TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and other info available at http://cs.stanford.edu/~althoff/timemachine

Magnetic and orbital order in overdoped bilayer manganites

The magnetic and orbital orders for the bilayer manganites in the doping region $0.5 < x <1.0$ have been investigated from a model that incorporates the two $e_g$ orbitals at each Mn site, the inter-orbital Coulomb interaction and lattice distortions. The usual double exchange operates via the $e_g$ orbitals. It is shown that such a model reproduces much of the phase diagram recently obtained for the bilayer systems in this range of doping. The C-type phase with ($\pi,0,\pi$) spin order seen by Ling et al. appears as a natural consequence of the layered geometry and is stabilised by the static distortions of the system. The orbital order is shown to drive the magnetic order while the anisotropic hopping across the $e_g$ orbitals, layered nature of the underlying structure and associated static distortions largely determine the orbital arrangements.Comment: 8 pages, 5 figure

Mode Repulsion and Mode Coupling in Random Lasers

We studied experimentally and theoretically the interaction of lasing modes in random media. In a homogeneously broadened gain medium, cross gain saturation leads to spatial repulsion of lasing modes. In an inhomogeneously broadened gain medium, mode repulsion occurs in the spectral domain. Some lasing modes are coupled through photon hopping or electron absorption and reemission. Under pulsed pumping, weak coupling of two modes leads to synchronization of their lasing action. Strong coupling of two lasing modes results in anti-phased oscillations of their intensities.Comment: 13 pages, 4 figure

Relaxed stability conditions based on Taylor series membership functions for polynomial fuzzy-model-based control systems

© 2014 IEEE. In this paper, we investigate the stability of polynomial fuzzy-model-based (PFMB) control systems, aiming to relax stability conditions by considering the information of membership functions. To facilitate the stability analysis, we propose a general form of approximated membership functions, which is implemented by Taylor series expansion. Taylor series membership functions (TSMF) can be brought into stability conditions such that the relation between membership grades and system states is expressed. To further reduce the con-servativeness, different types of information are taken into account: the boundary of membership functions, the property of membership functions, and the boundary of operating domain. Stability conditions are obtained from Lyapunov stability theory by sum of squares (SOS) approach. Simulation examples demonstrate the effect of each piece of information

Modeling geometric-temporal context with directional pyramid co-occurrence for action recognition

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved