arXiv subjects

Lin Liu

Publications and source records attributed to Lin Liu.

At least 289 records · Page 16Linked to original sources

arXiv2020

Wavelet-Based Dual-Branch Network for Image Demoireing

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality. In this paper, we design a wavelet-based dual-branch network (WDNet) with a spatial attention mechanism for image demoireing. Existing image restoration methods working in the RGB domain have difficulty in distinguishing moire patterns from true scene texture. Unlike these methods, our network removes moire patterns in the wavelet domain to separate the frequencies of moire patterns from the image content. The network combines dense convolution modules and dilated convolution modules supporting large receptive fields. Extensive experiments demonstrate the effectiveness of our method, and we further show that WDNet generalizes to removing moire artifacts on non-screen images. Although designed for image demoireing, WDNet has been applied to two other low-levelvision tasks, outperforming state-of-the-art image deraining and derain-drop methods on the Rain100h and Raindrop800 data sets, respectively.

cs.CV↗

arXiv2020

On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators $\hatψ_{1}$ are the state-of-the-art, incorporating the good prediction performance of machine learning; the decreased bias of doubly robust estimators; and the analytic tractability and bias reduction of sample splitting with cross fitting. Nonetheless, even in the absence of confounding by unmeasured factors, the nominal $(1 - α)$ Wald confidence interval $\hatψ_{1} \pm z_{α/ 2} \widehat{\mathsf{se}} [\hatψ_{1}]$ may still undercover even in large samples, because the bias of $\hatψ_{1}$ may be of the same or even larger order than its standard error of order $n^{-1/2}$. In this paper, we introduce essentially assumption-free tests that (i) can falsify the null hypothesis that the bias of $\hatψ_{1}$ is of smaller order than its standard error, (ii) can provide an upper confidence bound on the true coverage of the Wald interval, and (iii) are valid under the null under no smoothness/sparsity assumptions on the nuisance parameters. The tests, which we refer to as \underline{A}ssumption \underline{F}ree \underline{E}mpirical \underline{C}overage \underline{T}ests (AFECTs), are based on a U-statistic that estimates part of the bias of $\hatψ_{1}$.

stat.ML↗

arXiv2020

Computational methods for cancer driver discovery: A survey

Motivation: Uncovering the genomic causes of cancer, known as cancer driver genes, is a fundamental task in biomedical research. Cancer driver genes drive the development and progression of cancer, thus identifying cancer driver genes and their regulatory mechanism is crucial to the design of cancer treatment and intervention. Many computational methods, which take the advantages of computer science and data science, have been developed to utilise multiple types of genomic data to reveal cancer drivers and their regulatory mechanism behind cancer development and progression. Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers. Results: We survey computational methods for identifying cancer drivers from genomic data. We categorise the methods into three groups, methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. We also conduct a case study to compare the performance of the current methods. We further analyse the advantages and limitations of the current methods, and discuss the challenges and future directions of the topic. In addition, we investigate the resources for discovering and validating cancer drivers in order to provide a one-stop reference of the tools to facilitate cancer driver discovery. The ultimate goal of the paper is to help those interested in the topic to establish a solid background to carry out further research in the field.

q-bio.GN↗

arXiv2020

Clinically Relevant Mediation Analysis using Controlled Indirect Effect

Mediation analysis allows one to use observational data to estimate the importance of each potential mediating pathway involved in the causal effect of an exposure on an outcome. However, current approaches to mediation analysis with multiple mediators either involve assumptions not verifiable by experiments, or estimate the effect when mediators are manipulated jointly which precludes the practical design of experiments due to curse of dimensionality, or are difficult to interpret when arbitrary causal dependencies are present. We propose a method for mediation analysis for multiple manipulable mediators with arbitrary causal dependencies. The proposed method is clinically relevant because the decomposition of the total effect does not involve effects under cross-world assumptions and focuses on the effects after manipulating (i.e. treating) one single mediator, which is more relevant in a clinical scenario. We illustrate the approach using simulated data, the "framing" dataset from political science, and the HIV-Brain Age dataset from a clinical retrospective cohort study. Our results provide potential guidance for clinical practitioners to make justified choices to manipulate one of the mediators to optimize the outcome.

stat.ME↗

arXiv2020

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks

Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we have statically applied five most frequently used augmentation techniques (random rotation, horizontal flip, vertical flip, random crop, and translation) to prostate Diffusion-weighted Magnetic Resonance Imaging training dataset of 217 patients separately and evaluated the effect of each method on the accuracy of prostate cancer detection. The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep Convolutional Neural Network (CNN) were trained on the five augmented sets separately. We used Area Under Receiver Operating Characteristic (ROC) curve (AUC) to evaluate the performance of the trained CNNs on a separate test set of 95 patients, using a validation set of 102 patients for finetuning. The shallow network outperformed the deep network with the best 2D slice-based AUC of 0.85 obtained by the rotation method.

q-bio.QM↗

arXiv2020

Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the attribute-level feature embedding, which might perform poorly in complicated heterogeneous conditions. To address this problem, we propose a hierarchical feature embedding (HFE) framework, which learns a fine-grained feature embedding by combining attribute and ID information. In HFE, we maintain the inter-class and intra-class feature embedding simultaneously. Not only samples with the same attribute but also samples with the same ID are gathered more closely, which could restrict the feature embedding of visually hard samples with regard to attributes and improve the robustness to variant conditions. We establish this hierarchical structure by utilizing HFE loss consisted of attribute-level and ID-level constraints. We also introduce an absolute boundary regularization and a dynamic loss weight as supplementary components to help build up the feature embedding. Experiments show that our method achieves the state-of-the-art results on two pedestrian attribute datasets and a facial attribute dataset.

cs.CV↗

arXiv2020

Receive Antenna Selection for Secure Pre-coding Aided Spatial Modulation

In this paper, we make an investigation of receive antenna selection (RAS) strategies in the secure pre-coding aided spatial modulation (PSM) system with the aid of artificial noise. Due to a lack of the closed-form expression for secrecy rate (SR) in secure PSM systems, it is hard to optimize the RAS. To address this issue, the cut-off rate is used as an approximation of the SR. Further, two low-complexity RAS schemes for maximizing SR, called Max-SR-L and Max-SR-H, are derived in the low and high signal-to-noise ratio (SNR) regions, respectively. Due to the fact that the former works well in the low SNR region but becomes worse in the medium and high SNR regions while the latter also has the similar problem, a novel RAS strategy Max-SR-A is proposed to cover all SNR regions. Simulation results show that the proposed Max-SR-H and Max-SR-L schemes approach the optimal SR performances of the exhaustive search (ES) in the high and low SNR regions, respectively. In particular, the SR performance of the proposed Max-SR-A is close to that of the optimal ES and better than that of the random method in almost all SNR regions.

cs.IT↗

arXiv2019

Linking Graph Entities with Multiplicity and Provenance

Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base population. It is the task of accurately identifying multiple, differing, and possibly contradicting representations of the same real-world entity in data. In this work, we propose an entity linking and resolution system capable of linking entities across different databases and mentioned-entities extracted from text data. Our entity linking/resolution solution, called Certus, uses a graph model to represent the profiles of entities. The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values. Provenance descriptions of a value provide the settings of the value, such as validity periods, sources, security requirements, etc. This paper presents the architecture for the entity linking system, the logical, physical, and indexing models used in the system, and the general linking process. Furthermore, we demonstrate the performance of update operations of the physical storage models when the system is implemented in two state-of-the-art database management systems, HBase and Postgres.

cs.DB↗

arXiv2019

Causality-based Feature Selection: Methods and Evaluations

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world data sets. Finally, we discuss some challenging problems to be tackled in future causality-based feature selection research.

