32109322
RIBRA-An Error-Tolerant Algorithm for the NMR Backbone Assignment Problem
We develop an iterative relaxation algorithm, called RIBRA, for NMR protein backbone assignment. RIBRA applies nearest neighbor and weighted maximum independent set algorithms to solve the problem. To deal with noisy NMR spectral data, RIBRA is executed in an iterative fashion based on the quality of spectral peaks. We first produce spin system pairs using the spectral data without missing peaks, then the data group with one missing peak, and finally, the data group with two missing peaks. We test RIBRA on two real NMR datasets: hb-SBD and hbLBD, and perfect BMRB data (with 902 proteins) and four synthetic BMRB data which simulate four kinds of errors. The accuracy of RIBRA on hbSBD and hbLBD are 91.4% and 83.6%, respectively. The average accuracy of RIBRA on perfect BMRB datasets is 98.28%, and 98.28%, 95.61%, 98.16% and 96.28% on four kinds of synthetic datasets, respectively.
341424
Domain Event Extraction and Representation with Domain Ontology
With domain ontology, a meaningful index of document indexing, such as the domain events structure in this paper, can be defined. Since the construction of domain ontology is costly, an automatic domain ontology acquisition is preferred. We find that named entity can be a clue of domain event acquisition from the training corpus. Therefore , we add the named entity recognition module into the automatic domain ontology acquisition process. Thus, a subject-verb-object-modifier (SVOM) indexing can be constructed. Our experimental results demonstrate that the automatic event extraction and ontology acquisition can be good resources for text categorization and further information processing.
17603178
A Linear Time Algorithm for Finding a Maximal Planar Subgraph Based on PC-Trees
In Shih & Hsu, a planarity test was introduced utilizing a data structure called PC-trees, generalized from PQ-trees. They illustrated that a PC-tree is more natural in representing planar graphs. Their algorithm starts by constructing a depth-first-search tree and adds all back edges to a vertex one by one. An important feature in the S&H algorithm is that, at each iteration, at most two terminal nodes need to be computed and the unique tree path between these two nodes provides essentially the boundary path of the newly formed biconnected component. 
 
In this paper we modify their PC-tree algorithm and introduce the deferred planarity test (DPT), which has the added benefit of finding a maximal planar subgraph (MPS) in linear time when the given graph is not planar. DPT is an incremental algorithm, which only computes a partial terminal path at each iteration. DPT continually deletes back edges that could create a violation to the formation of those partial terminal paths so that, at the end, the subgraph constructed is guaranteed to be planar. 
 
The key to the efficiency of the S&H and the DPT algorithms lies in their management on the creation and destruction of biconnected components in which the PC-tree plays a major role. Previously, there have been reports that the MPS problem can be solved in linear time. However, there was no concrete data structure realizing them.
5724385
An iterative relaxation technique for the NMR backbone assignment problem
NMR spectroscopy is one of the popular experiments to determine protein structures. An important stage of protein structure determination by using NMR is protein backbone resonance assignment (or backbone assignment for short). Due to the messiness and disorder of NMR spectral data, backbone assignment is usually a tedious and time-consuming manual work. This raises a great interest in developing an efficient and automatic method to perform backbone assignment. An iterative algorithm is proposed that is equipped with two operations: grouping and linking. Grouping is responsible for peak picking and part of connectivity determination. Ideally, those peaks with the same H/sup N/ and N chemical shifts can be grouped together; peaks belonging to the same group can be used to determine the order of two consecutive spin systems. However, in real situation, grouping is a difficult task due to false positives and false negatives. We sometimes add hypothetic peaks to tackle false negatives and use linking operation to remove false positives. Linking is responsible for part of connectivity determination and backbone assignment. Given a protein sequence and partial connectivity information, we try to link connected spin systems as much as possible. False positive spin systems may create conflicts in the linking stage. To find a good assignment on a noisy dataset, the backbone assignment problem is modeled as a maximum independent set problem. Although the problem is NP-complete, there are heuristic methods that obtain pretty good results.
5367915
A Maximum Entropy Approach to Biomedical Named Entity Recognition
Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using ME for NER, inaccurate boundary detection of NEs and misclassification may occur. Some NEs are partially recognized by ME. In the post-processing stage, we use dictionary-based and rule-based methods to extend boundary of partially recognized NEs and to adjust classification. We use GENIA corpus 3.01 to conduct 10-fold cross-verification experiments. To evaluate the performance, we consider the longest NE annotations. We evaluate our approach using standard precision (P), recall (R), and F-score, where F-score is defined as 2PR/(P+R). The precision, recall and F-score ([P, R, F]) of our ME module for overall 23 categories is [0.512, 0.538, 0.525], and after the post-processing the performance becomes [0.729, 0.711, 0.72] for [P, R, F]. For protein, DNA and RNA classes, our method achieves [P, R, F] of [0.77, 0.80, 0.785], [0.653, 0.748, 0.7], and [0.716, 0.788, 0.752], respectively. The post-processing stage significantly improves the performance of our ME-based NER module.
