arXiv subjects

Yun Deng

Publications and source records attributed to Yun Deng.

6 recordsLinked to original sources

arXiv2026

Population-scale Ancestral Recombination Graphs with tskit 1.0

Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics. The tskit library has become key infrastructure for the field, providing an expressive and general representation of ARGs together with a suite of efficient fundamental operations. In this note, we announce tskit version 1.0, describe its underlying rationale, and document its stability guarantees. These guarantees provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

q-bio.PE↗

arXiv2025

Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species

The ongoing explosion of genome sequence data is transforming how we reconstruct and understand the histories of biological systems. Across biological scales, from individual cells to populations and species, trees-based models provide a common framework for representing ancestry. Once limited to species phylogenetics, "tree thinking" now extends deeply to population genomics and cell biology, revealing the genealogical structure of genetic and phenotypic variation within and across organisms. Recently, there have been great methodological and computational advances on tree-based methods, including methods for inferring ancestral recombination graphs in populations, phylogenetic frameworks for comparative genomics, and lineage-tracing techniques in developmental and cancer biology. Despite differences in data types and biological contexts, these approaches share core statistical and algorithmic challenges: efficiently inferring branching histories from genomic information, integrating temporal and spatial signals, and connecting genealogical structures to evolutionary and functional processes. Recognizing these shared foundations opens opportunities for cross-fertilization between fields that are traditionally studied in isolation. By examining how tree-based methods are applied across cellular, population, and species scales, we identify the conceptual parallels that unite them and the distinct challenges that each domain presents. These comparisons offer new perspectives that can inform algorithmic innovations and lead to more powerful inference strategies across the full spectrum of biological systems.

q-bio.PE↗

arXiv2018

Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement

Recently, deep learning(DL) methods have been proposed for the low-dose computed tomography(LdCT) enhancement, and obtain good trade-off between computational efficiency and image quality. Most of them need large number of pre-collected ground-truth/high-dose sinograms with less noise, and train the network in a supervised end-to-end manner. This may bring major limitations on these methods because the number of such low-dose/high-dose training sinogram pairs would affect the network's capability and sometimes the ground-truth sinograms are hard to be obtained in large scale. Since large number of low-dose ones are relatively easy to obtain, it should be critical to make these sources play roles in network training in an unsupervised learning manner. To address this issue, we propose an unsupervised DL method for LdCT enhancement that incorporates unlabeled LdCT sinograms directly into the network training. The proposed method effectively considers the structure characteristics and noise distribution in the measured LdCT sinogram, and then learns the proper gradient of the LdCT sinogram in a pure unsupervised manner. Similar to the labeled ground-truth, the gradient information in an unlabeled LdCT sinogram can be used for sufficient network training. The experiments on the patient data show effectiveness of the proposed method.

cs.CV↗

arXiv2016

Fast Compatibility Testing for Phylogenies with Nested Taxa

Semi-labeled trees are phylogenies whose internal nodes may be labeled by higher-order taxa. Thus, a leaf labeled Mus musculus could nest within a subtree whose root node is labeled Rodentia, which itself could nest within a subtree whose root is labeled Mammalia. Suppose we are given collection $\mathcal P$ of semi-labeled trees over various subsets of a set of taxa. The ancestral compatibility problem asks whether there is a semi-labeled tree $\mathcal T$ that respects the clusterings and the ancestor/descendant relationships implied by the trees in $\mathcal P$. We give a $\tilde{O}(M_{\mathcal{P}})$ algorithm for the ancestral compatibility problem, where $M_{\mathcal{P}}$ is the total number of nodes and edges in the trees in $\mathcal P$. Unlike the best previous algorithm, the running time of our method does not depend on the degrees of the nodes in the input trees.

cs.DS↗

arXiv2015

Fast Compatibility Testing for Rooted Phylogenetic Trees

We consider the following basic problem in phylogenetic tree construction. Let $\mathcal{P} = \{T_1, \ldots, T_k\}$ be a collection of rooted phylogenetic trees over various subsets of a set of species. The tree compatibility problem asks whether there is a tree $T$ with the following property: for each $i \in \{1, \dots, k\}$, $T_i$ can be obtained from the restriction of $T$ to the species set of $T_i$ by contracting zero or more edges. If such a tree $T$ exists, we say that $\mathcal{P}$ is compatible. We give a $\tilde{O}(M_\mathcal{P})$ algorithm for the tree compatibility problem, where $M_\mathcal{P}$ is the total number of nodes and edges in $\mathcal{P}$. Unlike previous algorithms for this problem, the running time of our method does not depend on the degrees of the nodes in the input trees. Thus, it is equally fast on highly resolved and highly unresolved trees.

cs.DS↗

arXiv2006

Crosstalk-free Conjugate Networks for Optical Multicast Switching

High-speed photonic switching networks can switch optical signals at the rate of several terabits per second. However, they suffer from an intrinsic crosstalk problem when two optical signals cross at the same switch element. To avoid crosstalk, active connections must be node-disjoint in the switching network. In this paper, we propose a sequence of decomposition and merge operations, called conjugate transformation, performed on each switch element to tackle this problem. The network resulting from this transformation is called conjugate network. By using the numbering-schemes of networks, we prove that if the route assignments in the original network are link-disjoint, their corresponding ones in the conjugate network would be node-disjoint. Thus, traditional nonblocking switching networks can be transformed into crosstalk-free optical switches in a routine manner. Furthermore, we show that crosstalk-free multicast switches can also be obtained from existing nonblocking multicast switches via the same conjugate transformation.

cs.NI↗