Suicidal behavior among elementary school students and current needs in prevention practices: A survey of Virginia school counselors

Most of the research on suicidal behavior has focused on the middle and high school level, and an extensive review of the literature shows that more information is needed on the current needs and prevention practices at the elementary school level. In Virginia, school psychologists rated school counselors the top professional in elementary schools to lead suicide intervention and prevention efforts. Due to this, the current study examined 161 Virginia school counselors’ responses to an online survey to further explore intervention and prevention efforts among school professionals. Both school counselors and school psychologists noted that receiving additional training and having established crisis plans are important in regards to suicidal behavior. While both professionals agree that suicide at the elementary level is something that should be taken seriously, the results found that open communication and discussion among professionals is an area that could be improved

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201

Supersonic Flow past a Family of Blunt Axisymmetric Bodies

Some 100 numerical computations have been carried out for unyawed bodies of revolution with detached bow waves. The gas is assumed perfect with gamma = 5/3, 7/5, or 1. Free-stream Mach numbers are taken as 1.2, 1.5, 2, 3, 4, 6, 10, and infinity. The results are summarized with emphasis on the sphere and paraboloid

Policy committee for adaptation in multi-domain spoken dialogue systems

Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487

Policy committee for adaptation in multi-domain spoken dialogue systems

Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.The research leading to this work was funded by the EPSRC grant EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740487

Rocket Engine Plume Diagnostics at Stennis Space Center

The Stennis Space Center has been at the forefront of development and application of exhaust plume spectroscopy to rocket engine health monitoring since 1989. Various spectroscopic techniques, such as emission, absorption, FTIR, LIF, and CARS, have been considered for application at the engine test stands. By far the most successful technology h a been exhaust plume emission spectroscopy. In particular, its application to the Space Shuttle Main Engine (SSME) ground test health monitoring has been invaluable in various engine testing and development activities at SSC since 1989. On several occasions, plume diagnostic methods have successfully detected a problem with one or more components of an engine long before any other sensor indicated a problem. More often, they provide corroboration for a failure mode, if any occurred during an engine test. This paper gives a brief overview of our instrumentation and computational systems for rocket engine plume diagnostics at SSC. Some examples of successful application of exhaust plume spectroscopy (emission as well as absorption) to the SSME testing are presented. Our on-going plume diagnostics technology development projects and future requirements are discussed

Multi-domain neural network language generation for spoken dialogue systems

Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.Toshiba Research Europe Ltd.This is the accepted manuscript. It is currently embargoed pending publication

Dialogue manager domain adaptation using Gaussian process reinforcement learning

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”

Sphingosine kinase 2 inhibition synergises with bortezomib to target myeloma by enhancing endoplasmic reticulum stress

Published: April 14, 2017The proteasome inhibitor bortezomib has proven to be invaluable in the treatment of myeloma. By exploiting the inherent high immunoglobulin protein production of malignant plasma cells, bortezomib induces endoplasmic reticulum (ER) stress and the unfolded protein response (UPR), resulting in myeloma cell death. In most cases, however, the disease remains incurable highlighting the need for new therapeutic targets. Sphingosine kinase 2 (SK2) has been proposed as one such therapeutic target for myeloma. Our observations that bortezomib and SK2 inhibitors independently elicited induction of ER stress and the UPR prompted us to examine potential synergy between these agents in myeloma. Targeting SK2 synergistically contributed to ER stress and UPR activation induced by bortezomib, as evidenced by activation of the IRE1 pathway and stress kinases JNK and p38MAPK, thereby resulting in potent synergistic myeloma apoptosis in vitro. The combination of bortezomib and SK2 inhibition also exhibited strong in vivo synergy and favourable effects on bone disease. Therefore, our studies suggest that perturbations of sphingolipid signalling can synergistically enhance the effects seen with proteasome inhibition, highlighting the potential for the combination of these two modes of increasing ER stress to be formally evaluated in clinical trials for the treatment of myeloma patients.Craig T. Wallington-Beddoe, Melissa K. Bennett, Kate Vandyke, Lorena Davies, Julia R. Zebol, Paul A.B. Moretti, Melissa R. Pitman, Duncan R. Hewett, Andrew C.W. Zannettino and Stuart M. Pitso

Discriminating between Cognitive and Supportive Group Therapies for Chronic Mental Illness

This descriptive and comparative study employed a Q-sort process to describe common factors of therapy in two group therapies for inpatients with chronic mental illness. While pharmacological treatments for chronic mental illness are prominent, there is growing evidence that cognitive therapy is also efficacious. Groups examined were part of a larger study comparing the added benefits of cognitive versus supportive group therapy to the treatment milieu. In general, items described the therapist’s attitudes and behaviors, the participants’ attitudes and behaviors, or the group interactions. Results present items that were most and least characteristic of each therapy and items that discriminate between the two modalities. Therapists in both groups demonstrated good therapy skills. However, the cognitive group was described as being more motivated and active than the supportive group, indicating that the groups differed in terms of common as well as specific factors of treatment