arXiv subjects

R. Tang

Publications and source records attributed to R. Tang.

3 recordsLinked to original sources

arXiv2025

Efficient k-mer Dataset Compression Using Eulerian Covers of de Bruijn Graphs and BWT

Transforming an input sequence into its constituent k-mers is a fundamental operation in computational genomics. To reduce storage costs associated with k-mer datasets, we introduce and formally analyze MCTR, a novel two-stage algorithm for lossless compression of the k-mer multiset. Our core method achieves a minimal text representation (W) by computing an optimal Eulerian cover (minimum string count) of the dataset's de Bruijn graph, enabled by an efficient local Eulerization technique. The resulting strings are then further compressed losslessly using the Burrows-Wheeler Transform (BWT). Leveraging de Bruijn graph properties, MCTR is proven to achieve linear time and space complexity and guarantees complete reconstruction of the original k-mer multiset, including frequencies. Using simulated and real genomic data, we evaluated MCTR's performance (list and frequency representations) against the state-of-the-art lossy unitigging tool greedytigs (from matchtigs). We measured core execution time and the raw compression ratio cmpr = weight(M)/weight(W), where M is the input sequence data). Benchmarks confirmed MCTR's data fidelity but revealed performance trade-offs inherent to lossless representation. GreedyTigs was significantly faster. Regarding raw compression, GreedyTigs achieved high ratios (cmpr approx 14) on noisy real data for its lossy sequence output. On real data, MCTR (frequency) showed moderate raw compression (cmpr approx 1.5-2.7), while MCTR (list) showed none (cmpr approx 1). Importantly, the full MCTR+BWT pipeline significantly outperforms BWT alone for enhanced lossless compression. Our results establish MCTR as a valuable, theoretically grounded tool for applications demanding efficient, lossless storage and analysis of k-mer multisets, complementing lossy methods optimized for sequence summarization.

math.CO↗

arXiv2023

Local Contrastive Learning for Medical Image Recognition

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings.

cs.CV↗

arXiv2021

Topological edge states of Kekul\'e-type photonic crystals induced by a synchronized rotation of unit cells

Generating and manipulating Dirac points in artificial atomic crystals has received attention especially in photonic systems due to their ease of implementation. In this paper, we propose a two-dimensional photonic crystal made of a Kekul\'e lattice of pure dielectrics, where the internal rotation of cylindrical pillars induces optical Dirac-degeneracy breaking. Our calculated dispersion reveals that the synchronized rotation reverses bands and switches parity as well so as to induce a topological phase transition. Our simulation demonstrates that such topologically protected edge states can achieve robust transmission in defect waveguides under deformation, and therefore provides a pragmatically tunable scheme to achieve reconfigurable topological phases.

physics.optics↗