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Run Zheng

dblp:388/8044 · DBLP profile ↗
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4ranked-venue papers
3as first author
4since 2021 · last 2026
0000-0002-9117-6639ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Theory of computation · 2 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Coding theory · 100%
Artificial intelligence
1 paper
Segmentation and scene understanding · 100%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes › cyclic codes
BCH codes
1.922026
The Dimension and Bose Distance of Some BCH Codes of Length $\frac{q^{m}-1}{\lambda}$ · IEEE Trans. Inf. Theory 2026
The Dimension and Bose Distance of Certain Primitive BCH Codes · IEEE Trans. Inf. Theory 2025
Coding theory
error-correcting codes
1.922026
The Dimension and Bose Distance of Some BCH Codes of Length $\frac{q^{m}-1}{\lambda}$ · IEEE Trans. Inf. Theory 2026
The Dimension and Bose Distance of Certain Primitive BCH Codes · IEEE Trans. Inf. Theory 2025
Computer vision › Segmentation and scene understanding
instance segmentation
1.012026
FloorPlanFormer: Multi-Task Transformer Network for Floor Plan Recognition with Outer-to-Inner Feature Refinement · AAAI 2026
Computer vision › Segmentation and scene understanding › image segmentation
multi-task segmentation
1.012026
FloorPlanFormer: Multi-Task Transformer Network for Floor Plan Recognition with Outer-to-Inner Feature Refinement · AAAI 2026
Computer vision › Segmentation and scene understanding
semantic segmentation
1.012026
FloorPlanFormer: Multi-Task Transformer Network for Floor Plan Recognition with Outer-to-Inner Feature Refinement · AAAI 2026
Coding theory › error-correcting codes › block codes
linear code
0.312026
The Dimension and Bose Distance of Some BCH Codes of Length $\frac{q^{m}-1}{\lambda}$ · IEEE Trans. Inf. Theory 2026
Coding theory › error-correcting codes › block codes › linear code › code parameters
optimal linear codes
0.312026
The Dimension and Bose Distance of Some BCH Codes of Length $\frac{q^{m}-1}{\lambda}$ · IEEE Trans. Inf. Theory 2026
Coding theory › error-correcting codes
cyclic codes
0.312025
The Dimension and Bose Distance of Certain Primitive BCH Codes · IEEE Trans. Inf. Theory 2025
Coding theory › error-correcting codes › algebraic coding theory
narrow-sense primitive BCH codes
0.312025
The Dimension and Bose Distance of Certain Primitive BCH Codes · IEEE Trans. Inf. Theory 2025

Methods — techniques the papers use, named apart from their topics

finite field theory · 1.9cyclotomic cosets · 1.9swin transformer · 1.0mask transformer · 1.0attention mechanism · 1.0
YearPublicationVenuePosition
2026 FloorPlanFormer: Multi-Task Transformer Network for Floor Plan Recognition with Outer-to-Inner Feature Refinement
abstract
Floor plan recognition requires accurate segmentation and classification of entrance doors, outer contours (walls and windows) and inner contours (various room types) , despite strong spatial dependencies and large stylistic differences between different datasets. To overcome these challenges, we propose FloorPlanFormer, a multi-task learning network divided into three phases: the first phase introduces a Swin Transformer backbone with a pixel decoder to extract fine-grained pixel-level semantics; the second phase employs prompt encoder and mask decoder, and a novel Global Contextual Attention Module (GCAM) is designed to generate clear, high-quality outer contour masks; the third stage uses mask transformer decoder to recognize targets and designs a Masked Feature Refinement Module (MFRM) to accurately delineate the inner contour by modeling the relationship between the local inner and outer contours. Finally, we constructed FloorPlan8K, a dataset containing 8200 images and 77434 instances, on which our model was trained and evaluated, and the results greatly outperformed the state-of-the-art general segmentation methods and specialized methods.
Yun Liang 0003, Run Zheng, Shuai Xie, Yishen Lin
AAAI3
2026 Synchronization of Asymmetric Numeral System Decoder
abstract
The “synchronization” property of Asymmetric Numeral Systems (ANS) is vital for massively parallel decoding on platforms like GPUs [2]. This allows a decoder, starting from an arbitrary position and state, to converge to the correct decoding sequence. While the synchronization of methods like Huffman coding is well-understood [1], a formal analysis for ANS has been missing. This work derives the single-step synchronization probability for ANS, a key metric for parallel performance.
Run Zheng, Zheting Dong, Zhaoyi Sun
DCC1
2026 The Dimension and Bose Distance of Some BCH Codes of Length $\frac{q^{m}-1}{\lambda}$
abstract
BCH codes are important error correction codes, widely utilized due to their robust algebraic structure, multierror correcting capability, and efficient decoding algorithms. Despite their practical importance and extensive study, their parameters, including dimension, minimum distance and Bose distance, remain largely unknown in general. This paper addresses this challenge by investigating the dimension and Bose distance of BCH codes of length (qm−1)/λ over the finite field Fq, where λ is a positive divisor ofq−1. Specifically, for narrowsense BCH codes of this length withm≥ 4, we derive explicit formulas for their dimension for designed distances 2 ≤ δ ≤ (q⌊(2m−1)/3⌋+1−1)/λ+1. We also provide explicit formulas for their Bose distance in the range 2 ≤ δ ≤ (q⌊(2m−1)/3⌋+1 − 1)/λ. These ranges for δ are notably larger than the previously known results for this class of BCH codes. Furthermore, we extend these findings to determine the dimension and Bose distance for certain non-narrow-sense BCH codes of the same length. Several optimal linear codes can be obtained from these BCH codes.
Run Zheng, Nung-Sing Sze
IEEE Trans. Inf. Theory1
2025 The Dimension and Bose Distance of Certain Primitive BCH Codes
abstract
Bose-Ray-Chaudhuri-Hocquenghem (BCH) codes are a significant class of cyclic codes that play an important role in both theoretical research and practical applications. Their strong error-correcting abilities and efficient encoding and decoding methods make BCH codes widely applicable in various areas, including communication systems, data storage devices, and consumer electronics. Although BCH codes have been extensively studied, the parameters of BCH codes are not known in general. Letqbe a prime power andmbe a positive integer. Denote byC(q,m,δ))the narrow-sense primitive BCH code with lengthqm− 1 and designed distance δ. As of now, the dimensions ofC(q,m,δ)are fully understood only form≤ 2. Form≥ 4, the dimensions ofC(q,m,δ)are known only for the range 2 ≤ δ ≤q⌊(m+1)/2⌋+1and for a limited number of special cases. In this paper, we determined the dimension and Bose distance ofC(q,m,δ)form≥ 4 and δ ∈ [2,q⌊(2m−1)/3⌋+1]. Additionally, we have also extended our results to primitive BCH codes that are not necessarily narrow-sense.
Run Zheng, Nung-Sing Sze
IEEE Trans. Inf. Theory1