A Nearest-Neighbor Approach to Indicative Web Summarization

Through their role of content proxy, in particular on search engine result pages, Web summaries play an essential part in the discovery of information and services on the Web. In their simplest form, Web summaries are snippets based on a user-query and are obtained by extracting from the content of Web pages. The focus of this work, however, is on indicative Web summarization, that is, on the generation of summaries describing the purpose, topics and functionalities of Web pages. In many scenarios — e.g. navigational queries or content-deprived pages — such summaries represent a valuable commodity to concisely describe Web pages while circumventing the need to produce snippets from inherently noisy, dynamic, and structurally complex content. Previous approaches have identified linking pages as a privileged source of indicative content from which Web summaries may be derived using traditional extractive methods. To be reliable, these approaches require sufficient anchortext redundancy, ultimately showing the limits of extractive algorithms for what is, fundamentally, an abstractive task. In contrast, we explore the viability of abstractive approaches and propose a nearest-neighbors summarization framework leveraging summaries of conceptually related (neighboring) Web pages. We examine the steps that can lead to the reuse and adaptation of existing summaries to previously unseen pages. Specifically, we evaluate two Text-to-Text transformations that cover the main types of operations applicable to neighbor summaries: (1) ranking, to identify neighbor summaries that best fit the target; (2) target adaptation, to adjust individual neighbor summaries to the target page based on neighborhood-specific template-slot models. For this last transformation, we report on an initial exploration of the use of slot-driven compression to adjust adapted summaries based on the confidence associated with token-level adaptation operations. Overall, this dissertation explores a new research avenue for indicative Web summarization and shows the potential value, given the diversity and complexity of the content of Web pages, of transferring, and, when necessary, of adapting, existing summary information between conceptually similar Web pages

An Ordinal Approach to Affective Computing

Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods. First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets. Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation. Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset. Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate

Genetic variability in Arbuscular Mycorrhizal Fungi : effect on gene transcription of "Oryza Sativa"

AbstractArbuscular Mycorrhizal Fungi (AMF) form obligate symbioses with the majority of land plants. These fungi influence the diversity and productivity of plants. AMF are unusual organisms, harbouring genetically different nuclei in a common cytoplasm (known as heterokaryosis). Genetic variability has been shown between AMF individuals coming from the same population. Recent findings showed that genetic exchange between genetically different AMF individuals was possible. Additionnaly, segregation was shown to occur at spore formation in AMF. These two processes were shown to increase genetic variability between AMF individuals.Because of the difficulty to study these organisms, almost nothing is known about the effect of intra-specific genetic variability in AMF on the plant transcriptome. The aim of this thesis was to bring insights into the effect of intra-specific genetic variability in AMF on plant gene transcription. We demonstrated that genetic exchange could influence expression of some symbiosis specific plant genes and the timing of the colonization of the fungi in plant roots. We also showed that segregation could have a large impact on plant gene transcription. Taken together, these results demonstrated that AMF intra-specific variability could profoundly affect the life of plants by altering various molecular pathways. Moreover, results obtained on rice open a field of research on AMF genetics in impromvment of growth in agricultural plants and should be taken into account for future experiments.RésuméLes champignons endomycorhiziens arbusculaires (CEA) forment une symbiose obligatoire avec la majorité des plantes sur terre. Ces champignons peuvent influencer la diversité et la productivité des plantes avec lesquelles ils forment la symbiose. Les CEA sont des organismes particuliers de part le fait qu'ils possèdent des noyaux génétiquement différents (appelés hétérocaryosis) dans un cytoplasme commun. Il a été montré qu'il existait de la variabilité génétique intra-specific chez les CEA. De plus, des études recentes ont montré que l'échange génétique chez les CEA était possible entre des individus génétiquement différents tout comme la ségrégation qui a aussi été démontrée au moment de la formation des nouvelles spores chez les CEA. Ces deux processus ont été montrés comme pouvant créer aussi de la variabilité génétique intra-specific.Du fait de la difficulté de travailler avec les CEA et à cause de la nouveauté de ces recherches, très peu de choses sont connues sur l'effet de l'échange génétique et de la ségrégation chez les CEA sur les plantes, et particulièrement au niveau moléculaire. Le but de cette thèse a été d'apporter la lumière sur les effets de la viariabilité génétique intra-specific chez les CEA, sur la transcription des gènes chez la plante. Nous avons pu montrer que l'échange génétique pouvait avoir des effets sur l'expression de gènes spécifiques à cette symbiose mais aussi pouvait influencer le timing de colonisation des racines de plantes par les CEA. Nous avons aussi montré que la ségrégation pouvait grandement influencer le transcriptome complet de la plante, et pas seulement les voies métaboliques spécifiques à la symbiose comme cela avait été montré auparavant.L'ensemble de ces résultats démontre l'importance de la variation intra-specific chez les CEA sur les plantes et leur implication sur leur cycle de vie en changeant l'expression de voies métaboliques. De plus, ces résultats obtenus sur le riz ouvrent un champ de recherches sur les plantes destinées à l'agriculture et devraient être pris en compte pour des expériences futures

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

Proteomics

Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and high-density antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein