ppxscal/embedding_network huggingface dataset:
An abbreviated concept-based query language and its exploratory evaluation
Authors: Vesper Owei, Shamkant B. Navathe, Hyeun-Suk Rhee
Venue: Journal of Systems and Software (2002)

Research on the use of conceptual information in database queries has primarily focused on semantic query optimization. Studies on the important aspects of conceptual query formulation are currently not as extensive. Only a relatively small number of works exist in this area. The existing concept-based query languages are similar in the sense that they require the user to specify the entire query path in formulating a query. In this study, we present the Conceptual Query Language (CQL), which does not require entire query paths to be specified but only their terminal points. CQL is an abbreviated concept-based query language that allows for the conceptual abstraction of database queries and exploits the rich semantics of semantic data models to ease and facilitate query formulation. CQL was developed with the aim of providing typical end-users like secretaries and administrators an easy-to-use database query interface for querying and report generation. A CQL prototype has been implemented and currently runs as a front-end to an underlying relational DBMS. A statistical experiment conducted to probe end-users' reaction to using CQL vis-à-vis SQL as a database query language indicates that end-users perform better with CQL and have a better perception of it than of SQL. This paper discusses the design of CQL, the strategies for CQL query processing, and the comparative study between CQL and SQL.
Relax, but don't be too lazy
Authors: Joris Van der Hoeven
Venue: Journal of Symbolic Computation (2002)

Assume that we wish to expand the product h = fg of two formal power series f and g. Classically, there are two types of algorithms to do this: zealous algorithms first expand f and g up to order n, multiply the results and truncate at order n. Lazy algorithms on the contrary compute the coefficients of f, g and h gradually and they perform no more computations than strictly necessary at each stage. In particular, at the moment we compute the coefficient hi of zi in h, only f0,..., fi and g0,..., gi are known.Lazy algorithms have the advantage that the coefficients of f and g may actually depend on "previous" coefficients of h, as long as they are computed before they are needed in the multiplication, i.e. the coefficients fi and gi may depend on h0,..., hi-1. For this reason, lazy algorithms are extremely useful when solving functional equations in rings of formal power series. However, lazy algorithms have the disadvantage that the classical asymptotically fast multiplication algorithms on polynomials--such as the divide and conquer algorithm and fast Fourier multiplication--cannot be used.In a previous paper, we therefore introduced relaxed algorithms, which share the property concerning the resolution of functional equations with lazy algorithms, but perform slightly more computations than lazy algorithms during the computation of a given coefficient of h. These extra computations anticipate the computations of the next coefficients of h and dramatically improve the asymptotic time complexities of such algorithms.In this paper, we survey several classical and new zealous algorithms for manipulating formal power series, including algorithms for multiplication, division, resolution of differential equations, composition and reversion. Next, we give various relaxed algorithms for these operations. All algorithms are specified in great detail and we prove theoretical time and space complexity bounds. Most algorithms have been experimentally implemented in C++ and we provide benchmarks. We conclude by some suggestions for future developments and a discussion of the fitness of the lazy and relaxed approaches for specific applications.This paper is intended both for those who are interested in the most recent algorithms for the manipulation of formal power series and for those who want to actually implement a power series library into a computer algebra system.
An EGA approach to the compile-time assignment of data to multiple memories in digital-signal processors
Authors: Gary Gréwal, Tom Wilson, Andrew Morton
Venue: ACM SIGARCH Computer Architecture News (2003)

In this paper, we present a methodology, based on an Enhanced Genetic Algorithm (EGA), for assigning data objects to dual-bank memories. Our approach is global, and special effort is made to identify those objects that could potentially benefit from an assignment to a specific memory, or perhaps duplication in both memories. The enhancements to the genetic algorithm include a directed mutation operator and a new type of elitism. Together, these enhancements improve the performance of the genetic algorithm and allow the EGA to run unsupervised. The EGA has been incorporated into a retargetable, optimizing compiler for embedded systems, currently under development at the University of Guelph.
A new data-mapping scheme for latency-tolerant distributed sparse triangular solution
Authors: Keita Teranishi, Padma Raghavan, Esmond Ng
Venue: Proceedings of the 2002 ACM/IEEE conference on Supercomputing (2002)

