The rigid hybrid number for two phylogenetic trees

Recently there has been considerable interest in the problem of finding a phylogenetic network with a minimum number of reticulation vertices which displays a given set of phylogenetic trees, that is, a network with minimum hybrid number. Such networks are useful for representing the evolution of species whose genomes have undergone processes such as lateral gene transfer and recombination that cannot be represented appropriately by a phylogenetic tree. Even so, as was recently pointed out in the literature, insisting that a network displays the set of trees can be an overly restrictive assumption when modeling certain evolutionary phenomena such as incomplete lineage sorting.} In this paper, we thus consider the less restrictive notion of rigidly displaying which we introduce and study here. More specifically, we characterize when two trees can be rigidly displayed by a certain type of phylogenetic network called a temporal tree-child network in terms of fork-picking sequences. These are sequences of special subconfigurations of the two trees related to the well-studied cherry-picking sequences. We also show that, in case it exists, the rigid hybrid number for two phylogenetic trees is given by a minimum weight fork-picking sequence for the trees. Finally, we consider the relationship between the rigid hybrid number and three closely related numbers; the weak, beaded, and temporal hybrid numbers. In particular, we show that these numbers can all be different even for a fixed pair of trees, and also present an infinite family of pairs of trees which demonstrates that the difference between the rigid hybrid number and the temporal-hybrid number for two phylogenetic trees on the same set of $n$ leaves can grow at least linearly with $n$

School greening : right or privilege? examining urban nature within and around primary schools through an equity lens

Unidad de excelencia María de Maeztu CEX2019-000940-MA mounting body of research shows strong positive associations between urban nature and child well-being, including benefits related to mental and physical health. However, there is also evidence that children are spending less time in natural environments than previous generations, especially those living in deprived neighborhoods. To date, most studies analyzing children's (unequal) exposure or access to urban green and blue spaces focus on residential metrics while a school-based perspective, also an essential part of children's daily experience, is still understudied. The overall goal of this research is to assess spatially the amount and main components of green infrastructure within and around a sample of primary schools (n = 324) in the city of Barcelona, Spain, and to examine the equity implications of its distributional patterns. A multi-method approach based on GIS, correlation and cluster analyses, and an online survey, is used to identify these patterns of inequity according to three main dimensions: socio-demographic disparities across neighborhoods; school type (public, charter and private); and the frequency of outdoor educational activities organized by schools. Results show that schools located in the wealthiest neighborhoods are generally greener, but inequities are not observed for school surrounding green infrastructure indicators such as access to public green spaces or between public and charter schools. Survey results also indicate that greener schools generally organize more nature-based outdoor activities than those with less exposure to urban nature. In the light of these findings, we contend that multiple indicators of green infrastructure and different dimensions of equity should be considered to improve justice in the implementation of school-based re-naturing and outdoor educational programs

Contrasting Distributions of Urban Green Infrastructure across Social and Ethno-racial Groups

Links between urban green infrastructure (UGI) and public health benefits are becoming well established. Despite this, how UGI is distributed varies widely. Although not a universal finding, sectors of society that are disadvantaged often suffer from poor provision, something which might be due to which UGI are examined. We assess the distribution of street trees and public greenspaces (two types of publicly-owned and accessible UGI) across the city of Bradford, UK which is characterised by high levels of inequality and variation in ethno-racial background. We do this through statistical and spatial analyses. Street tree density was distributed unevenly and was highest in neighbourhoods with a high proportion of Asian/Asian British residents and with lower socio-economic status. Conversely, neighbourhoods with better access to public greenspaces were characterised by high income and/or a high proportion of White households. While the quality of public greenspace was spatially clustered, there were only limited spatial associations with ethno-racial group or socio-economic status. Population density was a key determinant of the distribution of UGI, suggesting understanding UGI distributions should also focus on urban form. Nevertheless, within the same city we show that equitable distribution of UGI differs according to the form and characteristics of UGI. To fully realise the public health benefits of UGI, it is necessary to map provision and understand the causal drivers of unequal distributions. This would facilitate interventions that promote equitable distributions of UGI based on the needs of the target populations

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

CLIFF is the Computational Linguists\u27 Feedback Forum. We are a group of students and faculty who gather once a week to hear a presentation and discuss work currently in progress. The \u27feedback\u27 in the group\u27s name is important: we are interested in sharing ideas, in discussing ongoing research, and in bringing together work done by the students and faculty in Computer Science and other departments. However, there are only so many presentations which we can have in a year. We felt that it would be beneficial to have a report which would have, in one place, short descriptions of the work in Natural Language Processing at the University of Pennsylvania. This report then, is a collection of abstracts from both faculty and graduate students, in Computer Science, Psychology and Linguistics. We want to stress the close ties between these groups, as one of the things that we pride ourselves on here at Penn is the communication among different departments and the inter-departmental work. Rather than try to summarize the varied work currently underway at Penn, we suggest reading the abstracts to see how the students and faculty themselves describe their work. The report illustrates the diversity of interests among the researchers here, as well as explaining the areas of common interest. In addition, since it was our intent to put together a document that would be useful both inside and outside of the university, we hope that this report will explain to everyone some of what we are about

Theory-based Explorations of Associations between Human Brain Structure and Intelligence from Childhood to Early Adulthood

Intelligence is often defined as the ability of an agent to learn, adapt to its environment, and solve novel challenges. However, despite over 100 years of theoretical development (e.g., general intelligence), widespread explanatory power (up to 50% of variance in cognitive scores), and the ability of intelligence measures to predict important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive ability remain poorly understood. This dissertation aims to make progress in this pursuit by exploring how human brain structure and intelligence correlate and co-develop with each other from childhood to early adulthood (ages 5 – 22 years). This endeavour is undertaken in three large cohorts (N range: 337 – 2072), guided by theory (e.g., crystallised and fluid intelligence), and implemented using rigorous, cutting-edge quantitative methods (i.e., structural equation modelling and network science). The results of this research provide robust evidence that the brain-behaviour relationships in intelligence are complex (i.e., consists of many independent yet interacting parts) and change nonlinearly during development. The first study sought to elucidate the factorial structure and white matter substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N = 551 (N = 165 neuroimaging), age range: 5 – 18 years; NKI-Rockland: N = 337 (N = 65 neuroimaging), age range: 6 – 18 years). In both samples, it was found (using structural equation modelling (SEM)) that cognitive ability is best modelled as two separable yet related constructs, crystallised and fluid intelligence, which became more distinct (i.e., less correlated) across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently of the superior longitudinal fasciculus, was strongly associated with crystallised (gc) and fluid (gf) abilities. Finally, SEM trees, which combines traditional SEM with decision trees, provided evidence for developmental reorganisation of gc and gf and their white matter substrates such that the relationships among these factors dropped between ages 7 – 8 years before increasing around age 10. Together, these results suggested that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence. The second study builds upon the first by again examining the neurocognitive structure of intelligence, this time from a network perspective. The network or mutualism theory of intelligence presupposes direct (statistical) interactions among cognitive abilities (e.g., maths, memory, and vocabulary) throughout development. Therefore, this project used network analytic methods (specifically graphical LASSO) to simultaneously model brain-behaviour relationships essential for general intelligence in a large (behavioural, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165), developmental (ages 5 – 18 years) cohort of struggling learners (CALM). Results indicated that both the single-layer (cognitive or neural nodes) and multilayer (combined cognitive and neural variables) networks consisted of mostly positive, small partial correlations, providing further support for the mutualism/network theory of cognitive ability. Moreover, using community detection (i.e., the Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), convergent evidence suggested that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behaviour. Overall, these findings suggest specific behavioural and neural variables that may have greater influence among (or might be more influenced by) other nodes within general intelligence. The final study investigated the longitudinal relationships between human cortical grey matter structure and measures of decision-making, risk-related behaviours, and spatial working memory from adolescence to early adulthood (ages 14 – 22 years). In the IMAGEN study (maximum N across time points/waves = 2072), latent growth curve models were used to estimate the baseline and longitudinal associations between behavioural measures and cortical surface area, thickness, and volume. Univariate models (only behavioural or neural measures) revealed that performance in decision-making, risk-related behaviours, and spatial working memory, as well as brain structure changed nonlinearly from mid-adolescence (age 14) to early adulthood (age 22). Furthermore, bivariate models (combined behavioural and neural measures) provided evidence for adaptive reorganisation (behaviour intercept predicts changes in brain structure) but not structural scaffolding (brain structure intercept predicts changes in behaviour). Furthermore, findings suggested that there were no correlated changes between behavioural and brain structure slopes (rates of change from mid-adolescence to early adulthood). This dissertation concludes by summarising the core results, addressing key limitations, and discussing avenues for future research. Taken together, this thesis hopes to convince cognitive neuroscientists that to understand cognitive ability and its neural determinants, they (we) must work more diligently toward building coherent, rigorous, and testable neurocognitive theories of intelligence—particularly under the conceptual and analytic paradigm of complex systems.The Cambridge Commonwealth, European & International Trus

06101 Abstracts Collection -- Spatial Data:mining, processing and communicating

From 05.03.06 to 10.03.06, the Dagstuhl Seminar 06101 ``Spatial Data: mining, processing and communicating\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Application of machine learning techniques to weather forecasting

84 p.El pronóstico del tiempo es, incluso hoy en día, una actividad realizada principalmente por humanos. Si bien las simulaciones por computadora desempeñan un papel importante en el modelado del estado y la evolución de la atmósfera, faltan metodologías para automatizar la interpretación de la información generada por estos modelos. Esta tesis doctoral explora el uso de metodologías de aprendizaje automático para resolver problemas específicos en meteorología y haciendo especial énfasis en la exploración de metodologías para mejorar la precisión de los modelos numéricos de predicción del tiempo. El trabajo presentado en este manuscrito comprende dos enfoques diferentes a la aplicación de algoritmos de aprendizaje automático a problemas de predicción meteorológica. En la primera parte, las metodologías clásicas, como la regresión multivariada no paramétrica y los árboles binarios, se utilizan para realizar regresiones en datos meteorológicos. Esta primera parte, está centrada particularmente en el pronóstico del viento, cuya naturaleza circular crea desafíos interesantes para los algoritmos clásicos de aprendizaje automático. La segunda parte de esta tesis explora el análisis de los datos meteorológicos como un problema de predicción estructurado genérico utilizando redes neuronales profundas. Las redes neuronales, como las redes convolucionales y recurrentes, proporcionan un método para capturar la estructura espacial y temporal inherente en los modelos de predicción del tiempo. Esta parte explora el potencial de las redes neuronales convolucionales profundas para resolver problemas difíciles en meteorología, como el modelado de la precipitación a partir de campos de modelos numéricos básicos. La investigación que sustenta esta tesis sirve como un ejemplo de cómo la colaboración entre las comunidades de aprendizaje automático y meteorología puede resultar mutuamente beneficiosa y conducir a avances en ambas disciplinas. Los modelos de pronóstico del tiempo y los datos de observación representan ejemplos únicos de conjuntos de datos grandes (petabytes), estructurados y de alta calidad, que la comunidad de aprendizaje automático exige para desarrollar la próxima generación de algoritmos escalables

Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions