Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

Advances in precision medicine: tailoring individualised therapies

The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs

Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy

The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models

Simultaneous interval regression for K-nearest neighbor

International audienceIn some regression problems, it may be more reasonable to predict intervals rather than precise values. We are interested in finding intervals which simultaneously for all input instances x ∈X contain a β proportion of the response values. We name this problem simultaneous interval regression. This is similar to simultaneous tolerance intervals for regression with a high confidence level γ ≈ 1 and several authors have already treated this problem for linear regression. Such intervals could be seen as a form of confidence envelop for the prediction variable given any value of predictor variables in their domain. Tolerance intervals and simultaneous tolerance intervals have not yet been treated for the K-nearest neighbor (KNN) regression method. The goal of this paper is to consider the simultaneous interval regression problem for KNN and this is done without the homoscedasticity assumption. In this scope, we propose a new interval regression method based on KNN which takes advantage of tolerance intervals in order to choose, for each instance, the value of the hyper-parameter K which will be a good trade-off between the precision and the uncertainty due to the limited sample size of the neighborhood around each instance. In the experiment part, our proposed interval construction method is compared with a more conventional interval approximation method on six benchmark regression data sets

The first long-read nuclear genome assembly of Oryza australiensis, a wild rice from northern Australia

Oryza australiensis is a wild rice native to monsoonal northern Australia. The International Oryza Map Alignment Project emphasises its significance as the sole representative of the EE genome clade. Assembly of the O. australiensis genome has previously been challenging due to its high Long Terminal Repeat (LTR) retrotransposon (RT) content. Oxford Nanopore long reads were combined with Illumina short reads to generate a high-quality ~ 858 Mbp genome assembly within 850 contigs with 46× long read coverage. Reference-guided scaffolding increased genome contiguity, placing 88.2% of contigs into 12 pseudomolecules. After alignment to the Oryza sativa cv. Nipponbare genome, we observed several structural variations. PacBio Iso-Seq data were generated for five distinct tissues to improve the functional annotation of 34,587 protein-coding genes and 42,329 transcripts. We also report SNV numbers for three additional O. australiensis genotypes based on Illumina re-sequencing. Although genetic similarity reflected geographical separation, the density of SNVs also correlated with our previous report on variations in salinity tolerance. This genome re-confirms the genetic remoteness of the O. australiensis lineage within the O. officinalis genome complex. Assembly of a high-quality genome for O. australiensis provides an important resource for the discovery of critical genes involved in development and stress tolerance.Aaron L. Phillips, Scott Ferguson, Nathan S. Watson, Haigh, Ashley W. Jones, Justin O. Borevitz, Rachel A. Burton, Brian J. Atwel

Expressive Dominant Versus Receptive Dominant Language Patterns in Young Children: Findings from the Study to Explore Early Development

We examined language profiles of 2571 children, 30–68 months old, with autism spectrum disorder (ASD), other developmental disabilities (DD), and typical development from the general population (POP). Children were categorized as expressive dominant (ED), receptive dominant (RD), or nondominant (ND). Within each group, the ED profile was the least frequent. However, children in the ASD group were more likely to display an ED profile than those in the DD or POP groups, and these children were typically younger, had lower nonverbal cognitive skills, and displayed more severe social-affect symptoms of ASD compared to their peers with RD or ND profiles. These findings have research and clinical implications related to the focus of interventions targeting young children with ASD and other DDs

Three-dimensional random Voronoi tessellations: From cubic crystal lattices to Poisson point processes

We perturb the SC, BCC, and FCC crystal structures with a spatial Gaussian noise whose adimensional strength is controlled by the parameter a, and analyze the topological and metrical properties of the resulting Voronoi Tessellations (VT). The topological properties of the VT of the SC and FCC crystals are unstable with respect to the introduction of noise, because the corresponding polyhedra are geometrically degenerate, whereas the tessellation of the BCC crystal is topologically stable even against noise of small but finite intensity. For weak noise, the mean area of the perturbed BCC and FCC crystals VT increases quadratically with a. In the case of perturbed SCC crystals, there is an optimal amount of noise that minimizes the mean area of the cells. Already for a moderate noise (a>0.5), the properties of the three perturbed VT are indistinguishable, and for intense noise (a>2), results converge to the Poisson-VT limit. Notably, 2-parameter gamma distributions are an excellent model for the empirical of of all considered properties. The VT of the perturbed BCC and FCC structures are local maxima for the isoperimetric quotient, which measures the degre of sphericity of the cells, among space filling VT. In the BCC case, this suggests a weaker form of the recentluy disproved Kelvin conjecture. Due to the fluctuations of the shape of the cells, anomalous scalings with exponents >3/2 is observed between the area and the volumes of the cells, and, except for the FCC case, also for a->0. In the Poisson-VT limit, the exponent is about 1.67. As the number of faces is positively correlated with the sphericity of the cells, the anomalous scaling is heavily reduced when we perform powerlaw fits separately on cells with a specific number of faces

Exploring Appropriation of Global Cultural Rituals

Adolescents, as a consequence of identification with popular culture, have been described as having homogenous consumption patterns. More recently, however, it has been recognised that ‘glocalisation’ (global practices reworked to fit local contexts) affords an opportunity for differentiation. This paper considers a recent UK phenomenon, namely that of the US high school prom, and seeks to explore the ways in which this ritual has been adopted or adapted as part of youth culture. The method employed here was mixed methods and included in-depth interviews with those who attended a prom in the last three years as well as a questionnaire distributed amongst high school pupils who were anticipating a high school prom. The findings illustrate that the high school prom in the UK is becoming increasingly integrated into the fabric of youth culture although, depending on the agentic abilities employed by the emerging adults in the sample, there is differing appropriation of this ritual event particularly in relation to attitudes towards and motivations for attending the prom. A typology of prom attendees is posited. This paper contributes to our understanding of this practice in a local context

f(R)f(R) gravity constrained by PPN parameters and stochastic background of gravitational waves

We analyze seven different viable $f(R)$-gravities towards the Solar System tests and stochastic gravitational waves background. The aim is to achieve experimental bounds for the theory at local and cosmological scales in order to select models capable of addressing the accelerating cosmological expansion without cosmological constant but evading the weak field constraints. Beside large scale structure and galactic dynamics, these bounds can be considered complimentary in order to select self-consistent theories of gravity working at the infrared limit. It is demonstrated that seven viable $f(R)$-gravities under consideration not only satisfy the local tests, but additionally, pass the above PPN-and stochastic gravitational waves bounds for large classes of parameters.Comment: 23 pages, 8 figure