This paper concerns latency-tolerant schemes for the efficient parallel solution of sparse triangular linear systems on distributed memory multiprocessors. Such triangular solution is required when sparse Cholesky factors are used to solve for a sequence of right-hand-side vectors or when incomplete sparse Cholesky factors are used to precondition a Conjugate Gradients iterative solver. In such applications, the use of traditional distributed substitution schemes can create a performance bottleneck when the latency of interprocessor communication is large. We had earlier developed the Selective Inversion (SI) scheme to reduce communication latency costs by replacing distributed substitution by parallel matrix vector multiplication. We now present a new two-way mapping of the triangular sparse matrix to processors to improve the performance of SI by halving its communication latency costs. We provide analytic results for model sparse matrices and we report on the performance of our scheme for parallel preconditioning with incomplete sparse Cholesky factors.
A novel net weighting algorithm for timing-driven placement
Authors: Tim (Tianming) Kong
Venue: Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design (2002)

Net weighting for timing-driven placement has been very popular in industry and academia. It has various advantages such as low complexity, high flexibility and ease of implementation. Existing net weighting algorithms, however, are often ad-hoc. There is generally no known good net weighting algorithms. In this paper, we present a novel net weighting algorithm based on the concept of path-counting, and apply it in timing-driven FPGA placement application. Theoretically this is the first ever known accurate, all-path counting algorithm. Experimental data shows that compared with the weighting algorithm used in state-of-the-art FPGA placement package VPR[1], this new algorithm can achieve the longest path delay reduction of up to 38.8%, 15.6% on average with no runtime overhead and only a 4.1% increase in total wirelength.
Micromotives in Global Environmental Policy
Authors: Jason F. Shogren
Venue: Interfaces (2002)

Protecting ourselves cost-effectively from such global environmental threats as climate change requires a good understanding of the behavioral underpinnings that drive choice under risk and uncertainty, lead to the design of efficient and effective protocols for cooperative action across nations, and create incentives for different control strategies within nations. Researchers can use experimental economic methods to gain insight into these micromotives of climate policy by testing the robustness of theoretical predictions, and by recognizing new patterns of choice. Experiments can be used to sharpen the best guesses guiding policy by identifying how decision makers can try to get more environmental progress at less cost by accounting for the relevant determinants of behavior.
Load balancing distributed file system servers: a rule-based approach
Authors: Alexandra Glagoleva, Archana Sathaye
Venue: Web-enabled systems integration: practices and challenges (2003)

In this paper, we present a new methodology for managing read-write filesets across multiple file servers of a Distributed File System, thus balancing the load of file access requests across servers. The proposed methodology is based on a rule-based data mining technique and graph theory algorithms. The rule-based technique generates rules from access request data to identify present file access patterns in the system. We then use the rules, graph analysis and statistical information (usage and size of the filesets) to relocate the filesets between different file servers. The algorithm for fileset relocation is based on the graph coloring problem. We tested our algorithms on data collected for five months on DFS file servers in a production environment. Experiments with the data show that our methodology can make intelligent decisions about file system transfers in order to balance the access request load across DFS servers.
A TASOM-based algorithm for active contour modeling
Authors: Hamed Shah-Hosseini, Reza Safabakhsh
Venue: Pattern Recognition Letters (2003)

Active contour modeling is a powerful technique for modeling object boundaries. Various methods introduced for this purpose, however, have certain difficulties such as getting stuck in local minima, poor modeling of long concavities, and producing inaccurate results when the initial contour is chosen simple or far from the object boundary. A modified form of time adaptive self-organizing map network with a variable number of neurons is proposed here for active contour modeling which does not show such difficulties and automatically determines the required number of control points. The initial contour for the object boundary can be defined inside, outside, or across the boundary. This contour can be open or closed, may be as simple as desired, and can be placed far from the object boundary. In addition, the boundary may contain long concavities. The proposed algorithm is tested for modeling different objects and shows very good performance.
Feature Selection Via Mathematical Programming
Authors: P. S. Bradley, O. L. Mangasarian, W. N. Street
Venue: INFORMS Journal on Computing (1998)

The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints. Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage method for reducing neural network complexity. One feature selection algorithm via concave minimization reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4.
The Specifications Exchange Service of an RM-ODP Framework
Authors: Xavier Blanc, Marie-Pierre Gervais, Juliette Le Delliou
Venue: Proceedings of the 4th International conference on Enterprise Distributed Object Computing (2000)

LIP6 and EDF have started to develop an RM-ODP framework for building open distributed systems. The first concern of this development was the construction of the Specification Exchange Service. This service, the so-called Backbone, enables to exchange RM-ODP specifications over any ORB or file system. This article presents the functionalities of the Backbone in the RM-ODP framework and its explains how meta-modeling standards such as MOF and XMI have been used to create it.
Three extensions to register integration
Authors: Vlad Petric, Anne Bracy, Amir Roth
Venue: Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture (2002)

