Text data extraction for a prospective, research-focused data mart: implementation and validation Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review.Methods: Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. The abstraction and aggregation of clinical data from electronic health records is a necessary component of translational research. We have presented a way to abrogate the need for researchers to re-enter numerical data available in structured reports in clinical systems. We recognize that regular expressions are only appropriate for highly structured, machine-produced data and span the gap between structured data entry and natural language processing techniques for semantically interpreting text such as clinical notes. Based on other work, about 40% of the data elements involved in translational research are captured in electronic health records at various levels of structure, so this process is of general utility [13] [14] [15] . Automating the abstraction process and representing structured textual data as discrete, atomically coded values streamlines the availability of these data for translational research and outcomes analysis. We have designed a reusable SQL Server Integration Services package (Regextractor) for extracting coded data from text fields in the electronic health record and demonstrated the creation of a discretely coded PFT data mart using Regextractor. Regextractor has also been used to create cardiac catheterization and echocardiographic data marts at our institution [16] . The initial PFT data mart was built by coupling Regextractor to the existing extract transform and load process using SQL Server Integration Services. Reports pulled from the resulting PFT data mart were validated against manual chart abstraction. There were six discrepancies out of 1100 (99.5% congruency) between the results obtained from the manual chart abstraction process and the PFT data mart. In all cases, the discrepancies were due to mistakes made during data entry. Translational research requires the longitudinal collection of anthropometric, demographic, laboratory, and diagnostic data for specific patient cohorts. Most institutions now have electronic health record systems to collect and retain these data. Electronic health records are often document-centered systems [1] that require special processing [2] [3] [4] to collect and aggregate data, especially when clinical data is stored as text (e.g. clinical notes, diagnostic studies such as pulmonary function tests, and some quantitative laboratory results). Electronic health records enable the examination of these data on a perencounter or per-patient basis, but are not designed to enable the aggregation of data across a series of encounters or for a defined cohort of patients, as is typically required in clinical research. Lack of aggregated clinical data necessitates costly and error-prone manual chart reviews [5] . There are several technical projects designed to aid in transforming clinical text into discrete, analyzable data. These projects are typically bundled as large frameworks requiring additional servers and computing environments to implement [2, [6] [7] [8] .. Data warehouses that are large databases used for reporting and analyses, greatly facilitate data aggregation by integrating data from a number of sources, including electronic health records. Data warehouses provide a consistent, defined access mechanism to electronic health record data through standard database reporting and analytics tools. Data warehouses are often used to spawn specialized data marts that contain pre-processed or transformed versions of specific crosssections of data available in the warehouse. Coupling a data mart to a data warehouse through one or more structured and reproducible transformation processes allows data warehouse architects to easily maintain a well defined data source while providing clinical researchers with necessary clinical research data.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Deep Inspection of Unreachable BitTorrent Swarms †a) , Nonmember and Akihiro NAKAO †b) , Member SUMMARY BitTorrent is one of the most popular P2P file sharing applications worldwide. Each BitTorrent network is called a swarm, and millions of peers may join multiple swarms. However, there are many unreachable peers (NATed (network address translated), firewalled, or inactive at the time of measurement) in each swarm; hence, existing techniques can only measure a part of all the peers in a swarm. In this paper, we propose an improved measurement method for BitTorrent swarms that include many unreachable peers. In essence, NATed peers and those behind firewalls are found by allowing them to connect to our crawlers by actively advertising our crawlers' addresses. Evaluation results show that the proposed method increases the number of unique contacted peers by 112% compared to the conventional method. Moreover, the proposed method increases the total volume of downloaded pieces by 66%. We investigate the sampling bias among the proposed and conventional methods, and we find that different measurement methods yield significantly different results. In recent years, BitTorrent has become the most popular P2P networks throughout the Internet. Due to a growing number of video sharing websites such as YouTube, BitTorrent traffic is steadily increasing year by year [1] . As BitTorrent becomes popular, many researchers, companies and copyright holders devote their efforts to understanding and characterizing BitTorrent networks. Especially, the accurate measurement of BitTorrent networks, i.e., the minimization of sampling biases in BitTorrent measurements, can benefit studies on BitTorrent in the following ways. It may lead to a better understanding of the topology and characteristics of BitTorrent swarms, a collection of peers that participate in the distribution of a specific file through a BitTorrent application. Accurate swarm information is valuable for understanding and improving the performance of the BitTorrent protocol and traffic control methods such as P4P [2] , Ono [3] and so on. However, several issues continue to hinder the accurate measurement of BitTorrent swarms.. First, there are many unreachable peers in each swarm and unreachable peers causes a serious sampling bias in peer-level measurement methods, i.e., when a crawler tries to contact each peer to obtain detailed information [4] - [8] .. Peer-level measurement methods can obtain information such as IP address, service port number, latency, download rate, connection status, available pieces of the content (piece-bitmap), and neighboring peer addresses. However, each swarm includes many unreachable peers (NATed, firewalled, or inactive at the time of the measurement). Owing to their inability to connect the crawler to unreachable peers, conventional peer-level measurement studies can measure only a part of all the peers in the swarm [9] . Second, the peer-level measurement method may degrade the performance of BitTorrent systems. In order to contact peers, the crawler has to contact trackers in advance. A tracker is a server that keeps track of which peers are in the swarm. Peers report information to the tracker periodically and, in exchange, receive information about other peers to which they can connect. However, each tracker retains crawler addresses for a long time, and peers may obtain inactive crawler addresses until the trackers discard them following the measurement. When peers try to contact the inactive crawler, they cannot download their desired files and this may slow down file completion. We must measure swarms with a minimal impact on the BitTorrent performance.. In this paper, we propose an improved measurement method for unreachable peers with minimal impact on the BitTorrent performance. In developing our method, we focus on a BitTorrent peer that simultaneously works as a client and server to the other peers in the swarm. In the conventional peer-level measurement method, the crawler does not work as a server and disregards all incoming accesses for unreachable peers. To improve connectivity between