Rong Zhou

JBHI Journal 2020 Journal Article

Bayesian Inference of Lymph Node Ratio Estimation and Survival Prognosis for Breast Cancer Patients

• Jing Teng
• Assem Abdygametova
• Jing Du
• Bian Ma
• Rong Zhou
• Yu Shyr
• Fei Ye

Objective: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. Methods: Data on 5, 279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006-2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. Results: The final survival model with LNR outperformed the other models considered (C-statistic 0. 71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. Conclusion: The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients. Significance: We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models.

IJCAI Conference 2019 Conference Paper

LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

• Weibin Meng
• Ying Liu
• Yichen Zhu
• Shenglin Zhang
• Dan Pei
• Yuqing Liu
• Yihao Chen
• Ruizhi Zhang

Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.

TIST Journal 2018 Journal Article

Interactive Visual Graph Mining and Learning

• Ryan A. Rossi
• Nesreen K. Ahmed
• Rong Zhou
• Hoda Eldardiry

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

AAAI Conference 2016 Conference Paper

Toward Interactive Relational Learning

• Ryan Rossi
• Rong Zhou

AAAI Conference 2011 Conference Paper

Succinct Set-Encoding for State-Space Search

• Tim Schmidt
• Rong Zhou

We introduce the level-ordered edge sequence (LOES), a succinct encoding for state-sets based on prefix-trees. For use in state-space search, we give algorithms for member testing and element hashing with runtime dependent only on state size, as well as time and memory efficient construction of and iteration over such sets. Finally we compare LOES to binary decision diagrams (BDDs) and explicitly packed setrepresentation over a range of IPC planning problems. Our results show LOES produces succinct set-encodings for a wider range of planning problems than both BDDs and explicit state representation, increasing the number of problems that can be solved cost-optimally.

IJCAI Conference 2009 Conference Paper

• Rong Zhou
• Eric A. Hansen

Breadth-first and depth-first search are basic search strategies upon which many other search algorithms are built. In this paper, we describe an approach to integrating these two strategies in a single algorithm that combines the complementary strengths of both. We show the benefits of this approach using the treewidth problem as an example.

IJCAI Conference 2009 Conference Paper

• Ethan Burns
• Seth Lemons
• Rong Zhou
• Wheeler Ruml

To harness modern multi-core processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to best-first heuristic search in a sharedmemory setting. Each thread attempts to expand the most promising open nodes. By using abstraction to partition the state space, we detect duplicate states without requiring frequent locking. We allow speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, verifying its correctness using temporal logic. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that A* implemented in our framework yields faster search than improved versions of previous parallel search proposals. Our approach extends easily to other best-first searches, such as Anytime weighted A*.

IJCAI Conference 2007 Conference Paper

• Rong Zhou
• Eric A. Hansen

There is currently much interest in using external memory, such as disk storage, to scale up graph-search algorithms. Recent work shows that the local structure of a graph can be leveraged to substantially improve the efficiency of external-memory graph search. This paper introduces a technique, called edge partitioning, which exploits a form of local structure that has not been considered in previous work. The new technique improves the scalability of structured approaches to external-memory graph search, and also guarantees the applicability of these approaches to any graph-search problem. We show its effectiveness in an external-memory graph-search algorithm for domain-independent STRIPS planning.

AAAI Conference 2007 Conference Paper

Parallel Structured Duplicate Detection

• Rong Zhou

We describe a novel approach to parallelizing graph search using structured duplicate detection. Structured duplicate detection was originally developed as an approach to externalmemory graph search that reduces the number of expensive disk I/O operations needed to check stored nodes for duplicates, by using an abstraction of the search graph to localize memory references. In this paper, we show that this approach can also be used to reduce the number of slow synchronization operations needed in parallel graph search. In addition, we describe several techniques for integrating parallel and external-memory graph search in an efficient way. We demonstrate the effectiveness of these techniques in a graphsearch algorithm for domain-independent STRIPS planning.

AAAI Conference 2006 Conference Paper

A Breadth-First Approach to Memory-Efficient Graph Search

• Rong Zhou

Recent work shows that the memory requirements of A* and related graph-search algorithms can be reduced substantially by only storing nodes that are on or near the search frontier, using special techniques to prevent node regeneration, and recovering the solution path by a divide-and-conquer technique. When this approach is used to solve graph-search problems with unit edge costs, we have shown that a breadth-first search strategy can be more memory-efficient than a best-first strategy. We provide an overview of our work using this approach, which we call breadth-first heuristic search.

AIJ Journal 2006 Journal Article

Breadth-first heuristic search

• Rong Zhou
• Eric A. Hansen

Recent work shows that the memory requirements of A* and related graph-search algorithms can be reduced substantially by only storing nodes that are on or near the search frontier, using special techniques to prevent node regeneration, and recovering the solution path by a divide-and-conquer technique. When this approach is used to solve graph-search problems with unit edge costs, we show that a breadth-first search strategy can be more memory-efficient than a best-first strategy. We also show that a breadth-first strategy allows a technique for preventing node regeneration that is easier to implement and can be applied more widely. The breadth-first heuristic search algorithms introduced in this paper include a memory-efficient implementation of breadth-first branch-and-bound search and a breadth-first iterative-deepening A* algorithm that is based on it. Computational results show that they outperform other systematic search algorithms in solving a range of challenging graph-search problems.

AAAI Conference 2006 Conference Paper

Domain-Independent Structured Duplicate Detection

• Rong Zhou

The scalability of graph-search algorithms can be greatly extended by using external memory, such as disk, to store generated nodes. We consider structured duplicate detection, an approach to external-memory graph search that limits the number of slow disk I/O operations needed to access search nodes stored on disk by using an abstract representation of the graph to localize memory references. For graphs with sufficient locality, structured duplicate detection outperforms other approaches to external-memory graph search. We develop an automatic method for creating an abstract representation that reveals the local structure of a graph. We then integrate this approach into a domain-independent STRIPS planner and show that it dramatically improves scalability for a wide range of planning problems. The success of this approach strongly suggests that similar local structure can be found in many other graph-search problems.

AAAI Conference 2005 Conference Paper

External-Memory Pattern Databases Using Structured Duplicate Detection

• Rong Zhou

A pattern database is a lookup table that stores an exact evaluation function for a relaxed search problem, which provides an admissible heuristic for the original search problem. In general, the larger the pattern database, the more accurate the heuristic function. We consider how to build large pattern databases that are stored in external memory, such as disk, and how to use an externalmemory pattern database efficiently in heuristic search. To limit the number of slow disk I/O operations needed to construct and query an external-memory pattern database, we adapt an approach to external-memory graph search called structured duplicate detection that localizes memory references by leveraging an abstraction of the state space. We present results that show this approach increases the scalability of heuristic search by allowing larger and more accurate pattern database heuristics.

AAAI Conference 2004 Conference Paper

Space-Efficient Memory-Based Heuristics

• Rong Zhou
• Eric A. Hansen

A memory-based heuristic is a heuristic function that is stored in a lookup table. Very accurate heuristics have been created by building very large lookup tables, sometimes called pattern databases. Most previous work assumes that a memorybased heuristic is computed for the entire state space, and the cost of computing it is amortized over many problem instances. But in some cases, it may be useful to compute a memory-based heuristic for a single problem instance. If the start and goal states of the problem instance are used to restrict the region of the state space for which the heuristic is needed, the time and space used to compute the heuristic may be substantially reduced. In this paper, we review recent work that uses this idea to compute space-efficient heuristics for the multiple sequence alignment problem. We then describe a novel development of this idea that is simpler and more general. Our approach leads to improved performance in solving the multiple sequence alignment problem, and is general enough to apply to other domains.

AAAI Conference 2004 Conference Paper

Structured Duplicate Detection in External-Memory Graph Search

• Rong Zhou
• Eric A. Hansen

We consider how to use external memory, such as disk storage, to improve the scalability of heuristic search in statespace graphs. To limit the number of slow disk I/O operations, we develop a new approach to duplicate detection in graph search that localizes memory references by partitioning the search graph based on an abstraction of the state space, and expanding the frontier nodes of the graph in an order that respects this partition. We demonstrate the effectiveness of this approach both analytically and empirically.

IJCAI Conference 2003 Conference Paper

Sparse-Memory Graph Search

• Rong Zhou
• Eric A. Hansen

We describe a framework for reducing the space complexity of graph search algorithms such as A* that use Open and Closed lists to keep track of the frontier and interior nodes of the search space. We propose a sparse representation of the Closed list in which only a fraction of already expanded nodes need to be stored to perform the two functions of the Closed List - preventing duplicate search effort and allowing solution extraction. Our proposal is related to earlier work on search algorithms that do not use a Closed list at all [Korf and Zhang, 2000]. However, the approach we describe has several advantages that make it effective for a wider variety of problems.