Reconstruction of Network Evolutionary History from Extant Network Topology and Duplication History

Genome-wide protein-protein interaction (PPI) data are readily available thanks to recent breakthroughs in biotechnology. However, PPI networks of extant organisms are only snapshots of the network evolution. How to infer the whole evolution history becomes a challenging problem in computational biology. In this paper, we present a likelihood-based approach to inferring network evolution history from the topology of PPI networks and the duplication relationship among the paralogs. Simulations show that our approach outperforms the existing ones in terms of the accuracy of reconstruction. Moreover, the growth parameters of several real PPI networks estimated by our method are more consistent with the ones predicted in literature.Comment: 15 pages, 5 figures, submitted to ISBRA 201

Heat transport and spin-charge separation in the normal state of high temperature superconductors

Hill et al. have recently measured both the thermal and charge conductivities in the normal state of a high temperature superconductor. Based on the vanishing of the Wiedemann-Franz ratio in the extrapolated zero temperature limit, they conclude that the charge carriers in this material are not fermionic. Here I make a simple observation that the prefactor in the temperature dependence of the measured thermal conductivity is unusually large, corresponding to an extremely small energy scale $T_0 \approx 0.15$ K. I argue that $T_0$ should be interpreted as a collective scale. Based on model-independent considerations, I also argue that the experiment leads to two possibilities: 1) The charge-carrying excitations are non-fermionic. And much of the heat current is in fact carried by distinctive charge-neutral excitations; 2) The charge-carrying excitations are fermionic, but a subtle ordering transition occurs at $T_0$.Comment: 3 pages, 1 figur

Destruction of the Kondo effect in a multi-channel Bose-Fermi Kondo model

We consider the SU(N) x SU(kappa N) generalization of the spin-isotropic Bose-Fermi Kondo model in the limit of large N. There are three fixed points corresponding to a multi-channel non-Fermi liquid phase, a local spin-liquid phase, and a Kondo-destroying quantum critical point (QCP). We show that the QCP has strong similarities with its counterpart in the single-channel model, even though the Kondo phase is very different from the latter. We also discuss the evolution of the dynamical scaling properties away from the QCP.Comment: 2 papes, 2 figures, submittet to SCES'0

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.Comment: This manuscript is the accepted version for CVPR 201

High-speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201

Optimistic versus Pessimistic--Optimal Judgemental Bias with Reference Point

This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over-pessimism depends on the real chance of success and optimistic decision makers prefer receiving early information. In the portfolio choice problem, pessimistic investors tend to trade conservatively, however, they might trade aggressively if they are sophisticated enough to recognise the biases since low expectation can reduce their fear of loss

Quantum Criticality and the Kondo Lattice

Quantum phase transitions (QPTs) arise as a result of competing interactions in a quantum many-body system. Kondo lattice models, containing a lattice of localized magnetic moments and a band of conduction electrons, naturally feature such competing interactions. A Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange interaction among the local moments promotes magnetic ordering. However, a Kondo exchange interaction between the local moments and conduction electrons favors the Kondo-screened singlet ground state. This chapter summarizes the basic physics of QPTs in antiferromagnetic Kondo lattice systems. Two types of quantum critical points (QCPs) are considered. Spin-density-wave quantum criticality occurs at a conventional type of QCP, which invokes only the fluctuations of the antiferromagnetic order parameter. Local quantum criticality describes a new type of QCP, which goes beyond the Landau paradigm and involves a breakdown of the Kondo effect. This critical Kondo breakdown effect leads to non-Fermi liquid electronic excitations, which are part of the critical excitation spectrum and are in addition to the fluctuations of the magnetic order parameter. Across such a QCP, there is a sudden collapse of the Fermi surface from large to small. I close with a brief summary of relevant experiments, and outline a number of outstanding issues, including the global phase diagram.Comment: 27 pages, 6 figures; Chapter of the book "Understanding Quantum Phase Transitions", ed. Lincoln D. Carr (CRC Press/Taylor & Francis, Boca Raton, 2010