ppxscal/embedding_network huggingface dataset:
Survey and Review
Authors: Randy LeVeque
Venue: SIAM Review (2005)

Anyone who regularly uses noise-cancelling headphones on airplanes appreciates the ability to actively control the solution to wave equations. Active control of noise over a larger scale---for example, in the entire airplane cabin---is currently being pursued by industry using actuators or smart materials. The mathematics involved in this technology raises interesting and challenging problems in control theory and numerical analysis. The Survey and Review article in this issue concerns some related mathematical problems where the goal is to drive the solution of a partial differential equation to zero by choosing an appropriate boundary condition. In simple cases the exact boundary control can be determined analytically, but for practical versions of this problem numerical methods must often be employed. A natural way to proceed is to discretize the PDE and determine the discrete control needed for the resulting discrete algebraic system. One might hope that by refining the grid, increasingly accurate approximations to the exact control needed for the PDE would be obtained. Unfortunately this is often not the case---the discrete control for the discrete system may not converge to the exact control as the mesh is refined. In the following paper, Enrique Zuazua explores this topic and the closely related issue of observability of a wave equation. While naive numerical discretization often fails, numerical methods can be applied if suitable filtering or regularization are applied, and a survey of several different approaches along these lines is also included. So put on those noise-cancelling headphones and settle down for a good read.
On Constructing k-Connected k-Dominating Set in Wireless Networks
Authors: Fei Dai, Jie Wu
Venue: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01 (2005)

An important problem in wireless networks, such as wireless ad hoc and sensor networks, is to select a few nodes to form a virtual backbone that supports routing and other tasks such as area monitoring. Previous work in this area has focused on selecting a small virtual backbone for high efficiency. We propose to construct a k-connected k-dominating set (k-CDS) as a backbone to balance efficiency and fault tolerance. Three localized k-CDS construction protocols are proposed. The first protocol randomly selects virtual backbone nodes with a given probability p_k, where p_k depends on network condition and the value of k. The second protocol is a deterministic approach. It extends Wu and Dai's coverage condition, which is originally designed for 1-CDS construction, to ensure the formation of a k-CDS. The last protocol is a hybrid of probabilistic and deterministic approaches. It provides a generic framework that can convert many existing CDS algorithms into k-CDS algorithms. These protocols are evaluated via a simulation study.
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
Authors: Rong Yan, Milind Naphade
Venue: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01 (2005)

For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose a novel multi-view semi-supervised learning algorithm called semi-supervised cross feature learning(SCFL). The proposed algorithm has two advantages over co-training. First, SCFL can theoretically guarantee its performance not being significantly degraded even when the assumption of view sufficiency fails. Also, SCFL can also handle additional views of unlabeled data even when these views are absent from the training data. As demonstrated in the TRECVIDý03 semantic concept extraction task, the proposed SCFL algorithm not onlysignificantly outperforms the conventional co-training algorithms, but also comes close to achieving the performance when the unlabeled set were to be manually annotated and used for training along with the labeled data set.
Fuzzy financial profitability analyses of demand side management alternatives from participant perspective
Authors: J. N. Sheen
Venue: Information Sciences&mdash;Informatics and Computer Science: An International Journal (2005)

This paper derives fuzzy profitability models for the financial evaluation of different demand side management (DSM) alternatives. The present value of cost (PVC) and equivalent uniform annual cost (EUAC) models are selected to determine the least-cost solution, while the net present value (NPV), pay back year (PBY) and benefit/cost ratio (BCR) models are proposed for the execution of cost-benefit analysis. Since fuzzy results are in the form of a complex non-linear representation, and do not always provide a totally ordered set in the same way that crisp numbers do, the current paper approximates the resulting fuzzy profitability indexes by a triangular fuzzy number initially, and then uses the Mellin transform to obtain the means and variances of the approximated fuzzy numbers in order to determine their relative ranking in a decision-making process. The performance of the proposed models is verified through the simulation of a numerical example and by considering their application to two practical DSM programs in Taiwan. In the first case study, the fuzzy least-cost solution is used to decide upon the installation of either a conventional air conditioning system or a cooling energy storage (CES) air conditioning system. In the second case study relating to cogeneration, a fuzzy cost-benefit analysis is applied to compare the relative profitabilities of an Extracted Condensing Steam Turbine Generator system and a Backpressure Steam Turbine Generator system. These investigations confirm not only that the results of the proposed fuzzy economic models are consistent with those of the conventional crisp models, but also demonstrate that the proposed methods represent readily implemented possibility analysis tools for use in the arena of uncertain financial decision-making.
On uniform amplification of hardness in NP
Authors: Luca Trevisan
Venue: Proceedings of the thirty-seventh annual ACM symposium on Theory of computing (2005)