Register integration (or just integration) is a register renaming discipline that implements instruction reuse via physical register sharing. Initially developed to perform squash reuse, the integration mechanism can exploit more reuse scenarios. Here, we describe three extensions to the original design that expand its applicability and boost its performance impact. First, we extend squash reuse to general reuse. Whereas squash reuse maintains the concept of an instruction instance "owning" its output register, we allow multiple instructions to simultaneously share a single register. Next, we replace the PC indexing scheme with an opcode-based indexing scheme that exposes more integration opportunities. Finally, we introduce an extension called reverse integration in which we speculatively create integration entries for the inverses of operations---for instance, when renaming an add, we create an entry for the inverse subtract. Reverse integration allows us to reuse operations that the program itself has not executed yet. We use reverse integration to implement speculative memory bypassing for stack-pointer based loads (register fills and restores).Our evaluation shows that these extensions increase the integration rate---the number of retired instructions that integrate older results and bypass the execution engine---to an average of 15% on the SPEC2000 integer benchmarks. On a 4-way superscalar processor with an aggressive memory system, this translates into an average IPC improvement of 7%. The fact that integrating instructions completely bypass the execution engine raises the possibility of using integration as a low-complexity substitute for execution bandwidth and issue buffering. Our experiments show that such a trade-off is possible, enabling a range of IPC/complexity designs.
Optimal task scheduling at run time to exploit intra-tile parallelism
Authors: Fabrice Rastello, Amit Rao, Santosh Pande
Venue: Parallel Computing (2003)

In this paper we address the issue of iteration space tiling to minimize the completion time of loops when executed on multicomputers. The previous work on tiling assumes atomic execution of tiles to minimize synchronization costs. In this work, we remove the restriction of atomicity of tiles so that internal parallelism within tiles is exploited by overlapping computation with communication on multicomputers. The effectiveness of tiling is then critically dependent on the execution order of tasks within a tile. In this paper we present a theoretical framework based on equivalence classes that provides an optimal task ordering under assumptions of fixed and variable orderings of tasks in individual tiles. Our framework is able to handle loop invariant compile-time unknown dependences by efficiently generating optimal task orderings at run-time and results in lower loop completion times. Our solution is an improvement over previous approaches [Proceedings of Euromicro Workshop on Parallel and Distributed Processing, IEEE Computer Society Press, 1995, pp. 571-580; Proceedings of the International Conference on Application Specific Array Processors (ASAP), 1993, pp. 53-64]. Unlike [Proceedings of Euromicro Workshop on Parallel and Distributed Processing, IEEE Computer Society Press, 1995, pp. 571-580; Proceedings of the International Conference on Application Specific Array Processors (ASAP), 1993, pp. 53-64], our approach is optimal for all problem instances with one dependence vector in one-dimension. We show that the performance improvement over previous results is good.
A performance assessment of Bayesian networks as a predictor of breast cancer survival
Authors: Amy Moore, Albert Hoang
Venue: Second international workshop on Intelligent systems design and application (2002)

This paper is intended to assess the survival analysis of Bayesian Network models, Neural Network models, and Logistic Regression models. Our analysis will be performed on the SEER data set, a registry of women with breast cancer from the National Cancer Institute. Each model will include the following prognostic variables; progesterone (PR), estrogen (ER), lymph involvement (N), morphology (M), extension and tumor size (T), and histological grade (G). These variables have proven to be significant to the model in regards to breast cancer survival. We have found that a Bayesian network model, which is a combination of an automatically generated network by the BKD software and human expert knowledge, performs comparatively better than the Neural Networks and logistic regression models. The Bayesian network also offers the advantage of explaining the causal relationships among the variables, thus it is the most promising model
The evolution of Prot&eacute;gé: an environment for knowledge-based systems development
Authors: John H. Gennari, Mark A. Musen, Ray W. Fergerson, William E. Grosso, Monica Crubézy, Henrik Eriksson, Natalya F. Noy, Samson W. Tu
Venue: International Journal of Human-Computer Studies (2003)

The Protégé project has come a long way since Mark Musen first built the Protégé meta-tool for knowledge-based systems in 1987. The original tool was a small application, aimed at building knowledge-acquisition tools for a few specialized programs in medical planning. From this initial tool, the Protégé system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Protégé-2000, can be run on a variety of platforms, supports customized user-interface extensions, incorporates the Open Knowledge-Base Connectivity (OKBC) knowledge model, interacts with standard storage formats such as relational databases, XML, and RDF, and has been used by hundreds of individuals and research groups. In this paper, we follow the evolution of the Protégé project through three distinct re-implementations. We describe our overall methodology, our design decisions, and the lessons we have learned over the duration of the project. We believe that our success is one of infrastructure: Protégé is a flexible, well-supported, and robust development environment. Using Protégé, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains.
Ordered sets and complete lattices: a primer for computer science
Authors: Hilary A. Priestley
Venue: Algebraic and coalgebraic methods in the mathematics of program construction (2002)

