NASTRAN Installation: Implementation Steps and Possible Problems Encountered

NASTRAN, from its inception, was designed to operate on several diverse computer system. It is currently installed and operating on the CDC 6600, the IBM 360, and the UNIVAC 1108. This paper discusses the steps found by CSC to be necessary in installing NASTRAN on a computer system and the possible obstacles that might be encountered in undertaking NASTRAN installation. Reference is made to actual problems that arose during installation on the above machines. With a knowledge of what has happened to date in setting up NASTRAN, the future user will be better able to cope with and understand the implications of installing NASTRAN on his computer

Recognition of Dialogue Acts in Multiparty Meetings using a Switching DBN

This paper is concerned with the automatic recognition of dialogue acts (DAs) in multiparty conversational speech. We present a joint generative model for DA recognition in which segmentation and classification of DAs are carried out in parallel. Our approach to DA recognition is based on a switching dynamic Bayesian network (DBN) architecture. This generative approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. The switching DBN coordinates the recognition process by integrating the component models. The factored language model, which is estimated from multiple conversational data corpora, is used in conjunction with additional task-specific language models. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. We have carried out experiments on the AMI corpus of multimodal meeting recordings, using both manually transcribed speech, and the output of an automatic speech recognizer, and using different configurations of the generative model. Our results indicate that the system performs well both on reference and fully automatic transcriptions. A further significant improvement in recognition accuracy is obtained by the application of the discriminative reranking approach based on conditional random fields

Automatic recognition of multiparty human interactions using dynamic Bayesian networks

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting actions describe the same process at a coarse level, highlighting interactions between different meeting participants and showing overall group intentions. A framework based on probabilistic graphical models such as dynamic Bayesian networks (DBNs) has been investigated for both tasks. Our first set of experiments is concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these lowlevel multimodal features to complex group behaviours proposing a multistreammodelling framework based on dynamic Bayesian networks. Later experiments are concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative approach based on a switching DBN for DA recognition in which segmentation and classification of DAs are carried out in parallel. This approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. The DBN based approach yielded significant improvements when applied both to the meeting action and the dialogue act recognition task. On both tasks, the DBN framework provided an effective factorisation of the state-space and a flexible infrastructure able to integrate a heterogeneous set of resources such as continuous and discrete multimodal features, and statistical language models. Although our experiments have been principally targeted on multiparty meetings; features, models, and methodologies developed in this thesis can be employed for a wide range of applications. Moreover both group meeting actions and DAs offer valuable insights about the current conversational context providing valuable cues and features for several related research areas such as speaker addressing and focus of attention modelling, automatic speech recognition and understanding, topic and decision detection

On Free-Riders and Sovereign Default: The Rise of Non-Traditional Bilateral Lenders and the Resulting Challenges to International Debt Renegotiations

Since the onset of the Global Financial Crisis in 2007, new cross-border lending to emerging and developing countries has been dominated by ‘non-traditional’ lenders. As a result, defaulting countries at present typically owe the plurality of their debt to these newly dominant lenders. These new lenders - often Chinese institutions - are not members of the Paris Club and primarily lend to low-income countries. This development has represented a serious challenge to the pre-existing Paris Club framework for dealing with sovereign debt defaults. Moreover, some observers have claimed that the terms demanded by these new lenders have been unusually unfavorable to low-income borrowers, fueling concerns about the use of ‘debt-trap diplomacy’. Based upon empirical analysis of newly-compiled comprehensive data, this thesis finds that while certain non-traditional bilateral creditors do engage in problematic lending practices - including contract provisions that forbid the use of the Paris Club or comparability of treatment - there currently exists little evidence to suggest that recent increases in Chinese lending have led to higher observed incidence of debt distress. Furthermore, there exists mixed evidence on whether Paris Club treatment results in improved macroeconomic outcomes for debtor countries. Notably, Paris Club debt restructuring does not appear to reduce the incidence of serial defaults when compared to sovereign debtors that do not receive such treatment

A General Pathway to Heterobimetallic Triple‐Decker Complexes

A systematic study on the reactivity of the triple-decker complex [(Cp'''Co)(2)(mu,eta(4):eta(4)-C7H8)] (A) (Cp'''=1,2,4-tritertbutyl-cyclopentadienyl) towards sandwich complexes containing cyclo-P-3, cyclo-P-4, and cyclo-P-5 ligands under mild conditions is presented. The heterobimetallic triple-decker sandwich complexes [(Cp*Fe)(Cp'''Co)(mu,eta(5):eta(4)-P-5)] (1) and [(Cp'''Co)(Cp'''Ni)(mu,eta(3):eta(3)-P-3)] (3) (Cp*=1,2,3,4,5-pentamethylcyclopentadienyl) were synthesized and fully characterized. In solution, these complexes exhibit a unique fluxional behavior, which was investigated by variable temperature NMR spectroscopy. The dynamic processes can be blocked by coordination to {W(CO)(5)} fragments, leading to the complexes [(Cp*Fe)(Cp'''Co)(mu(3),eta(5):eta(4):eta(1)-P-5){W(CO)(5)}] (2 a), [(Cp*Fe)(Cp'''Co)(mu(4),eta(5):eta(4):eta(1):eta(1)-P-5){(W(CO)(5))(2)}] (2 b), and [(Cp'''Co)(Cp'''Ni)(mu(3),eta(3):eta(2):eta(1)-P-3){W(CO)(5)}] (4), respectively. The thermolysis of 3 leads to the tetrahedrane complex [(Cp'''Ni)(2)(mu,eta(2):eta(2)-P-2)] (5). All compounds were fully characterized using single-crystal X-ray structure analysis, NMR spectroscopy, mass spectrometry, and elemental analysis

Acceptability and Perceived Time to Implement Interventions for Children with Adhd Moderated by General Education Teachers' Training in Adhd and Disability Law, and Eligibility for Disabling Conditions

Educational Psycholog

Multi-Stream Segmentation of Meetings

This paper investigates the automatic segmentation of meetings into a sequence of group actions or phases. Our work is based on a corpus of multiparty meetings collected in a meeting room instrumented with video cameras, lapel microphones and a microphone array. We have extracted a set of feature streams, in this case extracted from the audio data, based on speaker turns, prosody and a transcript of what was spoken. We have related these signals to the higher level semantic categories via a multistream statistical model based on dynamic Bayesian networks (DBNs). We report on a set of experiments in which different DBN architectures are compared, together with the different feature streams. The resultant system has an action error rate of 9%

Dynamic Bayesian Networks for Meeting Structuring

This paper is about the automatic structuring of multiparty meetings using audio information. We have used a corpus of 53 meetings, recorded using a microphone array and lapel microphones for each participant. The task was to segment meetings into a sequence of meeting actions, or phases. We have adopted a statistical approach using dynamic Bayesian networks (DBNs). Two DBN architectures were investigated: a two-level hidden Markov model (HMM) in which the acoustic observations were concatenated; and a multistream DBN in which two separate observation sequences were modelled. Additionally we have also explored the use of counter variables to constrain the number of action transitions. Experimental results indicate that the DBN architectures are an improvement over a simple baseline HMM, with the multistream DBN with counter constraints producing an action error rate of 6%

DBN based joint dialogue act recognition of multiparty meetings

Joint Dialogue Act segmentation and classification of the new AMI meeting corpus has been performed through an integrated framework based on a switching dynamic Bayesian network and a set of continuous features and language models. The recognition process is based on a dictionary of 15 DA classes tailored for group decision-making. Experimental results show that a novel interpolated Factored Language Model results in a low error rate on the automatic segmentation task, and thus good recognition results can be achieved on AMI multiparty conversational speech