430 research outputs found
UMDA/S: An Effective Iterative Compilation Algorithm for Parameter Search
The search process is critical for iterative compilation because the large size of the search space and the cost of evaluating the candidate implementations make it infeasible to find the true optimal value of the optimization parameter by brute force. Considering it as a nonlinear global optimization problem, this paper introduces a new hybrid algorithm -- UMDA/S: Univariate Marginal Distribution Algorithm with Nelder-Mead Simplex Search, which utilizes the optimization space structure and parameter dependency to find the near optimal parameter. Elitist preservation, weighted estimation and mutation are proposed to improve the performance of UMDA/S. Experimental results show the ability of UMDA/S to locate more excellent parameters, as compared to existing static methods and search algorithms
A Comprehensive Review of Community Detection in Graphs
The study of complex networks has significantly advanced our understanding of
community structures which serves as a crucial feature of real-world graphs.
Detecting communities in graphs is a challenging problem with applications in
sociology, biology, and computer science. Despite the efforts of an
interdisciplinary community of scientists, a satisfactory solution to this
problem has not yet been achieved. This review article delves into the topic of
community detection in graphs, which serves as a crucial role in understanding
the organization and functioning of complex systems. We begin by introducing
the concept of community structure, which refers to the arrangement of vertices
into clusters, with strong internal connections and weaker connections between
clusters. Then, we provide a thorough exposition of various community detection
methods, including a new method designed by us. Additionally, we explore
real-world applications of community detection in diverse networks. In
conclusion, this comprehensive review provides a deep understanding of
community detection in graphs. It serves as a valuable resource for researchers
and practitioners in multiple disciplines, offering insights into the
challenges, methodologies, and applications of community detection in complex
networks
Maine Impact Week 2021 Faculty Mentor Impact Awards : Yonggang “Tim” Lu
Earlier this year, we asked students to nominate faculty members who had an important impact on them and the response was incredible. Through online videos and announcements, we are featuring the nine faculty members who won 2021 Faculty Mentor Impact Awards.
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The Bayesian two-sample t-test
In this article we show how the pooled-variance two-sample t-statistic arises from a Bayesian formulation of the two-sided point null testing problem, with emphasis on teaching. We identify a reasonable and useful prior giving a closed-form Bayes factor that can be written in terms of the distribution of the two-sample t-statistic under the null and alternative hypotheses respectively. This provides a Bayesian motivation for the two-sample t-statistic, which has heretofore been buried as a special case of more complex linear models, or given only roughly via analytic or Monte Carlo approximations. The resulting formulation of the Bayesian test is easy to apply in practice, and also easy to teach in an introductory course that emphasizes Bayesian methods. The priors are easy to use and simple to elicit, and the posterior probabilities are easily computed using available software, in some cases using spreadsheets
Analysis of the Hydroelastic Performance of Very Large Floating Structures Based on Multimodules Beam Theory
The hydroelastic behavior of very large floating structures (VLFSs) is investigated based on the proposed multimodules beam theory (MBT). To carry out the analysis, the VLFS is first divided into multiple submodules that are connected through their gravity center by a spatial beam with specific stiffness. The external force exerted on the submodules includes the wave hydrodynamic force as well as the beam bending force due to the relative displacements of different submodules. The wave hydrodynamic force is computed based on three-dimensional potential theory. The beam bending force is expressed in the form of a stiffness matrix. The motion response defined at the gravity center of the submodules is solved by the multibody hydrodynamic control equations; then both the displacement and the structure bending moment of the VLFS are determined from the stiffness matrix equations. To account for the moving point mass effects, the proposed method is extended to the time domain based on impulse response function (IRF) theory. The method is verified by comparison with existing results. Detailed results through the displacement and bending moment of the VLFS are provided to show the influence of the number of the submodules and the influence of the moving point mass
Community Detection Using Revised Medoid-Shift Based on KNN
Community detection becomes an important problem with the booming of social
networks. As an excellent clustering algorithm, Mean-Shift can not be applied
directly to community detection, since Mean-Shift can only handle data with
coordinates, while the data in the community detection problem is mostly
represented by a graph that can be treated as data with a distance matrix (or
similarity matrix). Fortunately, a new clustering algorithm called Medoid-Shift
is proposed. The Medoid-Shift algorithm preserves the benefits of Mean-Shift
and can be applied to problems based on distance matrix, such as community
detection. One drawback of the Medoid-Shift algorithm is that there may be no
data points within the neighborhood region defined by a distance parameter. To
deal with the community detection problem better, a new algorithm called
Revised Medoid-Shift (RMS) in this work is thus proposed. During the process of
finding the next medoid, the RMS algorithm is based on a neighborhood defined
by KNN, while the original Medoid-Shift is based on a neighborhood defined by a
distance parameter. Since the neighborhood defined by KNN is more stable than
the one defined by the distance parameter in terms of the number of data points
within the neighborhood, the RMS algorithm may converge more smoothly. In the
RMS method, each of the data points is shifted towards a medoid within the
neighborhood defined by KNN. After the iterative process of shifting, each of
the data point converges into a cluster center, and the data points converging
into the same center are grouped into the same cluster
Information diffusion in mobile social networks: The speed perspective
Abstract—The emerging of mobile social networks opens op-portunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric k-center problem which is NP-hard, and the best known approximation algorithm for the asymmetric k-center problem has approximation ratio of log ∗ n and time complexity O(n5). Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time. I
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An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control
Article asserts that spare parts inventory management is complex due to the combined impact of intermittent and variable demand patterns. This study proposes a novel nonparametric Bayesian forecasting approach with its roots in the empirical Bayes paradigm
Federated Learning with Extremely Noisy Clients via Negative Distillation
Federated learning (FL) has shown remarkable success in cooperatively
training deep models, while typically struggling with noisy labels. Advanced
works propose to tackle label noise by a re-weighting strategy with a strong
assumption, i.e., mild label noise. However, it may be violated in many
real-world FL scenarios because of highly contaminated clients, resulting in
extreme noise ratios, e.g., 90%. To tackle extremely noisy clients, we study
the robustness of the re-weighting strategy, showing a pessimistic conclusion:
minimizing the weight of clients trained over noisy data outperforms
re-weighting strategies. To leverage models trained on noisy clients, we
propose a novel approach, called negative distillation (FedNed). FedNed first
identifies noisy clients and employs rather than discards the noisy clients in
a knowledge distillation manner. In particular, clients identified as noisy
ones are required to train models using noisy labels and pseudo-labels obtained
by global models. The model trained on noisy labels serves as a `bad teacher'
in knowledge distillation, aiming to decrease the risk of providing incorrect
information. Meanwhile, the model trained on pseudo-labels is involved in model
aggregation if not identified as a noisy client. Consequently, through
pseudo-labeling, FedNed gradually increases the trustworthiness of models
trained on noisy clients, while leveraging all clients for model aggregation
through negative distillation. To verify the efficacy of FedNed, we conduct
extensive experiments under various settings, demonstrating that FedNed can
consistently outperform baselines and achieve state-of-the-art performance. Our
code is available at https://github.com/linChen99/FedNed.Comment: Accepted by AAAI 202
Sperm proteins SOF1, TMEM95, and SPACA6 are required for sperm-oocyte fusion in mice
Noda, T., Lu, Y., Fujihara, Y., Oura, S., Koyano, T., Kobayashi, S., . . . Ikawa, M. (2020). Sperm proteins SOF1, TMEM95, and SPACA6 are required for sperm-oocyte fusion in mice. Proceedings of the National Academy of Sciences of the United States of America, 117(21) doi:10.1073/pnas.192265011
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