scholarly journals Statistics of spatial rotations and projection directions considering molecular symmetry in 3D electron cryo-microscopy

2020 ◽  
Author(s):  
Qi Zhang ◽  
Hai Lin ◽  
Mingxu Hu
Keyword(s):  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Molecular Symmetry ◽  
Domain Specific ◽  
Learning Techniques ◽  
Statistical Justification ◽  
Projection Images ◽  
The Mean ◽  
Spatial Rotations ◽  
Unique Games

AbstractElectron cryo-microscopy (cryoEM) three-dimensional (3D) reconstruction is based on estimations of orientations of projection images or 3D volumes. It is common that the macromolecules studied by cryoEM have molecular symmetry, which, unfortunately, has not been taken into consideration by any statistics for either spatial rotations or projection directions at this point. Meanwhile, there are growing needs to adopt advanced statistical methods, and further, modern machine learning techniques in cryoEM. Since those methodologies are built heavily upon statistical learning cornerstones, the absence of their domain-specific statistical justification limits their applications in cryoEM. In this research, based on the concept of non-unique-games (NUG), we propose two key statistical measurements, the mean and the variance, of both spatial rotations and projection directions when molecular symmetry is considered. Such methods are implemented in the open-source python package pySymStat.

scholarly journals Statistics of spatial rotations in 3D electron cryo-microscopy by unit quaternion description

2019 ◽  
Author(s):  
Mingxu Hu ◽  
Qi Zhang ◽  
Jing Yang ◽  
Xueming Li
Keyword(s):  
Statistical Methods ◽  
Three Dimensional ◽  
Molecular Symmetry ◽  
Directional Statistics ◽  
Statistical Tools ◽  
Unit Quaternion ◽  
Statistical Tool ◽  
Average Rotation ◽  
Projection Images ◽  
Spatial Rotations

AbstractElectron cryo-microscopy (cryoEM) involves the estimation of orientations of projection images or three-dimensional (3D) volumes. However, the lack of statistical tools of rotations in cryoEM fails to answer the growing demands for adopting advanced statistical methods. In this study, we develop a comprehensive statistical tool specialized for cryoEM based on an unit quaternion description of spatial rotations. Some basic properties and definitions of the quaternion, as well as a way to use the unit quaternion to describe and perform rotations, are first recalled. Then, based on the unit quaternion, the distance and geodesic between rotations are designed for cryoEM to enable comparisons and interpolations between rotations, which are prerequisites of statistics of rotations in 3D cryoEM. Further, methods of directional statistics specialized for cryoEM are developed, including calculations of the average rotation, sampling, and inference with uniform and angular central Gaussian (ACG) distribution, as well as an estimation of the rotation precision. Finally, the method of handling molecular symmetry is introduced. Using the unit quaternion system for cryoEM, we provide comprehensive mathematical tools for the analysis of spatial rotations in cryoEM.

Towards Games for Knowledge Acquisition and Modeling

2010 ◽  
Vol 2 (4) ◽  
pp. 48-66 ◽  
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Machine Learning Techniques ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Modelling Approach ◽  
Representation Of Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

scholarly journals Geomorphometry today

2021 ◽  
Vol 27 (2) ◽  
pp. 394-448
Author(s):  
Igor Florinsky
Keyword(s):  
Noise Suppression ◽  
Three Dimensional ◽  
Aerial Survey ◽  
Machine Learning Techniques ◽  
Multi Scale ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Analytical Review ◽  
The Subject

Topography is the most important component of the geographical shell, one of the main elements of geosystems, and the framework of a landscape. geomorphometry is a science, the subject of which is modeling and analyzing the topography and the relationships between topography and other components of geosystems. Currently, the apparatus of geomorphometry is widely used to solve various multi-scale problems of the Earth sciences. As part of the RFBR competition “Expansion”, we present an analytical review of the development of theory, methods, and applications of geomorphometry for the period of 2016–2021. For the analysis, we used a sample of 485 of the strongest and most original papers published in international journals belonging to the JCR Web of Science Core Collection quartile I and II (Q1–Q2), as well as monographs from leading international publishers. We analyze factors caused a progress in geomorphometry in recent years. These include widespread use of unmanned aerial survey and digital photogrammetry, development of tools and methods for survey of submarine topography, emergence of new publicly available digital elevation models (DEMs), development of new methods of DEM preprocessing for their filtering and noise suppression, development of methods of two-dimensional and three-dimensional visualization of DEMs, introduction of machine learning techniques, etc. We consider some aspects of the geomorphometric theory developed in 2016–2021. In particular, a new classification of morphometric values is presented. We discuss new computational methods for calculating morphometric models from DEM, as well as the problems facing the developers and users of such methods. We consider application of geomorphometry for solving multiscale problems of geomorphology, hydrology, soil science, geology, glaciology, speleology, plant science and forestry, zoogeography, oceanology, planetology, landslide studies, remote sensing, urban studies, and archaeology.

scholarly journals Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques

2020 ◽  
Vol 498 (3) ◽  
pp. 4518-4532
Author(s):  
W D Jennings ◽  
C A Watkinson ◽  
F B Abdalla
Keyword(s):  
Machine Learning ◽  
Power Spectrum ◽  
Brightness Temperature ◽  
Bubble Size ◽  
Prediction Error ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Ionization Fraction ◽  
Point Correlation ◽  
The Mean

