Chotirawee Chatpattanasiri (21533220), Federica Ninno (21533223), Catriona Stokes (21533226) et al. · Figshare · 2025
<div> High-fidelity numerical simulations such as Computational Fluid Dynamics (CFD) have been proven effective in analysing haemodynamics, offering insight into many vascular conditions. However, these methods often face challenges of high computational cost and long processing times. Data-driven approaches such as Reduced Order Modeling (ROM) and Machine Learning (ML) are increasingly being explored alongside CFD to advance biomechanical research and application. This study presents an integration of Proper Orthogonal Decomposition (POD)-based ROM with neural network-based ML models to predict Wall Shear Stress (WSS) in patient-specific vascular pathologies. CFD was used to generate WSS data, followed by POD to construct the ROM. The ML models were trained to predict the ROM coefficients from the inlet flowrate waveform, which can be routinely collected in the clinic. Two ML models were explored: a simpler flowrate-coefficients mapping model and a more advanced autoregressive model. Both models were tested against two case studies: flow in Peripheral Arterial Disease (PAD) and flow in Aortic Dissection (AD). Despite the limited training data sets (three flowrate waveforms for the PAD case and two for the AD case), the models were able to predict the haemodynamic indices, with the flowrate-coefficients mapping model outperforming the autoregressive model in both case studies. The accuracy is higher in the PAD case study, with reduced accuracy in the more complex case study of AD. Additionally, the computational cost analysis reveals a significant reduction in computational demands, with speed-up ratios in the order of 10<sup>4</sup> for both case studies. This approach shows an effective integration of ROM and ML techniques for fast and reliable evaluations of haemodynamic properties that contribute to vascular conditions, setting the stage for clinical translation. </div>
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Simon Boutry (10956118), Raphaël Helaers (796995), Tom Lenaerts (21921) et al. · Figshare · 2023
<p>Computational time of our 4 ensemble methods and 59 state-of-the-art tests, based on 18,000 empirical power simulations. The average, minimum and maximum computational time are given in seconds. Best: minimal time needed to perform 20,000 genetic regions based on the minimum computational time, given in hours. Worst: maximal time needed to perform 20,000 genetic regions based on the maximum computational time, given in hours. The laste 18 columns show the average computational time for each empirical power experiment ID (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1011488#pcbi.1011488.t001" target="_blank">Table 1</a>), in seconds.</p> <p>(XLSX)</p>
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Sayaka Miura (4780230), Koichiro Tamura (4181785), Qiqing Tao (135228) et al. · Figshare · 2020
<p> We used simulated influenza A datasets (one IBR and one ABR datasets) that contained 289 sequences. From these datasets, we sampled 50, 100, and 150 sequences. For BEAST, we used a log-normal rate model, and correct models were selected for MCMCTree.</p>
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Genki Terashi (818178), Mayuko Takeda-Shitaka (818179) · Figshare · 2015
<p><sup>a</sup>Average computational time for an alignment pair, excluding the preprocessing step.</p><p><sup>b</sup>Average computational time required to preprocess a PDB file.</p><p>Computational time.</p>
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José G. Fuentes Cabrera (17458283), Hugo A. Pérez Vicente (17458286), Sebastián Maldonado (17458289) et al. · Figshare · 2023
<div><p>Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost. This study introduces an innovative approach by combining traditional discretization techniques with clustering-based discretization, specifically <i>k</i> means and Gaussian mixture models. The study proposes two combinations: Discrete Competitive Combination (DCC) and Discrete Exhaustive Combination (DEC). Discrete Competitive Combination selects features based on the discretization method that performs better on each feature, whereas Discrete Exhaustive Combination includes every discretization method to complement the information not captured by each technique. The proposed combinations were tested on 11 different credit risk datasets by fitting a logistic regression model using the weight of evidence transformation over the training partition and contrasted over the validation partition. The experimental findings showed that both combinations similarly outperform individual methods for the logistic regression without compromising the computational efficiency. More importantly, the proposed method is a feasible and competitive alternative to conventional methods without reducing explainability.</p></div>
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Mehran Aflakparast (8213769), Mathisca de Gunst (198647), Wessel van Wieringen (9174694) · Figshare · 2020
<p>Computational time to estimate mixture graphical networks as a function of (a) the data dimension with a fixed sample size of 100 and (b) the sample size with a fixed dimension of 10.</p>
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Lenna X. Peterson (3916196), Yoichiro Togawa (4762818), Juan Esquivel-Rodriguez (4762821) et al. · Figshare · 2018