We continue the study of amplification of average-case complexity within NP, and we focus on the uniform case.We prove that if every problem in NP admits an efficient uniform algorithm that (averaged over random inputs and over the internal coin tosses of the algorithm) succeeds with probability at least 1 ⁄ 2 +1 (log n )α, then for every problem in NP there is an efficient uniform algorithm that succeeds with probability at least 1 - 1 poly(n). Above, α > 0 is an absolute constant.Previously, Trevisan (FOCS'03) presented a similar redution between success 3⁄4 + 1 (log n) and 1 - 1 (log n)α Stronger reductions, due to O'Donnell (STOC'02) and Healy, Vadhan and Viola (FOCS'04) are known in the non-uniform case.
An on-line algorithm for creating self-organizing fuzzy neural networks
Authors: Gang Leng, Girijesh Prasad, Thomas Martin McGinnity
Venue: Neural Networks (2004)

This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically.
Mobile search with text messages: designing the user experience for google SMS
Authors: Rudy Schusteritsch, Shailendra Rao, Kerry Rodden
Venue: CHI '05 extended abstracts on Human factors in computing systems (2005)

SMS (Short Message Service) is already a hugely popular communication technology for mobile phones, with users sending billions of text messages to each other every year. The goal of the Google SMS service is to provide this large existing base of users with access to the types of information they are most likely to need when mobile. Users simply send their query as a text message and receive their results in the reply. This enables users to search for information without having to upgrade their phone or subscribe to specialized mobile data services. In this paper we describe how we worked with the Google SMS team on the iterative design of the service's user experience. In particular, we focus on how we attempted to overcome two major constraints: the technical limitations of the SMS standard, and users' current conceptual models of both SMS and Google search.
A preliminary survey of artificial intelligence machines
Authors: Harold Boley
Venue: Issue 72 (July 1980) (1980)

This paper is first attempt at a survey of concrete and abstract machines relevant to AI research, inspired by some recent developments in personal computers. In particular, we consider machines supporting higher-level programming languages used in AI, mainly LISP. The emphasis will be on gaining a global perspective on the emerging field of AI machines rather than on a detailed treatment of specific issues. Thus, readers who are specialists in one of the fields touched (e.g., in parallel and associative machines) will not learn anything new about their particular field (on the contrary, they may contribute additional AI-relevant material to later versions of the survey). However, particularly for AI researchers worrying about the efficiency of their systems, this survey may provide a quick access to this important new research areas. For more specific questions the references in the extensive topic-indexed bibliography should be consulted.
Area-efficient two-dimensional architectures for finite field inversion and division
Authors: Zhiyuan Yan, Dilip V. Sarwate
Venue: Proceedings of the 15th ACM Great Lakes symposium on VLSI (2005)

Many high-throughput two-dimensional architectures for finite field inversion and division are based on reformulations of the extended Euclidean algorithm (EEA). These reformulated EEAs usually keep track of two pairs of data polynomials (or registers), and the operations of the reformulated EEAs are only within each pair of polynomials. In this paper, we propose a new reformulated EEA wherein the operations within the two pairs of polynomials are identical. Hence, the two pairs of polynomials in our new reformulated EEA can be concatenated into one pair. By utilizing some inherent properties of the EEA, we further reduce the computational complexity of our reformulated EEA by 25%. Based on our reformulated EEA, we propose new two-dimensional inversion and division architectures. How much hardware saving the reduced computational complexity translates into depends on how control mechanisms are implemented. Regardless of the implementation of control signals, our new architectures require smaller numbers of gates and latches while achieving comparable or better throughput, latency, and critical path delay in comparison to the best architectures in the literature.
Inherently Workload-Balanced Clustered Microarchitecture
Authors: Jaume Abella, Antonio Gonzalez
Venue: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01 (2005)

