Lin Huang (288599), Sivan Bercovici (542442), Jesse M. Rodriguez (542443) et al. · Figshare · 2015
<p>SpeeDB’s running time is shown in comparison to the running times of IBD detection methods PARENTE, GERMLINE, and fastIBD. SpeeDB+PARENTE denotes the total time for using SpeeDB followed by running PARENTE only on the candidate segments output by SpeeDB. Calculations based on analyzing 6,400 pairs of individuals (one query vs. 6,400 indexed individuals for SpeeDB and SpeeDB+PARENTE). SpeeDB was run with thresholds corresponding to 99% sensitivity.</p>
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University of Debrecen Electronic Archive (University of Debrecen) · 2008
1.Játék "A" hangra = Play in "A" sound /László Dés +2.Júlia mosolya = Julia's smile /László Dés +3.Talizmán = Talisman /Tamás Tóth +4.Szöszi álma = Blonde's dream /László Dés +5.Hajnaltól reggelig = From daybreak to morning /László Dés +6.Altató = Lullaby /László Dés +7.Out of Brasil /László Dés - Ferenc Schnéberger - Kornél Horváth +8.Hommage a Gismonti /László Dés - Ferenc Schnéberger - Kornél Horváth +9.Song for V. M. /László Dés +10.One string /László Dés - Ferenc Schnéberger - Kornél Horváth +11.Aura /László Dés
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Can Zhao (706105), Jiabing Liu (9773382), Fuyong Zheng (11632005) et al. · Figshare · 2021
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Bettina Knapp (110084), Lars Kaderali (110095) · Figshare · 2015
<p>The figure shows the computation time required to infer networks of different sizes, for the LP (red solid circles and line) and DEPN (green dashed circles and line) approaches, in seconds. Note the logarithmic scale of the y-axis. Computation time is for full evaluation of stratified 10-fold crossvalidation. Computations for networks for 44 and 52 nodes with DEPN were aborted after 1000 hours (green star) without a solution.</p>
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Wei Zhang (405), Jae-Woong Chang (839929), Lilong Lin (839930) et al. · Figshare · 2016
<p>The plots show the CPU time (Intel Xeon E5-1620 with 3.70GHZ) for running the Net-RSTQ algorithm one three networks, the small transcript network, the large transcript network, and an artificial huge network of 10000 transcripts.</p>
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Vittorio Fortino (633002), Pia Kinaret (633003), Nanna Fyhrquist (140718) et al. · Figshare · 2015
<p>Evaluation of the running time represented as the mean over 30 bootstrap iterations. All methods investigated in this study were run single-threaded. For the proposed method the running time is compiled considering the sum of the execution times spent for the feature selection and prioritization steps.</p><p>Running time.</p>
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2015
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Zilong Deng (4594117), Yizhang Wang (5532749), Mustafa Muwafak Alobaedy (21533504) · Figshare · 2025
<div> Federated clustering is a distributed clustering algorithm that does not require the transmission of raw data and is widely used. However, it struggles to handle Non-IID data effectively because it is difficult to obtain accurate global consistency measures under Non-Independent and Identically Distributed (Non-IID) conditions. To address this issue, we propose a federated k-means clustering algorithm based on a cluster backbone called FKmeansCB. First, we add Laplace noise to all the local data, and run k-means clustering on the client side to obtain cluster centers, which faithfully represent the cluster backbone (<i>i.e.</i>, the data structures of the clusters). The cluster backbone represents the client’s features and can approximatively capture the features of different labeled data points in Non-IID situations. We then upload these cluster centers to the server. Subsequently, the server aggregates all cluster centers and runs the k-means clustering algorithm to obtain global cluster centers, which are then sent back to the client. Finally, the client assigns all data points to the nearest global cluster center to produce the final clustering results. We have validated the performance of our proposed algorithm using six datasets, including the large-scale MNIST dataset. Compared with the leading non-federated and federated clustering algorithms, FKmeansCB offers significant advantages in both clustering accuracy and running time. </div>
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Shaoqiang Zhang (3144117), Yong Chen (109188) · Figshare · 2016
<p>The running times of CLIMP and MCL on graphs with different densities with either one or four threads.</p>
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Xusheng Zhao (4491715), Yunqing Rao (14803448), Peng Qi (291905) et al. · Figshare · 2023
