arXiv subjects

Sichen Wang

Publications and source records attributed to Sichen Wang.

3 recordsLinked to original sources

arXiv2026

The Price of Order in the Logarithmic Method

The logarithmic method is a classical static-to-dynamic transformation: it stores one dynamic ordered set as several immutable static components and rebuilds them by merges. The same component-and-merge discipline underlies write-optimized ordered indexes, where cheap insertions must be reconciled with exact ordered queries. In this paper, we study the insertion-only version after $n$ insertions, over abstract keys, in a strongly materialized merge-stack model with sequential component merges and one forward scan of the live components per query. We bound the product between the total amount of data written during the $n$ insertions and the worst-case amount of data read by a single query, known as the write-read product. The optimal bounds are as follows: - Membership and local certificates: $\Theta(n\log^2 n)$. - Order and range queries with named keys or endpoints: $\Theta(n\log^3 n)$. - Select: $\Theta(n^2)$. Thus, the logarithmic method does not impose a universal dynamic overhead: under materialized one-way access, the optimum depends on what information the query reveals before the scan starts. This pinpoints the access-model obstruction behind the extra logarithm for exact order and range queries, and the quadratic barrier for select.

cs.DS↗

arXiv2026

Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies

Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute. We argue for depth over fidelity and propose probabilistic elite membership (PEM), which replaces hard rank-based weights in evolution strategies with conditional expected rank weights that integrate over ranking uncertainty. PEM preserves the conditional mean update while reducing conditional update dispersion, a Rao-Blackwellization of the noisy rank-based step. We instantiate PEM via residual bootstrapping (RB-PEM) with capped per-generation overhead, complemented by an adaptive probe-and-switch mechanism for low-noise regimes. Across the COCO bbob-noisy suite and external tasks including RL policy search and hyperparameter optimization, RB-PEM achieves consistent gains in high-misranking, budget-constrained settings.

cs.NE↗

arXiv2022

Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows

The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery. But it is hard to figure out the statistical pattern and apply intelligent processing due to the lack of high-quality training data. To solve this issue, we first collect a novel benchmark on this setting with four diverse scenarios including concerts, sports games, gala shows, and contests, where each scenario contains 6 synchronized tracks recorded by different cameras. It contains 88-hour raw videos that contribute to the 14-hour edited videos. Based on this benchmark, we further propose a new approach temporal and contextual transformer that utilizes clues from historical shots and other views to make shot transition decisions and predict which view to be used. Extensive experiments show that our method outperforms existing methods on the proposed multi-camera editing benchmark.

cs.CV↗