1,140 research outputs found
A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems
In order to sustain the user-base for a web service, it is important
to know the return time of a user to the service. We propose a
Bayesian point process, log Gaussian Cox process (LGCP), to model
and predict return time of users. It allows encoding the prior do-
main knowledge and non-parametric estimation of latent intensity
functions capturing user behaviour. We capture the similarities
among the users in their return time by using a multi-task learning
approach. We show the effectiveness of the proposed approaches
on predicting the return time of users to last.fm music service
An Outlook on the Localisation and Structure-Function Relationships of R Proteins in Solanum
The co-evolution of plants and plant-pathogens shaped a multi-layered defence system in plants, in which Resistance proteins (R proteins) play a significant role. A fundamental understanding of the functioning of these R proteins and their position in the broader defence system of the plant is essential. Sub-project 3 of the BIOEXPLOIT programme studies how R proteins are activated upon effector recognition and how recognition is conveyed in resistance signalling pathways, using the solanaceous R proteins Rx1 (from S. tuberosum spp. andigena; conferring extreme resistance against Potato Virus X), I-2 (from S. lycopersicon; mediating resistance to Fusarium oxysporum) and Mi-1.2 (from S. lycopersicon; conferring resistance to Meloidogyne incognita) as model systems. The results obtained in this project will serve as a model for other R proteins and will be translated to potential applications or alternative strategies for disease resistance. These include the modification of the recognition specificity of R proteins with the aim to obtain broad spectrum resistance to major pathogens in potato
Flow probe of symmetry energy in relativistic heavy-ion reactions
Flow observables in heavy-ion reactions at incident energies up to about 1
GeV per nucleon have been shown to be very useful for investigating the
reaction dynamics and for determining the parameters of reaction models based
on transport theory. In particular, the elliptic flow in collisions of
neutron-rich heavy-ion systems emerges as an observable sensitive to the
strength of the symmetry energy at supra-saturation densities. The comparison
of ratios or differences of neutron and proton flows or neutron and hydrogen
flows with predictions of transport models favors an approximately linear
density dependence, consistent with ab-initio nuclear-matter theories.
Extensive parameter searches have shown that the model dependence is comparable
to the uncertainties of existing experimental data. Comprehensive new flow data
of high accuracy, partly also through providing stronger constraints on model
parameters, can thus be expected to improve our knowledge of the equation of
state of asymmetric nuclear matter.Comment: 20 pages, 24 figures, review to appear in EPJA special volume on
nuclear symmetry energ
Longitudinal Modeling of Social Media with Hawkes Process based on Users and Networks
Online social networks provide a platform for
sharing information at an unprecedented scale. Users generate
information which propagates across the network resulting in
information cascades. In this paper, we study the evolution of
information cascades in Twitter using a point process model
of user activity. We develop several Hawkes process models
considering various properties including conversational structure,
users’ connections and general features of users including the
textual information, and show how they are helpful in modeling
the social network activity. We consider low-rank embeddings
of users and user features, and learn the features helpful in
identifying the influence and susceptibility of users. Evaluation
on Twitter data sets associated with civil unrest shows that
incorporating richer properties improves the performance in
predicting future activity of users and memes
CO 2 + Methanol + Glycerol: Multiphase behaviour
The phase behaviour of the system CO2 + methanol + glycerol has been studied at low concentrations of this tri-alcohol. The synthetic method was implemented in a visual sapphire cell. New phase diagrams were obtained at temperatures of 313.15 and 333.15 K. Different methanol/glycerol molar ratios of 113, 50 and 30 were considered and its influence on phase behaviour accounted for. Pressures were varied from 6.03 to 11.44 MPa. A specific experiment is reported, starting from an initial volume of 4 mL of liquid mixture methanol + glycerol (methanol/glycerol molar ratio 30), where, with a stepwise addition of CO2, diverse fluid phase phenomena were detected, such as VLE, Critical Point, VLLE, VLLLE, LLE and LE
Hawkes processes for continuous time sequence classification : an application to rumour stance classification in Twitter
Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content
- …