the crawler and unreachable peers, our crawler opens service ports and accepts all incoming accesses from peers to retrieve detailed information. Moreover, to increase the availability of files, our crawler aggressively advertises reachable peer addresses to the target swarm.. There are four contributions of this paper, as follows: First, through our extensive study of peers' connectivity included in the collected information, we find that, on average, 8.16% of peers are reachable in each swarm. This is because most peers are NATed or behind a firewall. Further, we investigate how long each tracker retains inactive peer addresses. We show that 99.11% of trackers keep inactive peer addresses for at least 30 minutes. This out-of-date information directly affects the peer-level measurements.. Second, we develop an improved peer-level measure-ment method. Although unreachable peers do not accept a connection from our crawler, they may access our crawler if they know its address. For this reason, our crawler aggressively advertises its address to the target swarm and accepts all incoming accesses. Our method increases the number of unique peers contacted by 112.75% compared with the conventional methods. Third, to achieve our measurement purpose, our crawler collects peers' incoming connections. However, our crawler does not have any pieces of the file, and this may slow down file completion. To avoid this problem, our crawler advertises the set of reachable peer addresses to incoming peers. Because our crawler cooperates by advertising reachable peer addresses to the target swarm, our method increases the total volume of downloaded pieces by more than 66.07% compared to the case without our cooperation.. Last, to evaluate the impact of different measurement methods on peer-to-peer (P2P) characterization, we compare the characteristics of the target P2P network measured concurrently using different methods, such as churn [11] and peer's degree distribution. We show that the results exhibit significant differences according to the measurement methods, and that our method captures the most detailed information of all, revealing that the other methods may result in false characterization of P2P networks due to a lack of detail.. To the best of our knowledge, this is the first study into peer-level measurement that considers both the unreachable peers problem and the performance degradation problem. Our main contribution in this paper is that our scrutiny of P2P characteristics not only reinforces the sampling bias inherent in the existing approaches, but also suggests the possibility of improving the efficiency of P2P file sharing applications.. The remainder of the paper is organized as follows. In Sect. 2, we briefly explain the BitTorrent protocol. In Sect. 3, we discuss related work on BitTorrent measurements, and we expose the problems of the peer-level measurements in Sect. 4. Section 5 introduces our proposed measurement method, and and Sect. 6 explains the method employed by the implemented crawler. In Sect. 7, we show the effectiveness of our measurement method. In Sect. 8, we compare the measurement results obtained with different measurement methods. We then discuss the key implications of our findings on the design of the BitTorrent system in Sect. 9, and present our conclusions in Sect. 10. In this paper, we propose a peer-level measurement method for unreachable peers in a swarm, and we evaluate our method on a real BitTorrent swarm. The main contribution of this study is that our scrutiny of P2P characteristics not only reinforces the significance of the sampling bias in existing approaches but also suggests the possibility of improving the efficiency of P2P file sharing applications. In detail, the contributions of this paper are four-fold.. First, our measurement results indicate that the current BitTorrent system is composed of many NATed and firewalled peers and that each tracker retains inactive peers for a long time. Unfortunately, these factors mean that we can only measure a fraction of the peers in a swarm.. Second, to solve the unreachable peer problem, we develop a method that accepts all incoming accesses from unreachable peers, and we show that our method successfully increases the number of unique contacted peers by 112.75%, as compared to conventional methods. Finally, our crawler connects all the leechers in the swarm within six minutes and also reveals all peers' download rates.. Third, we advertise reachable peer addresses to avoid a slowdown in the file completion time by unreachable peers. Our result shows that advertising reachable peer addresses increases the total volume of downloaded pieces by 66.07%.. Finally, we measure the characteristics of the target P2P network concurrently using different methods, and we show that the results exhibit significant differences according to the method of measurement. Our proposed method captures the most detailed information of those tested, and reveals that the other methods are subject to significant sampling bias due to their relative lack of detail.. In the future, we plan to scale our measurements to a wider scope. We are currently performing geographically distributed crawling on PlanetLab and CoreLab [30] . In addition, we plan to apply our method to other P2P file sharing protocols besides BitTorrent. In this section, we discuss the possibility of applying our findings to improving the efficiency of P2P file-sharing systems. In particular, we focus on improving file availability by advertising reachable peer addresses to a swarm. In Sect. 7.2.4, our method is shown to aggressively diffuse reachable peer addresses to improve the connectivity of peers. This leads to an increase of approximately 70% in download performance. This result implies that the connectivity of peers would significantly affect file availability in the BitTorrent system. Therefore, to maintain good connectivity for each peer, the BitTorrent system must be able to efficiently handle the significant fraction of unreachable peers (see Sect. 4).. We suggest three strategies to improve the connectivity of peers in a swarm. First, the BitTorrent system should enable NAT traversal for peers in each swarm. NAT traversal techniques allow unreachable peers to contact each other. There are many NAT traversal techniques for Internet applications [25] , and most of them require assistance from a server at a publicly-routable IP address. We believe that, with a few modifications, a BitTorrent tracker can work as this server. Moreover, Guha et al. [26] develop STUNT (Simple Traversal of UDP Through NATs and TCP too) library, which extends STUN to include TCP functionality. BitTorrent client programs would use the library with a few modifications, and they would communicate through the existing NAT infrastructure without sacrificing the benefits of TCP. In addition, Lai et al. [27] implement their NAT traversal technique on BitTorrent. Thanks to their efforts, we can easily achieve our first strategy for improving the connectivity of unreachable peers.. Second, trackers should discard inactive peer addresses after a shorter period of time. To avoid contamination of the swarm by inactive peer addresses, it is essential to perform more frequent clean-ups by removing inactive peer addresses from trackers. More specifically, peers should use DHT (distributed hash table)-trackers instead of normal trackers. DHT-trackers can efficiently handle the churn, meaning that they discard inactive peer addresses after a minimal time. However, the current DHT-trackers cannot work well due to unreachable peers [28] . Therefore, we should also enable NAT traversal for DHT-trackers.. Third, Wu et al. [22] observe that PEX can significantly reduce the download time for some swarms. We also confirm this trend in Sect. 7.2.4. Therefore, peers must be aggressively exchanging active peer addresses via PEX messages. When peers follow this behavior pattern, active peer addresses are well diffused to the whole swarm, increasing the file availability between peers.. Although there are many unreachable peers in not only BitTorrent but also