Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages. (C) 2014 Acoustical Society of America.

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.

Language model combination and adaptation using weighted finite state transducers

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaptation may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences. ©2010 IEEE.

Assistance in poverty alleviation through improved aquatic resources management in Asia-Pacific: DoF/NACA-STREAM/FAO Workshop on livelihoods approaches and analysis. Yangon, Union of Myanmar, 11-15 May 2004

This is the report of the “DoF/NACA-STREAM/FAO Workshop on Livelihoods Approaches and Analysis” that was conducted in Yangon, Union of Myanmar from 11-15 May 2004. The purpose of the workshop was to develop and document mechanisms for training in livelihoods approaches and analysis, and to build national capacity to conduct livelihoods studies. The workshop in Yangon was the first STREAM event in Myanmar, with colleagues coming to participate from Yangon and many Divisions and States throughout the country. The workshop in Yangon was the fourth in a series, the first of which was held in Iloilo City, Philippines, in November 2003, the second in Ranchi, India, in February 2004, and the third in Vientiane, Lao PDR in March 2004. A subsequent workshop will take place in Yunnan, China. The objectives of the workshop were to: Understand issues of interest to people whose livelihoods include aquatic resources management, especially those with limited resources Build “(national) livelihoods teams” to do livelihoods analyses and training, and share their experiences with communities and other stakeholders Share understandings of livelihoods approaches and analysis using participatory methods Review current NACA-STREAM livelihoods analysis documentation, adapt and supplement, towards the drafting of a Guide for Livelihoods Analysis Experience the use of participatory tools for livelihoods analysis Plan activities for carrying out livelihoods analyses, and Consider how to build capacity in monitoring and evaluation (M&E) and “significant change”. (Pdf contains 56 pages).

Research Data Management — Activities and Challenges Report on the Knowledge Exchange Workshop

Workshop Research Data Management – Activities and Challenges 14-15 November 2011, Bonn The Knowledge Exchange initiative organised a workshop to highlight current activities and challenges with respect to research data management in the Knowledge Exchange partner countries and beyond. The workshop brought together experts from data centres, libraries, computational centres, funding organisations, publishing services and other institutions in the field of research and higher education who are working to improve research data management and encourage effective reuse of research data. A considerable part of the programme was dedicated to sharing perspectives from these communities, leading to the development of a roadmap of practical actions for the Knowledge Exchange initiative, partner organisations and other stakeholders to progress over the next two years. On the first day, principal investigators and project managers from a great variety of recent projects shared their insights on objectives and methods for improving data management ranging from discipline-specific to more general approaches. A series of short presentations of selected projects was followed by an extensive poster session that functioned as a “trade fair” of current trends and activities in the field of research data management. Moreover, the poster session offered ample network opportunities for participants. The second day was dedicated to intensive group discussions looking at a number of data management challenges. First the most important findings from the "Surfboard for 'Riding the Wave'" report were presented. This included the state of the art on activities and challenges in the field of research data management. The subgroups will concentrate on the following key themes: funding, incentives, training and technical infrastructure. These discussions culminated in the identification of practical recommendations for future cooperation on practical as well as on strategic levels that should be taken forward by the KE partner organisations and beyond. These activities aim to improve the sustainability of services and infrastructures at both national and international levels.

Knowledge Exchange RDM Training & Skills Workshop, 9-10 Feb 2016, London

In today’s changing research environment, RDM is important in all stages of research. The skills and know-how in RDM that researchers and research support staff need, should be nurtured all though their career. At the end of 2015, KE initiated a project to compare approaches in RDM training within the partnership’s five member countries. The project was structured around two strands of activity: In the last months of 2015 a survey was conducted to collect information on current practice around RDM training, in order to provide an overview of the RDM training landscape. In February 2016 a workshop was held to share successful approaches to RDM training and capacity building provided within institutions and by infrastructure. The report describes the outputs of both the analysis of the survey and the outcomes of the workshop. The document provides an evidence base and informed suggestions to help improve RDM training practices in KE partner countries and beyond.