1251407
Reference metadata extraction using a hierarchical knowledge representation framework
The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.
2164726
A New Planarity Test
Abstract Given an undirected graph, the planarity testing problem is to determine whether the graph can be drawn in a plane without any crossing edges. Linear time planarity testing algorithms have previously been designed by Hopcroft and Tarjan, and by Booth and Lueker. However, their approaches are quite involved. Several other approaches have also been developed for simplifying the planarity test. In this paper, we developed a very simple linear time testing algorithm based only on a depth-first search tree. When the given graph is not planar, our algorithm immediately produces explicit Kuratowski's subgraphs. A new data structure, PC -trees, is introduced, which can be viewed as abstract subembeddings of actual planar embeddings. A graph-reduction technique is adopted so that the embeddings for the planar biconnected components constructed at each iteration never have to be changed. The recognition and embedding are actually done simultaneously in our algorithm (Booth and Lueker, 1976). The implementation of our algorithm is quite straightforward.
14423694
Characteristics of I/O traffic in personal computer and server workloads
Understanding the characteristics of I/O traffic is increasingly important as the performance gap between the processor and disk-based storage continues to widen. Moreover, recent advances in technology, coupled with market demands, have led to new and exciting developments in storage systems, particularly network storage, storage utilities, and intelligent self-optimizing storage. In this paper, we empirically examine the physical I/O traffic of a wide range of server and personal computer (PC) workloads, focusing on how these workloads will be affected by the recent developments in storage systems. As part of our analysis, we compare our results with historical data and re-examine some rules of thumb (e.g., one bit of I/O per second for each instruction per second of processing power) that have been widely used for designing computer systems. We find that the I/O traffic is bursty and appears to exhibit self-similar characteristics. Our analysis also indicates that there is little cross-correlation between traffic volumes of server workloads, which suggests that aggregating these workloads will likely help to smooth out the traffic and enable more efficient utilization of resources. We discover that there is significant potential for harnessing "free" system resources to perform background tasks such as optimization of disk block layout. In general, we observe that the characteristics of the I/O traffic are relatively insensitive to the extent of upstream caching, and thus our results still apply, on a qualitative level, when the upstream cache is increased in size.
11584462
The automatic improvement of locality in storage systems
Disk I/O is increasingly the performance bottleneck in computer systems despite rapidly increasing disk data transfer rates. In this article, we propose Automatic Locality-Improving Storage (ALIS), an introspective storage system that automatically reorganizes selected disk blocks based on the dynamic reference stream to increase effective storage performance. ALIS is based on the observations that sequential data fetch is far more efficient than random access, that improving seek distances produces only marginal performance improvements, and that the increasingly powerful processors and large memories in storage systems have ample capacity to reorganize the data layout and redirect the accesses so as to take advantage of rapid sequential data transfer. Using trace-driven simulation with a large set of real workloads, we demonstrate that ALIS considerably outperforms prior techniques, improving the average read performance by up to 50% for server workloads and by about 15% for personal computer workloads. We also show that the performance improvement persists as disk technology evolves. Since disk performance in practice is increasing by only about 8% per year, the benefit of ALIS may correspond to as much as several years of technological progress.
10542450
Capturing dynamic memory reference behavior with adaptive cache topology
Memory references exhibit locality and are therefore not uniformly distributed across the sets of a cache. This skew reduces the effectiveness of a cache because it results in the caching of a considerable number of less-recently-used lines which are less likely to be re-referenced before they are replaced. In this paper, we describe a technique that dynamically identifies these less-recently-used lines and effectively utilizes the cache frames they occupy to more accurately approximate the global least-recently-used replacement policy while maintaining the fast access time of a direct-mapped cache. We also explore the idea of using these underutilized cache frames to reduce cache misses through data prefetching. In the proposed design, the possible locations that a line can reside in is not predetermined. Instead, the cache is dynamically partitioned into groups of cache lines. Because both the total number of groups and the individual group associativity adapt to the dynamic reference pattern, we call this design the adaptive group-associative cache. Performance evaluation using trace-driven simulations of the TPC-C benchmark and selected programs from the SPEC95 benchmark suite shows that the group-associative cache is able to achieve a hit ratio that is consistently better than that of a 4-way set-associative cache. For some of the workloads, the hit ratio approaches that of a fully-associative cache.
17795095
A Simple Test for the Consecutive Ones Property
A (0,1)-matrix satisfies the consecutive ones property if there exists a column permutation such that the one's in each row of the resulting matrix are consecutive. Booth and Lueker [1976] designed a linear time testing algorithm for this property based on a data structure called “PQ-trees”. This procedure is very complicated and the linear time amortized analysis is also rather involved. We develop an off-line testing algorithm for the consecutive ones property without using PQ-trees. Our approach is based on a decomposition technique which separates the rows into “prime” subsets which admit unique column orderings that realize the consecutive ones property. The success of this approach is based on finding a good “row ordering” to be tested iteratively.