Egypt Is Using Banned U.S.-Made Cluster Munitions in Sinai, Rights Group Says

NYT > Middle East

Egypt's armed forces have used internationally outlawed cluster bombs, made in the United States, in operations aimed at crushing Islamic extremists in northern Sinai, Amnesty International said Wednesday.


Black Hole In Massive Star Cluster Revealed By Orbiting Star's Weird Motion

International Business Times

For the first time, scientists have discovered a stellar-mass black hole in a globular star cluster by detecting its gravitational pull on another object. An inactive black hole was discovered in the star cluster NGC 3201 because of the erratic motion being displayed by a star that was orbiting it.


Positive Unlabeled Learning for Time Series Classification

AAAI Conferences

In many real-world applications of the time series classification problem, not only could the negative training instances be missing, the number of positive instances available for learning may also be rather limited. This has motivated the development of new classification algorithms that can learn from a small set P of labeled seed positive instances augmented with a set U of unlabeled instances (i.e. PU learning algorithms). However, existing PU learning algorithms for time series classification have less than satisfactory performance as they are unable to identify the class boundary between positive and negative instances accurately. In this paper, we propose a novel PU learning algorithm LCLC (Learning from Common Local Clusters) for time series classification. LCLC is designed to effectively identify the ground truths’ positive and negative boundaries, resulting in more accurate classifiers than those constructed using existing methods. We have applied LCLC to classify time series data from different application domains; the experimental results demonstrate that LCLC outperforms existing methods significantly.


Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks

AAAI Conferences

Location-Based Social Networks (LBSN) present so far the most vivid realization of the convergence of the physical and virtual social planes. In this work we propose a novel approach on modeling human activity and geographical areas by means of place categories. We apply a spectral clustering algorithm on areas and users of two metropolitan cities on a dataset sourced from the most vibrant LBSN, Foursquare. Our methodology allows the identification of user communities that visit similar categories of places and the comparison of urban neighborhoods within and across cities. We demonstrate how semantic information attached to places could be plausibly used as a modeling interface for applications such as recommender systems and digital tourist guides.


Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model

AAAI Conferences

Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.