ppxscal/embedding_network huggingface dataset:
Informatics aided design of crystal chemistry
Authors: Krishna Rajan
Venue:  (2005)

The search and design of new materials can be significantly aided by combinatorial experiments. However the key to minimizing the search process in combinatorial experiments is to identify the key combinations that achieve the desired functionality in the class of materials being studied. The concept of virtual combinatorial experiments for materials selection and design strategy is useful to show how one may design combinatorial libraries a priori by integrating data mining techniques with physically robust multivariate data. In this thesis, using crystal chemistry of spinel nitrides as a framework for materials design, the methodology for integrating derived variables using newly proposed mixing rules based on statistical tools such as Partial Least Squares (PLS) and Principal Component Analysis (PCA) is described. Strategically selecting appropriate quantum mechanical and crystallographic data are used to predict and identify new alloy chemistries, modulus properties, and phase stabilities. This approach is unique in materials design because it overcomes problems of length scale by connecting the microscopic phenomena and macroscopic engineering properties. The integration of the physics based predictions with data mining predictions is used to propose new virtual compounds, especially with higher order or multicomponent chemistries. With the predicted target properties (bulk moduli and phase stabilities) by PLS, correlations between all variables in a created library of binary and ternary spinel nitrides is visualized in the dimensionally reduced structure maps created by PCA. Through these activities, materials informatics plays an important role in guiding the choice of the most promising chemistries that exhibit the desired functionality in the virtual combinatorial libraries of hypothetical materials.
Design Planning for Uniform Thermal Distribution
Authors: Rajendra M. Patrikar, Olivier Peyran
Venue: Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design (2006)

Thermal distribution has become an important reliability concern for today's integrated circuits. In traditional design flows, the temperature of the chip is assumed to be uniform across the substrate. However, non-uniform thermal distribution can be a major source of inaccuracy in delay and clock skew computations, and can have an impact on electro-migration reliability and self-heating effects. Hence, it has become necessary to obtain design with uniform temperature distribution to ensure minimum temperature gradient and avoid hot spots across the chip area. The uniform temperature distribution can be achieved by appropriate placement of circuit blocks during the physical design. However, placement should take into account factors such as area, aspect ratio and timing closure issues. Thus the design process becomes a multi-objective optimization problem, which we are solving using a genetic algorithm.
Data Systems Dynamic Simulation: a total system for data system design assessments and trade studies
Authors: James W. Hooper, Dale W. Rowe
Venue: ACM SIGSIM Simulation Digest (1978)

Data Systems Dynamic Simulation is a simulation system designed to reduce cost and time and increase the confidence and comprehensiveness of Data Systems Simulation. It is designed to simulate large data processing and communications systems from end-to-end or by subsystem.Those features relevant to system timing, control, sizing, personnel support activities, cost and external influences are modeled. Emphasis is placed on ease of use, comprehensive system performance measures, and extensive post simulation analysis capability. The system has been used to support trade studies of the National Aeronautics & Space Administration's data system needs in the 1985 to 1990 time frame.
Management of grid jobs and data within SAMGrid
Authors: A. Baranovski, G. Garzoglio, I. Terekhov, A. Roy, T. Tannenbaum
Venue: Proceedings of the 2004 IEEE International Conference on Cluster Computing (2004)

When designing SAMGrid, a project for distributing high-energy physics computations on a grid, we discovered that it was challenging to decide where to place user's jobs. Jobs typically need to access hundreds of files, and each site has a different subset of the files. Our data system SAM knows what portion of a user's data may be at each site, but does not know how to submit grid jobs. Our job submission system Condor-G knows how to submit grid jobs, but originally it required users to choose grid sites and gave them no assistance in choosing. This work describes how we enhanced Condor-G to interact with SAM to make good decisions about where jobs should be executed, and thereby improve the performance of grid jobs that access large amounts of data. All these enhancements are general enough to be applicable to grid computing beyond the data-intensive computing with SAMGrid.
User's Perceptions of an Automatic Activity Diary for Medical Annotation and Analysis
Authors: Joshua Edmison, David Lehn, Mark Jones, Thomas Martin
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

