arXiv subjects

Yang Xie

Publications and source records attributed to Yang Xie.

31 records · Page 2Linked to original sources

arXiv2023

Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction

Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive performance in drug discovery. However, such models lack authenticity and accuracy in drug target identification, leading to an increased misjudgment rate and reduced drug development efficiency. To address these issues, we focus on the problem of drug-target interactions, with knowledge mapping as the core technology. Specifically, a causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model. Experimental results demonstrate that the developed confidence measurement method based on causal intervention can significantly enhance the accuracy of DTI link prediction, particularly for high-precision models. The predicted results are more valuable in guiding the design and development of subsequent drug development experiments, thereby significantly improving the efficiency of drug development.

q-bio.QM↗

arXiv2022

Realize General Access Structure Based On Single Share

Traditional threshold secret sharing cannot realizing all access structures of secret sharing. So, Ito introduced the concept of Secret sharing scheme realizing general access structure. But Its scheme has to send multiple shares to each trustee. In this paper, we proposed two new secret sharing schemes realizing general access structures by only assigning one share to each trustee. Our proposed second scheme is a perfect secret sharing scheme. Furthermore, our schemes can realize any access structures.

cs.CR↗

arXiv2022

Defect-induced edge ferromagnetism and fractional spin excitations of the SU(4) $π$-flux Hubbard model on honeycomb lattice

Recently, a SU(4) $π$-flux Hubbard model on the honeycomb lattice has been proposed to study the spin-orbit excitations of $α$-ZrCl$_3$ [Phys.~Rev.~Lett. 121.097201~(2017)]. Based on this model with a zigzag edge, we show the edge defects can induce edge flat bands that result in a SU(4) edge ferromagnetism. We develop an effective one-dimensional interaction Hamiltonian to study the corresponding SU(4) spin excitations. Remarkably, SU(4) spin excitations of the edge ferromagnet appear as a continuum covering the entire energy region rather than usual magnons. Through further entanglement entropy analysis, we suggest that the continuum consists of fractionalized spin excitations from the disappeared magnons, except for that from the particle-hole Stoner excitations. Moreover, in ribbon systems with finite widths, the disappeared magnons can be restored in the gap formed by the finite-size effect and the optical branch of the restored magnons are found to be topological nontrivial.

cond-mat.str-el↗

arXiv2021

HBert + BiasCorp -- Fighting Racism on the Web

Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of the society. In this short piece of work, we present how we're tackling this societal issue with Natural Language Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively.

cs.CL↗

arXiv2021

Robust and Attack Resilient Logic Locking with a High Application-Level Impact

Logic locking is a hardware security technique to intellectual property (IP) against security threats in the IC supply chain, especially untrusted fabs. Such techniques incorporate additional locking circuitry within an IC that induces incorrect functionality when an incorrect key is provided. The amount of error induced is known as the effectiveness of the locking technique. "SAT attacks" provide a strong mathematical formulation to find the correct key of locked circuits. In order to achieve high SAT resilience(i.e. complexity of SAT attacks), many conventional logic locking schemes fail to inject sufficient error into the circuit. For example, in the case of SARLock and Anti-SAT, there are usually very few (or only one) input minterms that cause any error at the circuit output. The state-of-the-art stripped functionality logic locking (SFLL) technique introduced a trade-off between SAT resilience and effectiveness. In this work, we prove that such a trade-off is universal in logic locking. In order to attain high effectiveness of locking without compromising SAT resilience, we propose a novel logic locking scheme, called Strong Anti-SAT (SAS). In addition to SAT attacks, removal-based attacks are also popular against logic locking. Based on SAS, we propose Robust SAS (RSAS) which is resilient to removal attacks and maintains the same SAT resilience and as effectiveness as SAS. SAS and RSAS have the following significant improvements over existing techniques. (1) SAT resilience of SAS and RSAS against SAT attack is not compromised by increase in effectiveness. (2) In contrast to prior work focusing solely on the circuit-level locking impact, we integrate SAS-locked modules into a processor and show that SAS has a high application-level impact. (3) Our experiments show that SAS and RSAS exhibit better SAT resilience than SFLL and have similar effectiveness.

cs.CR↗

arXiv2020

Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process

The location, timing, and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. The related R/C++ source code is available at https://github.com/Minzhe/BOOST-GP.

stat.AP↗

arXiv2019

RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data

Formalin-fixed paraffin-embedded (FFPE) samples have great potential for biomarker discovery, retrospective studies and diagnosis or prognosis of diseases. Their application, however, is hindered by the unsatisfactory performance of traditional gene expression profiling techniques on damaged RNAs. NanoString nCounter platform is well suited for profiling of FFPE samples and measures gene expression with high sensitivity which may greatly facilitate realization of scientific and clinical values of FFPE samples. However, methodological development for normalization, a critical step when analyzing this type of data, is far behind. Existing methods designed for the platform use information from different types of internal controls separately and rely on an overly-simplified assumption that expression of housekeeping genes is constant across samples for global scaling. Thus, these methods are not optimized for the nCounter system, not mentioning that they were not developed for FFPE samples. We construct an integrated system of random-coefficient hierarchical regression models to capture main patterns and characteristics observed from NanoString data of FFPE samples and develop a Bayesian approach to estimate parameters and normalize gene expression across samples. Our method, labeled RCRnorm, incorporates information from all aspects of the experimental design and simultaneously removes biases from various sources. It eliminates the unrealistic assumption on housekeeping genes and offers great interpretability. Furthermore, it is applicable to freshly frozen or like samples that can be generally viewed as a reduced case of FFPE samples. Simulation and applications showed the superior performance of RCRnorm.

stat.ME↗

arXiv2018

ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network

The spatial distributions of different types of cells could reveal a cancer cell growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key hallmarks of cancer. However, manually recognizing and localizing all the cells in pathology slides are almost impossible. In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor, stromal and lymphocytes classification, and extraction of tumor microenvironment related features for lung cancer pathology images. The overall classification accuracy is 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a spatial map of tumor, stromal and lymphocyte cells. From this spatial map, we can extracted features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.

cs.CV↗

arXiv2018

Topological and magnetic phase transition in silicene-like zigzag nanoribbons

Spin-orbital interactions (SOI) in silicene results in the quantum spin Hall effect, while the Hubbard-induced Coulomb interaction in zigzag nanoribbons often generates a band gap with the anti-ferromagnetic (AF) spin orders on two edges. In this paper we systematically study these two joint contributions to the zigzag silicene-like nanoribbons (zSiNR). Some topological and magnetic phase transitions are investigated with different material parameters and external fields. We find when the ribbon width or the SOI value exceeds some critical value, the SOI may overcome the Coulomb interaction and the system transits from a band insulator to a topological insulator: the quantum-spin-Hall or the spin quantum-anomalous Hall state. We also find some magnetic phase transition exist in the Hubbard-dominated zSiNR systems when the exchange field or the electric field goes beyond some critical values. At last we observe a double topological/magnetic phase transition in a Hubbard-SOI-balanced zSiNR system before the magnetic and topological phases are destroyed by a strong electric field.

cond-mat.mes-hall↗

arXiv2017

Neural Trojans

While neural networks demonstrate stronger capabilities in pattern recognition nowadays, they are also becoming larger and deeper. As a result, the effort needed to train a network also increases dramatically. In many cases, it is more practical to use a neural network intellectual property (IP) that an IP vendor has already trained. As we do not know about the training process, there can be security threats in the neural IP: the IP vendor (attacker) may embed hidden malicious functionality, i.e. neural Trojans, into the neural IP. We show that this is an effective attack and provide three mitigation techniques: input anomaly detection, re-training, and input preprocessing. All the techniques are proven effective. The input anomaly detection approach is able to detect 99.8% of Trojan triggers although with 12.2% false positive. The re-training approach is able to prevent 94.1% of Trojan triggers from triggering the Trojan although it requires that the neural IP be reconfigurable. In the input preprocessing approach, 90.2% of Trojan triggers are rendered ineffective and no assumption about the neural IP is needed.

cs.CR↗

arXiv2017

The band calculation and local state analysis for armchair graphene-like nanoribbons with line defects

In this paper we propose an analytical method to calculate the band structures of graphene-like nanoribbons of the armchair type with arbitrary line defects or uniaxial strains. The model is based on the tight-binding model and the standing wave assumption for the armchair nanoribbons. It gives accurate band results for large supercell systems. Within this method, we analyze different local states near the line defect in the graphene and boron-nitride nanoribbons. We also derive the analytical expression for the local states in a semi-infinite graphene nanoribbon by this method and the transfer matrix technique. The criteria condition for the local states in the semi-infinite nanoribbons is also given.

cond-mat.mes-hall↗

arXiv2012

Anisotropic Impurity-States, Quasiparticle Scattering and Nematic Transport in Underdoped Ca(Fe1-xCox)2As2

Iron-based high temperature superconductivity develops when the `parent' antiferromagnetic/orthorhombic phase is suppressed, typically by introduction of dopant atoms. But their impact on atomic-scale electronic structure, while in theory quite complex, is unknown experimentally. What is known is that a strong transport anisotropy with its resistivity maximum along the crystal b-axis, develops with increasing concentration of dopant atoms; this `nematicity' vanishes when the `parent' phase disappears near the maximum superconducting Tc. The interplay between the electronic structure surrounding each dopant atom, quasiparticle scattering therefrom, and the transport nematicity has therefore become a pivotal focus of research into these materials. Here, by directly visualizing the atomic-scale electronic structure, we show that substituting Co for Fe atoms in underdoped Ca(Fe1-xCox)2As2 generates a dense population of identical anisotropic impurity states. Each is ~8 Fe-Fe unit cells in length, and all are distributed randomly but aligned with the antiferromagnetic a-axis. By imaging their surrounding interference patterns, we further demonstrate that these impurity states scatter quasiparticles in a highly anisotropic manner, with the maximum scattering rate concentrated along the b-axis. These data provide direct support for the recent proposals that it is primarily anisotropic scattering by dopant-induced impurity states that generates the transport nematicity; they also yield simple explanations for the enhancement of the nematicity proportional to the dopant density and for the occurrence of the highest resistivity along the b-axis.

cond-mat.supr-con↗

arXiv2012

Anisotropic Energy Gaps of Iron-based Superconductivity from Intra-band Quasiparticle Interference in LiFeAs

If strong electron-electron interactions between neighboring Fe atoms mediate the Cooper pairing in iron-pnictide superconductors, then specific and distinct anisotropic superconducting energy gaps Δ_i(k) should appear on the different electronic bands i. Here we introduce intra-band Bogoliubov quasiparticle scattering interference (QPI) techniques for determination of Δ_i(k) in such materials, focusing on LiFeAs. We identify the three hole-like bands assigned previously as γ, α_2 and α_1, and we determine the anisotropy, magnitude and relative orientations of their Δ_i(k). These measurements will advance quantitative theoretical analysis of the mechanism of Cooper pairing in iron-based superconductivity.

cond-mat.supr-con↗