Study of Heterogeneous Academic Networks

Academic networks are derived from scholarly data. They are heterogeneous in the sense that different types of nodes are involved, such as papers and authors. This dissertation studies such heterogeneous networks for measuring the academic influence and learning vector representations of authors. Academic influence has been traditionally measured by the citation count and metrics derived from it. PageRank based algorithms have been used to give higher weight to citations from more influential papers. A better metric is to add authors into the citation network so that the importance of authors and papers are evaluated recursively within the same framework. Based on such heterogeneous academic networks, we propose a new algorithm for ranking authors. Tested on two large networks, we find that our method outperforms the other 10 methods in terms of the number of award winners among top-ranked authors. We further improve the method by finding and dealing with the long reference issue. Moreover, we find the mutual citation in paper networks and the self citation issue in author networks. Our new method can reduce the impact of the above three issues and identify more rising stars. To learn efficient author representations from heterogeneous academic networks, we propose a new embedding method called Stratified Embedding for Heterogeneous Networks (SEHN) based on Skip-Gram Negative Sampling (SGNS). We conduct Random Walks to generate the traces that represent the structure of the network, then separate the traces into different layers so that each layer contains the nodes of one type only. Such stratification improves embeddings that are derived from the mixed traces by a large margin. SEHN improves the state-of-the-art Metapath2vec by up to 24% at a certain point. The efficacy of stratification is also demonstrated on two classic network embedding algorithms DeepWalk and Node2vec. The results are validated in two heterogeneous networks. We also demonstrate that SEHN outperforms the embedding of homogeneous author networks that are induced from their corresponding heterogeneous networks

A compilation of known QSOs for the Gaia mission

Quasars are essential for astrometric in the sense that they are spatial stationary because of their large distance from the Sun. The European Space Agency (ESA) space astrometric satellite Gaia is scanning the whole sky with unprecedented accuracy up to a few muas level. However, Gaia's two fields of view observations strategy may introduce a parallax bias in the Gaia catalog. Since it presents no significant parallax, quasar is perfect nature object to detect such bias. More importantly, quasars can be used to construct a Celestial Reference Frame in the optical wavelengths in Gaia mission. In this paper, we compile the most reliable quasars existing in literatures. The final compilation (designated as Known Quasars Catalog for Gaia mission, KQCG) contains 1843850 objects, among of them, 797632 objects are found in Gaia DR1 after cross-identifications. This catalog will be very useful in Gaia mission

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Electrical Control of Magnetization in Charge-ordered Multiferroic LuFe2O4

LuFe2O4 exhibits multiferroicity due to charge order on a frustrated triangular lattice. We find that the magnetization of LuFe2O4 in the multiferroic state can be electrically controlled by applying voltage pulses. Depending on with or without magnetic fields, the magnetization can be electrically switched up or down. We have excluded thermal heating effect and attributed this electrical control of magnetization to an intrinsic magnetoelectric coupling in response to the electrical breakdown of charge ordering. Our findings open up a new route toward electrical control of magnetization.Comment: 14 pages, 5 figure

Power allocation for cache-aided small-cell networks with limited backhaul

Cache-aided small-cell network is becoming an effective method to improve the transmission rate and reduce the backhaul load. Due to the limited capacity of backhaul, less power should be allocated to users whose requested contents do not exist in the local caches to maximize the performance of caching. In this paper, power allocation is considered to improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment (IA) is utilized to manage interferences among users. Specifically, three power allocation algorithms are proposed. First, we come up with a power allocation algorithm to maximize the sum transmission rate of the network, considering the limitation of backhaul. Second, in order to have more users meet their rate requirements, a power allocation algorithm to minimizing the average outage probability is also proposed. In addition, in order to further improve the users’ experience, a power allocation algorithm that maximizes the average satisfaction of all the users is also designed. Simulation results are provided to show the effectiveness of the three proposed power allocation algorithms for cache-aided small-cell networks with limited backhaul

Spatial search by quantum walk with a randomized local start state

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2004.Includes bibliographical references (p. 47-48).In this thesis, we present a quantum walk algorithm for spatial search of a periodic lattice. Our algorithm is a variation of the Childs and Goldstone algorithm for spatial search, but begins in a randomly selected local initial state rather than a uniformly delocalized one. We analytically calculate the running time of our algorithm on the complete graph and find it to be O([square root]N). We reduce the analysis of our algorithm to that of the Childs and Goldstone algorithm by comparing the eigenvalue conditions of the Hamiltonians used in the two algorithms. We numerically show that the two Hamiltonians have similar eigenvalue conditions when the starting state is a certain extremal vertex of the lattice. We also study the behavior of the algorithm when we move the start state away from this extremal vertex. Finally, we numerically analyze the behavior of our algorithm on 5 and 4 dimensional lattices. In the 5 dimensional case, we appear to be able to achieve a O([square root]N) running time. In the 4 dimensional case, previous analysis indicates there may be additional factors of logc N in the running time of our algorithm. Numerically, we are not able to determine whether this logarithmic factor exists. However, the numerical evidence does indicate that the running time of our algorithm is O([square root]N), up to some factor of logc N.by Fen Zhao.S.B

Further Study On U(1) Gauge Invariance Restoration

To further investigate the applicability of the projection scheme for eliminating the unphysical divergence $s/m_e^2$ due to U(1) gauge invariance violation, we study the process $e^-+W^+\to e^-+\bar t+b$ which possesses advantages of simplicity and clearness. Our study indicates that the projection scheme can indeed eliminate the unphysical divergence $s/m_e^2$ caused by the U(1) gauge invariance violation and the scheme can apply to very high energy region.Comment: Latex, 13 pages, 4 EPS fiure

Dynamic Fano Resonance of Quasienergy Excitons in Superlattices

The dynamic Fano resonance (DFR) between discrete quasienergy excitons and sidebands of their ionization continua is predicted and investigated in dc- and ac-driven semiconductor superlattices. This DFR, well controlled by the ac field, delocalizes the excitons and opens an intrinsic decay channel in nonlinear four-wave mixing signals.Comment: 4pages, 4figure

DAAM1 Is a Formin Required for Centrosome Re-Orientation during Cell Migration

BACKGROUND: Disheveled-associated activator of morphogenesis 1 (DAAM1) is a formin acting downstream of Wnt signaling that is important for planar cell polarity. It has been shown to promote proper cell polarization during embryonic development in both Xenopus and Drosophila. Importantly, DAAM1 binds to Disheveled (Dvl) and thus functions downstream of the Frizzled receptors. Little is known of how DAAM1 is localized and functions in mammalian cells. We investigate here how DAAM1 affects migration and polarization of cultured cells and conclude that it plays a key role in centrosome polarity. METHODOLOGY/PRINCIPAL FINDINGS: Using a specific antibody to DAAM1, we find that the protein localizes to the acto-myosin system and co-localizes with ventral myosin IIB-containing actin stress fibers. These fibers are particularly evident in the sub-nuclear region. An N-terminal region of DAAM1 is responsible for this targeting and the DAAM1(1-440) protein can interact with myosin IIB fibers independently of either F-actin or RhoA binding. We also demonstrate that DAAM1 depletion inhibits Golgi reorientation in wound healing assays. Wound-edge cells exhibit multiple protrusions characteristic of unpolarized cell migration. Finally, in U2OS cells lines stably expressing DAAM1, we observe an enhanced myosin IIB stress fiber network which opposes cell migration. CONCLUSIONS/SIGNIFICANCE: This work highlights the importance of DAAM1 in processes underlying cell polarity and suggests that it acts in part by affecting the function of acto-myosin IIB system. It also emphasizes the importance of the N-terminal half of DAAM1. DAAM1 depletion strongly blocks centrosomal re-polarization, supporting the concept that DAAM1 signaling cooperates with the established Cdc42 associated polarity complex. These findings are also consistent with the observation that ablation of myosin IIB but not myosin IIA results in polarity defects downstream of Wnt signaling. The structure-function analysis of DAAM1 in cultured cells parallels more complex morphological events in the developing embryo