Many health monitoring applications require that the patient keep a log of activities so that when the physiological data is analyzed off-line, the data can be correlated to what the patient was doing before or during health events of interest. However, patients often do a poor job of self-reporting, so it would be desirable to use context-awareness to automatically annotate the physiological data with the user's activity. This paper presents some of the issues that arise in the design and use of a health monitoring and activity annotation system, including results from an informal survey about users' concerns about the control of physiological and activity data.
Automated Traffic Classification and Application Identification using Machine Learning
Authors: Sebastian Zander, Thuy Nguyen, Grenville Armitage
Venue: Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary (2005)

The dynamic classification and identification of network applications responsible for network traffic flows offers substantial benefits to a number of key areas in IP network engineering, management and surveillance. Currently such classifications rely on selected packet header fields (e.g. port numbers) or application layer protocol decoding. These methods have a number of shortfalls e.g. many applications can use unpredictable port numbers and protocol decoding requires a high amount of computing resources or is simply infeasible in case protocols are unknown or encrypted. We propose a novel method for traffic classification and application identification using an unsupervised machine learning technique. Flows are automatically classified based on statistical flow characteristics. We evaluate the efficiency of our approach using data from several traffic traces collected at different locations of the Internet. We use feature selection to find an optimal feature set and determine the influence of different features.
Shape and the Stereo Correspondence Problem
Authors: Abhijit S. Ogale, Yiannis Aloimonos
Venue: International Journal of Computer Vision (2005)

We examine the implications of shape on the process of finding dense correspondence and half-occlusions for a stereo pair of images. The desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities. To zeroth order, piecewise continuity becomes piecewise constancy. Using this approximation, we first discuss an approach for dealing with such a fronto-parallel shapeless world, and the problems involved therein. We then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity. In particular, we emphasize the following geometric fact: a horizontally slanted surface (i.e., having depth variation in the direction of the separation of the two cameras) will appear horizontally stretched in one image as compared to the other image. Thus, while corresponding two images, N pixels on a scanline in one image may correspond to a different number of pixels M in the other image. This leads to three important modifications to existing stereo algorithms: (a) due to unequal sampling, existing intensity matching metrics must be modified, (b) unequal numbers of pixels in the two images must be allowed to correspond to each other, and (c) the uniqueness constraint, which is often used for detecting occlusions, must be changed to an interval uniqueness constraint. We also discuss the asymmetry between vertical and horizontal slant, and the central role of non-horizontal edges in the context of vertical slant. Using experiments, we discuss cases where existing algorithms fail, and how the incorporation of these new constraints provides correct results.
Computer Society Magazines Set Coverage for 2006
Authors: Bob Ward
Venue: Computer (2005)

In 2006, Computer Society publications will address broad trends, emerging issues, historical perspectives, and recent developments in all aspects of the computing profession.Fred Brooks, Jr. Receives 2004 IEEE/ACM Eckert-Mauchly AwardFrederick P. Brooks, Jr. received the 2004 IEEE/ACM Eckert-Mauchly Award in recognition of his outstanding contributions to the field of computer and digital systems architectures.History-Themed Student Web Design Contest Offers $5,000 First PrizeEntrants competing for the $5,000 first-place cash prize in the Computer History Competition 60, an international competition for full-time undergraduate students, will create live Web sites that outline specific aspects of computer history.
A model for handling approximate, noisy or incomplete labeling in text classification
Authors: Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties associated with the labeling process. Given a labeled or partially labeled training corpus of text documents, the model estimates the joint distribution of training documents and class labels by using a generalization of the Expectation Maximization algorithm. The estimates can be used in standard classification models to reduce error rates. Since uncertainties in the labeling are taken into account, the model provides an elegant mechanism to deal with noisy labels. We provide an intuitive modification to the EM iterations by re-estimating the empirical. distribution in order to reinforce feature values in unlabeled data and to reduce the influence of noisily labeled examples. Considerable improvement in the classification accuracies of two popular classification algorithms on standard labeled data-sets with and without artificially introduced noise, as well as in the presence and absence of unlabeled data, indicates that this may be a promising method to reduce the burden of manual labeling.
Research on Association Rules Mining AlgorithmWith Item Constraints
Authors: Nan Lu, Jing-Zhou Zhou, Wang Zhe, Chun-Guang Zhou
Venue: Proceedings of the 2005 International Conference on Cyberworlds (2005)

The issues in the field of association rules mining with specific items are discussed first. To solve the problems in the ordinary algorithm, we put forward a new but efficient mining algorithm with itemconstraints, called EclatII. We then give an analysis on the performance of the algorithm as well as on its strategy. The experimental result shows that the Eclatll algorithm is more robust in items of using "low support" and "long pattern" association rules than others.
Demystifying 3D ICs: The Pros and Cons of Going Vertical
Authors: W. Rhett Davis, John Wilson, Stephen Mick, Jian Xu, Hao Hua, Christopher Mineo, Ambarish M. Sule, Michael Steer, Paul D. Franzon
Venue: IEEE Design Test (2005)

As 3D technologies become technologically viable, there is increasinginterest in determining the achievable payoff. This article first presents an overview of 3D technologies and introduces the motivation for moving from 2D to 3D. It then presents a case study of a fast-Fourier-transform design to illustrate the advantages of going to the third dimension.
Applying artificial intelligence data mining tools to the challenges of program evaluation
Authors: Sharon Epple Beitel
Venue:  (2005)

Educational data warehouses, particularly in this accountability age of No Child Left Behind, are replete with a variety data. The data represents a wide range of variables (input, output and process). Up until now, educational leaders have not been able to analyze the variety and plethora of data they have at hand to answer complex questions of root cause. The data warehouse in a suburban, New England K&ndash;8 school district was analyzed using Artificial Intelligence data mining tools. These tools have proved useful and beneficial in the business world for many years. The superintendent, like most educational leaders, asked a predictive question regarding the efficacy of a newly implemented reading program in the lower elementary grades. First, a traditional analysis was conducted on first and second grade data which determined that students who had received a new reading program performed statistically significantly better than those who did not receive the new program. Next, through the use of a classification and regression tree (CART) algorithm, the lower elementary data was successfully mined. From an extended selection of second grade variables (reading program, writing sample scores, gender, attendance, parent/guardian, ethnicity, kindergarten experience, readiness screening and teacher), the CART algorithm identified the reading program variable as the most important variable in relation to the target variable (Gates Reading Test comprehension section). This result, generalized to the district studied, indicated that the reading program variable was predictive of achievement on the target variable (Gates Reading Test comprehension section). This analysis provided meaningful and thought-provoking information to the superintendent and assistant superintendent regarding their reading programs. Data mining analyses can now be conducted over the course of a few days. The analyses also stirred questions of which program variables are predictive of other target variables preK&ndash;8. The application of Artificial Intelligence data mining tools on educational data warehouses could enhance, supplement or even fast-track traditional program evaluation processes for educational leaders.
Improving GridFTP Performance with Split TCP Connections
Authors: Philip Rizk, Cameron Kiddle, Rob Simmonds
Venue: Proceedings of the First International Conference on e-Science and Grid Computing (2005)

Many grid computing projects involve sharing and transferring large volumes of data. It is often important to maximise data throughput. Grid computing environments can be highly heterogeneous in nature and could involve large round trip times between collaborating sites. The actual throughput achieved could be much lower than the available bandwidth due to TCP dynamics. One technique that helps improve TCP performance in heterogeneous network environments is split TCP connections, where the connection is split at one or more points in the network. This paper examines the use of this technique for GridFTP file transfers. Results are promising showing up to four times improvement over GridFTP file transfers involving just a single end-to-end TCP connection.
Fractional Model of Market Behavior: a New Modeling Approach
Authors: Vladimir V. Kulish, Weng-Kong Chan
Venue: Proceedings of the 2005 International Conference on Cyberworlds (2005)

Stochastic models are a standard tool for analyzing the behavior of markets in response to changes that happen in the environments, in which these markets function. This paper presents a novel model of market analysis that uses the fractional calculus as its main tool. The model is based on the assumption that the market value is proportional to the energy-informational content of the value bearing item. The model is developed starting from fundamental principles, such as energy conservation which is valid for all cases. The evolution of the market value is described by means of the Volterra integral equation that describes the major and minor trends in the value variation. Using the data available for public access on the Internet at http://www.gold.org, the model developed in this study has been validated by showing that the evolution of the gold price can be quite adequately modeled by considering single major disturbance acting upon the world market.
The design of smart homes for people with dementia&#x2014;user-interface aspects
Authors: R. Orpwood, C. Gibbs, T. Adlam, R. Faulkner, D. Meegahawatte
Venue: Universal Access in the Information Society (2005)

