arXiv subjects

Yulin Zhang

Publications and source records attributed to Yulin Zhang.

45 records · Page 3Linked to original sources

arXiv2021

A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data

DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene selection in high dimensional data(over 450 thousand). Variance filtering is first introduced for dimension reduction and Recursive feature elimination (RFE) is then used for feature selection. We address the problem of selecting a small subsets of genes from large number of methylated sites, and our parsimonious model is demonstrated to be efficient, achieving an accuracy over 91%, outperforming other studies which use DNA micro-arrays and RNA-seq Data . The performance of 20 models, which are based on 4 estimators (Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5 classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is examined. Results suggest that the combined model of extra tree plus catboost classifier offers the best performance in cancer identification, with an overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and 50 features respectively. The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies. Therefore, our method may be utilzed as an auxiliary diagnostic method to determine the actual clinicopathological status of a specific cancer.

q-bio.TO↗

arXiv2020

Accelerating combinatorial filter reduction through constraints

Reduction of combinatorial filters involves compressing state representations that robots use. Such optimization arises in automating the construction of minimalist robots. But exact combinatorial filter reduction is an NP-complete problem and all current techniques are either inexact or formalized with exponentially many constraints. This paper proposes a new formalization needing only a polynomial number of constraints, and characterizes these constraints in three different forms: nonlinear, linear, and conjunctive normal form. Empirical results show that constraints in conjunctive normal form capture the problem most effectively, leading to a method that outperforms the others. Further examination indicates that a substantial proportion of constraints remain inactive during iterative filter reduction. To leverage this observation, we introduce just-in-time generation of such constraints, which yields improvements in efficiency and has the potential to minimize large filters.

cs.RO↗

arXiv2020

Abstractions for computing all robotic sensors that suffice to solve a planning problem

Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions---i.e., plan and sensor characterization pairs---if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design trade-offs. Generalizing prior work where sensors are modeled abstractly as sensor maps on p-graphs, the present paper increases the potential sensors which can be sought significantly. But doing so enlarges a problem currently on the outer limits of being considered tractable. Toward taming this complexity, two contributions are made: (1) we show how to represent the search space for this more general problem and describe data structures that enable whole sets of sensors to be summarized via a single special representative; (2) we give a means by which other structure (either task domain knowledge, sensor technology or fabrication constraints) can be incorporated to reduce the sets to be enumerated. These lead to algorithms that we have implemented and which suffice to solve particular problem instances, albeit only of small scale. Nevertheless, the algorithm aids in helping understand what attributes sensors must possess and what information they must provide in order to ensure a robot can achieve its goals despite non-determinism.

cs.RO↗

arXiv2019

Finding plans subject to stipulations on what information they divulge

Motivated by applications where privacy is important, we consider planning problems for robots acting in the presence of an observer. We first formulate and then solve planning problems subject to stipulations on the information divulged during plan execution --- the appropriate solution concept being both a plan and an information disclosure policy. We pose this class of problem under a worst-case model within the framework of procrustean graphs, formulating the disclosure policy as a particular type of map on edge labels. We devise algorithms that, given a planning problem supplemented with an information stipulation, can find a plan, associated disclosure policy, or both if some exists. Both the plan and associated disclosure policy may depend subtlety on additional information available to the observer, such as whether the observer knows the robot's plan (e.g., leaked via a side-channel). Our implementation finds a plan and a suitable disclosure policy, jointly, when any such pair exists, albeit for small problem instances.

cs.RO↗

arXiv2018

What does my knowing your plans tell me?

For robots acting in the presence of observers, we examine the information that is divulged if the observer is party to the robot's plan. Privacy constraints are specified as the stipulations on what can be inferred during plan execution. We imagine a case in which the robot's plan is divulged beforehand, so that the observer can use this {\em a priori} information along with the disclosed executions. The divulged plan, which can be represented by a procrustean graph, is shown to undermine privacy precisely to the extent that it can eliminate action-observation sequences that will never appear in the plan. Future work will consider how the divulged plan might be sought as the output of a planning procedure.

cs.RO↗

arXiv2018

The eigenstructures of real (skew) circulant matrices with some applications

The circulant matrices and skew-circulant matrices are two special classes of Toeplitz matrices and play vital roles in the computation of Toeplitz matrices. In this paper, we focus on real circulant and skewcirculant matrices. We first investigate their real Schur forms, which are closely related to the family of discrete cosine transform (DCT) and discrete sine transform (DST). Using those real Schur forms, we then develop some fast algorithms for computing real circulant, skew-circulant and Toeplitz matrix-real vector multiplications. Also, we develop a DCT-DST version of circulant and skew-circulant splitting (CSCS) iteration for real positive definite Toeplitz systems. Compared with the fast Fourier transform (FFT) version of CSCS iteration, the DCTDST version is more efficient and saves a half storage. Numerical experiments are presented to illustrate the effectiveness of our method.

math.NA↗

arXiv2018

Complete characterization of a class of privacy-preserving tracking problems

We examine the problem of target tracking whilst simultaneously preserving the target's privacy as epitomized by the robotic panda tracking scenario, which O'Kane introduced at the 2008 Workshop on the Algorithmic Foundations of Robotics in order to elegantly illustrate the utility of ignorance. The present paper reconsiders his formulation and the tracking strategy he proposed, along with its completeness. We explore how the capabilities of the robot and panda affect the feasibility of tracking with a privacy stipulation, uncovering intrinsic limits, no matter the strategy employed. This paper begins with a one-dimensional setting and, putting the trivially infeasible problems aside, analyzes the strategy space as a function of problem parameters. We show that it is not possible to actively track the target as well as protect its privacy for every nontrivial pair of tracking and privacy stipulations. Secondly, feasibility can be sensitive, in several cases, to the information available to the robot initially. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The robot's power to achieve privacy-preserving tracking is bounded, converging asymptotically with increasing sensing power. We analyze the entire space of possible tracking problems, characterizing every instance as either achievable, constructively by giving a policy where one exists (some of which depend on the initial information), or proving the instance impossible. Finally, to relate some of the impossibility results in one dimension to their higher-dimensional counterparts, including the planar panda tracking problem studied by O'Kane, we establish a connection between tracking dimensionality and the sensing power of a one-dimensional robot.

cs.RO↗

arXiv2016

Characterizations and representations of core and dual core inverses

In this paper, double commutativity and the reverse order law for the core inverse are considered. Then, new characterizations of the Moore-Penrose inverse of a regular element are given by one-sided invertibilities in a ring. Furthermore, the characterizations and representations of the core and dual core inverses of a regular element are considered.

math.RA↗

arXiv2012

Structure-preserving Schur methods for computing square roots of real skew-Hamiltonian matrices

Our contribution is two-folded. First, starting from the known fact that every real skew-Hamiltonian matrix has a real Hamiltonian square root, we give a complete characterization of the square roots of a real skew-Hamiltonian matrix W. Second, we propose a structure exploiting method for computing square roots of W. Compared to the standard real Schur method, which ignores the structure, our method requires significantly less arithmetic.

math.NA↗