Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.

Predictors of Nasal Obstruction: Quantification and Assessment Using Multiple Grading Scales

Objective. To evaluate the association between nasal obstruction and (1) demographic factors, (2) medical history, (3) physical tests, and (4) nasal exam findings. Study Design. CASE SERIES: Methods. Chart review at a tertiary medical center. Results. Two hundred-forty consecutive patients (52.1 ± 17.5 years old, with a Nasal Obstruction Symptom Evaluation (NOSE) score of 32.0 ± 24.1) were included. Demographic factors and inferior turbinate sizes were not associated with NOSE score or Nasal Obstruction Visual Analog Scale (NO-VAS). A significant association was found between higher NOSE score on univariate analysis and positive history of nasal trauma (p = 0.0136), allergic rhinitis (p < 0.0001), use of nasal steroids (p = 0.0108), higher grade of external nasal deformity (p = 0.0149), higher internal nasal septal deviation grade (p = 0.0024), and narrow internal nasal valve angle (p < 0.0001). Multivariate analysis identified the following as independent predictors of high NOSE score: NO-VAS: ≥50 (Odds Ratio (OR) = 17.6 (95% CI 5.83-61.6), p < 0.0001), external nasal deformity: grades 2-4 (OR = 4.63 (95% CI 1.14-19.9), p = 0.0339), and allergic rhinitis: yes (OR = 5.5 (95% CI 1.77-18.7), p = 0.0041). Conclusion. Allergic rhinitis, NO-VAS score ≥ 50, and external nasal deformity (grades 2-4) were statistically significant independent predictors of high NOSE scores on multivariate analysis. Inferior turbinate size was not associated with NOSE scores or NO-VAS.

Recurrent bacteremia with Streptococcus dysgalactiae: a case-control study.

Beta-hemolytic streptococci of groups C and G, designated as Streptococcus dysgalactiae (SD), can cause severe and recurring invasive infections. In this case-control study, we aimed to identify clinical and molecular risk factors for recurrence of SD bacteremia. Twenty-two cases of recurrent SD bacteremia were identified, and median time between episodes was 6 months. The most frequent clinical manifestation was skin and soft tissue infection. Cases and 92 controls, with single-episode SD bacteremia, showed similar demographics, had similar Charlson comorbidity scores, and had similar clinical presentations. Thirty-day fatality was 13% among controls, whereas none of 22 cases died. In 19 cases (86%), the same emm type was encountered in both episodes. SD isolates from recurrent episodes and from single episodes had a similar emm type distribution. Thus, we did not identify clinical risk factors for recurrences. The high proportion of identical emm types in recurrent episodes indicates a host-specific colonization.

Precision medicine in patients with allergic diseases: Airway diseases and atopic dermatitis-PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology

In this consensus document we summarize the current knowledge on major asthma, rhinitis, and atopic dermatitis endotypes under the auspices of the PRACTALL collaboration platform. PRACTALL is an initiative of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology aiming to harmonize the European and American approaches to best allergy practice and science. Precision medicine is of broad relevance for the management of asthma, rhinitis, and atopic dermatitis in the context of a better selection of treatment responders, risk prediction, and design of disease-modifying strategies. Progress has been made in profiling the type 2 immune response-driven asthma. The endotype driven approach for non-type 2 immune response asthma, rhinitis, and atopic dermatitis is lagging behind. Validation and qualification of biomarkers are needed to facilitate their translation into pathway-specific diagnostic tests. Wide consensus between academia, governmental regulators, and industry for further development and application of precision medicine in management of allergic diseases is of utmost importance. Improved knowledge of disease pathogenesis together with defining validated and qualified biomarkers are key approaches to precision medicine.

Effect of Cage-Induced Stereotypies on Measures of Affective State and Recurrent Perseveration in CD-1 and C57BL/6 Mice.