This paper reports on the user requirements analysis, design and evaluation of smart home solutions for people with dementia. In order to be appropriate for people with dementia, smart home technologies should keep interaction with users to a minimum. In the work presented in this paper, discussions with carers, as well as a more formal user-survey, provided a good understanding of the issues that are faced by this group, and useful indications of potential design solutions. This preliminary survey work led to a design approach that used carer emulation, familiarity of appearance, incorporation of verbal prompts and reminders, and careful user-monitoring, to achieve effective devices. This design approach is illustrated through several examples of specific designs. Evaluations of stand-alone systems were carried out prior to smart home installation, and illustrated the complex interface that exists between assistive technology and people with dementia.
Efficient matched processing for localisation of a moving acoustic source
Authors: Stephen J. Searle
Venue: Signal Processing (2005)

In recent years matched processing techniques have emerged as a solution for the problem of target motion analysis. A target's motion parameters are estimated by determining which of a bank of replica signals best correlates with the signal received from that target. Matched processing methods have been used in low SNR environments such as the underwater acoustic environment because they outperform pseudomeasurement-based target motion analysis. However, matched processing is notoriously computationally demanding, requiring evaluation of complex equations for replica generation, and batch processing via an exhaustive search of a multidimensional parameter space. This study presents a matched processing method for localisation of a slow-moving low-frequency acoustic source by matching Fourier coefficients from a single frequency bin. A simple range-dependent propagation model is assumed, and the Doppler effect is carefully modelled to produce realistic replicas. An efficient matching algorithm is presented which computes cost values in a recursive fashion, discarding poorly scoring replicas before they are fully evaluated. Simulated examples demonstrate the ability of the technique to resolve the locus of low-Doppler targets at extremely low SNR with a fraction of the cost associated with exhaustive search batch processing.
Characterization of the Indoor/Outdoor to Indoor MIMO Radio Channel at 2.140 GHz
Authors: Hung Tuan Nguyen, Jørgen Bach Andersen, Gert Frølund Pedersen
Venue: Wireless Personal Communications: An International Journal (2005)

In this paper the radio channel characteristics of the 8 × 4 MIMO system consisting of a base station and a small terminal equipped with multiple antennas for indoor-indoor and outdoor-indoor scenarios are presented. We study the large-scale variation and small-scale characteristics of the measured channel coefficients. Although the mean received power is very much dependent on the measured location, the channel capacity seems to be unchanged when the receiver's location is altered. The data collected from different scenarios (e.g. measurement locations, antenna setting) were used to investigate the advantage of having the knowledge of the channel at both ends of the transmission link. It is shown that using the water filling algorithm there is indeed an increase in the channel capacity. At low SNR, the benefit of knowing the channel at both link ends observed in the measurement data is much higher than which can be obtained in the channel matrix with usual assumption on identical independently distributed components. Using the small-scale and large-scale information in the formulation of the channel capacity we show that in our measurement, the variation of the mean received power has a greater influence on the change of the overall system performance than the change in the environmental multipath scattering property.
Visualizing Global Manifold Based on Distributed Local Data Abstractions
Authors: Xiaofeng Zhang, William K. Cheung
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Mining distributed data for global knowledge is getting more attention recently. The problem is especially challenging when data sharing is prohibited due to local constraints like limited bandwidth and data privacy. In this paper, we investigate how to derive the embedded manifold (as a 2-D map)for a horizontally partitioned data set, where data cannot be shared among the partitions directly. We propose a model-based approach which computes hierarchical local data abstractions, aggregates the abstractions, and finally learns a global generative model — generative topographic mapping (GTM) based on the aggregated data abstraction. We applied the proposed method to two benchmarking data sets and demonstrated that the accuracy of the derived manifold can effectively be controlled by adjusting the data granularity level of the adopted local abstraction.