Quantum Algorithms with Fixed Points: The Case of Database Search

The standard quantum search algorithm lacks a feature, enjoyed by many classical algorithms, of having a fixed-point, i.e. a monotonic convergence towards the solution. Here we present two variations of the quantum search algorithm, which get around this limitation. The first replaces selective inversions in the algorithm by selective phase shifts of $\frac{\pi}{3}$. The second controls the selective inversion operations using two ancilla qubits, and irreversible measurement operations on the ancilla qubits drive the starting state towards the target state. Using $q$ oracle queries, these variations reduce the probability of finding a non-target state from $\epsilon$ to $\epsilon^{2q+1}$, which is asymptotically optimal. Similar ideas can lead to robust quantum algorithms, and provide conceptually new schemes for error correction.

Creation of a Huge Annotated Database for Tamil and Kannada OHR

This paper describes the efforts at MILE lab, IISc, to create a 100,000-word database each in Kannada and Tamil for the design and development of Online Handwritten Recognition. It has been collected from over 600 users in order to capture the variations in writing style. We describe features of the scripts and how the number of symbols were reduced to be able to effectively train the data for recognition. The list of words include all the characters, Kannada and Indo-Arabic numerals, punctuations and other symbols. A semi-automated tool for the annotation of data from stroke to word level is used. It segments each word into stroke groups and also acts as a validation mechanism for segmentation. The tool displays the stroke, stroke groups and aksharas of a word and hence can be used to study the various styles of writing, delayed strokes and for assigning quality tags to the words. The tool is currently being used for annotating Tamil and Kannada data. The output is stored in a standard XML format.

Kannada WordNet - a lexical database

This paper presents the preliminary analysis of Kannada WordNet and the set of relevant computational tools. Although the design has been inspired by the famous English WordNet, and to certain extent, by the Hindi WordNet, the unique features of Kannada WordNet are graded antonyms and meronymy relationships, nominal as well as verbal compoundings, complex verb constructions and efficient underlying database design (designed to handle storage and display of Kannada unicode characters). Kannada WordNet would not only add to the sparse collection of machine-readable Kannada dictionaries, but also will give new insights into the Kannada vocabulary. It provides sufficient interface for applications involved in Kannada machine translation, spell checker and semantic analyser.

DoSA: Database of Structural Alignments

Protein structure alignment is a crucial step in protein structure-function analysis. Despite the advances in protein structure alignment algorithms, some of the local conformationally similar regions are mislabeled as structurally variable regions (SVRs). These regions are not well superimposed because of differences in their spatial orientations. The Database of Structural Alignments (DoSA) addresses this gap in identification of local structural similarities obscured in global protein structural alignments by realigning SVRs using an algorithm based on protein blocks. A set of protein blocks is a structural alphabet that abstracts protein structures into 16 unique local structural motifs. DoSA provides unique information about 159 780 conformationally similar and 56 140 conformationally dissimilar SVRs in 74 705 pairwise structural alignments of homologous proteins. The information provided on conformationally similar and dissimilar SVRs can be helpful to model loop regions. It is also conceivable that conformationally similar SVRs with conserved residues could potentially contribute toward functional integrity of homologues, and hence identifying such SVRs could be helpful in understanding the structural basis of protein function.

Real-time magnetic resonance imaging and electromagnetic articulography database for speech production research (TC)

USC-TIMIT is an extensive database of multimodal speech production data, developed to complement existing resources available to the speech research community and with the intention of being continuously refined and augmented. The database currently includes real-time magnetic resonance imaging data from five male and five female speakers of American English. Electromagnetic articulography data have also been presently collected from four of these speakers. The two modalities were recorded in two independent sessions while the subjects produced the same 460 sentence corpus used previously in the MOCHA-TIMIT database. In both cases the audio signal was recorded and synchronized with the articulatory data. The database and companion software are freely available to the research community. (C) 2014 Acoustical Society of America.

Haemophilus influenzae Genome Database (HIGDB): A single point web resource for Haemophilus influenzae

Background: Haemophilus influenzae (H. Influenzae) is the causative agent of pneumonia, bacteraemia and meningitis. The organism is responsible for large number of deaths in both developed and developing countries. Even-though the first bacterial genome to be sequenced was that of H. Influenzae, there is no exclusive database dedicated for H. Influenzae. This prompted us to develop the Haemophilus influenzae Genome Database (HIGDB). Methods: All data of HIGDB are stored and managed in MySQL database. The HIGDB is hosted on Solaris server and developed using PERL modules. Ajax and JavaScript are used for the interface development. Results: The HIGDB contains detailed information on 42,741 proteins, 18,077 genes including 10 whole genome sequences and also 284 three dimensional structures of proteins of H. influenzae. In addition, the database provides ``Motif search'' and ``GBrowse''. The HIGDB is freely accessible through the URL:http://bioserverl.physicslisc.ernetin/HIGDB/. Discussion: The HIGDB will be a single point access for bacteriological, clinical, genomic and proteomic information of H. influenzae. The database can also be used to identify DNA motifs within H. influenzae genomes and to compare gene or protein sequences of a particular strain with other strains of H. influenzae. (C) 2014 Elsevier Ltd. All rights reserved.