cs.LG↗

arXiv2019

A Deep-learning-based Joint Inference for Secure Spatial Modulation Receiver

As a green and secure wireless transmission way, secure spatial modulation (SM) is becoming a hot research area. Its basic idea is to exploit both the index of activated transmit antenna and amplitude phase modulation (APM) signal to carry messages, improve security, and save energy. In this paper, we reviewed its crucial techniques: transmit antenna selection (TAS), artificial noise (AN) projection, power allocation (PA), and joint detection at desired receiver. To achieve the optimal performance of maximum likelihood (ML) detector, a deep-neural-network (DNN) joint detector is proposed to jointly infer the index of transmit antenna and signal constellation point with a lower-complexity. Here, each layer of DNN is redesigned to optimize the joint inference performance of two distinct types of information: transmit antenna index and signal constellation point. Simulation results show that the proposed DNN method performs 3dB better than the conventional DNN structure and is close to ML detection in the low and medium signal-to-noise ratio regions in terms of the bit error rate (BER) performance, but its complexity is far lower-complexity compared to ML. Finally, three key techniques TAS, PA, and AN projection at transmitter can be combined to make SM a true secure modulation.

eess.SP↗

arXiv2019

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to 22 times speedup has been achieved.

cs.LG↗

arXiv2018

A Unified View of Causal and Non-causal Feature Selection

In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we are able to interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-word data.

cs.AI↗

arXiv2018

An exploration of algorithmic discrimination in data and classification

Algorithmic discrimination is an important aspect when data is used for predictive purposes. This paper analyzes the relationships between discrimination and classification, data set partitioning, and decision models, as well as correlation. The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.

cs.CY↗

arXiv2018

FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce discrimination in their predictions. This paper proposes a method to post-process the predictions of a predictive model to make the processed predictions non-discriminatory. The method considers multiple protected variables together. Multiple protected variables make the problem more challenging than a simple protected variable. The method uses a well-cited discrimination metric and adapts it to allow the specification of explanatory variables, such as position, profession, education, that describe the contexts of the applications. It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time. The proposed method is independent of classification methods. It can handle the cases that existing methods cannot handle: satisfying multiple protected attributes at the same time, allowing multiple explanatory attributes, and being independent of classification model types. An evaluation using four real world data sets shows that the proposed method is as effectively as existing methods, in addition to its extra power.

cs.AI↗

arXiv2018

High-Performance Hybrid Silicon and Lithium Niobate Mach-Zehnder Modulators for 100 Gbit/s and Beyond

Optical modulators are at the heart of optical communication links. Ideally, they should feature low insertion loss, low drive voltage, large modulation bandwidth, high linearity, compact footprint and low manufacturing cost. Unfortunately, these criteria have only been achieved on separate occasions.Based on a Silicon and Lithium Niobate hybrid integration platform, we demonstrate Mach-Zehnder modulators that simultaneously fulfill these criteria. The presented device exhibits an insertion loss of 2.5 dB, voltage-length product of 2.2 Vcm, high linearity, electro-optic bandwidth of at least 70 GHz and modulation rates up to 112 Gbit/s. The high-performance modulator is realized by seamless integration of high-contrast waveguide based on Lithium Niobate - the most mature modulator material - with compact, low-loss silicon circuits. The hybrid platform demonstrated here allows for the combination of 'best-in-breed' active and passive components, opening up new avenues for enabling future high-speed, energy efficient and cost-effective optical communication networks.

physics.app-ph↗

arXiv2018

Car-following behavior of connected vehicles in a mixed traffic flow: modeling and stability analysis

Vehicle-to-vehicle communications can change the driving behavior of drivers significantly by providing them rich information on downstream traffic flow conditions. This study seeks to model the varying car-following behaviors involving connected vehicles and human-driving vehicles in mixed traffic flow. A revised car-following model is developed using an intelligent driver model (IDM) to capture drivers' perceptions of their preceding traffic conditions through vehicle-to-vehicle communications. Stability analysis of the mixed traffic flow is conducted for a specific case. Numerical results show that the stable region is apparently enlarged compared with the IDM.

eess.SY↗

arXiv2018

Discovering Context Specific Causal Relationships

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data.

cs.AI↗

arXiv2018

A Structural Representation Learning for Multi-relational Networks

Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.

cs.SI↗