1,721,016 research outputs found
Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence
Full text linkIn contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. This option is useful in cases of uncertainty, where the learner does not feel confident enough on the entire label set. Adopting a decision-theoretic perspective, we propose a formal framework of MLC with partial abstention, which builds on two main building blocks: First, the extension of underlying MLC loss functions so as to accommodate abstention in a proper way, and second the problem of optimal prediction, that is, finding the Bayes-optimal prediction minimizing this generalized loss in expectation. It is well known that different (generalized) loss functions may have different risk-minimizing predictions, and finding the Bayes predictor typically comes down to solving a computationally complexity optimization problem. In the most general case, given a prediction of the (conditional) joint distribution of possible labelings, the minimizer of the expected loss needs to be found over a number of candidates which is exponential in the number of class labels. We elaborate on properties of risk minimizers for several commonly used (generalized) MLC loss functions, show them to have a specific structure, and leverage this structure to devise efficient methods for computing Bayes predictors. Experimentally, we show MLC with partial abstention to be effective in the sense of reducing loss when being allowed to abstain
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
ELECTROSPRAY METHOD: PROCESSING PARAMETERS INFLUENCE ON MORPHOLOGY AND SIZE OF PCL PARTICLES
Full text linkThe polymeric microparticles using electrospray technique have been used effectively as the drug carrier, whereby controlled release of drug. The electrosprayed particles morphology and size dictated the degradation of polymer matrix, therefore they influenced the release profile from drug loaded microparticles. The effects of electrospray processing parameters (flow rate, applied voltage and distance from the tip of needle to collector) on morphology and size of polycaprolactone (PCL) particles were investigated by scanning electron microscopy (SEM) and ImageJ software. In this research, the PCL solution was prepared by dissolving PCL in Dichloromethane at 4.5 % solution. In addition, processing parameters such as the flow rate (0.5 mL/h, 1 mL/h, 1.5 mL/h, 2 mL/h and 4 mL/h), the applied voltage (15 kV, 18 kV and 24 kV) and the collecting distance (15 cm, 20 cm, and 25 cm) were changed to examine the effects of them on size and morphology of PCL particles. The results indicated that at the suitable electrospraying parameters (18 kV, 1.5 mL/h, 20–25 cm), microparticles have obtained the uniform and stable morphology while at higher flow rate (2 mL/h and 4 mL/h), the particles were deformed and had bigger size.
Imprécision en apprentissage statistique
No full textWe have focused on imprecision modeling in machine learning problems, where available data or knowledge suffers from important imperfections. In this work, imperfect data refers to situations where either some features or the labels are imperfectly known, that is can be specified by sets of possible values rather than precise ones. Learning from partial data are commonly encountered in various fields, such as bio-statistics, agronomy, or economy. These data can be generated by coarse or censored measurements, or can be obtained from expert opinions. On the other hand, imperfect knowledge refers to the situations where data are precisely specified, however, there are classes, that cannot be distinguished due to a lack of knowledge (also known as epistemic uncertainty) or due to a high uncertainty (also known as aleatoric uncertainty). Considering the problem of learning from partially specified data, we highlight the potential issues of dealing with multiple optimal classes and multiple optimalmodels in the inference and learning step, respectively. We have proposed active learning approaches to reduce the imprecision in these situations. Yet, the distinction epistemic/aleatoric uncertainty has been well-studied in the literature. To facilitate subsequent machine learning applications, we have developed practical procedures to estimate these degrees for popular classifiers. In particular, we have explored the use of this distinction in the contexts of active learning and cautious inferences.Nous nous sommes concentrés sur la modélisation et l'imprécision dans les problèmes d'apprentissage automatique, où les données ou connaissances disponibles souffrent d'imperfections importantes. Dans ce travail, les données imparfaites font référence à des situations où certaines caractéristiques ou les étiquettes sont