Projected Power Iteration for Network Alignment

The network alignment problem asks for the best correspondence between two given graphs, so that the largest possible number of edges are matched. This problem appears in many scientific problems (like the study of protein-protein interactions) and it is very closely related to the quadratic assignment problem which has graph isomorphism, traveling salesman and minimum bisection problems as particular cases. The graph matching problem is NP-hard in general. However, under some restrictive models for the graphs, algorithms can approximate the alignment efficiently. In that spirit the recent work by Feizi and collaborators introduce EigenAlign, a fast spectral method with convergence guarantees for Erd\H{o}s-Reny\'i graphs. In this work we propose the algorithm Projected Power Alignment, which is a projected power iteration version of EigenAlign. We numerically show it improves the recovery rates of EigenAlign and we describe the theory that may be used to provide performance guarantees for Projected Power Alignment.Comment: 8 page

Spatial and kinematic alignments between central and satellite halos

Based on a cosmological N-body simulation we analyze spatial and kinematic alignments of satellite halos within six times the virial radius of group size host halos (Rvir). We measure three different types of spatial alignment: halo alignment between the orientation of the group central substructure (GCS) and the distribution of its satellites, radial alignment between the orientation of a satellite and the direction towards its GCS, and direct alignment between the orientation of the GCS and that of its satellites. In analogy we use the directions of satellite velocities and probe three further types of alignment: the radial velocity alignment between the satellite velocity and connecting line between satellite and GCS, the halo velocity alignment between the orientation of the GCS and satellite velocities and the auto velocity alignment between the satellites orientations and their velocities. We find that satellites are preferentially located along the major axis of the GCS within at least 6 Rvir (the range probed here). Furthermore, satellites preferentially point towards the GCS. The most pronounced signal is detected on small scales but a detectable signal extends out to 6 Rvir. The direct alignment signal is weaker, however a systematic trend is visible at distances < 2 Rvir. All velocity alignments are highly significant on small scales. Our results suggest that the halo alignment reflects the filamentary large scale structure which extends far beyond the virial radii of the groups. In contrast, the main contribution to the radial alignment arises from the adjustment of the satellite orientations in the group tidal field. The projected data reveal good agreement with recent results derived from large galaxy surveys. (abridged)Comment: accepted for publication in Ap

In-the-wild Facial Expression Recognition in Extreme Poses

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.Comment: Published on ICGIP201

Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states

Network community detection via iterative edge removal in a flocking-like system

We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks