactions on mobile phones. Unlike most existing state-of-the-art systems which produce only point cloud based 3D models online or surface mesh offline, we propose a novel online incremental mesh generation approach to achieve fast online dense surface mesh reconstruction to satisfy the demand of real-time AR applications. For each keyframe of 6DoF tracking, we perform a robust monocular depth estimation, with a multi-view semi-global matching method followed by a depth refinement post-processing. The proposed mesh generation module incrementally fuses each estimated keyframe depth map to an online dense surface mesh, which is useful for achieving realistic AR effects such as occlusions and collisions. We verify our real-time reconstruction results on two mid-range mobile platforms. The experiments with quantitative and qualitative evaluation demonstrate the effectiveness of the proposed monocular 3D reconstruction system, which can handle the occlusions and collisions between virtual objects and real scenes to achieve realistic AR effects.

In this paper, we present a routing algorithm based on the Time-Division-Multiplexer technique for routing table based Network-on-Chip (NoC) routers to decrease the demand of the system bandwidth while ensuring deadlock free. To fully use the communication resources of NoC — channels, banker algorithm is adopted to allocate and recycle the resources, and a weighted maze algorithm is utilized to determine if there is an available path for the current communication process. Experimental results show that the bandwidth requirement with the proposed algorithm decreases by 71.4% compared with the odd-even algorithm.

As an error correction code, Low Density Parity Check (LDPC) code has been widely used in various communication standards such as WiMAX and DVB-S2. But these continuously-evolving communication standards and the high development cost and low-flexibility of hardwired ASIC solutions have pushed LDPC researchers to turn to more cost-efficient and flexible implementation, and thus the multi-core processor based implementation of LDPC decoder is gaining increasing attention in the last few years. However, the performance of the multi-core processor based implementation is far below the hardwired ASICs, with one of the key reasons that the cost of communication between processors is very high. Three approaches are proposed in this paper to reduce the communication cost, including: optimized algorithm partitioning to reduce communication traffic, utilizing imbalanced communication between tasks to optimize mapping and reduce overall communication distance, and simplified data sending-receiving mechanism to reduce the cost of identifying received data. By using these approaches, the communication time of the proposed implementation of LDPC decoder only accounts for 12.2% of total decoding time, which generally occupies 50% decoding time in the previously reported LDPC decoders on multi-core processors. And our work can achieve better throughput performance under the same hardware condition compared with other state-of-the-art works.

Network-on-Chip (NoC) architecture is drawing intensive attention since it promises to maintain high performance in handling complex communication issues as the number of on-chip components increases. Mapping a given application onto the multi-core processors on NoC to obtain a high performance is a significant challenge. In this paper, we propose an optimized branch-and-bound (B&B) mapping algorithm to reduce the communication energy or improve the mapping efficiency by binding the tasks together when they have a large communication volume. Experimental results show that the proposed algorithm can achieve high performance in a short time compared with the traditional algorithm. For example, when mapping 64 tasks onto an 8×8 NoC system, with the approximate run time, 14.72% and 64.11% average energy consumption is saved compared with the original B&B and simulated annealing (SA) algorithms, respectively.