Multi-view constrained clustering with an incomplete mapping between views

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios

Alien Registration- Gagnon, Marie Yvonne (Auburn, Androscoggin County)

https://digitalmaine.com/alien_docs/22695/thumbnail.jp

La sécurité alimentaire durable au Nunavik : les enjeux juridiques de la commercialisation de la viande de caribou et de ses sous-produits par les Inuits

Le régime alimentaire des Inuits du Nunavik s’est modifié considérablement au cours des dernières décennies. Leur alimentation traditionnelle, constituée de viande et de poisson, a évolué vers un régime composé majoritairement de produits alimentaires importés du Sud, bien que l’apport en protéines demeure largement basé sur la consommation de gibier et de poisson. En raison du coût important lié à cette nouvelle forme d’alimentation, la transformation des habitudes alimentaires des Inuits modifie l’état de la sécurité alimentaire au Nunavik. Parce qu’elle permettrait de générer des revenus supplémentaires aux Inuits, la commercialisation de la viande de caribou et de ses sous-produits pourrait contribuer à renforcer l’accessibilité économique des Inuits aux produits du Sud et ainsi participer à l’atteinte d’une sécurité alimentaire durable au Nunavik. La Convention de la Baie-James et du Nord québécois et ses conventions complémentaires, qui établissent le régime de chasse des Inuits, permettent effectivement à ces derniers de pratiquer la chasse commerciale du caribou tout en leur assurant un accès sécurisé à cette ressource faunique dans une perspective de développement durable. Toutefois, certaines dispositions de ces conventions posent problème, principalement en cas de diminution importante des populations de caribous. De plus, des difficultés sont prévisibles quant au respect des règles nationales et internationales liées au commerce des produits alimentaires, notamment en ce qui a trait aux règles d’innocuité et de salubrité.Over the past decades, the food patterns of the Inuit of Nunavik have undergone substantial changes. Their traditional diet, mainly of meat and fish, has evolved towards a regimen made up largely of food products imported from the south, despite the fact that their intake of proteins is mainly based on the consumption of game animals and fish. Owing to the high cost of their new eating habits, the change in diet among the Inuit has modified food security in Nunavik. Since this enables the Inuit to generate extra income, the marketing of caribou meat and its derivative products could contribute to reinforcing Inuit economic access to products from the south and thereby participate in the attainment of sustainable food security for Nunavik. The Agreement concerning James Bay and Northern Québec and its complementary agreements, which establish the Inuit hunting regime, do in effect allow them to practice commercial hunting while ensuring for themselves secure access to this wildlife resource with a view to sustainable development. Nonetheless, some provisions of these agreements are problematical, especially in the case of a significant decline in caribou populations. Moreover, difficulties are foreseen regarding compliance with national and international rules relevant to the marketing of food products, particularly with regard to rules of safety and health

Mindfulness-Based Educational Module for Nurses Caring for Pediatric Mental Health Patients

BACKGROUND: Emergency pediatric nurses have the added stress of not only caring for medical patients but must also care for patients experiencing mental health issues. Many nurses feel unprepared to care for this specialized patient population. To bridge this gap, mindfulness-based interventions (MBIs) have been shown to provide nurses with the skills needed to care for mental health patients. This same concept can be applied to pediatric patients. Pediatric-focused mindfulness-based techniques can be used to help pediatric patients manage their symptoms. LOCAL PROBLEM: The setting for this project was an urban pediatric emergency department in East Tennessee. At the state and national level, there is a lack of available inpatient psychiatric beds for children requiring higher level of care. This has contributed to longer lengths of stay for patients holding in pediatric emergency departments. The purpose of this evidence-based practice project was for pediatric emergency room nurses to participate in a computer-based educational module that teaches implementing pediatric-focused MBIs in a pediatric setting. METHODS: The Johns Hopkins Evidence-Based Practice Model and Lewin’s 3-Step Change Theory were used to guide this project’s development, plan, and intervention implementation. Pre-module, immediate post-module, and 1-month post-module self-reports were measured with the use of the C-Scale Confidence Tool. INTERVENTIONS: To best determine the impact of this intervention, nurse confidence was measured using the C-Scale Confidence tool. Nurses’ self-reports before and after the intervention were collected to see if there was an improvement in their confidence in caring for pediatric mental health patients. RESULTS: Nurses\u27 self-reports showed an increase in nursing confidence after the implementation of a mindfulness-based educational module. Self-report nursing confidence scores increased from mean confidence of 3.18 to 4.13 (pre-module compared to the 1-month follow-up evaluation). CONCLUSIONS: Use of a mindfulness-based educational module resulted in a significant increase in nursing confidence when caring for pediatric mental health patients

Hérédité et longévité au Québec ancien

Par l’entremise du Registre de la population du Québec ancien (RPQA), banque de données élaborée dans le cadre du Programme de recherche en démographie historique, nous avons observé l’existence d’une composante familiale de la longévité. En effet, l’âge au décès des parents semble influencer l’âge au décès de leurs fils et filles, particulièrement pour les parents décédés après 70 ans. Nous avons aussi observé une convergence significative de l’âge au décès dans les fratries. Cependant, il existe également une relation entre les âges au décès des conjoints, ce qui laisse supposer que la composante familiale est due à la part environnementale de l’héritabilité plutôt qu’à la part génétique.For a long time, people have had a belief in a form of heritability in regard to longevity. According to this belief, reaching very great ages would be specific to some families. By using the Registre de la population du Québec ancien (RPQA), a database put together by the Programme de recherche en démographie historique (PRDH), we have established the existence of a familial component of human longevity. Indeed, our results suggest that the parents’ ages at death could have had a significant influence on their children’s ages at death, particularly for parents who died at age 70 or over. Moreover, our analysis revealed a significant association between brothers’ and sisters’ ages at death. However, there is also a clear relationship between spouses’ ages at death, which implies that the familial component of longevity could be more strongly related to the environmental component of heritability than to its genetic component

An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices

BACKGROUND: Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors. RESULTS: We demonstrate the three fundamental types of question that our software can address by taking as a specific example the connections between 49 measures of amino acid biophysical properties (e.g., size, charge and hydrophobicity), a generalized model of amino acid substitution (as represented by the PAM74-100 matrix), and the mutational distance that separates amino acids within the standard genetic code (i.e., the number of point mutations required for interconversion during protein evolution). We show that our software allows a user to recapture the insights from several key publications on these topics in just a few minutes. CONCLUSION: Our software facilitates rapid, interactive exploration of three interconnected topics: (i) the multidimensional molecular descriptors of the twenty proteinaceous amino acids, (ii) the correlation of these biophysical measurements with observed patterns of amino acid substitution, and (iii) the causal basis for differences between any two observed patterns of amino acid substitution. This software acts as an intuitive bioinformatic exploration tool that can guide more comprehensive statistical analyses relating to a diverse array of specific research questions

Confidence-Based Feature Acquisition

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability)

Algorithms for Learning Preferences for Sets of Objects

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features