Correlated Cascades: Compete or Cooperate

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives

Classification Under Human Assistance

Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by this empirical observation, our goal is to design classifiers that are optimized to operate under different automation levels. More specifically, we focus on convex margin-based classifiers and first show that the problem is NP-hard. Then, we further show that, for support vector machines, the corresponding objective function can be expressed as the difference of two functions f = g - c, where g is monotone, non-negative and {\gamma}-weakly submodular, and c is non-negative and modular. This representation allows a recently introduced deterministic greedy algorithm, as well as a more efficient randomized variant of the algorithm, to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone

Recurrent spatio-temporal modeling of check-ins in location-based social networks.

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic-decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters by using an efficient EM algorithm, which distributes over the users, and has a linear time complexity. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives

RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks

Users in social networks whose posts stay at the top of their followers'{} feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple -- it is given by the position of her most recent post on each of her follower's feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post. Experiments on both synthetic and real data gathered from Twitter show that our algorithm is able to consistently make a user's posts more visible over time, is robust to volume changes on her followers' feeds, and significantly outperforms the state of the art.Comment: To appear at the 10th ACM International Conference on Web Search and Data Mining (WSDM

Evaluation of the reliability and construct validity of test of gross motor development-2 (Ulrich 2) in children of Semnan province

Introduction: The purpose of this study was to assess the validity and reliability of the second edition of test of gross motor development (TGMD-2; Ulrich) in 7-11 aged children of Semnan province, Iran.Materials and Methods: TGMD-2 measures 12 fundamental movement skills divided evenly into locomotor and object control subtests. 1277 children (651 girls and 626 boys) aged from seven to eleven years were participated.Results: Cronbach's alpha coefficients for the two subtests were ranged from 0.60 to 0.78, and test-retest reliability was from 0.86 to 0.89. Two-factor structure of TGMD-2 and proper assignment of skills to locomotor and object control factors were supported for our population.Conclusion: Based on our findings, we conclude that the TGMD-2 is an appropriate tool to examine the gross motor skills in this populatio

Antioxidant and hepatoprotective effects of Artemisia dracunculus against CCl4-induced hepatotoxicity in rats

Objective: The present study was conducted to investigate the antioxidant and hepatoprotective activity of the hydro-alcoholic extract of aerial parts of Artemisia dracunculus (HAAD) against CCl4-induced hepatotoxicity in rats. Materials and Methods: The antioxidant activity was evaluated by reducing power, 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and 2, 20-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays. Rats were pre-treated with either 50, 100, and 200 mg/kg of HAAD or silymarin (100 mg/kg; served as the positive control group) for 15 days and they received a single dose of CCl4 on the last day. Hepatoprotective effects were investigated by assessment of serum biochemical enzymes such as alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), total protein (TP), total bilirubin (TB), malondialdehyde (MDA), and antioxidant enzymes (SOD, CAT, GST and GSH), along with histopathological studies. Results: Total phenolic content was 197.22±3.73 mg gallic acid equivalent/g HAAD dry weight. HAAD indicated powerful activity in FRAP, DPPH and ABTS tests. Acute toxicity study showed that the extract had an LD50 of >5000 mg/kg. Oral treatment with HAAD exhibited a significant decrease in the levels of AST, ALT, ALP and TB and an increase in the level of TP. The extract significantly diminished MDA levels. The activities of the antioxidant enzymes were significantly augmented in rats pretreated with HAAD 200 mg/kg. Histopathological examination demonstrated lower liver damage in HAAD-treated groups as compared to CCl4 groups. Conclusion: Our findings indicated hepatoprotective effects of the hydro-alcoholic extract of A. dracunculus on CCl4-induced hepatic damage in rats and suggested that these effects may be produced by reducing oxidative stress

Cheshire: An Online Algorithm for Activity Maximization in Social Networks

User engagement in social networks depends critically on the number of online actions their users take in the network. Can we design an algorithm that finds when to incentivize users to take actions to maximize the overall activity in a social network? In this paper, we model the number of online actions over time using multidimensional Hawkes processes, derive an alternate representation of these processes based on stochastic differential equations (SDEs) with jumps and, exploiting this alternate representation, address the above question from the perspective of stochastic optimal control of SDEs with jumps. We find that the optimal level of incentivized actions depends linearly on the current level of overall actions. Moreover, the coefficients of this linear relationship can be found by solving a matrix Riccati differential equation, which can be solved efficiently, and a first order differential equation, which has a closed form solution. As a result, we are able to design an efficient online algorithm, Cheshire, to sample the optimal times of the users' incentivized actions. Experiments on both synthetic and real data gathered from Twitter show that our algorithm is able to consistently maximize the number of online actions more effectively than the state of the art

Real data temporal measures.

<p>The percent of check-ins which their times are closer than a threshold to the real time (<i>left</i>). The number of users which their average distance of predicted check-in times to the real times are less than a threshold (<i>right</i>).</p