Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning

Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Our method relies on combining diffusion geometry, a central approach to manifold learning, and hyperbolic geometry. Specifically, using diffusion geometry, we build multi-scale densities on the data, aimed to reveal their hierarchical structure, and then embed them into a product of hyperbolic spaces. We show theoretically that our embedding and distance recover the underlying hierarchical structure. In addition, we demonstrate the efficacy of the proposed method and its advantages compared to existing methods on graph embedding benchmarks and hierarchical datasets

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks

Desempenho de porcas e leitoes em maternidades com diferentes sistemas de acondionamento termico no inverno.

O objetivo desta pesquisa foi estudar os efeitos de sistemas de acondicionamento termico, em maternidades para suinos, sobre as caracteristicas fisiologicas e o desempenho dos animais, durante o inverno. Um experimento em delineamento inteiramente casualizado em parcelas subdivididas, com dois tratamentos e dois periodos nas sub-parcelas, foi realizado. Os tratamentos usados foram: sala convencional (SSV) e sala com amplas aberturas de janelas e sistema de regulacao das aberturas por deio de cortinas (SAC). Foram coletados os seguintes dados: temperatural retal (TR), e frequencia respiratoria (FR) das porcas, perda de peso das porcas (PPP) e ganho de peso dos leitoes (GPL), consumo de racao (CRP), consumo de agua (CA), intervalo de desmamecio (IDC). As porcas do tratamento SAC apresentaram os menores valores de TR e FR. Nao houve diferencas entre os tratamentos sobre CRP e IDC. Houve diferenca entre os tratamentos para GPP, constatando-se que no tratamento SAC as porcas apresentaram as menores perdas. Nao houve diferenca entre os tratamento no GPL. Houve efeito do sistema de acondicionamento sobre as caracteristicas fisiologicas e o ganho de peso das porcas. O tratamento SAC e o melhor para as porcas no inverno

Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

Spacecraft Formation Dynamics and Design

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76961/1/AIAA-13002-440.pd