arXiv subjects

Zekai Wu

Publications and source records attributed to Zekai Wu.

7 recordsLinked to original sources

arXiv2026

FGIM: a Fast Graph-based Indexes Merging Framework for Approximate Nearest Neighbor Search

As the state-of-the-art methods for high-dimensional data retrieval, Approximate Nearest Neighbor Search (ANNS) approaches with graph-based indexes have attracted increasing attention and play a crucial role in many real-world applications, e.g., retrieval-augmented generation (RAG) and recommendation systems. Unlike the extensive works focused on designing efficient graph-based ANNS methods, this paper delves into merging multiple existing graph-based indexes into a single one, which is also crucial in many real-world scenarios (e.g., cluster consolidation in distributed systems and read-write contention in real-time vector databases). We propose a Fast Graph-based Indexes Merging (FGIM) framework with three core techniques: (1) Proximity Graphs (PGs) to k Nearest Neighbor Graph (k-NNG) transformation used to extract potential candidate neighbors from input graph-based indexes through cross-querying, (2) k-NNG refinement designed to identify overlooked high-quality neighbors and maintain graph connectivity, and (3) k-NNG to PG transformation aimed at improving graph navigability and enhancing search performance. Then, we integrate our FGIM framework with the state-of-the-art ANNS method, HNSW, and other existing mainstream graph-based methods to demonstrate its generality and merging efficiency. Extensive experiments on six real-world datasets show that our FGIM framework is applicable to various mainstream graph-based ANNS methods, achieves up to 3.5X speedup over HNSW's incremental construction and an average of 7.9X speedup for methods without incremental support, while maintaining comparable or superior search performance.

cs.DB↗

arXiv2026

MERIT: Efficient In-Place Deletion for Dynamic Graph-Based Approximate Nearest Neighbor Indexes

Graph-based indexes have become the dominant approach to approximate nearest neighbor search (ANNS) over high-dimensional data and play a crucial role in real-world applications such as retrieval-augmented generation, recommendation systems, and vector databases. Despite extensive progress in static graph construction and search, efficient in-place deletion remains challenging because obsolete vectors must be removed without allowing stale incoming edges to consume search capacity or expensive graph-wide maintenance to interrupt online services, e.g., retrieval-augmented generation (RAG) and recommendation platforms. To address this problem, we propose MERIT (MST-based Efficient Repair with In-place updaTes), an in-place update framework with three core techniques: (1) bounded search-based recovery that combines a deleted vertex's outgoing neighbors with its readily searchable in-neighbors, (2) $k_r$-Minimum Spanning Tree (MST) local repair that promotes local connectivity while retaining multiple routing choices for graph search, and (3) versioned-edge invalidation that immediately filters all stale incoming edges to the deleted vertex and progressively removes them as adjacency lists are rewritten. Its integration with the hierarchical HNSW index and the single-layer Vamana index demonstrates applicability across distinct graph structures. Extensive experiments on multiple real-world datasets show that MERIT processes deletion at nearly the cost of inserting one vector, achieves up to $3.02\times$--$18.87\times$ faster deletion than state-of-the-art (SOTA) methods, and keeps search recall stable or even improves it as deletions accumulate.

cs.DB↗

arXiv2026

FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision

Precise motion timing (PMT) is crucial for swift motion analysis. A millisecond difference may determine victory or defeat in sports competitions. Despite substantial progress in human pose estimation (HPE), PMT remains largely overlooked by the HPE community due to the limited availability of high-temporal-resolution labeled datasets. Today, PMT is achieved using high-speed RGB cameras in specialized scenarios such as the Olympic Games; however, their high costs, light sensitivity, bandwidth, and computational complexity limit their feasibility for daily use. We developed FlashCap, the first flashing LED-based MoCap system for PMT. With FlashCap, we collect a millisecond-resolution human motion dataset, FlashMotion, comprising the event, RGB, LiDAR, and IMU modalities, and demonstrate its high quality through rigorous validation. To evaluate the merits of FlashMotion, we perform two tasks: precise motion timing and high-temporal-resolution HPE. For these tasks, we propose ResPose, a simple yet effective baseline that learns residual poses based on events and RGBs. Experimental results show that ResPose reduces pose estimation errors by ~40% and achieves millisecond-level timing accuracy, enabling new research opportunities. The dataset and code will be shared with the community.

cs.CV↗

arXiv2026

Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots

Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate between humanoid robot and human poses using only kinematic information. To facilitate this evaluation, we present the Human-Humanoid Motion (HHMotion) dataset, which consists of 1,000 motion sequences spanning 15 action categories, performed by 11 humanoid models and 10 human subjects. All motion sequences are converted into SMPL-X representations to eliminate the influence of visual appearance. We recruited 30 annotators to rate the human-likeness of each pose on a 0-5 scale, resulting in over 500 hours of annotation. Analysis of the collected data reveals that humanoid motions still exhibit noticeable deviations from human movements, particularly in dynamic actions such as jumping, boxing, and running. Building on HHMotion, we formulate a human-likeness evaluation task that aims to automatically predict human-likeness scores from motion data. Despite recent progress in multimodal large language models, we find that they remain inadequate for assessing motion human-likeness. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods. The dataset, code, and benchmark will be publicly released to support future research in the community.

cs.CV↗

arXiv2025

IDInit: A Universal and Stable Initialization Method for Neural Network Training

Deep neural networks have achieved remarkable accomplishments in practice. The success of these networks hinges on effective initialization methods, which are vital for ensuring stable and rapid convergence during training. Recently, initialization methods that maintain identity transition within layers have shown good efficiency in network training. These techniques (e.g., Fixup) set specific weights to zero to achieve identity control. However, settings of remaining weight (e.g., Fixup uses random values to initialize non-zero weights) will affect the inductive bias that is achieved only by a zero weight, which may be harmful to training. Addressing this concern, we introduce fully identical initialization (IDInit), a novel method that preserves identity in both the main and sub-stem layers of residual networks. IDInit employs a padded identity-like matrix to overcome rank constraints in non-square weight matrices. Furthermore, we show the convergence problem of an identity matrix can be solved by stochastic gradient descent. Additionally, we enhance the universality of IDInit by processing higher-order weights and addressing dead neuron problems. IDInit is a straightforward yet effective initialization method, with improved convergence, stability, and performance across various settings, including large-scale datasets and deep models.

cs.LG↗

arXiv2025

A Complete Landscape of EFX Allocations on Graphs: Goods, Chores and Mixed Manna

We study envy-free up to any item (EFX) allocations on simple graphs where vertices and edges represent agents and items respectively. An agent (vertex) is only interested in items (edges) that are incident to her and all other items always have zero marginal value to her. Christodoulou et al. [EC, 2023] first proposed this setting and studied the case of goods where every item has non-negative marginal values to every agent. In this work, we significantly generalize this setting and provide a complete set of results by considering the allocation of arbitrary items that can be goods, chores, or mixed manna under doubly monotone valuations with a mild assumption. For goods, we complement the results by Christodoulou et al. [EC, 2023] by considering another weaker notion of EFX in the literature and showing that an orientation -- a special allocation where each edge must be allocated to one of its endpoint agents -- that satisfies the weaker notion always exists and can be computed in polynomial time, contrary to the stronger notion for which an orientation may not exist and determining its existence is NP-complete. For chores, we show that an envy-free allocation always exists, and an EFX orientation may not exist but its existence can be determined in polynomial time. For mixed manna, we consider the four notions of EFX in the literature. We prove that an allocation that satisfies the strongest notion of EFX may not exist and determining its existence is NP-complete, while one that satisfies any of the other three notions always exists and can be computed in polynomial time. We also prove that an orientation that satisfies any of the four notions may not exist and determining its existence is NP-complete.

cs.GT↗

arXiv2024

Allocating Mixed Goods with Customized Fairness and Indivisibility Ratio

We consider the problem of fairly allocating a combination of divisible and indivisible goods. While fairness criteria like envy-freeness (EF) and proportionality (PROP) can always be achieved for divisible goods, only their relaxed versions, such as the ''up to one'' relaxations EF1 and PROP1, can be satisfied when the goods are indivisible. The ''up to one'' relaxations require the fairness conditions to be satisfied provided that one good can be completely eliminated or added in the comparison. In this work, we bridge the gap between the two extremes and propose ''up to a fraction'' relaxations for the allocation of mixed divisible and indivisible goods. The fraction is determined based on the proportion of indivisible goods, which we call the indivisibility ratio. The new concepts also introduce asymmetric conditions that are customized for individuals with varying indivisibility ratios. We provide both upper and lower bounds on the fractions of the modified item in order to satisfy the fairness criterion. Our results are tight up to a constant for EF and asymptotically tight for PROP.

cs.GT↗