other P2P-like distributed systems, our method is effective for other P2P-like distributed systems. More specifically, our method works if P2P-like distributed systems have a peer discovery service. In BitTorrent, trackers work as the peer discovery service and trackers distribute our crawler's IP addresses to unreachable peers. Because unreachable peers try to contact to our crawler, our method can contact many unreachable peers in a short time. Fortunately, most of other P2P-like distributed systems also have a peer discovery service. For example, Winny, that is, one of the most popular P2P file sharing networks in Japan, contains over 50% of unreachable peers. But Winny has the peer discovery service and we have conducted measurements on Winny through our proposed method [29] . We can easily distribute our crawler's addresses to all peers through a peer discovery service and this fact also means our proposed method is effective for most of P2P-like distributed systems. In our attempt to obtain detailed information from as many peers as possible, we have implemented a high-performance crawler. Our crawler collects detailed information about peers in a systematic and automated manner through the method described in Sect. 5. Our crawler can open any number of service ports (1-65,536) and accepts all incoming accesses from peers. Because about 25% of incoming packets are encrypted, we also implement BitTorrent protocol encryption [23] to decode them. Our crawler is developed on a single PC server (CPU: Intel Core i7 2.8 GHz, Memory: 16 GB, HDD: 3 TB). The PC server is connected to the internet via a 100 Mbps access link. The crawler is developed in C# and runs on Windows XP 64-bit edition. To effectively use the access link, our crawler uses asynchronous I/O completion ports (IOCP) and can support more than 10,000 concurrent T CP connections. In addition, we change the socket-related registries (MaxUserPort, TCPMaxHalfOpen, ForwardBufferMemory, and so on) in the Windows OS.. Some BitTorrent applications support the IPv6 addressing protocol [21] and BitTorrent trackers support IPv6 peers [24] . Thanks to tracker's extended messages [24] , our crawler can identify IPv4 peers and IPv6 peers. When our crawler sends the subset of reachable peer addresses, our crawler can select the appropriate addressing protocols for each peer. For this reason, our proposed method also supports IPv6 peers.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Link-topic model for biomedical abbreviation disambiguation Introduction: The ambiguity of biomedical abbreviations is one of the challenges in biomedical text mining systems. In particular, the handling of term variants and abbreviations without nearby definitions is a critical issue. In this study, we adopt the concepts of topic of document and word link to disambiguate biomedical abbreviations. Methods: We newly suggest the link topic model inspired by the latent Dirichlet allocation model, in which each document is perceived as a random mixture of topics, where each topic is characterized by a distribution over words. Thus, the most probable expansions with respect to abbreviations of a given abstract are determined by word-topic, document-topic, and word-link distributions estimated from a document collection through the link topic model. The model allows two distinct modes of word generation to incorporate semantic dependencies among words, particularly long form words of abbreviations and their sentential co-occurring words; a word can be generated either dependently on the long form of the abbreviation or independently. The semantic dependency between two words is defined as a link and a new random parameter for the link is assigned to each word as well as a topic parameter. Because the link status indicates whether the word constitutes a link with a given specific long form, it has the effect of determining whether a word forms a unigram or a skipping/consecutive bigram with respect to the long form. Furthermore, we place a constraint on the model so that a word has the same topic as a specific long form if it is generated in reference to the long form. Consequently, documents are generated from the two hidden parameters, i.e. topic and link, and the most probable expansion of a specific abbreviation is estimated from the parameters. Results: Our model relaxes the bag-of-words assumption of the standard topic model in which the word order is neglected, and it captures a richer structure of text than does the standard topic model by considering unigrams and semantically associated bigrams simultaneously. The addition of semantic links improves the disambiguation accuracy without removing irrelevant contextual words and reduces the parameter space of massive skipping or consecutive bigrams. The link topic model achieves 98.42% disambiguation accuracy on 73,505 MEDLINE abstracts with respect to 21 three letter abbreviations and their 139 distinct long forms. As biomedical research has grown rapidly, text mining and automatic data construction systems based on text processing and machine learning techniques have become essential in the biomedical domain. In particular, many important terms including clinical diseases, procedures, genes, proteins, and chemicals in bio text are often expressed as abbreviations. 1 Furthermore, biomedical and each abbreviation has, on average, 16.6 multiple long forms [5] . In addition, the same abbreviation may be used to denote RNA, protein, disease, or gene across a variety of entity categories [5, 6] . A common issue with biomedical abbreviations is the multiplicity of lexical variants associated with a given abbreviation [7] [8] [9] [10] . For instance, ''CML'' has several different long forms of the same sense: ''chronic myelocytic leukemia,'' ''chronic myelogenous leukemia,'' and ''chronic myeloid leukemia''. Similarly the abbreviation ''APC'' can have many spelling variants, such as ''Apc,'' ''aPC,'' ''ApC,'' ''aPC,'' ''APc,'' ''apc,'' and ''AP-C.'' These term variations cause uncertainties regarding exact term identification/normalization when the terms in text are mapped to unique identifiers of a reference database [8, 9] .. Furthermore, abbreviations usually appear without their definitions, which it makes the task of identification more difficult [10] [11] [12] . Based on the definition given by Gaudan et al. [11] , these are known as global abbreviations, which are distinct from local abbreviations that are explicitly stated with their intended long forms in text. Genes and proteins are referred to in text in a range of different ways as full names and abbreviation symbols. In fact, a considerable number of gene names are expressed as global abbreviations [13] [14] [15] . Some studies have reported that only 30% of gene abbreviations are accompanied by their corresponding long forms in text [13, 14] . In addition, disambiguation of global abbreviations with multiple definitions is essential in establishing relationships among entities such as protein interaction map or genetic network that require correct definition pairs to be recognized [8] .. Especially, gene terms may refer to general English words [3, 5, 14, 16] and have different meanings depending on the context. Since common English words that are also gene names, such as ''end'', ''leg'', ''white'', and ''key'' often generate false positives in NLP text-mining applications, one main issue is to resolve abbreviation ambiguities.. Many studies have thus far been conducted on biomedical abbreviations in terms of (1) recognition and (2) disambiguation. An abbreviation recognition (extraction) task detects abbreviations and their long forms explicitly defined in biomedical texts [10] . It has been studied using various methods including: set of rules or patterns [17] , co-occurrence statistics by neighboring words of an abbreviation [1, 18] , and machine learning [19] [20] [21] . The process of extracting abbreviation-long form pairs from a raw text is still a major issue because the pairs are not sufficiently listed in most databases and new abbreviations appear at a fast rate. In fact, some research has reported that the sense inventory generated from the UMLS Metathesaurus could cover only 35% of long forms for abbreviations, which showed a low coverage for abbreviations [5, 14] .. On the other hand, abbreviation disambiguation has been studied to a lesser extent than the abbreviation-long form pair extraction [10] . As abbreviation disambiguation identifies its intended sense (long form) in a given context, it is, in effect, equivalent to word sense disambiguation (WSD), which has been studied extensively during the past years. Most approaches have focused on the neighboring words of ambiguous words because these contextual words can provide strong and consistent clues for determining the sense of an abbreviation [11, 15, 21, 22] . Thus, the context of an abbreviation is usually compared with previously-learned contexts to disambiguate its sense by using a context similarity. The similarity has been often assessed by determining how many words are shared or overlap between the contexts. However, such word-based context similarity can be a problem due to the lack of consistent vocabularies and variations in lexical choice.. In this study, we propose the novel link topic model based on the latent Dirichlet allocation (LDA) [23] for disambiguating biomedical abbreviation in MEDLINE abstracts. The model chooses the most probable sense that generates the entire document with its underlying topic structure and link dependency between words. In the model, each document exhibits multiple topics with different proportions and each word is generated from one of the topics and link information. The link can be interpreted as a topical and contextual relatedness between two words, particularly long form words of abbreviations and their sentential co-occurring words. The existence of a link between two words is inferred by the Gibbs sampling method. To model the link association with the topic model, a probabilistic distribution that corresponds to the strength of the associations is specified so that two words constitute a link if they tend to co-occur with one another and to have similar topics with high frequency. That is, a link dependency is parameterized by topics and co-occurrence frequencies of two words.. As a consequence, the proposed model avoids the insensitivity to word order of previous topic models based on a bag-of-words assumption, and the representations extracted by the model capture a richer latent structure of a text in terms of unigrams, consecutive bigrams, skip bigrams, and semantic dependencies between words. Compared to previous studies such as bigram [24] , n-gram [25] , and collocation [26] topic models in which word associations are sequential dependencies based on word order rather than semantic context, the word associations represented by the link captures both sequential dependencies and semantic context well.. An abbreviation is disambiguated by computing the likelihood of a document. That is, the long form among candidates that is most likely to be generated given underlying topic and link structures of a document is determined. We use Gibbs sampling for approximate posterior inference for topics and word link distributions that best explain a collection of documents (observations). Twenty-one abbreviations were studied, with more challenging rare sense tested in comparison to previous works. This study suggested a new approach for the disambiguation of biomedical abbreviations based on topic and link. We designed the link topic model to reflect topics of documents, topics of words, and semantic relatedness of words with respect to long forms. The disambiguation experiments were conducted on MEDLINE abstracts containing 21 three-letter target abbreviations and their 139 definitions after we grouped long-form variants. The results show that the expansions of target abbreviations were correctly identified based on the link topic model with an accuracy of 98.42%, which is quite promising.. In addition, the experimental results revealed that two distinct processes of word generation and the addition of semantic links between a specific long form and words could improve disambiguation accuracy. This model was effective in reducing the parameter space of massive skipping bigrams and removing contextual words irrelevant to the long forms of an abbreviation by linking words and related long forms.. In a future study, we will investigate how other observable information used for document indexing, such as MeSH terms, can also be incorporated into the topic model. According to the results of the link topic model, abbreviations having low disambiguation accuracy such as ''ASP,'' ''DIP,'' ''MAC,'' ''PCA,'' and ''PCP'' actually have more senses (long forms) than do other abbreviations. Table 10 represents specific confused examples about actual long forms and predicted ones obtained by the link topic model. It shows pairs whose error counts are more than 10 and the confusion ratio is greater than 50%. According to the table, closely related long form pairs such as ''aspartic_acid'' and ''aspartate,'' ''primary_care_provider'' and ''primary_care_phy-sician,'' and ''polyethylene_glycol,'' and ''pegylated'' were indistinguishable from one another. This means that the model effectively grasped similar senses well from a topic perspective. In addition, senses among viruses, diseases, and proteins were often indistinguishable from one another. For example, ''posterior ciliary artery'' and ''posterior cerebral artery'' were indistinguishable due to similar contexts even though they denote two very distinct anatomical entities.. In this study, we managed abbreviations that appeared in MED-LINE abstracts based on the assumption that an abbreviation has one sense per document, which is likely to be the case in most research abstracts. However, in complete biomedical texts, it is not uncommon to have occurrence of one abbreviation that represents different senses within a single document. Thus, the use of an entire document and topic based on one-sense-per-document assumption can imply a strict applicability. To address this limitation, disambiguation accuracy was also evaluated in cases in which only the words in sentences that included the abbreviation are supplied as the context, at the testing stage. We define the words as local context. As shown in Table 11 , the link topic model can also be applied to the local context with comparable accuracy. Regarding the link topic model, the most probable long forms with respect to target abbreviations were determined based on word-topic, document-topic, and word-link distributions estimated from a collection of documents by means of Eq. (10) . As a result, the link topic model achieved a high accuracy of 98.42% on abbreviation disambiguation (21 abbreviations and their 139 long forms) without the removal of additional commonly used domain-specific words. Table 6 shows our study's performance in comparison with the model of Stevenson et al. which reported the highest accuracy among studies of biomedical abbreviation disambiguation [15] . They tested their methodology on three sets: the first on 18 abbreviations (48 long forms), the second on 16 abbreviations (41 long forms), and the third on 14 abbreviations (37 long forms). In that study, 55,655 MEDLINE abstracts were retrieved but rare senses (less than 1% of the occurrences) were discarded from the dataset. Column 5 shows the actual numbers of long forms used in the experiments and the total number and rare numbers of long forms for each abbreviation are enclosed in parentheses. Columns 7 and 8 in Table 6 show the average disambiguation results of the research for the data sets consisting of 100, 200 and 300 examples with respect to each abbreviation, respectively. They evaluated the disambiguation performances on randomly selected examples from the retrieved abstracts by 10-fold cross validation. As shown in the table, although rare senses of target abbreviations were all maintained in our data set, the link topic model performed competitively. Table 7 shows the performance comparisons with two other state-of-the-art systems in the same condition. Our link topic model was evaluated on the same sets of abbreviations and long forms used in the studies of Stevenson et al. [15] and Liu et al. [21] , in order to ensure comparability. In the research of Liu et al., the disambiguation performance was measured on 14 abbreviations and their 49 long forms (14,870 abstracts) by the 10-fold cross validation method. In the table, the L. Accuracy denotes the result presented by Liu et al. [21] and the S. Accuracy, that measured by Stevenson et al. [15] . The results by the link topic model are shown on the right-hand side of each work. Table 8 shows more details about performances of the link topic model from 10-fold cross validation. We performed a significance test to compare our accuracy with ones of the two systems. According to the statistical test (p < 0.05 in a paired t-test), the link topic model achieved statistically significant improvements in disambiguation accuracy over the other two systems under the same experimental condition. Table 9 shows the w (link) probabilities of some skipping/consecutive bigrams associated with the long form ''acetaldehyde'' obtained through Gibbs sampling estimation in Eq. (7). For example, ''MPP+'' actually co-occurs with the long form word ''acetaldehyde'' 10 times in sentences or abstracts under the defined constraints. In fact, every occurrence forms a link to ''acetaldehyde'' according to the link structure inferred by the Gibbs sampling method under the link topic model. The link information reflects the fact that ''acetaldehyde'' and ''MPP+'' are closely related semantically and have similar topic distributions. In other words, ''MPP+'' is generated from a distribution that depends on the specific long form ''acetaldehyde''. By contrast, the words -''blood'' and ''acetaldehyde'' are never linked according to the estimated link structure even though they often co-occur either in sentences or in abstracts. This means that all occurrences of ''blood'' are generated independently of the word ''acetaldehyde.'' In fact, since ''blood'' frequently occurs with other senses of the abbreviation ''ACE,'' it is not much helpful to disambiguate the sense of ''acetaldehyde''. It is important to note that the link infor- mation is effective in disregarding frequent co-occurring words that are irrelevant to the recognition of long-form words. Fig. 2 visualizes the semantic relatedness between a specific long form word and other words based on the link generation probability w. The probability indicates the degree to which a word is dependent on a specific long form. The numbers in parentheses refer to the counts of sentential co-occurrences for each pair consisting of a word and a specific long form, and edge thickness indicates the link strength of the pair. For instance, words such as ''MPP+,'' ''ACR,'' ''DBF,'' ''FRM,'' and ''MPTP'' have strong dependencies with the long form ''acetaldehyde.'' The relatedness network can be a benefit of the link topic model. We performed 10-fold cross-validation to assess the model performance. This involves dividing the set of 73,505 abstracts with 274,434 unique terms into 10 disjoint sets of approximately equal size. For each of 10 experiments, we used nine folds for training and the remaining one for testing.. Our objective is to assign long forms to target abbreviations of each abstract. We evaluated the performance of topic models based on disambiguation accuracy, which represents the ratio of abstracts whose long forms are correctly identified with respect to target abbreviations. The parameters of each topic model were inferred from Gibbs sampling through 3,000 iterations with symmetric priors, Kðnumber of topicsÞ ¼ 300; a ¼ We conducted the disambiguation tests with the basic LDA topic model and the link topic model. The experiments of the topic models were conducted on 21 abbreviations and their 139 long forms (6.62 senses per abbreviation on average) after grouping long term variants. Basically, the parameters of topic models were inferred after removing PubMed stop words from the data set. We further selected 132 of the most common lexical words (weight w, -d < 0.01) based on the term weighting factor in Eq. (8) . The influence of the common words removal on disambiguation was examined under the basic LDA model. However, to verify the benefit of the link information, the parameters of the link topic model were inferred after removing only PubMed stop words from the dataset.. For a more precise comparison, the disambiguation accuracies of the link topic model were evaluated on the same set of abbreviations and long forms as in the studies of Stevenson et al. [15] and Liu et al. [21] . In addition, we examined the two words that form a dependency based on the w (link) probabilities of skipping/consecutive bigrams obtained from the Gibbs sampling estimation in Eq. (7). Table 3 shows the average cross-validated disambiguation accuracies of the topic models including that of the link topic model. Column 4 in Table 3 shows the results from the majority sense baseline model which simply determines the most frequent sense (long form) as the meaning of each abbreviation. The accuracy was approximately 65%. Column 5 presents the disambiguation accuracy of the basic LDA topic model [23] . The long forms were identified with Eq. (9) by using the topic distributions of words and documents. Consequently, the basic LDA model based on a topic structure of an entire document showed a quite compet- itive result (96.56%) regarding the biomedical abbreviation disambiguation problem. The disambiguation accuracy of the basic LDA topic model was improved slightly by 0.06% after removing 132 commonly used domain-specific words and PubMed stop words, as shown in Column 6. Table 4 shows some examples of the domain-specific words (weight w,d < 0.01) and their term weights. According to Table 4 , most of the words correspond to common words that appear frequently in biomedical texts. In Table 5 , more detailed performances of the models from 10-fold cross validation are shown.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Modeling and Control Strategies of Fuzzy Logic Controlled Inverter System for Grid Interconnected Variable Speed Wind Generator Abstract-Today, variable speed operation of a permanent magnet synchronous generator (PMSG) is becoming popular in the wind power industry (PI). A variable speed wind turbine (VSWT)-driven PMSG, in general, is connected to the grid using a fully controlled frequency converter (FC). Along with the generator side converter, the FC necessitates the grid side inverter system that has a great impact on the stability issue of the VSWT-PMSG, especially in the case of network disturbance. The well-known cascaded-controlled inverter system has widely been reported in much of the literature, where multiple PI controllers are used in inner and outer loops. However, a fuzzy logic controller deals well with the nonlinearity of the power system, compared to a PI controller. This paper presents a simple fuzzy logic controlled inverter system for the control of a grid side inverter system, which suits well for VSWT-PMSG operation in a wide operating range. This is one of the salient features of this paper. This paper presented a novel architecture for the fuzzy logic controlled inverter system suitable for variable speed wind turbine driven PMSG. The inverter of the VSWT-PMSG had to withstand against network disturbance and normal operation. The FLC controlled inverter system can successfully handle those issues working in a wide operating range. The intricacy of the controller design can also be avoided. The performance of the overall system was evaluated using both dynamic and transient analyses. The transient performance of the proposed control was compared with the well-known cascaded control. This control strategy was even suitable for other direct drive or one-stage gearbox enabled wind turbine generator systems for grid connectivity. Finally, it was concluded that the proposed FLC controlled inverter system might be a good choice for the application of the grid connected wind turbine generator system.

