Index File Research Articles

The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation

Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines.Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu.Contact: Kocher.JeanPierre@mayo.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Mental Files, Blown Up by Indexed Files

Our paper discusses Recanati’s application of the mental files apparatus to reports of beliefs and other attitudes. While mental files appear early on in Recanati’s work on belief-reports, his latest book introduces the concept of indexed files (a.k.a. vicarious files) and puts it to work to explain how we can report other people’s attitudes and to account for opacity phenomena. Our goal is twofold: we show that the approach in Recanati’s Mental Files (Oxford: Oxford University Press, 2012) departs significantly from his earlier proposals as well as from the very spirit of truth-conditional pragmatics; and we argue that the indexed files approach, qua an attempt to provide a semantics for belief-reports, is untenable.

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Currently, cloud computing systems use simple key-value data processing, which cannot support similarity search effectively due to lack of efficient index structures, and with increase of dimensionality, existing tree-like index structures could lead to problem of the curse of dimensionality. In this paper, a novel VF-CAN indexing scheme is proposed. VF-CAN integrates content addressable network (CAN) based routing protocol and improved vector approximation file (VA-file) index. There are two index levels in this scheme: global index and local index. The local index VAK-file is built for data in each storage node. VAK-file is k-means clustering result of VA-file approximation vectors according to their degree of proximity. Each cluster forms a separate local index file and each file stores approximate vectors that are contained in cluster. The vector of each cluster center is stored in cluster center information file of corresponding storage node. In global index, storage nodes are organized into an overlay network CAN, and in order to reduce cost of calculation, only clustering information of local index is issued to entire overlay network through CAN interface. The experimental results show that VF-CAN reduces index storage space and improves query performance effectively.

A two‐layer model for improving the energy efficiency of file sharing peer‐to‐peer networks

SummaryAs peer‐to‐peer networks gather and share large sets of computing resources, their aggregate energy consumption is an important problem to be addressed, given the economic and environmental impact of energy production and use. This is particularly true for file sharing peer‐to‐peer networks, considered the vast number of nodes participating to these systems. To address such problem, this paper proposes a peer‐to‐peer file sharing model organized in two logical layers. The lower layer is composed of a set of peers providing the files to be shared. The upper layer includes a set of super peers organized to form a distributed hash table, whose purpose is indexing files and peers, including their availability status. Following a sleep‐and‐wake approach, energy saving is achieved by letting peers cyclically switch between normal and sleep modes, where the time passed in normal mode by a peer depends on the number of files it provides. An energy‐saving algorithm and an associated file search and retrieval strategy are proposed within this model. Simulation results show that more than 50% of energy can be saved using the proposed model and that more than 80% of file downloads start with no additional delay compared with a network in which all peers are always powered on. Copyright © 2014 John Wiley & Sons, Ltd.

Hospitalists And Hospitals’ Bottom Line: How Do Hospitals That Use Hospitalists Fare?

Hospitalists are group of hospital-based physicians that specialize in the provision of hospital medicine. Since the mid- 1990s, hospitalists have grown unprecedentedly. Studies on hospitalists have focused for the most part on efficiency as measured by costs and length of stay (LOS). Very few investigations have attempted to explore the relationship between hospitals’ use of hospitalists and hospitals’ bottom line. The purpose of this study is to explore the association between the use of hospitalists and financial performance. The data for this study was extracted from the AHA Annual Survey, the Area Health Resource File (AHRF), and CMS’ costs reports and Case Mix Index (CMI) files. This longitudinal study (2007- 2010) consisted of a national sample of U.S. acute care hospitals. A panel design with facility and year fixed effects regression was performed to assess this relationship. We found that an increase in the use of hospitalists is associated with higher costs and revenue. Our study also showed that an increase in the use of a low proportion of hospitalists is associated with both low costs and revenues while an increase in the use of a high proportion of hospitalists is associated with both high costs and revenues. This increased/decreased costs and revenues offset one another and does not translate into increased profitability. As stakeholders in the health care industry explore strategies to reduce costs, the adoption of hospitalists is a staffing strategy which may necessitate further exploration to determine if this staffing strategy holds its potential promises.

COSMID: A Web-based Tool for Identifying and Validating CRISPR/Cas Off-target Sites.

Precise genome editing using engineered nucleases can significantly facilitate biological studies and disease treatment. In particular, clustered regularly interspaced short palindromic repeats (CRISPR) with CRISPR-associated (Cas) proteins are a potentially powerful tool for modifying a genome by targeted cleavage of DNA sequences complementary to designed guide strand RNAs. Although CRISPR/Cas systems can have on-target cleavage rates close to the transfection rates, they may also have relatively high off-target cleavage at similar genomic sites that contain one or more base pair mismatches, and insertions or deletions relative to the guide strand. We have developed a bioinformatics-based tool, COSMID (CRISPR Off-target Sites with Mismatches, Insertions, and Deletions) that searches genomes for potential off-target sites (http://crispr.bme.gatech.edu). Based on the user-supplied guide strand and input parameters, COSMID identifies potential off-target sites with the specified number of mismatched bases and insertions or deletions when compared with the guide strand. For each site, amplification primers optimal for the chosen application are also given as output. This ranked-list of potential off-target sites assists the choice and evaluation of intended target sites, thus helping the design of CRISPR/Cas systems with minimal off-target effects, as well as the identification and quantification of CRISPR/Cas induced off-target cleavage in cells.

Personal Video Recorder Function Based on Android Platform Set-top Box

In order to satisfy the constantly increasing appetite of consumers for content in today’s world, Personal Video Recorder (PVR) function is implemented in digital TV set-top box (STB) based on Android platform. An algorithm that adjusts video Presentation Time Stamp (PTS) based on audio PTS is presented, which aims at the problem that audio is not synchronized with video existing during playback. And a method of generating index file based on the information of I-picture is presented, which aims at the implementation of trick modes. Namely, the location of I-picture which is used to generate index is recorded while audio and video data are being sent to storage medium in Packetized Elementary Streams (PES) format. The test results show that the TV STB designed can implement synchronization of audio and video accurately and reliably and the recorded programs can be played on the STB at different speeds. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3231

Self-similarity-based partial near-duplicate video retrieval and alignment

There have been recent studies on partial near-duplicate videos, which involve segments of videos that are near duplicates of each other. State-of-the-art searching schemes usually segment the input video into clips and implement clip-level near-duplicate retrieval. However, the segmentation results are always poorly aligned, which lead to a difficult “unbalance” problem. In this paper, we introduce a self-similarity-based feature representation called the Self-Similarity Belt (SSBelt), which derives from the Self-Similarity Matrix (SSM). In addition, a distinctive pattern in SSBelt called the Interest Corner is detected and described by a bag-of-words representation. The visual words are then combined into visual shingles and indexed by an inverted file index for fast retrieval. Another important task is to accurately align the unbalanced clips, for which we propose the Intensity Mark (IMark) and design a coarse-to-fine near-duplicate video localization scheme. Experimental results show the effectiveness of our approach for both web-based near-duplicate video and unbalanced video datasets. The near-duplicate alignment capacity of IMark is also shown to be effective.

APL@Voro: A Voronoi-Based Membrane Analysis Tool for GROMACS Trajectories

APL@Voro is a new program developed to aid in the analysis of GROMACS trajectories of lipid bilayer simulations. It can read a GROMACS trajectory file, a PDB coordinate file, and a GROMACS index file to create a two-dimensional geometric representation of a bilayer. Voronoi diagrams and Delaunay triangulations--generated for different selection models of lipids--support the analysis of the bilayer. The values calculated on the geometric structures can be visualized in a user-friendly interactive environment and, then, plotted and exported to different file types. APL@Voro supports complex bilayers with a mix of various lipids and proteins. For the calculation of the projected area per lipid, a modification of the well-known Voronoi approach is presented as well as the presentation of a new approach for including atoms into an existing triangulation. The application of the developed software is discussed for three example systems simulated with GROMACS. The program is written in C++, is open source, and is available free of charge.

Design of a Shape File Index for Fast Map Matching in Electric Vehicle Services

Mainly targeting at a small-city level road network, this paper builds an in-memory index for fast map matching, one of the most fundamental building blocks for vehicle information services. For the ESRI shape file sequentially storing road objects such as intersections and links, our indexing scheme obtains the offset of each link and creates the corresponding record consisting of link id, two end points, and the offset. Along with node entries, each of which includes all emanating links for a node, this index makes it possible to traverse the shape file just like Dijkstra’s shortest path algorithm, while all index traversal steps are done within memory. The previous location of a vehicle gives the start point of a map match process, significantly reducing the number of links to check if they include a specific point. This scheme particularly helps us to build an analyzer application for a series of spatiotemporal streams such as battery discharge dynamics.

Design and implementation of distributed retrieval system for electronic products information

In order to obtain the useful information that can satisfy the user requirements,this paper proposed a distributed information retrieval system based on Hadoop and Lucene handling the Web electronic products information retrieval.In order to improve the retrieval efficiency,using the Map and Reduce method of Hadoop technology implemented the storage of distributed index files and using Lucene technology implemented the file access of distributed index files.At the same time,it also proposed an improved method at fine grain retrieval level,which reduced the index building time.The experiment demonstrates that our distributed information retrieval system has a good retrieval performance for Web electronic products information.

효율적 범위 검색을 위한 플래시 메모리 기반 B+-트리

지난 수십 년간 B+-트리는 디스크 기반 데이터베이스를 위한 색인 구조로 가장 널리 사용되고 있다. 디스크 기반 B+-트리에서의 노드 갱신은 해당 노드가 저장된 디스크 페이지를 제자리 갱신함으로써 간단히 수행되며, 이런 제자리 갱신 비용은 크지 않다. 반면에 B+-트리를 플래시 메모리에 저장하여 사용할 때는 플래시 메모리의 과도한 제자리 갱신 비용 문제로 인해 기존 디스크 기반 B+-트리 알고리즘을 그대로 사용하기 어렵다. 이런 이유로 기존 플래시 메모리 기반 B+-트리 연구에서는 실시간으로 발생하는 갱신 연산 정보를 추가적인 임시 공간에 저장하는 방식을 사용하였다. 이런 방식은 B+-트리의 제자리 갱신 횟수를 쉽게 줄일 수 있다는 장점이 있지만 저장 공간의 추가 사용과 키 검색 시간을 지연시킬 수 있다는 문제가 있다. 특히 단말노드 계층의 링크 연결을 사용한 범위 검색을 효과적으로 수행할 수 없다는 문제를 가지고 있다. 이런 문제점을 해결하기 위해 본 논문에서는 단말노드들과 이들의 부모노드를 p-node 블록이라는 하나의 플래시 메모리 블록에 저장할 수 있는 알고리즘을 제안한다. During the past decades, the B+-tree has been most widely used as an index file structure for disk-resident databases. For the disk based B+-tree, a node update can be cheaply performed just by modifying its associated disk page in place. However, in case that the B+-tree is stored on flash memory, the traditional algorithms of the B+-tree come to be useless due to the prohibitive cost of in-place updates on flash memory. For this reason, the earlier schemes for flash memory B+-trees usually take an approach that saves B+-tree changes from real-time updates into extra temporary storage. Although that approach can easily prevent frequent in-place updates in the B+-tree, it can suffer from a waste of storage space and prolonged search times. Particularly, it is not allowable to process range searches on the leaf node level. To resolve such problems, we devise a new scheme in which the leaf nodes and their parent node are stored together in a single flash block, called the p-node block.

The Design of Distributed File System Based on HDFS

HDFS is a distributed file system designed to access large files, which is inefficient for storing small files. For this issue, a new storage architecture based on the HDFS is designed to solve the problem of low efficiency of HDFS storing small files in this article. This paper mainly uses SequenceFile to merge small files and against to the shortcoming that SequenceFile merges small files, the paper provides the solution and designs a new system structure based on HDFS. The system mainly increases the file judgment unit to mark and identify small files, creates a local index file which is helpful to improve the retrieval efficiency of small files to record the size and offset of the small files and finally uses binary serialization to merge the small files, which makes small files be written into large files as time order.

