2,366 research outputs found
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
Space-based Aperture Array For Ultra-Long Wavelength Radio Astronomy
The past decade has seen the rise of various radio astronomy arrays,
particularly for low-frequency observations below 100MHz. These developments
have been primarily driven by interesting and fundamental scientific questions,
such as studying the dark ages and epoch of re-ionization, by detecting the
highly red-shifted 21cm line emission. However, Earth-based radio astronomy
below frequencies of 30MHz is severely restricted due to man-made interference,
ionospheric distortion and almost complete non-transparency of the ionosphere
below 10MHz. Therefore, this narrow spectral band remains possibly the last
unexplored frequency range in radio astronomy. A straightforward solution to
study the universe at these frequencies is to deploy a space-based antenna
array far away from Earths' ionosphere. Various studies in the past were
principally limited by technology and computing resources, however current
processing and communication trends indicate otherwise. We briefly present the
achievable science cases, and discuss the system design for selected scenarios,
such as extra-galactic surveys. An extensive discussion is presented on various
sub-systems of the potential satellite array, such as radio astronomical
antenna design, the on-board signal processing, communication architectures and
joint space-time estimation of the satellite network. In light of a scalable
array and to avert single point of failure, we propose both centralized and
distributed solutions for the ULW space-based array. We highlight the benefits
of various deployment locations and summarize the technological challenges for
future space-based radio arrays.Comment: Submitte
Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems
Source localization is the inverse problem of graph information dissemination
and has broad practical applications.
However, the inherent intricacy and uncertainty in information dissemination
pose significant challenges, and the ill-posed nature of the source
localization problem further exacerbates these challenges. Recently, deep
generative models, particularly diffusion models inspired by classical
non-equilibrium thermodynamics, have made significant progress. While diffusion
models have proven to be powerful in solving inverse problems and producing
high-quality reconstructions, applying them directly to the source localization
is infeasible for two reasons. Firstly, it is impossible to calculate the
posterior disseminated results on a large-scale network for iterative denoising
sampling, which would incur enormous computational costs. Secondly, in the
existing methods for this field, the training data itself are ill-posed
(many-to-one); thus simply transferring the diffusion model would only lead to
local optima.
To address these challenges, we propose a two-stage optimization framework,
the source localization denoising diffusion model (SL-Diff). In the coarse
stage, we devise the source proximity degrees as the supervised signals to
generate coarse-grained source predictions. This aims to efficiently initialize
the next stage, significantly reducing its convergence time and calibrating the
convergence process. Furthermore, the introduction of cascade temporal
information in this training method transforms the many-to-one mapping
relationship into a one-to-one relationship, perfectly addressing the ill-posed
problem. In the fine stage, we design a diffusion model for the graph inverse
problem that can quantify the uncertainty in the dissemination. The proposed
SL-Diff yields excellent prediction results within a reasonable sampling time
at extensive experiments
Technical benefits and cultural barriers of networked Autonomous Undersea Vehicles
Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 44-45).The research presented in this thesis examines the technical benefits to using a collaborative network of Autonomous Undersea Vehicles (AUVs) in place of individual vehicles. Benefits could be achieved in the areas of reduced power consumption, improved positional information and improved acoustic communication bandwidth. However, current culture of AUV development may impede this approach. The thesis uses the Object Process Methodology (OPM) and principles of System Architecture to trace the value of an AUV system from the scientist who benefits from the data to the vehicle itself. Sections 3 and 4 outline the needs for an AUV system as they currently exist and describe the key physics-based limitations of operations. Section 5 takes a broader look at the system goal as data delivery, not just the deployment of a vehicle, and introduces the concept of networked AUV. Section 6 describes a potential evolution of networked AUVs in increasing autonomy and collaboration. Finally, Section 7 examines AUV development cultures that could impede, or foster, networked vehicles.by Patrick L. Wineman.S.M
A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments
Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.This work was carried out with the financial support of FuturAAL-Ego (RTI2018-101045-A-C22) and FuturAAL-Context (RTI2018-101045-B-C21) granted by Spanish Ministry of Science, Innovation and Universities
Bioinformatics Methods For Studying Intra-Host and Inter-Host Evolution Of Highly Mutable Viruses
Reproducibility and robustness of genomic tools are two important factors to assess the reliability of bioinformatics analysis. Such assessment based on these criteria requires repetition of experiments across lab facilities which is usually costly and time consuming. In this study we propose methods that are able to generate computational replicates, allowing the assessment of the reproducibility of genomic tools. We analyzed three different groups of genomic tools: DNA-seq read alignment tools, structural variant (SV) detection tools and RNA-seq gene expression quantification tools. We tested these tools with different technical replicate data. We observed that while some tools were impacted by the technical replicate data some remained robust. We observed the importance of the choice of read alignment tools for SV detection as well. On the other hand, we found out that the RNA-seq quantification tools (Kallisto and Salmon) that we chose were not affected by the shuffled data but were affected by reverse complement data. Using these findings, our proposed method here may help biomedical communities to advice on the robustness and reproducibility factors of genomic tools and help them to choose the most appropriate tools in terms of their needs. Furthermore, this study will give an insight to genomic tool developers about the importance of a good balance between technical improvements and reliable results
A Survey on Graph Representation Learning Methods
Graphs representation learning has been a very active research area in recent
years. The goal of graph representation learning is to generate graph
representation vectors that capture the structure and features of large graphs
accurately. This is especially important because the quality of the graph
representation vectors will affect the performance of these vectors in
downstream tasks such as node classification, link prediction and anomaly
detection. Many techniques are proposed for generating effective graph
representation vectors. Two of the most prevalent categories of graph
representation learning are graph embedding methods without using graph neural
nets (GNN), which we denote as non-GNN based graph embedding methods, and graph
neural nets (GNN) based methods. Non-GNN graph embedding methods are based on
techniques such as random walks, temporal point processes and neural network
learning methods. GNN-based methods, on the other hand, are the application of
deep learning on graph data. In this survey, we provide an overview of these
two categories and cover the current state-of-the-art methods for both static
and dynamic graphs. Finally, we explore some open and ongoing research
directions for future work
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