O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


NMSDECAY: A Fortran Code for Supersymmetric Particle Decays in the Next-to-Minimal Supersymmetric Standard Model The code NMSDECAY allows to compute widths and branching ratios of sparticle decays in the Next-to-Minimal Supersymmetric Standard Model. It is based on a generalization of SDECAY, to include the extended Higgs and neutralino sectors of the NMSSM. Slepton 3-body decays, possibly relevant in the case of a singlino-like lightest supersymmetric particle, have been added. NMSDECAY will be part of the NMSSMTools package, which computes Higgs, sparticle masses and Higgs decays in the NMSSM. The search for supersymmetric particles (sparticles) is one of the most exciting tasks of present and future experiments in particle physics, notably at the LHC [1, 2] . These searches can only be successful if the possible signals of sparticles are known so that they can be simulated and be compared to the data. Sparticle signals are not unique, since they depend on the specific supersymmetric (SUSY) extension of the Standard Model (SM), i.e., on the particle content, couplings and spectrum, which determine the numerous possible decay cascades.. In recent years, SUSY decay cascades have been studied mostly in the framework of the Minimal Supersymmetric Standard Model (MSSM). In order to study the dependence of the decay cascades on the sparticle masses and couplings, it is very helpful to use numerical programs which perform the calculation of the large number of sparticle widths and possible branching ratios. The sparticle masses and couplings depend, in turn, on the parameters of the underlying Lagrangian. Therefore it is reasonable to interface programs for the sparticle decays with programs which compute the sparticle masses and couplings (so-called spectrum calculators).. In the case of the MSSM, one such interface is the program SUSY-HIT (SUspect-SdecaYHdecay-InTerface) [3] . It uses the program Suspect [4] for the calculation of the Higgs and sparticle spectra, SDECAY [5] for the sparticle branching ratios, and HDECAY [6] for the branching ratios of the Higgs sector. Another program including simultaneously the calculation of sparticle spectra and decays is SPheno [7, 8] .. However, the MSSM is not the only possible supersymmetric extension of the SM; it is just the one with a minimal Higgs sector, which in this case contains two SU(2) doublets H u (coupling to up-type quarks) and H d (coupling to down-type quarks and charged leptons). The price to pay for such a minimal Higgs content is a supersymmetric Higgs mass term µ in the MSSM superpotential, whose phenomenologically required order of magnitude (the weak or the SUSY breaking scale) is difficult to explain [9] . The µ-problem of the MSSM can be solved in the Next-to-Minimal Supersymmetric Standard Model (NMSSM -for recent reviews see [10, 11] ). The Higgs sector of the NMSSM consists of two SU(2) doublets H u and H d (as in the MSSM), to which a gauge singlet superfield S is added. Due to a λSH u H d coupling in the superpotential, the vacuum expectation value (vev) of S, s, generates a supersymmetric mass term µ eff = λs of the desired order of magnitude for H u and H d . In its simplest Z 3 invariant version, the superpotential of the NMSSM is scale invariant; it is in fact the simplest phenomenologically acceptable supersymmetric extension of the SM with this property.. The new superfield S implies various additional physical states when compared to the MSSM: a neutral CP-even Higgs boson, a CP-odd Higgs boson and a neutralino. These states can be relatively light; if the additional NMSSM-specific CP-even Higgs boson mixes very little with the Higgs bosons H u and H d of the MSSM, it will have very small couplings to SM gauge bosons, quarks and leptons and is hence much less constrained by the so far unsuccessful direct searches, notably at LEP [12] . In some regions of the parameter space of the NMSSM, CP-odd Higgs bosons can also be lighter than in the MSSM [13, 14] . If they are light enough, any of the NMSSM-specific additional Higgs and neutralino states can have an important impact on sparticle decays, both as potential final states and/or as virtual intermediate states in 3-body decays. Notably in the case of the constrained NMSSM (with universal soft SUSY breaking terms at the GUT scale [15, 16] ), a very weakly coupled singlino-like neutralino can be the lightest supersymmetric particle (LSP), thus affecting the final states of all sparticle decay cascades.. The impact of these NMSSM specific features on sparticle production and on cascade decays has already been addressed: sparticle decays into singlino-like neutralinos have been studied in [16] [17] [18] [19] [20] [21] , sparticle decays into neutralinos in [22] , decays of stops, sbottoms and staus in [23] , the associated production of a light CP-odd Higgs boson with a chargino pair in [24] , neutralino decays into light Higgs bosons in [25] , neutralino decays into soft leptons in [26] , and neutralino and chargino decays into many leptons in [27] .. The purpose of the code NMSDECAY is to compute all sparticle decays into all possible 2-and 3-body final states in the NMSSM: squarks, sleptons, gluino, neutralinos and charginos decaying into squarks, sleptons, gluino, neutralinos, charginos plus Higgs bosons, SM-like quarks, leptons and gauge bosons. R-parity is assumed to be conserved such that each sparticle can only decay into a lighter sparticle plus SM-like particles and/or Higgs bosons.. NMSDECAY is based on an extension of the MSSM-specific code SDECAY [5] , generalized to include the NMSSM-specific extensions of the Higgs and neutralino sectors. QCD corrections, loop-induced and 3-body decays are included as in SDECAY. In the NMSSM, a singlino-like LSP can lead to dominant 3-body decays of sleptons, even if their 2-body decays are kinematically possible. Such 3-body decays of sleptons (which were omitted in SDECAY) have been added to NMSDECAY.. NMSDECAY is interfaced with NMSSMTools 1 , and is also written in FORTRAN 77. The installation and compilation of NMSSMTools is a fairly straightforward procedure, described in the manual README, which can be found on the web site. The package NMSSMTools includes: NMHDECAY [28, 29] , where the spectrum for the general NMSSM is computed and Higgs decays are calculated similar to HDECAY [6] ; NMSPEC [30] where universal soft SUSY breaking terms at the GUT scale are assumed; a link to MicrOMEGAS [31, 32] , which allows for the calculation of the dark matter relic density and its detection cross sections. Sparticle decays of the NMSSM with gauge mediated SUSY breaking [33] are not yet included in NMSDECAY. Once NMSSMTools is compiled, the subroutines required for NMSDECAY are linked automatically to NMHDECAY and NM-SPEC. However, the sparticle total widths and branching ratios are computed and written only if a corresponding flag in the input file (switch 13) is switched on.. In the next section we give the Lagrangian and the particle content of the NMSSM, and in Section 3 we describe how NMSDECAY is linked to NMSSMTools, as well as the various calculations performed in the different subroutines. In the Appendix we describe the arguments of the subroutines and COMMON statements, which have to be defined if NMSDECAY is called from another spectrum calculator.