Stereotypies are abnormal repetitive behaviour patterns that are highly prevalent in laboratory mice and are thought to reflect impaired welfare. Thus, they are associated with impaired behavioural inhibition and may also reflect negative affective states. However, in mice the relationship between stereotypies and behavioural inhibition is inconclusive, and reliable measures of affective valence are lacking. Here we used an exploration based task to assess cognitive bias as a measure of affective valence and a two-choice guessing task to assess recurrent perseveration as a measure of impaired behavioural inhibition to test mice with different forms and expression levels of stereotypic behaviour. We trained 44 CD- 1 and 40 C57BL/6 female mice to discriminate between positively and negatively cued arms in a radial maze and tested their responses to previously inaccessible ambiguous arms. In CD-1 mice (i) mice with higher stereotypy levels displayed a negative cognitive bias and this was influenced by the form of stereotypy performed, (ii) negative cognitive bias was evident in back-flipping mice, and (iii) no such effect was found in mice displaying bar-mouthing or cage-top twirling. In C57BL/6 mice neither route-tracing nor bar-mouthing was associated with cognitive bias, indicating that in this strain these stereotypies may not reflect negative affective states. Conversely, while we found no relation of stereotypy to recurrent perseveration in CD-1 mice, C57BL/6 mice with higher levels of route-tracing, but not bar-mouthing made more repetitive responses in the guessing task. Our findings confirm previous research indicating that the implications of stereotypies for animal welfare may strongly depend on the species and strain of animal as well as on the form and expression level of the stereotypy. Furthermore, they indicate that variation in stereotypic behaviour may represent an important source of variation in many animal experiments.

Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.

The recurrent shortage of registered nurses : a new look at the issues.

Mode of access: Internet.

Recurrent maladies in scholarly writing.

Mode of access: Internet.

Obstruction marking manual /

"June 1, 1941."

The Grand Canyon of the Colorado; recurrent studies in impressions and appearances,

Mode of access: Internet.

The status of the airport and airway trust fund.

Mode of access: Internet.

Powered microdebridement treatment for recurrent respiratory papillomatosis

Laryngeal papillomatosis is a benign disease of lhe larynx caused by human papilloma virus. The disease has u variable clinical course and treatment focuses on debridement until clinical remission. The most common technique for removing the papilloma is by carbon dioxide laser ublution. Powered microdebridement. which is more familiar to endoscopic sinus surgeons, has been adapted for use in the larynx. We would like to report on this technique for removal of respiratory papillomas that we believe to be safer for both patients and staff. The cases of seven paediatric patients with recurrent respiratory papillomatosis treated with microdebridement of their papillomas have been retrospectively reviewed.

An evaluation of continuous positive airway pressure (CPAP) therapy in the treatment of hypernasality following traumatic brain injury - A report of 3 cases

Objective: To evaluate the effectiveness of continuous positive airway pressure (CPAP) therapy in the treatment of hypernasality following traumatic brain injury (17111). Design: An A-B-A experimental research design. Assessments were conducted prior to commencement of the program, midway, immediately posttreatment, and 1 month after completion of the CPAP therapy program. Participants: Three adults with dysarthria and moderate to severe hypernasality subsequent to TBI. Outcome Measures: Perceptual evaluation using the Frenchay Dysarthria Assessment, the Assessment of Intelligibility of Dysarthric Speech, and a speech sample analysis, and instrumental evaluation using the Nasometer. Results: Between assessment periods, varying degrees of improvement in hypernasality and sentence intelligibility were noted. At the 1-month post-CPAP assessment, all 3 participants demonstrated reduced nasalance values, and 2 exhibited increased sentence intelligibility. Conclusions: CPAP may be a valuable treatment of impaired velopharyngeal function in the TBI population.

Generalization by symbolic abstraction in cascaded recurrent networks

Generalization performance in recurrent neural networks is enhanced by cascading several networks. By discretizing abstractions induced in one network, other networks can operate on a coarse symbolic level with increased performance on sparse and structural prediction tasks. The level of systematicity exhibited by the cascade of recurrent networks is assessed on the basis of three language domains. (C) 2004 Elsevier B.V. All rights reserved.