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Tomáš Gedeon (2940378), Bree Cummins (322546), Shaun Harker (5131217) et al. · Figshare · 2018
<p>CPU time to compute all four resettable bistability and hysteresis queries for various networks.</p>
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Tao Yu (176965), Wei Huang (36889), Xin Tang (134573) et al. · Figshare · 2025
<div><p>In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions. Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. The effectiveness, efficiency, and robustness of the proposed TSC-SVM model are demonstrated through various real-world applications. The proposed algorithm is anticipated to expand the customer base for financial institutions while reducing economic losses.</p></div>
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Stefan Engelen (267607), Ladislas A. Trojan (267611), Sophie Sacquin-Mora (267617) et al. · Figshare · 2015
<p>JET computational time (in seconds) has been evaluated for 400 sequences\n homologous with that of <i>Escherichia coli</i> aspartate\n transcarbamoylase 9atc:A. Each of the trees used contains sequences, as indicated by the curve “number\n of sequences in trees”. For the evaluation, we used a Dual\n Intel Xeon (64-bit) 3.2GHz 2GB memory with Linux system.</p>
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S. M. A. Aftab (2623156), A. S. Mohd Rafie (2623162), N. A. Razak (2623165) et al. · Figshare · 2016
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Xun Fu (2081062), Jack Withers (18981088), Juri A. Miyamae (18981091) et al. · Figshare · 2024
<div><p>Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints—often more than three—and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim, customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that among various parameters—the number of tendon branches, the number of joints between branches, and the ratio of muscle fiber length to muscle tendon unit length—the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method. Notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim, we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.</p></div>
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Chi Wan Lim (553836), Yi Su (311753), Si Yong Yeo (553837) et al. · Figshare · 2015
<p>. In <b>A</b>, the mean CPU time (in milliseconds) incurred per vertex for normal frames in each dataset is shown. In <b>B</b>, the same information for frames with 2D contour topological changes is shown. The standard deviation in CPU time across different frames is shown as a line bar for each dataset.</p>
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Luo, Xuan · ResearchWorks at the University of Washington (University of Washington) · 2022
Thesis (Ph.D.)--University of Washington, 2022
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Liangjun Zhao (403690), Yun Zhang (131894), Linlu Dong (10169608) et al. · Figshare · 2022
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Eric Bennett, Leonard McMillan · ACM Transactions on Graphics · 2007
We present methods for generating novel time-lapse videos that address the inherent sampling issues that arise with traditional photographic techniques. Starting with video-rate footage as input, our post-process downsamples the source material into a time-lapse video and provides user controls for retaining, removing, and resampling events. We employ two techniques for selecting and combining source frames to form the output. First, we present a non-uniform sampling method, based on dynamic programming, which optimizes the sampling of the input video to match the user's desired duration and visual objectives. We present multiple error metrics for this optimization, each resulting in different sampling characteristics. To complement the non-uniform sampling, we present the virtual shutter , a non-linear filtering technique that synthetically extends the exposure time of time-lapse frames.
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Arslan Shafique (13363419), Abid Mehmood (13822340), Mourad Elhadef (13822343) et al. · Figshare · 2022
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2012
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Eric Bennett, Leonard McMillan · ACM Transactions on Graphics · 2007
We present methods for generating novel time-lapse videos that address the inherent sampling issues that arise with traditional photographic techniques. Starting with video-rate footage as input, our post-process downsamples the source material into a time-lapse video and provides user controls for retaining, removing, and resampling events. We employ two techniques for selecting and combining source frames to form the output. First, we present a non-uniform sampling method, based on dynamic programming, which optimizes the sampling of the input video to match the user's desired duration and visual objectives. We present multiple error metrics for this optimization, each resulting in different sampling characteristics. To complement the non-uniform sampling, we present the virtual shutter, a non-linear filtering technique that synthetically extends the exposure time of time-lapse frames.
Cited by 2
Ting Wang (16292), Zhao Ren (422101), Ying Ding (152708) et al. · Figshare · 2016
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