imparfaitement connues, c'est-à-dire peuvent être spécifiées par des ensembles de valeurs possibles plutôt que par des valeurs précises. Les apprentissages à partir de données partielles sont couramment rencontrés dans divers domaines, tels que la biostatistique, l'agronomie ou l'économie. Ces données peuvent être générées par des mesures grossières ou censurées, ou peuvent être obtenues à partir d'avis d'experts. D'autre part, la connaissance imparfaite fait référence aux situations où les données sont spécifiées avec précision, cependant, il existe des classes qui ne peuvent pas être distinguées en raison d'un manque de connaissances (également appelée incertitude épistémique) ou en raison d'une forte incertitude (également appelée incertitude aléatoire). Considérant le problème de l'apprentissage à partir de données partiellement spécifiées, nous soulignons les problèmes potentiels liés au traitement de plusieurs classes optimales et de plusieurs modèles optimaux dans l'étape d'inférence et d'apprentissage, respectivement. Nous avons proposé des approches d'apprentissage actif pour réduire l'imprécision dans ces situations. Pourtant, la distinction incertitude épistémique/aléatoire a été bien étudiée dans la littérature. Pour faciliter les applications ultérieures d'apprentissage automatique, nous avons développé des procédures pratiques pour estimer ces degrés pour les classificateurs populaires. En particulier, nous avons exploré l'utilisation de cette distinction dans les contextes d'apprentissage actif et prudent
Impact assessment of the pilot policy on benefit-sharing mechanism in management, protection and sustainable development of Special-Use Forests in Vietnam : case study at Bach Ma National Park
Full text linkThe research aims at assessing the impacts of the Benefit Sharing Mechanism (BSM) pilot policy on the socioeconomic factors and natural-resource management in the co-management area between the Bach Ma National Park and the Thuong Nhat community. This is a new approach in Special-Use Forests (SUFs) management in Vietnam, which is needed to assess the scaling up of a national policy on co-management of SUFs in the future. A case study was undertaken in the Bach Ma National Park, which was one of the two national parks chosen to pilot the BSM policy under the Prime Minister’s Decision No 126 (Government-126 2012). In this study, both qualitative and quantitative data was collected on BSM implementation schemes; their impacts on local natural-resource use; local awareness of rights, benefits, and responsibilities when participating in the BSM; and local perspectives on the BSM’s achievements, failures, and future potential. Analysis revealed that the local community strongly supported the BSM implementation in the Bach Ma NP, displaying a high level of demand for the measures and significant involvement. Furthermore, thanks to the local people’s participation in the BSM implementation, their household incomes increased and their awareness and practices of NTFP sustainable use and forest protection in the co-management area improved”. The BSM implementation also exposed three main areas of weakness. These included poor compliance with the sustainable-harvest regulations, the apparent over-complexity of the the harvest-application procedures, and the users’ low contributions to the village fund after the NTFP harvest. The current research revealed that, although the local awareness and compliance with the BSM procedures increased annually after BSM implementation, the level of self-management and compliance was still quite low and needed to be improved. Furthermore, some conflicts have arisen between NTFPs users from village to village in Thuong Nhat and between local traders and NTFP collectors. Besides, the research identified two major negative practices: overexploitation of NTFPs and taking advantage of the NTFPs harvest to cut trees or trap animals. This dissertation presents various possibilities for improvement of the BSM implementation through (i) raising local people’s awareness; (ii) simplifying BSM procedures; (iii) clarifying incentives and tasks of BSM actors to enhance co-patrolling and monitoring activities; and (iv) creating good incentives either based on the livelihood program for those who actively comply with the BSM regulations or direct payments to those who join forest-protection activities. Finally, the research scrutinized certain challenges to future duplication of the BSM in other SUFs in Vietnam. To expand the BSM approach to other SUFs, the Ministry of Agriculture and Rural Development needs to develop a guideline for BSM implementation to encourage the relevant stakeholders’ involvement. The guideline for national BSM implementation should take account