arXiv subjects

Ruoyu Wang

Publications and source records attributed to Ruoyu Wang.

At least 145 records · Page 8Linked to original sources

arXiv2023

A robust fusion-extraction procedure with summary statistics in the presence of biased sources

Information from various data sources is increasingly available nowadays. However, some of the data sources may produce biased estimation due to commonly encountered biased sampling, population heterogeneity, or model misspecification. This calls for statistical methods to combine information in the presence of biased sources. In this paper, a robust data fusion-extraction method is proposed. The method can produce a consistent estimator of the parameter of interest even if many of the data sources are biased. The proposed estimator is easy to compute and only employs summary statistics, and hence can be applied to many different fields, e.g. meta-analysis, Mendelian randomisation and distributed system. Moreover, the proposed estimator is asymptotically equivalent to the oracle estimator that only uses data from unbiased sources under some mild conditions. Asymptotic normality of the proposed estimator is also established. In contrast to the existing meta-analysis methods, the theoretical properties are guaranteed even if both the number of data sources and the dimension of the parameter diverge as the sample size increases, which ensures the performance of the proposed method over a wide range. The robustness and oracle property is also evaluated via simulation studies. The proposed method is applied to a meta-analysis data set to evaluate the surgical treatment for the moderate periodontal disease, and a Mendelian randomization data set to study the risk factors of head and neck cancer.

stat.ME↗

arXiv2023

Discovering and Understanding 2D Semiconductors with High Intrinsic Carrier Mobility at Room Temperature

Two-dimensional (2D) semiconductors have demonstrated great potential for next-generation electronics and optoelectronics. However, the current 2D semiconductors suffer from intrinsically low carrier mobility at room temperature, which significantly limits its applications. Here we discover a variety of new 2D semiconductors with mobility one order of magnitude higher than the current ones. The discovery is made by developing effective descriptors for high-throughput computationally screening of the 2D materials database, followed by using state-of-the-art first principles method to accurately calculate the mobility. Further analyses attribute their exceptional mobilities to small effective mass, high sound velocity, high optical phonon frequency, small ratio of Born charge vs. polarizability, and/or weak electron-phonon coupling. Our work opens up new materials to realize high device performance and/or exotic physics that are difficult to achieve previously, and improves the understanding of the carrier transport mechanism.

cond-mat.mtrl-sci↗

arXiv2023

Distributed nonparametric regression imputation for missing response problems with large-scale data

Nonparametric regression imputation is commonly used in missing data analysis. However, it suffers from the ``curse of dimension". The problem can be alleviated by the explosive sample size in the era of big data, while the large-scale data size presents some challenges on the storage of data and the calculation of estimators. These challenges make the classical nonparametric regression imputation methods no longer applicable. This motivates us to develop two distributed nonparametric regression imputation methods. One is based on kernel smoothing and the other on the sieve method. The kernel-based distributed imputation method has extremely low communication cost and the sieve-based distributed imputation method can accommodate more local machines. To illustrate the proposed imputation methods, response mean estimation is considered. Two distributed nonparametric regression imputation estimators are proposed for the response mean, which are proved to be asymptotically normal with asymptotic variances achieving the semiparametric efficiency bound. The proposed methods are evaluated through simulation studies and are illustrated by a real data analysis.

stat.ME↗

arXiv2022

An Aggregation of Aggregation Methods in Computational Pathology

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.

cs.CV↗

arXiv2022

Characterization of Excess Risk for Locally Strongly Convex Population Risk

We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong globally strongly convexity or global growth conditions e.g., PL-inequality, we only require the population risk to be \emph{locally} strongly convex around its local minima. Concretely, our bound under convex problems is of order $\tilde{\cO}(1/n)$. For non-convex problems with $d$ model parameters such that $d/n$ is smaller than a threshold independent of $n$, the order of $\tilde{\cO}(1/n)$ can be maintained if the empirical risk has no spurious local minima with high probability. Moreover, the bound for non-convex problem becomes $\tilde{\cO}(1/\sqrt{n})$ without such assumption. Our results are derived via algorithmic stability and characterization of the empirical risk's landscape. Compared with the existing algorithmic stability based results, our bounds are dimensional insensitive and without restrictions on the algorithm's implementation, learning rate, and the number of iterations. Our bounds underscore that with locally strongly convex population risk, the models trained by any proper iterative algorithm can generalize well, even for non-convex problems, and $d$ is large.

cs.LG↗

arXiv2022

Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates

In a completely randomized experiment, the variances of treatment effect estimators in the finite population are usually not identifiable and hence not estimable. Although some estimable bounds of the variances have been established in the literature, few of them are derived in the presence of covariates. In this paper, the difference-in-means estimator and the Wald estimator are considered in the completely randomized experiment with perfect compliance and noncompliance, respectively. Sharp bounds for the variances of these two estimators are established when covariates are available. Furthermore, consistent estimators for such bounds are obtained, which can be used to shorten the confidence intervals and improve the power of tests. Confidence intervals are constructed based on the consistent estimators of the upper bounds, whose coverage rates are uniformly asymptotically guaranteed. Simulations were conducted to evaluate the proposed methods. The proposed methods are also illustrated with two real data analyses.

math.ST↗

arXiv2022

TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge

The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before localising the tumor bulk region for TILs detection. Finally, we use these outputs to generate a TILs score for each case. On preliminary testing, our approach achieved a tumor-stroma weighted Dice score of 0.791 and a FROC score of 0.572 for lymphocytic detection. For predicting survival, our model achieved a C-index of 0.719. These results achieved first place across the preliminary testing leaderboards of the TiGER challenge.

eess.IV↗

arXiv2022

Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images

The aetiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco and infection with human papillomavirus (HPV). As the HPV infection influences the prognosis, treatment and survival of patients with HNSCC, it is important to determine the HPV status of these tumours. In this paper, we propose a novel triplet-ranking loss function and a multiple instance learning pipeline for HPV status prediction. This achieves a new state-of-the-art performance in HPV detection using only the routine H&E stained WSIs on two HNSCC cohorts. Furthermore, a comprehensive tumour microenvironment profiling was performed, which characterised the unique patterns between HPV+/- HNSCC from genomic, immunology and cellular perspectives. Positive correlations of the proposed score with different subtypes of T cells (e.g. T cells follicular helper, CD8+ T cells), and negative correlations with macrophages and connective cells (e.g. fibroblast) were identified, which is in line with clinical findings. Unique gene expression profiles were also identified with respect to HPV infection status, and is in line with existing findings.

cs.CV↗

arXiv2022

Context-Auditor: Context-sensitive Content Injection Mitigation

Cross-site scripting (XSS) is the most common vulnerability class in web applications over the last decade. Much research attention has focused on building exploit mitigation defenses for this problem, but no technique provides adequate protection in the face of advanced attacks. One technique that bypasses XSS mitigations is the scriptless attack: a content injection technique that uses (among other options) CSS and HTML injection to infiltrate data. In studying this technique and others, we realized that the common property among the exploitation of all content injection vulnerabilities, including not just XSS and scriptless attacks, but also command injections and several others, is an unintended context switch in the victim program's parsing engine that is caused by untrusted user input. In this paper, we propose Context-Auditor, a novel technique that leverages this insight to identify content injection vulnerabilities ranging from XSS to scriptless attacks and command injections. We implemented Context-Auditor as a general solution to content injection exploit detection problem in the form of a flexible, stand-alone detection module. We deployed instances of Context-Auditor as (1) a browser plugin, (2) a web proxy (3) a web server plugin, and (4) as a wrapper around potentially-injectable system endpoints. Because Context-Auditor targets the root cause of content injection exploitation (and, more specifically for the purpose of our prototype, XSS exploitation, scriptless exploitation, and command injection), our evaluation results demonstrate that Context-Auditor can identify and block content injection exploits that modern defenses cannot while maintaining low throughput overhead and avoiding false positives.

cs.CR↗

arXiv2022

Out-of-distribution Generalization with Causal Invariant Transformations

In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the OOD generalization problem, with the idea resting on the causal mechanism that is invariant across domains of interest. To leverage the generally unknown causal mechanism, existing works assume a linear form of causal feature or require sufficiently many and diverse training domains, which are usually restrictive in practice. In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature. Our approach is based on transformations that modify the non-causal feature but leave the causal part unchanged, which can be either obtained from prior knowledge or learned from the training data in the multi-domain scenario. Under the setting of invariant causal mechanism, we theoretically show that if all such transformations are available, then we can learn a minimax optimal model across the domains using only single domain data. Noticing that knowing a complete set of these causal invariant transformations may be impractical, we further show that it suffices to know only a subset of these transformations. Based on the theoretical findings, a regularized training procedure is proposed to improve the OOD generalization capability. Extensive experimental results on both synthetic and real datasets verify the effectiveness of the proposed algorithm, even with only a few causal invariant transformations.

stat.ML↗

arXiv2022

ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. However, the prediction performance (accuracy, recall, precision), usability, and efficiency of existing methods still have much room to be improved. Here, we report ECRECer, a cloud platform for accurately predicting EC numbers based on novel deep learning techniques. To build ECRECer, we evaluate different protein representation methods and adopt a protein language model for protein sequence embedding. After embedding, we propose a multi-agent hierarchy deep learning-based framework to learn the proposed tasks in a multi-task manner. Specifically, we used an extreme multi-label classifier to perform the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against four representative methods demonstrate that ECRECer delivers the highest performance, which improves accuracy and F1 score by 70% and 20% over the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous enzymes in the Swiss-Prot database with incomplete EC numbers to their full fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-), ECRECer annotated it with "1.14.11.38", which supported by further protein structure analysis based on AlphaFold2. Finally, we established a webserver (https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve usability.

cs.LG↗

arXiv2021

Active Learning-Based Optimization of Scientific Experimental Design

Active learning (AL) is a machine learning algorithm that can achieve greater accuracy with fewer labeled training instances, for having the ability to ask oracles to label the most valuable unlabeled data chosen iteratively and heuristically by query strategies. Scientific experiments nowadays, though becoming increasingly automated, are still suffering from human involvement in the designing process and the exhaustive search in the experimental space. This article performs a retrospective study on a drug response dataset using the proposed AL scheme comprised of the matrix factorization method of alternating least square (ALS) and deep neural networks (DNN). This article also proposes an AL query strategy based on expected loss minimization. As a result, the retrospective study demonstrates that scientific experimental design, instead of being manually set, can be optimized by AL, and the proposed query strategy ELM sampling shows better experimental performance than other ones such as random sampling and uncertainty sampling.

cs.LG↗

arXiv2021

Essence of Factual Knowledge

Knowledge bases are collections of domain-specific and commonsense facts. Recently, the sizes of KBs are rocketing due to automatic extraction for knowledge and facts. For example, the number of facts in WikiData is up to 974 million! According to our observation, current KBs, especially domain KBs, show strong relevance in relations according to some topics. These patterns can be used to conclude and infer for part of facts in the KBs. Therefore, the original KBs can be minimzed by extracting patterns and essential facts.

cs.DB↗

arXiv2021

Constructing Flow Graphs from Procedural Cybersecurity Texts

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures. If such texts are structured, we can readily visualize instruction-flows, reason or infer a particular step, or even build automated systems to help novice agents achieve a goal. However, this structure recovery task is a challenge because of such texts' diverse nature. This paper proposes to identify relevant information from such texts and generate information flows between sentences. We built a large annotated procedural text dataset (CTFW) in the cybersecurity domain (3154 documents). This dataset contains valuable instructions regarding software vulnerability analysis experiences. We performed extensive experiments on CTFW with our LM-GNN model variants in multiple settings. To show the generalizability of both this task and our method, we also experimented with procedural texts from two other domains (Maintenance Manual and Cooking), which are substantially different from cybersecurity. Our experiments show that Graph Convolution Network with BERT sentence embeddings outperforms BERT in all three domains

cs.CL↗

arXiv2021

Determination and estimation of optimal quarantine duration for infectious diseases with application to data analysis of COVID-19

Quarantine measure is a commonly used non-pharmaceutical intervention during the outbreak of infectious diseases. A key problem for implementing quarantine measure is to determine the duration of quarantine. In this paper, a policy with optimal quarantine duration is developed. The policy suggests different quarantine durations for every individual with different characteristic. The policy is optimal in the sense that it minimizes the average quarantine duration of uninfected people with the constraint that the probability of symptom presentation for infected people attains the given value closing to 1. The optimal solution for the quarantine duration is obtained and estimated by some statistic methods with application to analyzing COVID-19 data.

stat.AP↗

arXiv2021

Deep Weakly Supervised Positioning

PoseNet can map a photo to the position where it is taken, which is appealing in robotics. However, training PoseNet requires full supervision, where ground truth positions are non-trivial to obtain. Can we train PoseNet without knowing the ground truth positions for each observation? We show that this is possible via constraint-based weak-supervision, leading to the proposed framework: DeepGPS. Particularly, using wheel-encoder-estimated distances traveled by a robot along random straight line segments as constraints between PoseNet outputs, DeepGPS can achieve a relative positioning error of less than 2%. Moreover, training DeepGPS can be done as auto-calibration with almost no human attendance, which is more attractive than its competing methods that typically require careful and expert-level manual calibration. We conduct various experiments on simulated and real datasets to demonstrate the general applicability, effectiveness, and accuracy of DeepGPS, and perform a comprehensive analysis of its robustness. Our code is available at https://ai4ce.github.io/DeepGPS/.

cs.RO↗

arXiv2021

Scam Pandemic: How Attackers Exploit Public Fear through Phishing

As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends. We find that phishing attack traffic in March and April 2020 skyrocketed up to 220\% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims' uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem's collective response.

cs.CR↗

arXiv2020

Statistical Detection of Collective Data Fraud

Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.

cs.DB↗