Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Models that depict innovation as a smooth, well-behaved linear process badly misspecify the nature and direction of the causal factors at work. Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization.

An Overview of Innovation

Deep learning and its applications to machine health monitoring

Applications of machine learning to machine fault diagnosis: A review and roadmap

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.

Object Detection Method for Grasping Robot Based on Improved YOLOv5

Balanced structure and parameter search is critical to evolutionary design with genetic programming (GP). Structure Fitness Sharing (SFS), based on a structure labeling technique, is proposed to maintain the structural diversity of a population and combat premature convergence of structures. SFS achieves balanced structure and parameter search by applying fitness sharing to each unique structure in a population, to prevent takeover by the best structure and thereby maintain the diversity of both structures and parameters simultaneously. SFS does not require definition of a distance metric among structures, and is thus more universal and efficient than other fitness sharing methods for GP. The effectiveness of SFS is demonstrated on a real-world bond-graph-based analog circuit synthesis problem.

Structure Fitness Sharing (SFS) for evolutionary design by genetic programming

This paper addresses adaptive chaos control issue for the fractional-order arch MEMS resonator with fully unknown function, chaotic vibration and time delay under distributed electrostatic actuation. The phase diagrams and time histories are presented to investigate characteristic of chaotic attractor for the fractional-order arch MEMS resonator. In the process of controller design, the Chebyshev neural network with a weight and fractional-order adaptive law is employed to approximate complicated unknown function. In view of more advantages in modeling the behavior of the arch MEMS resonator in contrast to integer-order calculus, the fractional-order adaptive chaos control technology by merging Chebyshev neural network and backstepping is presented to guarantee the stability based on the fractional-order Lyapunov stability criterion. Finally, simulation studies are presented to illustrate feasibility of the proposed scheme.

https://link.springer.com/content/pdf/10.1007/s11071-017-3890-6.pdf

Adaptive chaos control of the fractional-order arch MEMS resonator

Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.

/pdf/tourism-review-sentiment-classification-using-a-4aqyugdrb0.pdf

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Superconductors have been one of the most intriguing materials since they were discovered more than a century ago. However, superconductors at room temperature have yet to be discovered. On the other hand, machine learning and especially deep learning has been increasingly used in material properties prediction and discovery in recent years. In this paper, we propose to combine the deep convolutional neural network (CNN) model with fully convolutional layers for feature extraction with gradient boosting decision tree (GBDT) for superconductors critical temperature ( $T\text{c}$ ) prediction. Our prediction model only uses the elemental property statistics of the materials as original input and learns a hierarchical representation of superconductors using convolutional layers. Computational experiments showed that our convolutional gradient boosting decision tree (ConvGBDT) model achieved the state-of-the-art results on three superconductor data sets: DataS, DataH, and DataK. By visually comparing the raw elemental feature distribution and the learned feature distribution, it is found that the convolutional layers of our ConvGBDT can learn features that can more effectively distinguish cuprate and iron-based superconductors. On the other hand, the GBDT part of our ConvGBDT model can learn the sophisticated mapping relationship between extracted features and the critical temperatures to obtain good prediction performance.

/pdf/computational-prediction-of-critical-temperatures-of-2m8gq5xw66.pdf

Shaobo Li

Papers

Object Detection Method for Grasping Robot Based on Improved YOLOv5

Structure Fitness Sharing (SFS) for evolutionary design by genetic programming

Adaptive chaos control of the fractional-order arch MEMS resonator

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Computational Prediction of Critical Temperatures of Superconductors Based on Convolutional Gradient Boosting Decision Trees