O O O O O O O O O O B I I I O O O O O O O O O O O B I O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


ImageAdmixture: Putting Together Dissimilar Objects from Groups Abstract-We present a semiautomatic image editing framework dedicated to individual structured object replacement from groups. The major technical difficulty is element separation with irregular spatial distribution, hampering previous texture, and image synthesis methods from easily producing visually compelling results. Our method uses the object-level operations and finds grouped elements based on appearance similarity and curvilinear features. This framework enables a number of image editing applications, including natural image mixing, structure preserving appearance transfer, and texture mixing. I MAGE analysis and editing provide an important approach to producing new content in computer graphics. We propose here a dedicated system for users to replace structured objects selected from groups while preserving element integrity and providing visual compatibility. It proffers an unconventional way to create new image content with structured objects, even when they are irregularly arranged. The objects are allowed to be dissimilar in appearance and structure, as shown in Fig. 1 .. Image and texture synthesis from multiple exemplars have been explored in previous work. Pixel-level texture approaches make use of similar statistical properties to generate new results [4] , [9] , [45] . Patch-based texture synthesis [14] is another set of powerful tools to selectively combine patches from different sources [23] . Recently, Risser et al. [35] employed multiscale neighborhood information to extend texture synthesis to structured image hybrids. They are useful for creating new image or texture results based on examples.. These methods are general; but in the context of object replacement within groups, they may not be able to produce visually plausible effects without extensive user interaction. Grouped elements can be rapidly and integrally perceived by the human visual system (HVS) [21] , which indicates only considering pixel-or patch-level operations may not be enough to understand the individual structured element properties. Our system is composed of object-level editing algorithms and does not assume any particular texture form or near-regular patterns. Our major objective is to deal with natural images containing piled or stacked objects, which are common in real scenes, from the individual perspective.. The naturalness of element replacement depends on the compatibility of input and the result structures. It is measured in our system by the element-separability scores, combining curvilinear features and multiscale appearance similarity. Objects are extracted based on these metrics, with initial center detection followed by a Dilate-Trim algorithm to form complete shapes. Our method also finds suitable candidates for element replacement to ensure the result quality. Our main contribution lies in an object-level manipulation method without regularity assumption and on a new scheme to measure object structure compatibility. In this paper, we have proposed a dedicated system to substitute structured elements within groups by others even though they are dissimilar in appearance. Different levels of mixture are allowed in our system, preserving local structures. Our method requires a very small amount of user interaction. The analysis and other operations to separate objects are automatic. The drawback is that we do not consider layers and depth, which may be problem in some cases. But overall our method is powerful enough to handle many examples that are challenging for other approaches. Shi-Min Hu received the PhD degree from Zhejiang University in 1996. Currently, he is working as a professor in the Department of Computer Science and Technology, Tsinghua University, Beijing. He is an associate editorin-chief of The Visual Computer, associate editor of Computer and Graphics and on the editorial board of Computer Aided Design. His research interests include digital geometry processing, video processing, rendering, computer animation, and computer-aided geometric design. He is a member of the IEEE.. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. Image composition is a well-studied problem in computer graphics. Early work of Porter and Duff [33] used an alpha matte to composite transparent objects. Recent advance in alpha matting [41] , [42] made it possible to generate natural and visually plausible image composite with global or local operation. For our problem, directly apply alpha matting to replace one object by another can generate visual artifacts when illumination conditions vary. Poisson blending [32] and its variations reduce color mismatching by computation in the gradient domain. Farbman et al. [16] achieved similar composition results efficiently. Research efforts have also been made to select candidates for composition from image database [25] or internet images [10] , which was applied to create a fantastic artform called Arcimboldo-like collage in [11] . Such methods minimize composition artifacts while keeping the original object shape complete. In our problem, it is essential to seek proper image content to replace items in groups via compatible structures. We also do not simply copy the whole region to the target image for blending. There are procedures to refine boundaries to improve the visual quality of the results when in common situations that the found source and target objects are diverse in structure.. Our work is also related to example-based texture synthesis. Efros and Leung [15] proposed a nonparametric pixel-wise method to synthesize new texture from a user provided example, trying to preserve as much local structures as possible. The efficiency and capability were improved by tree-structured vector quantization [44] , global optimization [22] , and multiscale synthesis [17] , [26] . Patchbased approaches [14] , [23] form another important line for texture synthesis with the main idea being to find an optimal seam between adjacent patches. Mask images [31] , [47] and feature maps [37] , [46] can be used to guide texture synthesis. These methods provide a powerful tool for creating visually similar content from a few examples [43] , but none of them can be applied directly to natural images for separating grouped elements, and then replacing objects in a visually plausible way. The main challenge is that the underlying structured element regions may be changed in this process, as demonstrated in Fig. 1 .. Synthesis and analysis may also be done at the texel level [34] . Dischler et al. [13] manually segmented texture into particles. Automatic methods [1] , [40] extracted texels based on subtree matching in low-level segmentation. They can extract texels that are significantly occluded. It is, however, time-consuming and can fail when segmentation is unreliable for grouped elements. Ijiri et al. [20] showed how to manipulate elements which are distributed near-regularly.. Combining features or visually meaningful content from different samples, i.e., image/texture mixing, allows users to produce results with large variation. In prior work such as image hybrids [35] , Risser et al. synthesized image hybrids from exemplars. Other methods [4] , [9] , [45] generated new texture from multiple examples. The mixed area produced by these methods tends to have similar statistics or visual characteristics to the source. In RepFinder [12] , although element replacement can be conceptually achieved, strict shape similarity has to be enforced and there is no consideration of object consistency. In contrast, our work places elements from different groups, where the visual characteristics are allowed to be greatly different. Recently, Ma et al. [30] mixed objects by modeling the combination of individual elements and their distributions. However, this method cannot solve our problem and requires users to manually produce the source elements.