Streptococcus pneumoniae Genome Database (SPGDB): A database for strain specific comparative analysis of Streptococcus pneumoniae genes and proteins

Streptococcus pneumoniae causes pneumonia, septicemia and meningitis. S. pneumoniae is responsible for significant mortality both in children and in the elderly. In recent years, the whole genome sequencing of various S. pneumoniae strains have increased manifold and there is an urgent need to provide organism specific annotations to the scientific community. This prompted us to develop the Streptococcus pneumoniae Genome Database (SPGDB) to integrate and analyze the completely sequenced and available S. pneumoniae genome sequences. Further, links to several tools are provided to compare the pool of gene and protein sequences, and proteins structure across different strains of S. pneumoniae. SPGDB aids in the analysis of phenotypic variations as well as to perform extensive genomics and evolutionary studies with reference to S. pneumoniae. (C) 2014 Elsevier Inc. All rights reserved.

NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection

NrichD ( ext-link-type=''uri'' xlink:href=''http://proline.biochem.iisc.ernet.in/NRICHD/'' xlink:type=''simple''>http://proline.biochem.iisc.ernet.in/NRICHD/)< /named-content> is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural `intermediately related sequences' is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been `enriched' with these artificial intermediary sequences. NrichD database currently contains 3 611 010 artificial sequences that have been generated between 27 882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

Monitoring hydrilla using two RAPD procedures and the Nonindigenous Aquatic Species database

Hydrilla ( Hydrilla verticillata (L.f.) Royle), an invasive aquatic weed, continues to spread to new regions in the United States. Two biotypes, one a female dioecious and the other monoecious have been identified. Management of the spread of hydrilla requires understanding the mechanisms of introduction and transport, an ability to map and make available information on distribution, and tools to distinguish the known U.S. biotypes as well as potential new introductions. Review of the literature and discussions with aquatic scientists and resource managers point to the aquarium and water garden plant trades as the primary past mechanism for the regional dispersal of hydrilla while local dispersal is primarily carried out by other mechanisms such as boat traffic, intentional introductions, and waterfowl. The Nonindigenous Aquatic Species (NAS) database is presented as a tool for assembling, geo-referencing, and making available information on the distribution of hydrilla. A map of the current range of dioecious and monoecious hydrilla by drainage is presented. Four hydrilla samples, taken from three discrete, non-contiguous regions (Pennsylvania, Connecticut, and Washington State) were examined using two RAPD assays. The first, generated using primer Operon G17, and capable of distinguishing the dioecious and monoecious U.S. biotypes, indicated all four samples were of the monoecious biotype. Results of the second assay using the Stoffel fragment and 5 primers, produced 111 markers, indicated that these samples do not represent new foreign introductions. The differences in the monoecious and dioecious growth habits and management are discussed.

Supporting the planning of a community fisheries monitoring and evaluation database: a collaborative project undertaken by CFDO, FLD, STREAM, VSO and AYAD

Cambodia is one of the poorest countries in the world; much of its population live in rural areas and many live below the local poverty line. The management of common property aquatic resources is of over-riding importance to food security and sustainable rural development in Cambodia. Aquatic resources are utilized principally by subsistence fishers and the landless, for whom aquatic resource use is an important livelihood activity. Subsistence fishers access mainly the rivers, lakes and inundated forests in Tonle Sap provinces, the lower Mekong and Bassac regions and the upper part of the Mekong. Freshwater capture fisheries probably contribute more to national food security and the national economy in Cambodia than in any other country in the world. (PDF contains 52 pages)

Photographic database development of Nigerian fisheries: a complementary source of information documentation

The paper traces the history of the different documentation media used for information dissemination. Such early media are clay tablets, papyrus, and vellum or parchment codex. The invention of printing however revolutionized the information industry, enabling the production of books in multiple copies. Photography came into documentation mainly to preserve rare materials and those that easily deteriorate. This paper reports the efforts of National Institute for Freshwater Fisheries Research (NIFFR) and Kainji Lake Fisheries Promotion Project (KLFPPP), Nigeria, to develop an Object Oriented Database (OOD) using photographs. The photographs are stored in digitized form on commercial computers, using the program ACDSee 32 for classification, description and retrieval. Specifically the paper focuses on photographs in fisheries as visual communication and expression. Presently, the database contains photo documents about the following aspects of Kainji Lake fisheries: fishing gears and crafts, fish preservation methods

Brand image and lexicographic analysis: an application to shopping malls

[EN] The concept of image in its different aspects is very important in today s society as well as in the business management field. Some authors reports that most of the studies that measure image do not take into account neither previous theoretical and conceptual models nor other possible empirical evidence alternatives. Given this need, a research regarding the concept of brand image applied to shopping malls was conducted based on the conceptual model of the consumer cognitive response in order to empirically explore and contrast it. For this reason, a survey was applied to 420 consumers in five shopping malls in Bogotá, achieving a database of 3.749 cases. The results show attribute-shopping mall associations expressed in unique, differentiated, and notorious vocabulary obtained applying lexicometric and multivariate analysis techniques. Attribute-shopping mall associations such as spacious , good location , good variety of stores , and the existence of movie theaters . Finally, this research aims to potentially improve the management of shopping malls and increase their attractiveness and customer loyalty by applying the development of service quality systems, integral communication, segmentation, and positioning.