<div><p>The rectangular packing problem has been extensively studied over the years due to its wide application in industry. However, most of the research efforts are devoted to positioning techniques of the rectangles for various problem variants, the efficient implementation of the packing procedure is relatively less studied. In this paper, we propose an efficient constructive algorithm for the rectangular packing problem with rotations. We design a preprocess procedure with four data structures to store the information used for item selection. The gaps on the skyline are categorized into three types according to their associated edges for the placement procedure, during which the item is searched and packed in a descending order of the fitness value. The entire constructive phase takes a time complexity of O(<i>n</i>log<i>n</i>). For the packing improvement phase, we optimize the packing through random perturbation on the sequence and orientation of the item. Three classes of stochastic problems are generated ranging from small-scale to extra-large-scale, the recorded running time confirms the efficiency of the proposed algorithm. We also test the proposed algorithm on the benchmark problem C21, N13, NT, Babu and CX, the computational results show that it delivers a good performance.</p></div>
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R. S. Gwynn · Project Muse (Johns Hopkins University) · 2011
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Patricia Wollstadt (605354), Ulrich Meyer (32182), Michael Wibral (383303) · Figshare · 2015
<p>Running times [log(s)] for dynamic programming (A, B) and backtracking (C, D) by number of vertices ∣<b>V</b>∣ and maximum path weight <i>w</i><sub><i>crit</i></sub>. Running times are shown for different graph types (SW: small-world, SF: scale-free, RN: random networks with density <i>ρ</i>). Red markers indicate cases of intractability (execution was aborted after a pre-defined limit of reconstructed alternative paths was reached).</p>
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Bulletin of Miscellaneous Information (Royal Gardens Kew)
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University of Washington Libraries · University of Washington Libraries · 2024
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Aubrey Dillon-Malone, Christy Brown, Sam Baneham et al. · Books Ireland · 1982
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Li C. Xia (193535), Jacob A. Cram (193537), Ting Chen (15205) et al. · Figshare · 2015
<p>GRAMMy is the fastest in all cases as compared to MEGAN and GAAS in processing time. The BLAT mapping time is excluded for all compared tools.</p>
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Xuechun Mao (8972447), Xiaqing Zhou (6651515), Xiaoming Zhao (484793) et al. · Figshare · 2025
<div> The widespread adoption of e-health systems raises critical concerns regarding data privacy and network security. Ensuring secure and reliable data sharing between patients and healthcare professionals remains a significant challenge. To address this, we propose a novel anonymous authentication scheme tailored for e-health environments, integrating zero-knowledge proof (ZKP) with multimodal biometrics. Our key contributions are as follows: (1) applying the Pedersen vector commitment algorithm to construct a biometric-based ZKP scheme, thereby ensuring enhanced security and privacy-preserving authentication; (2) utilizing multimodal cancelable biometrics generate (MCBG) technology, integrating fingerprint, face, and iris modalities to strengthen the security of the verification process; and (3) providing a detailed security analysis that demonstrates our scheme meets essential security requirements, including anonymity, authenticity, unlinkability, forward security, and resistance to replay attacks. Experimental results demonstrate stable proving and verification time of approximately 78 ms and 140 ms, respectively, regardless of the proof range, validating its efficiency and practicality for secure authentication in e-health systems. </div>
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Jansen, M Martijn · TU/e Research Portal (Eindhoven University of Technology)
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Takahiro Yoshida (461333), Ryohei Hisano (418707), Takaaki Ohnishi (418709) · Figshare · 2023
<div><p>Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as “bank.” In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.</p></div>
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Nan Luo (117827), Quansen Sun (420337), Qiang Chen (114038) et al. · Figshare · 2015
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