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Dual oscillator model of the respiratory neuronal network generating quantal slowing of respiratory rhythm We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382-397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na + current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821-826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000-18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC. Two putative respiratory rhythm generators, the parafacial respiratory group (pFRG) (Onimaru et al. 1988; Onimaru and Homma 2003) and the preBötzinger complex (preBötC) (Smith et al. 1991) , exist in the mammalian brainstem. The two neuronal groups, separately identified, are thought to play important roles in the generation and maintenance of respiratory rhythm (Feldman and Janczewski 2006; Oku et al. 2007 ) at least in the neonatal period, although the primary site of the respiratory rhythm generation is still controversial (Feldman and Del Negro 2006) . There are a number of neuronal behaviors that characterize the respiratory network in brainstem spinal cord preparations of neonatal rodents. First, the firing pattern of preinspiratory (Pre-I) neurons, a subset of neurons within pFRG, typically consists of three phases: (1) firing preceding the inspiratory phase, (2) suppression of firing during the inspiratory phase, originally referred to as inspiration-related inhibition of the Pre-I firing (IIPI), and (3) firing during the post-inspiratory phase, which is not seen in preBötC preinspiratory neurons (Smith et al. 2007 ). Second, IIPI consistently disappears after removing chloride-mediated inhibition, resulting in overlapping of Pre-I neuronal firing and inspiratory neuronal firing (Onimaru et al. 1990 ). Third, opioids do not affect Pre-I neurons but depress preBötC inspiratory neurons (Gray et al. 1999) . As a consequence of dynamic interactions between pFRG and preBötC, respiratory periods jump non-deterministically to integer multiples of the control period, and the phenomenon was referred to as quantal slowing (Mellen et al. 2003; Barnes et al. 2007) . Synaptic connections and interactions between the neuronal groups to explain the characteristic behaviors have been proposed (Onimaru et al. 1990; Ballanyi et al. 1999) . Specifically, the observation of quantal slowing has led to a hypothesis that two rhythmically active networks interact to generate respiratory rhythm and quantal slowing results from transmission failure from pFRG to preBötC networks (Mellen et al. 2003) . However, in such a complex dynamic system, it is imperative to evaluate descriptive assumptions by simulating phenomena using a computational model.. In the present study, we demonstrate a computational model consisting of two groups of bursting neurons, representing pFRG and preBötC neuronal groups. We assume that pFRG provides excitatory inputs to preBötC and preBötC provides inhibitory connections to pFRG (Onimaru et al. 1990; Ballanyi et al. 1999) . We then analyzed the influences of changes in strengths of excitatory and inhibitory synaptic connections between the two neuronal groups on the coupling pattern of bursting activities in these groups. Finally, we evaluated the effects of reducing excitability of preBötC neurons on the respiratory period, and considered the mechanism that causes non-deterministic jump of respiratory period to integer multiples of the control periods. Interestingly, we found that the quantal slowing occurs without assuming suppressed excitatory synaptic transmission.. Similar attempts to simulate quantal slowing phenomenon by dynamic interactions between the two rhythm generating networks has been made by Joseph and Butera (2005) and Wittmeier et al. (2008) earlier.. In both studies, pFRG and preBötC were represented by only a pair of pacemaker neurons, presuming that any 'lumped' effects of the interaction should apply at the full-blown network level (Wittmeier et al. 2008 ). Joseph and Butera (2005) used a simple canonical model of pacemaker neurons to represent the pFRG and preBötC. However, it is difficult to correlate cellular properties with model parameters in such a canonical model. Further, in their result, quantal slowing accompanied abrupt changes in firing pattern of Pre-I neurons, i.e., emergence of postinspiratory burst that was absent in the control period, which is inconsistent with experimental findings (Mellen et al. 2003) . Wittmeier et al. (2008) adopted a Hodgkin-Huxley type realistic neuronal model described by Butera et al. (1999a) and substituted stochastic synaptic transmission for the deterministic excitatory synaptic inputs to preBötC from pFRG to simulate quantal slowing. However, there is no physiological basis supporting that opioids cause stochastic synaptic transmission. Moreover, if the stochastic nature of synaptic transmission is essential for producing quantal slowing then the phenomenon is not entirely a characteristic of the model under consideration but also of a undefined additional mechanism resulting in stochastic dynamics of the synaptic transmission. In the present study, we have conducted a large scale simulation by modeling both pFRG and preBötC as groups of 81 pacemaker neurons to test whether the essential features of the activity of a rhythm generating network may be adequately captured by replacing it by a single pacemaker neuron, and more specifically, to test whether the quantal slowing phenomenon requires stochastic dynamics of the synaptic transmission. Our simulation predicted that the reduction of excitability in preBötC network could result in intermittent failure of preBötC neurons to produce synchronized bursting and could cause quantal slowing even if we did not assume stochastic dynamics of the synaptic transmission. We suggest that the quantal slowing phenomenon is an intrinsic characteristic of the rhythm-generating network and stochastic synaptic transmission is unnecessary to explain the quantal slowing phenomenon. Our results are in agreement with the general conception that quantal slowing phenomenon results due to general loss of sensitivity within the preBötC group but, more importantly, they suggest towards the possibility of a different mechanism for its cause. As mentioned earlier, in the dual oscillator model, NeuronGroup1 and NeuronGroup2 represents pFRG and preBötC, respectively. However, during the presentation of results and discussion, we restrict the terms NeuronGroup1 and NeuronGroup2 to refer to the modeled pFRG and preBötC, respectively. Further, throughout the present model study, we define the period where preBötC/NeuronGroup2 neurons burst synchronously as the inspiratory phase, and the rest of period where a majority of preBötC/NeuronGroup2 neurons are quiescent as the expiratory phase.

O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O