These notes deal with an interconnecting web of mathematical techniques all of which deserve a place in the armoury of the well-educated computer scientist. The objective is to present the ideas as a self-contained body of material, worthy of study in its own right, and at the same time to assist the learning of algebraic and coalgebraic methods, by giving prior familiarization with some of the mathematical background that arises there. Examples drawn from computer science are only hinted at: the presentation seeks to complement and not to preempt other contributions to these ACMMPC Proceedings.
End-User Modeling Improves R D Management at Agrevo Canada, Inc.
Authors: Calvin Sonntag, Thomas A. Grossman,  Jr.
Venue: Interfaces (1999)

The manager of strategic-project planning used spreadsheet modeling and optimization to change the process AgrEvo Canada uses to allocate resources to crop protection and biotechnology R&D projects. We developed a spreadsheet optimization model to show that a perceived bottleneck actually had excess capacity and that all projects could be funded by reallocating resources. To achieve benefits from these insights required resolving political issues rather than solving optimization problems. Descriptive models proved superior to prescriptive optimization results for achieving buy-in. Use of the model led to the shortest and most objective budgeting meeting ever. We brought clarity to a vexing internal debate and helped to retain R&D projects valued at tens of millions of dollars. Managers can effectively apply management science without intervention by management science professionals. End-user modelers are different from expert consultants. Additional field research on end-user modeling is needed.
SYMBIONT: a cooperative evolutionary model for evolving artificial neural networks for classification
Authors: Nicolás García-Pedrajas, César Hervás-Martínez, José Muñoz-Pérez
Venue: Technologies for constructing intelligent systems: tools (2002)

A new cooperative evolutionary model, called Symbiont, for evolving artificial neural networks is presented in this paper. This model is based on the idea of developing subnetworks, called nodules, that must cooperate to form a solution, instead of evolving a complete network. The performance of the model in solving two real-world problems of classification is compared with a multilayer perceptron trained using back-propagation. Symbiont has proved to show better generalization than the multilayer perceptron and to evolve smaller networks.
Summers Rubber Company Designs its Data Warehouse
Authors: Narasimhaiah Gorla, Steve Krehbiel
Venue: Interfaces (1999)

Businesses use data warehouses to provide the vast amount of information users need. In designing data warehouses, they must consider the level of granularity of data, time intervals, data integrity, use of data marts, data scrubbing, distributed design, and trade-offs between system operating costs and response time. We computed querying and update costs for a data warehouse at Summers Rubber Company. While the company staff benefited from the information provided by the data warehouse, there were problems related to data quality and user skills. Using the data warehouse at Summers took disk storage space of only 18 percent of the transaction database storage requirement and using data marts took only three percent.
A Novel Built-In Self-Repair Approach for Embedded RAMs
Authors: Shyue-Kung Lu
Venue: Journal of Electronic Testing: Theory and Applications (2003)

In this paper, a novel built-in self-repair approach, block-level reconfiguration architecture, is proposed. Our approach is based on the concept of divided word line (DWL) for high-capacity memories, including SRAMs and DRAMs. This concept is widely used in low-power memory designs. However, the characteristics of divided word line memories have not been used for fault-tolerant applications. Therefore, we propose the block_repair fault-tolerant architecture based on the structure of DWL for high-capacity memories. The redundant rows of a memory array are divided into blocks and reconfiguration is performed at the block level instead of the traditional row level. Our fault-tolerant architecture can improve the yield for memory fabrication significantly. Moreover, the characteristics of low power and fast access time of DWL memories are also preserved. The reconfiguration mechanism of our block_repair architecture requires negligible hardware overhead. According to experimental results, the hardware overheads are less than 0.73% and 0.48% for 256-Kbit SRAMs and 8-Mbit DRAMs, respectively. The repair rate of our approach with previous memory repair algorithms is compared. It is found that block_repair approach improves repair rate significantly. The yield improvement over traditional row-based approaches is also analyzed. Simulated results show that the present approach can significantly improve fabrication yield.
A Bilevel Model for Toll Optimization on a Multicommodity Transportation Network
Authors: Luce Brotcorne, Martine Labbé, Patrice Marcotte, Gilles Savard
Venue: Transportation Science (2001)

We consider the problem of determining a set of optimal tolls on the arcs of a multicommodity transportation network. The problem is formulated as a bilevel mathematical program where the upper level consists in a firm that raises revenues from tolls set on arcs of the network, while the lower level is represented by a group of users travelling on shortest paths with respect to a generalized travel cost.