There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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XGBoost: A Scalable Tree Boosting System

The invention discloses a multi-field coupling transient numerical method for hypersonic flow-heat transfer and structural response. The method comprises the following steps of: determining wall temperature and displacement boundary condition according to structure, performing data exchange at a fluid-solid coupling interface, and obtaining the current temperature and displacement boundary condition; simultaneously solving coupled solution format of each preset conservation equation to obtain the current heat flux and pressure; performing data exchange at the fluid-solid coupling interface to obtain the boundary condition of a solid region; according to the boundary condition of the solid region, solving out the wall temperature and structure displacement through a thermal mechanical unity coupling method; and executing the steps above repeatedly until meeting a preset stop condition. Using the method provided by the invention, multi-field coupling calculation where a hypersonic non-equilibrium flow solver and a structural thermal/mechanical unity coupling solver are coupled is realized, prediction on pneumatic thermodynamic environment and structural thermodynamic response of a hypersonic aircraft meets physical reality better, and calculation precision can be guaranteed.

Multi-field coupling transient numerical method for hypersonic flow-heat transfer and structural response

The tutorial focuses on two major themes of recent advances in recommender systems: Part A: Recommendations in a Marketplace: Multi-sided marketplaces are steadily emerging as valuable ecosystems in many applications (e.g. Amazon, AirBnb, Uber), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer). This tutorial focuses on designing search & recommendation frameworks that power such multi-stakeholder platforms. We discuss multi-objective ranking/recommendation techniques, discuss different ways in which stakeholders specify their objectives, highlight user specific characteristics (e.g. user receptivity) which could be leveraged when developing joint optimization modules and finally present a number of real world case-studies of such multi-stakeholder platforms. Part B: Automated Recommendation System: As the recommendation tasks are getting more diverse and the recommending models are growing more complicated, it is increasingly challenging to develop a proper recommendation system that can adapt well to a new recommendation task. In this tutorial, we focus on how automated machine learning (AutoML) techniques can benefit the design and usage of recommendation systems. Specifically, we start from a full scope describing what can be automated for recommendation systems. Then, we elaborate more on three important topics under such a scope, i.e., feature engineering, hyperparameter optimization/neural architecture search, and algorithm selection. The core issues and recent works under these topics will be introduced, summarized, and discussed. Finally, we finalize the tutorial with conclusions and some future directions.

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys

Perovskite Cs3Bi2I9 Hexagonal Prisms with Ordered Geometry for Enhanced Photocatalytic Hydrogen Evolution

Transfer Learning in Natural Language Processing

The adoption of a massive number of synchronized phasor measurement units (PMUs) supporting the wide-area measurement system (WAMS) in current electric transmission networks brings direct benefit in provision of accurate and timely network measurements, but also exposes a set of outstanding technical challenges in security aspect. This paper looks into the security problem of state estimation in WAMS in the context of cyber-physical system (CPS) which often exhibits complex structural characteristics and dynamic operational phenomenon. Typical attacks on PMUs and adverse impact on network state estimation are explored and studied through carrying out a set of simulation experiments using the IEEE 14-bus transmission network model. The preliminary numerical result quantifies the impact of PMU measurement data tampering on the state estimation accuracy and confirms that the PMU-based state estimation potentially can be significantly affected by various forms of cyber attacks.

Qiang Yang

Papers

Multi-field coupling transient numerical method for hypersonic flow-heat transfer and structural response

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys

Perovskite Cs3Bi2I9 Hexagonal Prisms with Ordered Geometry for Enhanced Photocatalytic Hydrogen Evolution

Transfer Learning in Natural Language Processing

Cyber security assessment in PMU-based state estimation of smart electric transmission networks