ObjectPatchNet: Towards scalable and semantic image annotation and retrieval

The ever increasing Internet image collection densely samples the real world objects, scenes, etc. and is commonly accompanied with multiple metadata such as textual descriptions and user comments. Such image data has potential to serve as a knowledge source for large-scale image applications. Facilitated by such publically available and ever-increasing loosely annotated image data on the Internet, we propose a scalable data-driven solution for annotating and retrieving Web-scale image data. We extrapolate from large-scale loosely annotated images a compact and informative representation, namely ObjectPatchNet. Each vertex in ObjectPatchNet, which is called as an ObjectPatchNode, is defined as a collection of discriminative image patches annotated with object category labels. The edge linking two ObjectPatchNodes models the co-occurrence relationship among different objects in the same image. Therefore, ObjectPatchNet models not only probabilistically labeled image patches, but also the contextual relationship between objects. It is well suited to scalable image annotation task. Besides, we further take ObjectPatchNet as a visual vocabulary with semantic labels, and hence are able to easily develop inverted file indexing for efficient semantic image retrieval. ObjectPatchNet is tested on both large-scale image annotation and large-scale image retrieval applications. Experimental results manifest that ObjectPatchNet is both discriminative and efficient in these applications.

Provable ownership of files in deduplication cloud storage

AbstractWith the rapid adoption of cloud storage services, a great deal of data is being stored at remote servers, so a new technology, client‐side deduplication, which stores only a single copy of repeating data, is proposed to identify the client's deduplication and save the bandwidth of uploading copies of existing files to the server. It was recently found, however, that this promising technology is vulnerable to a new kind of attack in which by learning just a small piece of information about the file, namely its hash value, an attacker is able to obtain the entire file from the server. In this paper, to solve this problem, we propose a cryptographically secure and efficient scheme for a client to prove to the server his ownership on the basis of actual possession of the entire original file instead of only partial information about it. Our scheme utilizes the technique of spot checking in which the client only needs to access small portions of the original file, dynamic coefficients and randomly chosen indices of the original files. Our extensive security analysis shows that the proposed scheme can generate provable ownership of the file and maintain high detection probability of client misbehavior. Both performance analysis and simulation results demonstrate that our proposed scheme is much more efficient than the existing schemes, especially in reducing the burden of the client. Copyright © 2013 John Wiley & Sons, Ltd.

Scaffold Hopping by Fragment Replacement

This work describes a data driven method for scaffold hopping by fragment replacement. A search database of scaffolds is created by cutting bonds of existing compounds in a combinatorial fashion. Three-dimensional structures of the scaffolds are then generated and made searchable based on the relative orientation of the broken bonds using an auxiliary index file. The retrieved scaffolds are ranked using volume overlap and electrostatic similarity scores. A similar approach has been used before in the program CAVEAT and others. The present work introduces a novel indexing scheme for the attachment vector geometry, which allows for fast searching. A scaffold shape descriptor is defined, which allows for queries with a single attachment vector (R-groups) and improves the shape similarity between the query and the suggested replacement fragments. The program, called Scaffold Hopping, is shown to retrieve relevant bioisosteric replacement scaffolds for a set of example queries in a reasonable time frame, making the program suitable to be used in drug design work.

Using Contextual Information to Assist Desktop Search

As the volume of personal data grows, it becomes increasingly difficult to rapidly find what we need on our desktop computers. Traditional content-based desktop search tools provide a potential solution by indexing file content, while lacking of contextual information about the user's activity. Context-enhanced search engines seem promising: they re-rank content-based results with the user's context. However, most of them are incomplete answers: they neither consider the factor of network application (especially, Web browser), nor take full advantage of the characteristics of user behavior and file metadata on desktop computers. To address those issues, we propose a concept of ”document-window switch tree” to represent the dynamic relationship from the user's context. We further collect relevant information to re-rank traditional content search results, such as reading time, frequency of window switch, file content and metadata. Search results on ranking accuracy generated by our method significantly outperform the context-enhanced search tool based on temporal locality.

KEYWORD SPOTTING FROM ONLINE CHINESE HANDWRITTEN DOCUMENTS USING ONE-VERSUS-ALL CHARACTER CLASSIFICATION MODEL

In this paper, we propose a method for text-query-based keyword spotting from online Chinese handwritten documents using character classification model. The similarity between the query word and handwriting is obtained by combining the character classification scores. The classifier is trained by one-versus-all strategy so that it gives high similarity to the target class and low scores to the others. Using character classification-based word similarity also helps overcome the out-of-vocabulary (OOV) problem. We use a character-synchronous dynamic search algorithm to efficiently spot the query word in large database. The retrieval performance is further improved by using competing character confusion and writer-adaptive thresholds. Our experimental results on a large handwriting database CASIA-OLHWDB justify the superiority of one-versus-all trained classifiers and the benefits of confidence transformation, character confusion and adaptive thresholds. Particularly, a one-versus-all trained prototype classifier performs as well as a linear support vector machine (SVM) classifier, but consumes much less storage of index file. The experimental comparison with keyword spotting based on handwritten text recognition also demonstrates the effectiveness of the proposed method.

WAVEFORM ANALYSIS FOR THE EXTRACTION OF POST-FIRE VEGETATION CHARACTERISTICS

Abstract. Full-waveform is becoming increasingly available in today's LiDAR systems and the analysis of the full return signal can provide additional information on the reflecting surfaces. In this paper we present the results of an assessment on full-waveform analysis, as opposed to the more classic discrete return analysis, for discerning vegetation cover classes related to post-fire renovation. In the spring of 2011 an OPTECH ALTM sensor was used to survey an Alpine area of almost 20 km2 in the north of Italy. A forest fire event several years ago burned large patches of vegetation for a total of about 1.5 km2 . The renovation process in the area is varied because of the different interventions ranging from no intervention to the application of re-forestation techniques to accelerate the process of re-establishing protection forest. The LiDAR data was used to divide the study site into areas with different conditions in terms of re-establishment of the natural vegetation condition. The LiDAR survey provided both the full-waveform data in Optech's CSD+DGT (corrected sensor data) and NDF+IDX (digitizer data with index file) formats, and the discrete return in the LAS format. The method applied to the full-waveform uses canopy volume profiles obtained by modelling, whereas the method applied to discrete return uses point geometry and density indexes. The results of these two methods are assessed by ground truth obtained from sampling and comparison shows that the added information from the full-waveform does give a significant better discrimination of the vegetation cover classes.

Automatic File Indexing Framework: An Effective Approach to Resolve Dangling File Pointers

managing files in a server system has the same magnitude as managing the World Wide Web due to the dynamic nature of the file system. Even searching for files over the file system is time consuming because finding a file on hard disk is a long-running task. Every file on the disk has to be read with dangling pointers to files which no longer exist because they have been changed, moved or deleted. This makes the user frustrated. The Automatic file indexing framework facilitates users to resolve file names and locate documents stored in file repositories. The main design objective of the framework is to maintain sub-indexes at the folder level that have the full knowledge of the revisions that are made at the folder level automatically. This research proposes a framework that manages the creation and maintenance of the file index, with the use of Resources Description Framework (RDF) and retrieval using semantic query languages i.e. SPARQL. The sub-indexes are maintained hierarchically starting from the leaf node to the root node recursively. The proposed framework will monitor the file system continuously and update individual folder descriptors (sub-indexes) stored on each node as the file system changes making the cached indexes resilient to any file changes. The framework is resilient of file or folder name changes. Further, the study explores avenues to build an offline semantic index that can be used by the clients to perform distribute file search without performing the search on the server itself. This is viable since the framework uses semantic languages to describe and build file descriptors that can easily integrate semantic indexing and hence this makes the index readily available for the Web.