MrBayes sMC3

2016 ◽  
Vol 32 (2) ◽  
pp. 246-265 ◽  
Author(s):  
Lídia Kuan ◽  
Frederico Pratas ◽  
Leonel Sousa ◽  
Pedro Tomás
Keyword(s):  
Dna Sequences ◽  
Software Package ◽  
State Of The Art ◽  
Phylogenetic Inference ◽  
Iterative Approach ◽  
Computational Power ◽  
Data Transfers ◽  
Bayesian Phylogenetic Inference ◽  
Level Parallelism ◽  
Number Of Iterations

MrBayes is a popular software package for Bayesian phylogenetic inference, which uses an iterative approach to derive an evolutionary tree for a collection of species whose DNA sequences are known. Computationally, MrBayes is characterized by a large number of iterations, each composed of a set of tasks that isolated are not very time-consuming, but are globally computationally demanding. To accelerate the latest MrBayes 3.2, this paper presents MrBayes sMC3, which relies on the computational power of an heterogeneous CPU+GPU platform. For this, MrBayes sMC3 exploits both task and data-level parallelism while minimizing the overheads associated with kernel launches and CPU-GPU data transfers. Experimental results indicate that the proposed parallel approach, together with the proposed set of optimizations, allow for an application acceleration of up to 10× regarding the original MrBayes, and up to 3× regarding the Beagle Library. Furthermore, by analyzing the convergence rate of MrBayes sMC3 with that of the state-of-the-art approaches, a significant reduction in execution time is observed.

scholarly journals A Termination Criterion for Probabilistic Point Clouds Registration

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 159-173
Author(s):  
Simone Fontana ◽  
Domenico Giorgio Sorrenti
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Computational Time ◽  
Termination Criteria ◽  
Practical Usefulness ◽  
Local Point ◽  
Termination Criterion ◽  
Probabilistic Point ◽  
Excessive Number ◽  
Number Of Iterations

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

scholarly journals Integrated multiscale biomaterials experiment and modelling: a perspective

2016 ◽  
Vol 6 (1) ◽  
pp. 20150098 ◽  
Author(s):  
Markus J. Buehler ◽  
Guy M. Genin
Keyword(s):  
State Of The Art ◽  
Biological Systems ◽  
The State ◽  
Computational Power ◽  
Multiscale Models ◽  
Predictive Capacity ◽  
Hierarchical Levels ◽  
Challenges And Opportunities

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

SDSE: A software package to simulate the evolution of a pair of DNA sequences

1989 ◽  
Vol 5 (1) ◽  
pp. 47-50
Author(s):  
José L. Oliver ◽  
Antonio Marín ◽  
Juan-Ramón Medina
Keyword(s):  
Dna Sequences ◽  
Software Package

scholarly journals An Examination of the Monophyly of Morning Glory Taxa Using Bayesian Phylogenetic Inference

2002 ◽  
Vol 51 (5) ◽  
pp. 740-753 ◽  
Author(s):  
Richard E. Miller ◽  
Thomas R. Buckley ◽  
Paul S. Manos
Keyword(s):  
Phylogenetic Inference ◽  
Morning Glory ◽  
Bayesian Phylogenetic Inference

scholarly journals Transfer Incremental Learning Using Data Augmentation

2018 ◽  
Vol 8 (12) ◽  
pp. 2512 ◽  
Author(s):  
Ghouthi Boukli Hacene ◽  
Vincent Gripon ◽  
Nicolas Farrugia ◽  
Matthieu Arzel ◽  
Michel Jezequel
Keyword(s):  
Incremental Learning ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Complexity ◽  
Computational Power ◽  
Learning Problem ◽  
Learning Techniques ◽  
Using Data ◽  
Selection Of

Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consists of learning incrementally new classes and examples over time. Combining the outstanding performances of Deep Neural Networks (DNNs) with the flexibility of incremental learning techniques is a promising venue of research. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA is based on pre-trained DNNs as feature extractors, robust selection of feature vectors in subspaces using a nearest-class-mean based technique, majority votes and data augmentation at both the training and the prediction stages. Experiments on challenging vision datasets demonstrate the ability of the proposed method for low complexity incremental learning, while achieving significantly better accuracy than existing incremental counterparts.

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