of the differences between local contexts, as well as create the necessary, flexible regulations to be applicable in practice. Therefore, based on the guideline, additional steps are necessary for the development of appropriate specific measures in the course of the BSM implementation at the provincial and local levels.Das Ziel der vorliegenden Arbeit ist, die Auswirkungen des Pilotprogramms “Mechanismus für eine gerechte Nutzenverteilung” (BSM) auf sozioökonomische Faktoren und das Naturressourcenmanagement in dem Ko-Management Gebiet des Bach Ma Nationalparks zu bewerten. Um dieses Ziel zu erreichen, wurde eine Fallstudie in dem Bach Ma Nationalpark durchgeführt, welcher zu den zwei ausgewählten Pilotregionen für das BSM Programm zählt. Im Rahmen der Studie wurden sowohl qualitative als auch quantitative Daten erhoben. Stärken und Schwächen des Programms werden in der vorliegenden Arbeit analysiert und bewertet. Basierend auf diesen Ergebnissen werden verschiedene Vorschläge zur Verbesserung des Programms vorgestellt, u.a. das Programm bei der Bevölkerung bekannt zu machen und die Prozesse des Programms zu vereinfachen. Schließlich werden Empfehlungen für die Übertragung des Programms auf andere Gebiete in Vietnam gegeben
Imprécision en apprentissage statistique
No full textWe have focused on imprecision modeling in machine learning problems, where available data or knowledge suffers from important imperfections. In this work, imperfect data refers to situations where either some features or the labels are imperfectly known, that is can be specified by sets of possible values rather than precise ones. Learning from partial data are commonly encountered in various fields, such as bio-statistics, agronomy, or economy. These data can be generated by coarse or censored measurements, or can be obtained from expert opinions. On the other hand, imperfect knowledge refers to the situations where data are precisely specified, however, there are classes, that cannot be distinguished due to a lack of knowledge (also known as epistemic uncertainty) or due to a high uncertainty (also known as aleatoric uncertainty). Considering the problem of learning from partially specified data, we highlight the potential issues of dealing with multiple optimal classes and multiple optimalmodels in the inference and learning step, respectively. We have proposed active learning approaches to reduce the imprecision in these situations. Yet, the distinction epistemic/aleatoric uncertainty has been well-studied in the literature. To facilitate subsequent machine learning applications, we have developed practical procedures to estimate these degrees for popular classifiers. In particular, we have explored the use of this distinction in the contexts of active learning and cautious inferences.Nous nous sommes concentrés sur la modélisation et l'imprécision dans les problèmes d'apprentissage automatique, où les données ou connaissances disponibles souffrent d'imperfections importantes. Dans ce travail, les données imparfaites font référence à des situations où certaines caractéristiques ou les étiquettes sont imparfaitement connues, c'est-à-dire peuvent être spécifiées par des ensembles de valeurs possibles plutôt que par des valeurs précises. Les apprentissages à partir de données partielles sont couramment rencontrés dans divers domaines, tels que la biostatistique, l'agronomie ou l'économie. Ces données peuvent être générées par des mesures grossières ou censurées, ou peuvent être obtenues à partir d'avis d'experts. D'autre part, la connaissance imparfaite fait référence aux situations où les données sont spécifiées avec précision, cependant, il existe des classes qui ne peuvent pas être distinguées en raison d'un manque de connaissances (également appelée incertitude épistémique) ou en raison d'une forte incertitude (également appelée incertitude aléatoire). Considérant le problème de l'apprentissage à partir de données partiellement spécifiées, nous soulignons les problèmes potentiels liés au traitement de plusieurs classes optimales et de plusieurs modèles optimaux dans l'étape d'inférence et d'apprentissage, respectivement. Nous avons proposé des approches d'apprentissage actif pour réduire l'imprécision dans ces situations. Pourtant, la distinction incertitude épistémique/aléatoire a été bien étudiée dans la littérature. Pour faciliter les applications ultérieures d'apprentissage automatique, nous avons développé des procédures pratiques pour estimer ces degrés pour les classificateurs populaires. En particulier, nous avons exploré l'utilisation de cette distinction dans les contextes d'apprentissage actif et prudent
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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