Recommandation conversationnelle : écoutez avant de parlez

In a world of globalization, where offers continues to grow, the ability to direct people to their specific need is essential. After being key differentiating factors for Netflix and Amazon, Recommender Systems in general are no where near a downfall. Still, one downside of the basic recommender systems is that they are mainly based on indirect feedback (our behaviour, mainly form the past) as opposed to explicit demand at a specific time. Recent development in machine learning brings us closer to the possibility for a user to express it’s specific needs in natural language and get a machine generated reply. This is what Conversational Recommendation is about. Conversational recommendation encapsulates several machine learning sub-tasks. In this work, we focus our study on methods for the task of item (in our case, movie) recommendation from conversation. To explore this setting, we use, adapt and extend state of the art transformer based neural language modeling techniques to the task of recommendation from dialogue. We study the performance of different methods using the ReDial dataset [24], a conversational- recommendation dataset for movies. We also make use of a knowledge base of movies and measure their ability to improve performance for cold-start users, items, and/or both. This master thesis is divided as follows. First, we review all the basics concepts and the previous work necessary to to this lecture. When then dive deep into the specifics our data management, the different models we tested, the set-up of our experiments and the results we got. Follows the original a paper we submitted at RecSys 2020 Conference. Note that their is a minor inconsistency since throughout the thesis, we use v to represent items but in the paper, we used i. Overall, we find that pre-trained transformer models outperform baselines even if the baselines have access to the user preferences manually extracted from their utterances.Dans un monde de mondialisation, où les offres continuent de croître, la capacité de référer les gens vers leurs besoins spécifiques est essentiel. Après avoir été un facteur de différenciation clé pour Netflix et Amazon, les systèmes de recommandation en général ne sont pas près de disparaître. Néanmoins, l’un des leurs inconvénients est qu’ils sont principalement basés sur des informations indirects (notre comportement, principalement du passé) par opposition à une demande explicite à un moment donné. Le développement récent de l’apprentissage automatique nous rapproche de la possibilité d’exprimer nos besoins spécifiques en langage naturel et d’obtenir une réponse générée par la machine. C’est ce en quoi consiste la recommandation conversationnelle. La recommandation conversationnelle englobe plusieurs sous-tâches d’apprentissage automatique. Dans ce travail, nous concentrons notre étude sur les méthodes entourant la tâche de recommandation d’item (dans notre cas, un film) à partir d’un dialogue. Pour explorer cette avenue, nous adaptons et étendons les techniques de modélisation du langage basées sur les transformeurs à la tâche de recommandation à partir du dialogue. Nous étudions les performances de différentes méthodes à l’aide de l’ensemble de données ReDial [24], un ensemble de données de recommandation conversationnelle pour les films. Nous utilisons également une base de connaissances de films et mesurons sa capacité à améliorer les performances lorsque peu d’information sur les utilisateurs/éléments est disponible. Ce mémoire par article est divisé comme suit. Tout d’abord, nous passons en revue tous les concepts de base et les travaux antérieurs nécessaires à cette lecture. Ensuite, nous élaborons les spécificités de notre gestion des données, les différents modèles que nous avons testés, la mise en place de nos expériences et les résultats que nous avons obtenus. Suit l’article original que nous avons soumis à la conférence RecSys 2020. Notez qu’il y a une incohérence mineure puisque tout au long du mémoire, nous utilisons v pour représenter les éléments mais dans l’article, nous avons utilisé i. Dans l’ensemble, nous constatons que les modèles de transformeurs pré-entraînés surpassent les modèles de bases même si les modèles de base ont accès aux préférences utilisateur extraites manuellement des dialogues

A Bound Vortex Surface Impingement Method for Adhered Dust Particle Removal

Methods of dust mitigation in Martian and Lunar environments are an increasingly active area of research within the uid dynamic and aerospace community. Martian and Lunar environments produce electrically charged particles, which easily adhere to exposed surfaces. Adhered regolith particles can interfere with human comfort and mechanical functionality. In this work we investigate the potential to enhance particle removal through bound vortex surface impingement. A bound vortex ow condition is created using a specialized nozzle con guration where a combination of positive pressure inlets and a central negative pressure outlet are used to control ow dynamics. Using the techniques of computational uid dynamic simulations and physical experiments, the e ectiveness of vortex-induced ow conditions is evaluated. A parametric study is performed to explore bound vortex formation over a range of nozzle con gurations and pressure conditions. Visualization of pathlines and measurement of shear stress under various geometric and pressure conditions provide insight into ow characteristics. It is found that an optimal range of key geometric and pressure parameters exist in the creation of bound vortex ow and such ow enhances particle transportation and removal. A subset of optimal computational con gurations is recreated experimentally to support the existence of bound vortex ow and its positive impact on the removal of particles

Étude sur l'abordabilité de l'écoconstruction : le cas de la maison ERE 132

Les secteurs de l’habitation et de la construction sont responsables d’une importante proportion des émissions de gaz à effet de serre et des pollutions à travers le monde. L’écoconstruction, de par ses multiples approches et interprétations, est une méthode de construction qui propose de réduire les impacts négatifs de ce secteur, d’optimiser l’efficacité énergétique des habitations tout en participant à rehausser la qualité de vie des résidents et à un développement durable de la société. Sujette à un fort engouement dans les dernières années, l’écoconstruction demeure cependant marginale dans le parc résidentiel du pays, principalement à cause des réticences par rapport à un investissement monétaire initial élevé. Le projet ÉcoRésidence de l’Est 132 tente de démonter les idées préconçues freinant le développement de l’écoconstruction et d’offrir une option d’écoconstruction abordable pour le marché de masse de l’habitation pour réduire les impacts du secteur de l’habitation sur notre environnement. Par contre, la réalité du projet ÉcoRésidence de l’Est 132, prototype de maison unifamiliale, et son rôle de vitrine pour diverses pratiques et technologies en écoconstruction font en sorte qu’il est très difficile d’en estimer un prix applicable pour un consommateur privé. De ce fait, une réplique de la maison ÉcoRésidence de l’Est 132 adaptée à un consommateur privé a été imaginée pour pouvoir, à la suite d’une évaluation des coûts, en estimer un coût de construction. Sur la base de discussions menées avec plusieurs experts et d’une abondante littérature sur le sujet, le coût estimé pour la maison adaptée est de 278 439 $. Ce coût positionne la maison adaptée dans une gamme de produits au prix un peu plus élevé que la moyenne, ce qui cadre avec l’objectif initial du projet qui visait cette gamme de prix. En effet, les caractéristiques de la maison et ses performances environnementales accrues engendrent nécessairement des surcoûts par rapport à une offre semblable conventionnelle. Cela dit, différentes méthodes et stratégies de construction employées dans la maison ÉcoRésidence de l’Est 132 mitigent de manière importante ces surcoûts et permettent d’offrir des performances environnementales et énergétiques intéressantes à un coût qui ne s’écarte pas de manière importante du marché de l’habitation de masse. Ainsi, la notion d’abordabilité de la maison est intrinsèquement rattachée à l’ouverture du consommateur à mettre l’accent sur la performance environnementale et la qualité de l’environnement intérieur plutôt que sur la superficie habitable et les éléments architecturaux flamboyants. Pour le consommateur ouvert à ces traits, l’offre de la maison ÉcoRésidence de l’Est 132 est abordable et réaliste, ouvrant la porte à une éventuelle pénétration de l’écoconstruction dans un marché de masse et à une démocratisation de la performance des bâtiments

Aeroacoustic Duster

The invention disclosed herein provides for high particle removal rate and/or heat transfer from surfaces. The device removes particulate matter from a surface using a bounded vortex generated over the surface, with suction in the vortex center and jets for blowing air along the periphery. The jets are tilted in the tangential direction to induce vortex motion within the suction region. The vortex is said to be bounded because streamlines originating in the downward jets are entrained back into the central vortex

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.ABCFS: The Australian Breast Cancer Family Registry (ABCFR; 1992-1995) was supported by the Australian NHMRC, the New South Wales Cancer Council, and the Victorian Health Promotion Foundation (Australia), and by grant UM1CA164920 from the USA National Cancer Institute. The Genetic Epidemiology Laboratory at the University of Melbourne has also received generous support from Mr B. Hovey and Dr and Mrs R.W. Brown to whom we are most grateful. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Breast Cancer Susceptibility Variants and Mammographic Density 5 Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. BBCC: This study was funded in part by the ELAN-Program of the University Hospital Erlangen; Katharina Heusinger was funded by the ELAN program of the University Hospital Erlangen. BBCC was supported in part by the ELAN program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg. EPIC-Norfolk: This study was funded by research programme grant funding from Cancer Research UK and the Medical Research Council with additional support from the Stroke Association, British Heart Foundation, Department of Health, Research into Ageing and Academy of Medical Sciences. MCBCS: This study was supported by Public Health Service Grants P50 CA 116201, R01 CA 128931, R01 CA 128931-S01, R01 CA 122340, CCSG P30 CA15083, from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. MCCS: Melissa C. Southey is a National Health and Medical Research Council Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. The study was supported by the Cancer Council of Victoria and by the Victorian Breast Cancer Research Consortium. MEC: National Cancer Institute: R37CA054281, R01CA063464, R01CA085265, R25CA090956, R01CA132839. MMHS: This work was supported by grants from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. (R01 CA128931, R01 CA 128931-S01, R01 CA97396, P50 CA116201, and Cancer Center Support Grant P30 CA15083). Breast Cancer Susceptibility Variants and Mammographic Density 6 NBCS: This study has been supported with grants from Norwegian Research Council (#183621/S10 and #175240/S10), The Norwegian Cancer Society (PK80108002, PK60287003), and The Radium Hospital Foundation as well as S-02036 from South Eastern Norway Regional Health Authority. NHS: This study was supported by Public Health Service Grants CA131332, CA087969, CA089393, CA049449, CA98233, CA128931, CA 116201, CA 122340 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. OOA study was supported by CA122822 and X01 HG005954 from the NIH; Breast Cancer Research Fund; Elizabeth C. Crosby Research Award, Gladys E. Davis Endowed Fund, and the Office of the Vice President for Research at the University of Michigan. Genotyping services for the OOA study were provided by the Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096. OFBCR: This work was supported by grant UM1 CA164920 from the USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. SASBAC: The SASBAC study was supported by Märit and Hans Rausing’s Initiative against Breast Cancer, National Institutes of Health, Susan Komen Foundation and Agency for Science, Technology and Research of Singapore (A*STAR). Breast Cancer Susceptibility Variants and Mammographic Density 7 SIBS: SIBS was supported by program grant C1287/A10118 and project grants from Cancer Research UK (grant numbers C1287/8459). COGS grant: Collaborative Oncological Gene-environment Study (COGS) that enabled the genotyping for this study. Funding for the BCAC component is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692), the National Institutes of Health (CA128978) and Post- Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAMEON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund.This is the author accepted manuscript. The final version is available via American Association for Cancer Research at http://cancerres.aacrjournals.org/content/early/2015/04/10/0008-5472.CAN-14-2012.abstract

Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions

Genes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene-environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10(−07)), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m(2) (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m(2) or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10(−05)). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci

Wound dressings for a proteolytic-rich environment

Wound dressings have experienced continuous and significant changes over the years based on the knowledge of the biochemical events associated with chronic wounds. The development goes from natural materials used to just cover and conceal the wound to interactive materials that can facilitate the healing process, addressing specific issues in non-healing wounds. These new types of dressings often relate with the proteolytic wound environment and the bacteria load to enhance the healing. Recently, the wound dressing research is focusing on the replacement of synthetic polymers by natural protein materials to delivery bioactive agents to the wounds. This article provides an overview on the novel protein-based wound dressings such as silk fibroin keratin and elastin. The improved properties of these dressings, like the release of antibiotics and growth factors, are discussed. The different types of wounds and the effective parameters of healing process will be reviewed

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER(+) or ER(-)) and human ERBB2 (HER2(+) or HER2(-)) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER(-) tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.352

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci