Algorithmic Selection of Sliding-Sticking Contacts in Robotic In-Hand Manipulation

2021 ◽  
pp. 1-10
Author(s):  
Rajesh Kumar ◽  
Sudipto Mukherjee
Keyword(s):  
Gradient Descent ◽  
Sliding Contacts ◽  
Movement Sequence ◽  
Kinematic Method ◽  
Selection Of

Abstract The paper describes a kinematic method for robotic in-hand manipulation of objects. The method focuses on repositioning the object using a combination of sticking and sliding robotic contacts. Two fingertips with sliding contacts are fixed in space while the remaining fingertips actively manipulate the object without a change in the point of contact with the object. When sliding over two fixed contacts, the object is constrained to a “three-parameter twist space” if it is not programmed to rotate about the line joining the two fixed contacts. A gradient-descent-based kinematic algorithm is developed to project the desired twist to the allowable twist space, generating a movement sequence of robotic fingertips. The transition from fixed support vis-á-vis the sticking contacts for manipulating the object also emerges from the algorithm.

scholarly journals BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

2021 ◽  
Author(s):  
Sibylle Hess ◽  
Gianvito Pio ◽  
Michiel Hochstenbach ◽  
Michelangelo Ceci
Keyword(s):  
Data Mining ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
High Dimensional ◽  
Optimization Scheme ◽  
Real World Data ◽  
Binary Constraints ◽  
Selection Of

AbstractMatrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures.

scholarly journals Optimization of the High Nonlinear S-Boxes Generation Method

2017 ◽  
Vol 70 (1) ◽  
pp. 93-105 ◽  
Author(s):  
Mariia Rodinko ◽  
Roman Oliynykov ◽  
Yurii Gorbenko
Keyword(s):  
Information Security ◽  
Gradient Descent ◽  
Symmetric Encryption ◽  
Computational Power ◽  
Security Systems ◽  
Methods And Techniques ◽  
National University ◽  
Encryption Algorithms ◽  
Proposed Modification ◽  
Selection Of

Abstract The known method of high nonlinear S-boxes generation based on the gradient descent [Kazymyrov, O. V.: Methods and Techniques of Generation of Nonlinear Substitutions for Symmetric Encryption Algorithms. The thesis for the scholarly degree of candidate of technical sciences, speciality 05.13.21 - - Information security systems, Kharkiv National University of Radioelectronics, Kharkiv, 2014. (In Russian)] requires consecutive applications of several criteria for each formed substitution. This paper presents an improvement of the considered method by the appropriate selection of the criteria application order which decreases the required computational power for S-box generation. The proposed modification allows generation of a byte substitution with nonlinearity 104, algebraic immunity 3 and 8-uniformity within approximately 30 minutes of a single PC running time.

LEARNING ACTIVATION RULES RATHER THAN CONNECTION WEIGHTS

1996 ◽  
Vol 07 (02) ◽  
pp. 129-147 ◽  
Author(s):  
ERIC L. GRUNDSTROM ◽  
JAMES A. REGGIA
Keyword(s):  
Gradient Descent ◽  
A Priori ◽  
Learning Rule ◽  
Rule Based ◽  
Activation Mechanisms ◽  
Activation Rule ◽  
Associative Recall ◽  
Selection Of ◽  
Competitive Activation ◽  
Standard Learning

In the construction of neural networks involving associative recall, information is sometimes best encoded with a local representation. Moreover, a priori knowledge can lead to a natural selection of connection weights for these networks. With predetermined and fixed weights, standard learning algorithms that work by altering connection strengths are unable to train such networks. To address this problem, this paper derives a supervised learning rule based on gradient descent, where connection weights are fixed and a network is trained by changing the activation rule. It incorporates both traditional and competitive activation mechanisms, the latter being an efficient method for instilling competition in a network. The learning rule has been implemented, and the results from several test networks demonstrate that it works effectively.

scholarly journals Attentional Selection of Multiple Goal Positions Before Rapid Hand Movement Sequences: An Event-related Potential Study

2009 ◽  
Vol 21 (1) ◽  
pp. 18-29 ◽  
Author(s):  
Daniel Baldauf ◽  
Heiner Deubel
Keyword(s):  
Hand Movement ◽  
Event Related Potential ◽  
Dot Probe ◽  
Manual Pointing ◽  
Movement Sequences ◽  
The Third ◽  
Movement Sequence ◽  
Pointing Movements ◽  
Task Irrelevant ◽  
Selection Of

A dot-probe paradigm was used to provide physiological evidence for the parallel selection of multiple movement goals before rapid hand movement sequences. Participants executed a sequence of manual pointing movements to two out of three possible goal positions. During movement preparation, a task-irrelevant visual transient (a dot probe) was flashed either at one of both movement goals, or at the third, movement-irrelevant location. The results revealed that the N1 component induced by the presentation of the dot was enhanced if the dot was flashed at one of the movement goals, indicating that both target positions were attended before the initialization of the movement sequence. A second experiment showed that movement-irrelevant locations between the movement goals were not attended, suggesting that attention splits into spatially distinct foci.

The Design of Strapdown Inertial AHRS Based on MEMS

2013 ◽  
Vol 756-759 ◽  
pp. 518-522
Author(s):  
Qing Hui Wang ◽  
Dan Li ◽  
Lei Chang ◽  
Wen Zhou Wang
Keyword(s):  
Real Time ◽  
Reference System ◽  
Gradient Descent ◽  
Low Cost ◽  
Proper Selection ◽  
Descent Algorithm ◽  
The Real ◽  
The Core ◽  
Gradient Descent Algorithm ◽  
Selection Of

A method of low cost strapdown inertial Attitude and Heading Reference System based on MEMS is implemented in this paper. Based on the analysis of the modules, the proper selection of the core processor and the inertial devices, the hardware components of the system is presented; Using the gradient-descent algorithm which is based on quaternion, it can finish the calculation of the attitude; This AHRS can realize the real-time extraction, calculation and the output of the information.

scholarly journals New tools for automated cryo-EM single-particle analysis in RELION-4.0

2021 ◽  
Author(s):  
Dari Kimanius ◽  
Liyi Dong ◽  
Grigory Sharov ◽  
Takanori Nakane ◽  
Sjors H.W. Scheres
Keyword(s):  
Structure Determination ◽  
Gradient Descent ◽  
Particle Analysis ◽  
Data Sets ◽  
Variable Metric ◽  
Gradient Descent Algorithm ◽  
Potential Applications ◽  
Flexible Framework ◽  
Robust Procedures ◽  
Selection Of

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a Variable-metric gradient Descent algorithm with Adaptive Moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on twelve publicly available cryo-EM data sets.

scholarly journals New tools for automated cryo-EM single-particle analysis in RELION-4.0

2021 ◽  
Author(s):  
Dari Kimanius ◽  
Liyi Dong ◽  
Grigory Sharov ◽  
Takanori Nakane ◽  
Sjors H.W. Scheres
Keyword(s):  
Structure Determination ◽  
Gradient Descent ◽  
Particle Analysis ◽  
Data Sets ◽  
Variable Metric ◽  
Gradient Descent Algorithm ◽  
Potential Applications ◽  
Flexible Framework ◽  
Robust Procedures ◽  
Selection Of

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a Variable-metric gradient Descent algorithm with Adaptive Moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on twelve publicly available cryo-EM data sets.

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