A study of women's participation in fish food security and poverty alleviation in Lagos and Ogun States

Women, all over the world have contributed in various ways to the social, political and economic development of the Society. In fact, the World Resource Institute recognizes that "women have profound and preserve effect onn the well-being of their families, communities and local ecosystems" (Gamble and Well 1997:211). Women constitute more than 50 percent of the Agricultural (Fisheries being a sub sector), labour force. A study on Women in Fisheries showed that they participate in all aspects of the sector (capture, culture, processing, marketing research, training and Extension services). This paper reports the result of the study on women's contributions in the development of the Fisheries Industry particularly their roles in Fish Food Security, Poverty Alleviation and high rates of women's adoption of Fisheries technologies. The Case-study research methodology is used to study the "How" and "Why" Women's Contribution in Fish Food Security and Poverty Alleviation is at the index level recorded for the gender. The study made use of "Case Study" Research Instrument; documents, interview, artefacts, direct observation and archival records. The sampling techniques were purposive for research audiences and simple random for fisher-folks in the chosen locations. Analysed data showed among others that in Fisheries Research women occupy very important positions as Heads of Division/Section, Fisheries Liasion/Extension Officers and Fisheries Laboratory Chiefs etc. The paper also gave results of women production, processing, marketing and other services statistics; it also discusses the "whys" of women's low capacity in fisheries development of the nation and finally suggested ways in improving women's optimal capacity utilization in fisheries development

Initiatives for the improvement of continuous management training

[EN] Purpose. This work aims to present, from the company viewpoint, a structured account of management proposals and practices directed toward improving the intensity and effectiveness of continuous management training (CMT). Design/methodology/approach. The article takes as its main theoretical referents the Theory of Human Capital, the Resource-Based Vision and the contributions made via the new institutional economy with regard to the problems of information asymmetry between companies, employees and training providers and completes the proposals that derive from this theoretical approach. To do this, experience-based contributions are collected from a selection of company training and HR managers from twelve Basque companies characterised by their strong investment in management training. The methodology used was qualitative and obtained by different qualitative techniques: Focus Groups, Nominal Groups and the Delphi Method, which make up the so-called Hybrid Delphi. Findings and implications. The proposals are aimed at the main agents in training activity: training providers, associations and public agents engaged in management training and, particularly, companies themselves. The initiatives seek above all to increase training market transparency, to improve mutual commitments between companies and managers, and to link training and development with culture and strategic management, so that firms make optimal investment in management training. Originality/value. The methodology used is original, and the contributions are consistent with the theory, have a proven practical utility, and are presented in a hierarchy, which facilitates decision making.

A Delphi Process to Optimize Quality and Performance of Drug Evaluation in Neonates

Background: Neonatal trials remain difficult to conduct for several reasons: in particular the need for study sites to have an existing infrastructure in place, with trained investigators and validated quality procedures to ensure good clinical, laboratory practices and a respect for high ethical standards. The objective of this work was to identify the major criteria considered necessary for selecting neonatal intensive care units that are able to perform drug evaluations competently. Methodology and Main Findings: This Delphi process was conducted with an international multidisciplinary panel of 25 experts from 13 countries, selected to be part of two committees (a scientific committee and an expert committee), in order to validate criteria required to perform drug evaluation in neonates. Eighty six items were initially selected and classified under 7 headings: "NICUs description - Level of care'' (21), "Ability to perform drug trials: NICU organization and processes (15), "Research Experience'' (12), "Scientific competencies and area of expertise'' (8), "Quality Management'' (16), "Training and educational capacity'' (8) and "Public involvement'' (6). Sixty-one items were retained and headings were rearranged after the first round, 34 were selected after the second round. A third round was required to validate 13 additional items. The final set includes 47 items divided under 5 headings. Conclusion: A set of 47 relevant criteria will help to NICUs that want to implement, conduct or participate in drug trials within a neonatal network identify important issues to be aware of. Summary Points: 1) Neonatal trials remain difficult to conduct for several reasons: in particular the need for study sites to have an existing infrastructure in place, with trained investigators and validated quality procedures to ensure good clinical, laboratory practices and a respect for high ethical standards. 2) The present Delphi study was conducted with an international multidisciplinary panel of 25 experts from 13 countries and aims to identify the major criteria considered necessary for selecting neonatal intensive care units (NICUs) that are able to perform drug evaluations competently. 3) Of the 86 items initially selected and classified under 7 headings - "NICUs description - Level of care'' (21), "Ability to perform drug trials: NICU organization and processes (15), "Research Experience'' (12), "Scientific competencies and area of expertise'' (8), "Quality Management'' (16), "Training and educational capacity'' (8) and "Public involvement'' (6) - 47 items were selected following a three rounds Delphi process. 4) The present consensus will help NICUs to implement, conduct or participate in drug trials within a neonatal network.

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Fish demand and supply projections

It has been predicted that the global demand for fish for human consumption will increase by more than 50% over the next 15 years. The FAO has projected that the increase in supply will originate primarily from marine fisheries, aquaculture and to a lesser extent from inland fisheries, but with a commensurate price increase. However, there are constraints to increased production in both marine and inland fisheries, such as overfishing, overexploitation limited potential increase and environmental degradation due to industrialization. The author sees aquaculture as having the greatest potential for future expansion. Aquaculture practices vary depending on culture, environment, society amd sources of fish. Inputs are generally low-cost, ecologically efficient and the majority of aquaculture ventures are small-scale and family operated. In the future, advances in technology, genetic improvement of cultured species, improvement in nutrition, disease management, reproduction control and environmental management are expected along with opportunities for complimentary activities with agriculture, industrial and wastewater linkages. The main constraints to aquaculture are from reduced access to suitable land and good quality water due to pollution and habitat degradation. Aquaculture itself carries minimal potential for aquatic pollution. State participation in fisheries production has not proven to be the best way to promote the fisheries sector. The role of governments is increasingly seen as creating an environment for economic sectors to make an optimum contribution, through support in areas such as infrastructure, research, training and extension and a legal framework. The author feels that a holistic approach integrating the natural and social sciences is called for when fisheries policy is being examined.

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value.

The use of agreement measures and latent class models to assess the reliability of classifying thermally marked otoliths

Otolith thermal marking is an efficient method for mass marking hatchery-reared salmon and can be used to estimate the proportion of hatchery fish captured in a mixed-stock fishery. Accuracy of the thermal pattern classification depends on the prominence of the pattern, the methods used to prepare and view the patterns, and the training and experience of the personnel who determine the presence or absence of a particular pattern. Estimating accuracy rates is problematic when no secondary marking is available and no error-free standards exist. Agreement measures, such as kappa (κ), provide a relative measure of the reliability of the determinations when independent readings by two readers are available, but the magnitude of κ can be influenced by the proportion of marked fish. If a third reader is used or if two or more groups of paired readings are examined, latent class models can provide estimates of the error rates of each reader. Applications of κ and latent class models are illustrated by a program providing contribution estimates of hatchery-reared chum and sockeye salmon in Southeast Alaska.

MMI training for continuous phoneme recognition on the TIMIT database

This paper reports our experiences with a phoneme recognition system for the TIMIT database which uses multiple mixture continuous density monophone HMMs trained using MMI. A comprehensive set of results are presented comparing the ML and MMI training criteria for both diagonal and full covariance models. These results using simple monophone HMMs show clear performance gains achieved by MMI training, and are comparable to the best reported by others including those which use context-dependent models. In addition, the paper discusses a number of performance and implementation issues which are crucial to successful MMI training.

Factor analysis based VTS and JUD noise estimation and compensation

Model based compensation schemes are a powerful approach for noise robust speech recognition. Recently there have been a number of investigations into adaptive training, and estimating the noise models used for model adaptation. This paper examines the use of EM-based schemes for both canonical models and noise estimation, including discriminative adaptive training. One issue that arises when estimating the noise model is a mismatch between the noise estimation approximation and final model compensation scheme. This paper proposes FA-style compensation where this mismatch is eliminated, though at the expense of a sensitivity to the initial noise estimates. EM-based discriminative adaptive training is evaluated on in-car and Aurora4 tasks. FA-style compensation is then evaluated in an incremental mode on the in-car task. © 2011 IEEE.