The performance of clustered microarchitectures relies on steering schemes that try to find the best trade-off between workload balance and inter-cluster communication penalties. In previously proposed clustered processors, reducing communication penalties and balancing the workload are opposite targets, since improving one usually implies a detriment in the other. In this paper we propose a new clustered microarchitecture that can minimize communication penalties without compromising workload balance. The key idea is to arrange the clusters in a ring topology in such a way that results of one cluster can be forwarded to the neighbor cluster with a very short latency. In this way, minimizing communication penalties is favored when the producer of a value and its consumer are placed in adjacent clusters, which also favors workload balance. The proposed microarchitecture is shown to outperform a state-of-the-art clustered processor. For instance, for an 8-cluster configuration and just one fully pipelined unidirectional bus, 15% speedup is achieved on average for FP programs.
Weak minimization of DFA: an algorithm and applications
Authors: B. Ravikumar, G. Eisman
Venue: Theoretical Computer Science (2004)

DFA minimization is an important problem in algorithm design and is based on the notion of DFA equivalence: Two DFA's are equivalent if and only if they accept the same set of strings. In this paper, we propose a new notion of DFA equivalence (that we call weak-equivalence): We say that two DFA's are weakly equivalent if they both accept the same number of strings of length k for every k. The motivation for this problem is as follows. A large number of counting problems can be solved by encoding the combinatorial objects we want to count as strings over a finite alphabet. If the collection of encoded strings is accepted by a DFA, then standard algorithms from computational linear algebra can be used to solve the counting problem efficiently. When applying this approach to large-scale applications, the bottleneck is the space complexity since the computation involves a matrix of order k × k if k is the size of the underlying DFA M. This leads to the natural question: Is there a smaller DFA that is weakly equivalent to M? We present an algorithm of time complexity O(k3) to find a compact DFA weakly equivalent to a given DFA. We illustrate, in the case of a tiling problem, that our algorithm reduces a (strongly minimal) DFA by a factor close to 1/2.
Using non-linear dynamical systems for web searching and ranking
Authors: Panayiotis Tsaparas
Venue: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2004)

In the recent years there has been a surge of research activity in the area of information retrieval on the World Wide Web, using link analysis of the underlying hypertext graph topology. Most of the algorithms in the literature can be described as dynamical systems, that is, the repetitive application of a function on a set of weights. Algorithms that rely on eigenvector computations, such as HITS and PAGERANK, correspond to linear dynamical systems. In this work we consider two families of link analysis ranking algorithms that no longer enjoy the linearity property of the previous approaches. We study in depth an interesting special case of these two families. We prove that the corresponding non-linear dynamical system converges for any initialization, and we provide a rigorous characterization of the combinatorial properties of the stationary weights. The study of the weights provides a clear and insightful view of the mechanics of the algorithm. We also present extensive experimental results that demonstrate that our algorithm performs well in practice.
Sparse matrix storage revisited
Authors: Malik Silva
Venue: Proceedings of the 2nd conference on Computing frontiers (2005)

In this paper, we consider alternate ways of storing a sparse matrix and their effect on computational speed. They involve keeping both the indices and the non-zero elements in the sparse matrix in a single data structure. These schemes thus help reduce memory system misses that occur when the usual indexing based storage schemes are used to store sparse matrices and give promising performance improvements.
Topic spotting common sense translation assistant
Authors: Jae-woo Chung, Rachel Kern, Henry Lieberman
Venue: CHI '05 extended abstracts on Human factors in computing systems (2005)

Our Translation Assistant applies common sense logic to the problem of translating speech in real time from one language to another. Using speech recognition combined with a software translator to do word-by-word translation is not feasible because speech recognition is notorious for poor results. Word-by-word translation requires grammatically correct input to translate accurately. Therefore, translation of speech that is potentially already fraught with errors is not expected to be good. Our Translation Assistant works around these problems by using the context of the conversation as a basis for translation. It takes the location and the speaker as input to establish the circumstances. Then it uses a common sense knowledge network to do topic-spotting using key words from the conversation. It only translates the most likely topics of conversation into the target language. This system does not require perfect speech recognition, yet enables end-users to have a sense of the conversation.
Real Time Texture Classification using Field Programmable Gate Arrays
Authors: Geoffrey Wall, Faizal Iqbal, Jason Isaacs, Xiuwen Liu, Simon Foo
Venue: Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop (2004)

In this paper we present a novel hardware / software approach to implement a highlyaccurate texture classification algorithm. We propose the use of field programmable gatearrays (FPGAs) to efficiently compute multiple convolutions in parallel that are required by the spectral histogram representation we employ. The combination of custom hardware and software allows us to have a classifier that is able to achieve results of over 99% accuracy ata rate of roughly 6000 image classifications per second on a challenging real texture dataset.
A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method
Authors: E. Delivopoulos, J. B. Theocharis
Venue: Information Sciences&mdash;Informatics and Computer Science: An International Journal (2004)

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.
Job Superscheduler Architecture and Performance in Computational Grid Environments
Authors: Hongzhang Shan, Leonid Oliker, Rupak Biswas
Venue: Proceedings of the 2003 ACM/IEEE conference on Supercomputing (2003)

Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex scientific problems. However, a number of major technical hurdles, including distributed resource management and effective job scheduling, stand in the way of realizing these gains. In this paper, we propose a novel grid superscheduler architecture and three distributed job migration algorithms. We also model the critical interaction between the superscheduler and autonomous local schedulers. Extensive performance comparisons with ideal, central, and local schemes using real workloads from leading computational centers are conducted in a simulation environment. Additionally, synthetic workloads are used to perform a detailed sensitivity analysis of our superscheduler. Several key metrics demonstrate that substantial performance gains can be achieved via smart superscheduling in distributed computational grids.
Combining FT-MPI with H2O: Fault-Tolerant MPI Across Administrative Boundaries
Authors: Dawid Kurzyniec, Vaidy Sunderam
Venue: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02 (2005)

We observe increasing interest in aggregating geographically distributed, heterogeneous resources to perform large scale computations. MPI remains the most popular programming paradigm for such applications; however, as the size of computing environments increases, fault tolerance aspects become critically important. We argue that the fault tolerance model proposed by FT-MPI fits well in geographically distributed environments, even though its current implementation is confined to a single administrative domain. We propose to overcome these limitations by combining FTMPI with the H2O resource sharing framework. Our approach allows users to run fault tolerant MPI programs on heterogeneous, geographically distributed shared machines, without sacrificing performance and with minimal involvement of resource providers.
Optimizing the Access Performance and Data Freshness of Distributed Cache Objects Considering User Access Pattern
Authors: Rohan Samarasinghe, Yoshihiro Yasutake, Takaichi Yoshida
Venue: Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2 (2005)

Caching has long been used in most fields of the computer systems to enhance the scalability of the objects, improve the performance and reduce the access latency. A significant effort has been made to introduce cache-coherent algorithms for maintaining the consistency of such data objects in cache by keeping a higher freshness of the data. Updating the cache objects considering the access behavior and user preferences is one of an attractive solutions to maintain the consistency. In this paper, we define Quality of Data(QoD) metric to evaluate the amount of freshness that is necessary to satisfy the user requirements. We then focus on the update scheduling method that analyzes the access behavior of the cache objects and predicts the time interval for updating the cache. Here, we introduce the "Average Update Interval method" that uses the most recent time between access values, to predict the time interval. Using our proposed algorithm, the user can not only access the preference view but also he can get the maximum QoD of the objects. Moreover we performed extensive experiments using web log data and simulation data. Then the results could conclude that the cache objects are maintaining more than 70% of consistency with the original objects.
Boosting SVM classifiers by ensemble
Authors: Yan-Shi Dong, Ke-Song Han
Venue: Special interest tracks and posters of the 14th international conference on World Wide Web (2005)

By far, the support vector machines (SVM) achieve the state-of-the-art performance for the text classification (TC) tasks. Due to the complexity of the TC problems, it becomes a challenge to systematically develop classifiers with better performance. We try to attack this problem by ensemble methods, which are often used for boosting weak classifiers, such as decision tree, neural networks, etc., and whether they are effective for strong classifiers is not clear.