SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.Comment: 10 pages, published in KDD 201

The Crest Phenotype in Chicken Is Associated with Ectopic Expression of HOXC8 in Cranial Skin

The Crest phenotype is characterised by a tuft of elongated feathers atop the head. A similar phenotype is also seen in several wild bird species. Crest shows an autosomal incompletely dominant mode of inheritance and is associated with cerebral hernia. Here we show, using linkage analysis and genome-wide association, that Crest is located on the E22C19W28 linkage group and that it shows complete association to the HOXC-cluster on this chromosome. Expression analysis of tissues from Crested and non-crested chickens, representing 26 different breeds, revealed that HOXC8, but not HOXC12 or HOXC13, showed ectopic expression in cranial skin during embryonic development. We propose that Crest is caused by a cis-acting regulatory mutation underlying the ectopic expression of HOXC8. However, the identification of the causative mutation(s) has to await until a method becomes available for assembling this chromosomal region. Crest is unfortunately located in a genomic region that has so far defied all attempts to establish a contiguous sequence

Comprehensive Mapping of Common Immunodominant Epitopes in the West Nile Virus Nonstructural Protein 1 Recognized by Avian Antibody Responses

West Nile virus (WNV) is a mosquito-borne flavivirus that primarily infects birds but occasionally infects humans and horses. Certain species of birds, including crows, house sparrows, geese, blue jays and ravens, are considered highly susceptible hosts to WNV. The nonstructural protein 1 (NS1) of WNV can elicit protective immune responses, including NS1-reactive antibodies, during infection of animals. The antigenicity of NS1 suggests that NS1-reactive antibodies could provide a basis for serological diagnostic reagents. To further define serological reagents for diagnostic use, the antigenic sites in NS1 that are targeted by host immune responses need to be identified and the potential diagnostic value of individual antigenic sites also needs to be defined. The present study describes comprehensive mapping of common immunodominant linear B-cell epitopes in the WNV NS1 using avian WNV NS1 antisera. We screened antisera from chickens, ducks and geese immunized with purified NS1 for reactivity against 35 partially overlapping peptides covering the entire WNV NS1. This study identified twelve, nine and six peptide epitopes recognized by chicken, duck and goose antibody responses, respectively. Three epitopes (NS1-3, 14 and 24) were recognized by antibodies elicited by immunization in all three avian species tested. We also found that NS1-3 and 24 were WNV-specific epitopes, whereas the NS1-14 epitope was conserved among the Japanese encephalitis virus (JEV) serocomplex viruses based on the reactivity of avian WNV NS1 antisera against polypeptides derived from the NS1 sequences of viruses of the JEV serocomplex. Further analysis showed that the three common polypeptide epitopes were not recognized by antibodies in Avian Influenza Virus (AIV), Newcastle Disease Virus (NDV), Duck Plague Virus (DPV) and Goose Parvovirus (GPV) antisera. The knowledge and reagents generated in this study have potential applications in differential diagnostic approaches and subunit vaccines development for WNV and other viruses of the JEV serocomplex

CloudyFL:a cloudlet-based federated learning framework for sensing user behavior using wearable devices

Abstract Wearable devices have been widely utilized by the general public for tracking physical activities. Many complex machine learning models leverage wearable devices to address application problems, such as predicting pedestrian behaviors and health management. These models often incur heavy computing load and energy cost, which is challenging for wearable devices. However, aggregating the data from different wearable devices to a central server introduces privacy concerns. To address these challenges, we propose an architecture, CloudyFL, by deploying cloudlets close to wearable devices. In CloudyFL, each cloudlet forms a trusted zone covering a subset of nearby wearable devices. Models are trained in this trusted zone, and then, only the model parameters are transmitted to a centralized aggregator using a federated learning framework. We additionally propose an LSTM-based model for user behavior sensing, with a neural network design to adjust to the non-IID data distribution on multiple cloudlets. Experimental results show that our training model within the CloudyFL architecture can achieve a performance better than existing methodologies