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.

/pdf/xgboost-a-scalable-tree-boosting-system-48ocu6x4c7.pdf

XGBoost: A Scalable Tree Boosting System

A new direction-of-arrival (DOA) estimation method for the sparse receiving array with gain/phase uncertainties is proposed. Because of the sparsity of the received signals, compressed sensing theory can be used to sample and recover receiving signals with less data. Owing to the existence of the gain/phase uncertainties, it would be difficult to estimate the DOA accurately when the sparse representation of the signals is not optimal. In order to reduce the influence of the gain/phase uncertainties on the sparse representation, the authors firstly transfer the array signal receiving model with the gain/phase uncertainties into an errors-in-variables (EIV) model, which treats the gain/phase uncertainties as an additive error matrix. Then a new DOA estimation method named simultaneous orthogonal matching pursuit-total least squares algorithm based on the EIV model is proposed. The DOAs will be obtained by estimating the sparse coefficients through iterations with the proposed method. Simulation results show that the sparse regularised total least squares algorithm is able to provide a more accurate DOA estimation with the gain/phase uncertainties than the existing calibration algorithms even with the sparse array.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2018.5087

DOA estimation based on compressed sensing with gain/phase uncertainties

In response to the occurrence of an air incident, controllers at one of the three Canadian Rescue Coordination Centers (RCC) must make a series of critical decisions on the appropriate procedures to follow. These procedures (called incident prosecution) include hypotheses formulation and information gathering, development of a plan for the search and rescue (SAR) missions and in the end, the generation of reports. We present in this paper the results of a project aimed at evaluating the applicability of CBR to help support incident prosecution in the RCC. We have identified three possible applications of CBR: Online help, real time support for situation assessment, and report generation. We present a brief description of the situation assessment agent system that we are implementing as a result of this study.

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Is CBR Applicable to the Coordination of Search and Rescue Operations? A Feasibility Study

In this article, I will discuss three challenges in today's data mining field. These challenges include: the transfer learning challenge, the social learning challenge and the mobile context mining challenge. I pick these three challenges because I think time is ripe for each of them to be addressed in a major way in the near future, given the current technological and societal readiness to tackle them. I also believe that each of the three challenges discussed in this article will help move the science and engineering of data mining forward, and have a great impact on society.

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Three challenges in data mining

A staged-framework for data preprocessing has been developed to support data mining and help service providers identify customers who might switch to a competitor. The framework pushes the cost sensitivity and data imbalance of customer retention data into the data preprocessing itself. Tests using data set from the ACM KDD Cup 1998 showed that the framework outperformed the winner of that data mining and knowledge discovery competition. The framework has also been incorporated into a software system, called ED-Money. To demonstrate the framework's ability to predict customer attrition with high accuracy, it was applied to some benchmark data and to a real customer attrition data set from a large Chinese mobile telecommunications company

/pdf/cost-sensitive-data-preprocessing-for-mining-customer-4s2mhiffs4.pdf

Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases

Random early detection (RED) was developed to improve the congestion avoidance behaviour of TCP/IP traffic, but can also benefit real-time interactive applications A novel solution is developed for a RED controlled buffer, modelled as an M/M/1 queue This gives explicit formulas relating load, performance, and RED configuration parameters Results for VoIP demonstrate that RED regulates the mean delay and delay variation under congested conditions, and that the new analysis accurately bounds performance

Qiang Yang

Papers

DOA estimation based on compressed sensing with gain/phase uncertainties

Is CBR Applicable to the Coordination of Search and Rescue Operations? A Feasibility Study

Three challenges in data mining

Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases

Excess-rate queuing theory for M/M/1/RED with application to VoIP QoS