ABSTRACT Three-point and high-order clustering statistics of the high-redshift 21 cm signal contain valuable information about the Epoch of Reionization (EoR). We present 3PCF-fast, an optimized code for estimating the three-point correlation function (3PCF) of 3D pixelized data such as the outputs from numerical and seminumerical simulations. After testing 3PCF-fast on data with known analytical 3PCF, we use machine learning techniques to recover the mean bubble size and global ionization fraction from correlations in the outputs of the publicly available 21cmfast code. We assume that foregrounds have been perfectly removed and negligible instrumental noise. Using ionization fraction data, our best multilayer perceptron (MLP) model recovers the mean bubble size with a median prediction error of around $10 {{\ \rm per\ cent}}$, or from the 21 cm differential brightness temperature with median prediction error of around $14 {{\ \rm per\ cent}}$. A further two MLP models recover the global ionization fraction with median prediction errors of around $4 {{\ \rm per\ cent}}$ (using ionization fraction data) or around $16 {{\ \rm per\ cent}}$ (using brightness temperature). Our results indicate that clustering in both the ionization fraction field and the brightness temperature field encode useful information about the progress of the EoR in a complementary way to other summary statistics. Using clustering would be particularly useful in regimes where high signal-to-noise ratio prevents direct measurement of bubble size statistics. We compare the quality of MLP models using the power spectrum, and find that using the 3PCF outperforms the power spectrum at predicting both global ionization fraction and mean bubble size.

scholarly journals Teaching robots social autonomy from in situ human guidance

2019 ◽  
Vol 4 (35) ◽  
pp. eaat1186 ◽  
Author(s):  
Emmanuel Senft ◽  
Séverin Lemaignan ◽  
Paul E. Baxter ◽  
Madeleine Bartlett ◽  
Tony Belpaeme
Keyword(s):  
Autonomous Systems ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Generic Properties ◽  
Domain Specific ◽  
Learning Techniques ◽  
The One ◽  
The Right ◽  
Social Autonomy

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.

Towards Games for Knowledge Acquisition and Modeling

2014 ◽  
pp. 281-299
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Elicitation ◽  
Machine Learning Techniques ◽  
Method Engineering ◽  
Formal Representation ◽  
Formal Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

scholarly journals Alzheimer's Disease: A Survey

2021 ◽  
Vol 8 (1) ◽  
pp. 33-39
Author(s):  
Harshitha ◽  
Gowthami Chamarajan ◽  
Charishma Y
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Neural Networks ◽  
Brain Area ◽  
Brain Mri ◽  
Three Dimensional ◽  
The Other ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Brain

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

Modeling of snow avalanche dynamics using open source software OpenFOAM

2021 ◽  
Author(s):  
Daria Romanova ◽  
Margarita Egiit
Keyword(s):  
Three Dimensional ◽  
Snow Avalanche ◽  
Machine Learning Techniques ◽  
Turbulent Regime ◽  
Snow Avalanches ◽  
Advantages And Disadvantages ◽  
Three Dimensional Modeling ◽  
The Neural Network ◽  
Learning Techniques ◽  
Protective Structures

<p>The work is devoted to the comparison of different approaches for modeling the dynamics of dense and powder snow avalanches. Various 3D and 2D approaches are considered. The accuracy of determining the avalanche run-out zone, the interaction of the flow with obstacles, the front speed, and various distributed parameters are evaluated. As objects for comparison, an experiment on the interaction of a slushflow with a combination of protective structures and a powder snow avalanche in the Khibiny mountains are modeled.</p><p> </p><p>Taking into account the advantages and disadvantages of various approaches based on basic solutions available in the OpenFOAM package, a specialized software avalancheFoam is being developed for three-dimensional modeling of the dynamics of snow avalanches, taking into account the complex turbulent regime and multiphase structure of the flow. Machine learning techniques are used to refine turbulent stresses. The neural network is trained on a dataset obtained from high-precision supercomputer simulation of the flow, and sets the form of additional refinement members of the mathematical model of less computational complexity. Various avalanche sites in the Khibiny mountains are modeled to validate the developed software.</p>

scholarly journals Grammar Filtering for Syntax-Guided Synthesis

2020 ◽  
Vol 34 (02) ◽  
pp. 1611-1618
Author(s):  
Kairo Morton ◽  
William Hallahan ◽  
Elven Shum ◽  
Ruzica Piskac ◽  
Mark Santolucito
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Automated Reasoning ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Domain Specific ◽  
Learning Techniques ◽  
Data Driven Approach ◽  
Domain Specific Knowledge

Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.

MACHINE APPROACHES TO PROTEIN FEATURE PREDICTION

1992 ◽  
Vol 03 (supp01) ◽  
pp. 183-193 ◽  
Author(s):  
David K. Tcheng ◽  
Shankar Subramaniam
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Protein Structure ◽  
Homology Modeling ◽  
Three Dimensional ◽  
Antibody Fragments ◽  
Machine Learning Techniques ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Protein Feature

Knowledge-based approaches are being increasingly used in predicting protein structure and motifs. Machine learning techniques such as neural networks and decision-trees have become invaluable tools for these approaches. This paper describes the use of machine learning in predicting sequence-based motifs in antibody fragments. Given the limited number of three dimensional structures and the plethora of sequences, this technique is useful for homology modeling of three dimensional structures of antibody fragments.

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