1,497 research outputs found
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
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
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
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
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding
ICD coding is designed to assign the disease codes to electronic health
records (EHRs) upon discharge, which is crucial for billing and clinical
statistics. In an attempt to improve the effectiveness and efficiency of manual
coding, many methods have been proposed to automatically predict ICD codes from
clinical notes. However, most previous works ignore the decisive information
contained in structured medical data in EHRs, which is hard to be captured from
the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal
Attention Network (TreeMAN) to fuse tabular features and textual features into
multimodal representations by enhancing the text representations with
tree-based features via the attention mechanism. Tree-based features are
constructed according to decision trees learned from structured multimodal
medical data, which capture the decisive information about ICD coding. We can
apply the same multi-label classifier from previous text models to the
multimodal representations to predict ICD codes. Experiments on two MIMIC
datasets show that our method outperforms prior state-of-the-art ICD coding
approaches. The code is available at https://github.com/liu-zichen/TreeMAN
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