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