ppxscal/embedding_network huggingface dataset:
Sigest
Authors: Staff
Venue: SIAM Review (2007)

In applied mathematics and scientific computation, we commonly generate mathematical models of great size and complexity. These can consist of hundreds of thousands or millions of parameters and algebraic or differential equations. Our field consistently makes great advances in the ability to solve larger and larger models both through improved algorithms and effective utilization of ever faster computers. Still, in many situations, it is very important to be able to produce simplified mathematical models that represent the problem well. The reasons for using simplified models range from the efficiency of solving them to their ability to focus upon the primary characteristics of the problem. An overall approach of producing such simplified models is called reduced-order models, and one of the key techniques for generating reduced-order models is proper orthogonal decomposition (POD). POD is applied in a variety of fields and applications under a variety of names. For example, in statistics, principal component analysis techniques are used to transform a large set of possibly correlated variables into a smaller set of uncorrelated variables. The method of empirical orthogonal functions performs a related transformation in the area of models of geophysical fluid dynamics. Naturally, a key question is how well reduced-order models reflect the original mathematical model. A variety of existing research has addressed this question. In this issue&rsquo;s SIGEST paper, &ldquo;Error Estimation for Reduced-Order Models of Dynamical Systems,&rdquo; by C. Homescu, L. Petzold, and R. Serban, which originally appeared in the SIAM Journal on Numerical Analysis in 2005, the authors take this question one step further. They pose, and address, the question of how well the results of the reduced-order model generated by POD reflect the solution to perturbations of the original model. These perturbations could come from changes to the initial conditions, or to the model parameters. Based on a combination of the small-sample statistical condition estimation method and error estimation using the adjoint method, the authors are able to establish ranges of perturbations to the original model for which the estimates produced by the reduced-order model retain a reasonable degree of validity. Their approach produces a type of condition number that can be calculated a priori and that indicates the sensitivity of the results of the reduced-order model to changes in the parameters of the original model. Their mathematical analysis is followed by several numerical examples that validate their mathematical estimates. We hope that this paper will help introduce SIAM readers to the important area of reduced-order models, as well as to important new research about the applicability of these models.
Speaker verification with low storage requirements
Authors: J. Schalkwyk
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

As speaker verification continues to gain acceptance for security applications, the day is drawing near when millions of users worldwide will be using some form of speaker verification system to gain access to secure information. These accesses will most probably be via the telephone network. Within this scenario a huge data flow across long distances is expected, in order to have the speaker's templates available for verification. This paper presents initial work on reducing the data requirements for text-dependent speaker verification. It is shown how data storage and accuracy can be traded off in such systems.
Predictive vector quantization of wideband LSF using narrowband LSF for bandwidth scalable coders
Authors: Hiroyuki Ehara, Toshiyuki Morii, Koji Yoshida
Venue: Speech Communication (2007)

For implementing a bandwidth-scalable coder, a wideband line spectral frequency (LSF) quantizer was developed. It works in combination with a narrowband LSF quantizer. A new predictive vector quantization was introduced to the wideband LSF quantizer. The predictive vector quantizer is based on the use of several predictive contributions, which include first-order auto regressive (AR) prediction and vector quantization (VQ) codebook mapping. One feature of the new predictive vector quantizer is exploitation of the correlation between wideband and narrowband LSFs quantized in the previous frame for estimating wideband LSF in the current frame. A 16-bit switched predictive three-stage vector quantizer was used to encode estimation residues. Results showed that introduction of the predictor brought about a performance improvement of 0.3dB in spectral distortion. This paper describes procedures of designing the predictor and the three-stage codebook, as well as simulation results.
Heterogeneous Clustered VLIW Microarchitectures
Authors: Alex Aleta, Josep M. Codina, Antonio Gonzalez, David Kaeli
Venue: Proceedings of the International Symposium on Code Generation and Optimization (2007)

Increasing performance, while at the same time reducing power consumption, is a major design tradeoff in current microprocessors. In this paper, we investigate the potential of using a heterogeneous clustered VLIW microarchitecture. In the proposed microarchitecture, each cluster, the interconnection network and the supporting memory hierarchy can run at different frequencies and voltages. Some of the clusters can then be configured to be performanceoriented and run at high frequency, while the other clusters can be configured to be low-power-oriented and run at lower frequencies, thus reducing overall consumption. For this heterogeneous design to be effective, we need to select the most suitable frequencies and voltages for each component. We propose a scheme to choose these parameters based on a model that estimates the energy consumption and the execution time of floating-point codes at compile time. Finally, we present a modulo scheduling technique based on graph partitioning that exploits the opportunities presented on heterogeneous clustered microarchitectures. Results show that the Energy-Delay2 product (ED2) can be significantly reduced by 15% on average for a microarchitecture with 4-clusters and by as much as 35% for selected programs.
The Confluence Property for Petri Nets and its Applications
Authors: Ioana Leahu, Ferucio Laurentiu Tiplea
Venue: Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2006)

A Petri net is confluent if its firing relation is confluent, i.e., for any two reachable markings there exists a marking reachable from both of them. We prove that confluence is a decidable property for Petri nets and it is preserved by asynchronous parallel composition. Applications to Petri net structural transformations and term rewriting systems are then pointed out.
Case-based introspective learning
Authors: Zhongzhi Shi, Sulan Zhang
Venue: Proceedings of the Fourth IEEE International Conference on Cognitive Informatics (2005)

Introspective learning, as a method to improve the learning efficiency, has become an active area of research. In this paper, introspective learning and a general introspective learning mode are discussed. Some related problems such as meta-level reasoning, taxonomy of failure, and the relation between case-based reasoning and introspective learning are represented. Based on the importance of case-based reasoning in introspective learning, a case representation and case retrieval mechanism appropriate to introspective learning are described in detail.
A Time-Frequency Based Method for the Detection of Epileptic Seizures in EEG Recordings
Authors: Alexandros T. Tzallas, Markos G. Tsipouras, Dimitrios I. Fotiadis
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

novel three-stage method for the analysis of electroencephalographic (EEG) signals, concerning epileptic seizures, is proposed. First, segments of the EEG signals are analyzed using a time-frequency distribution and then, several features are extracted for each segment, representing the energy distribution over the time-frequency plane. Those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments (existence of epileptic seizure or not). The evaluation results are very promising, indicating overall accuracy from 89.4% to 99%.
Head-pose and illumination invariant three-dimensional audio-visual speech recognition
Authors: Michael I. Miller
Venue:  (2007)

Speech perception is bimodal, employing not only the acoustic signal, but also visual cues. Audio-visual speech recognition aims to improve the performance of conventional automated speech recognition by incorporating visual information. Due to a fundamentally limited two-dimensional representation employed, current approaches for visual feature extraction lack invariance to speaker's pose and illumination in the environment. The research presented in this thesis aims to develop three-dimensional methods for visual feature extraction that alleviate the above-mentioned limitation.Following the concepts of Grenander's General Pattern Theory, the prior knowledge of speaker's face is described by a prototype, which consists of a 3-D surface and a texture. The variability in observed video images of a speaker associated with pose, articulatory facial motion, and illumination is represented by transformations acting on the prototype and forming the group of geometric and photometric variability. Facial motion is described as smooth deformations of the prototype surface and is learned from motion capture data. The effects of illumination are accommodated by analytically constructing surface scalar fields that express relative changes in the face surface irradiance.We derive a multi-resolution tracking algorithm for estimation of speaker's pose, articulatory facial motion and illumination from uncalibrated monocular video sequences. The inferred facial motion parameters are utilized as visual features in audio-visual speech recognition. An application of our approach to large-vocabulary audio-visual speech recognition is presented. Speaker-independent speech recognition combines audio and visual models at the utterance level. We demonstrate that the visual features derived using our 3-D approach significantly improve speech recognition performance across a wide range of acoustic signal-to-noise ratios.
ANEMONE: A Pan-European Testbed to Validate IPv6 Mobility Technologies
Authors: Laszlo Bokor, Nicolas Montavont, Paolo Di Francesco, Thierry Ernst, Tobias Hof, Jari Korva
Venue: Proceedings of the 2007 International Symposium on Applications and the Internet Workshops (2007)

The paper reports on an ongoing European Commission funded R&D project called ANEMONE [1] which aims at realizing a large-scale testbed for mobility technologies. This testbed will provide support of mobile users and devices, as well as enhanced services by integrating cutting edge IPv6 node and network mobility solutions. Among the key features of this deployment will be multihoming initiatives through the use of the current and emerging wireless access technologies. We review our progress of integrating existing testbeds and extensions covering campus and metropolitan areas with large spectrum of real end users, and discuss the conditions of creating this architecture.
Fast-Moving Zombies: Botnets Stay a Step Ahead of the Fixes
Authors: Greg Goth
Venue: IEEE Internet Computing (2007)

Malware authors have discovered a bonanza in always-on connections that often feature vulnerable operating systems and feckless computer owners. Industry experts say the crisis has just started.
Probabilistic models for topic detection and tracking
Authors: F. Walls
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01 (1999)

We present probabilistic models for use in detecting and tracking topics in broadcast news stories. Our information retrieval (IR) models are formally explained. The topic detection and tracking (TDT) initiative is discussed. The application of probabilistic models to the topic detection and tracking tasks is developed, and enhancements are discussed. We discuss four variations of these models, and we report our preliminary test results from the current TDT corpus.
Dual versus primal-dual interior-point methods for linear and conic programming
Authors: M. J. Todd
Venue: Mathematical Programming: Series A and B (2007)

We observe a curious property of dual versus primal-dual path-following interior-point methods when applied to unbounded linear or conic programming problems in dual form. While primal-dual methods can be viewed as implicitly following a central path to detect primal infeasibility and dual unboundedness, dual methods can sometimes implicitly move away from the analytic center of the set of infeasibility/unboundedness detectors.
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Authors: Ke Xu, Frédéric Boussemart, Fred Hemery, Christophe Lecoutre
Venue: Artificial Intelligence (2007)

In this paper, we show that the models of random CSP instances proposed by Xu and Li [K. Xu, W. Li, Exact phase transitions in random constraint satisfaction problems, Journal of Artificial Intelligence Research 12 (2000) 93-103; K. Xu, W. Li, Many hard examples in exact phase transitions with application to generating hard satisfiable instances, Technical report, CoRR Report cs.CC/0302001, Revised version in Theoretical Computer Science 355 (2006) 291-302] are of theoretical and practical interest. Indeed, these models, called RB and RD, present several nice features. First, it is quite easy to generate random instances of any arity since no particular structure has to be integrated, or property enforced, in such instances. Then, the existence of an asymptotic phase transition can be guaranteed while applying a limited restriction on domain size and on constraint tightness. In that case, a threshold point can be precisely located and all instances have the guarantee to be hard at the threshold, i.e., to have an exponential tree-resolution complexity. Next, a formal analysis shows that it is possible to generate forced satisfiable instances whose hardness is similar to unforced satisfiable ones. This analysis is supported by some representative results taken from an intensive experimentation that we have carried out, using complete and incomplete search methods.
Multi-dimensional parametric estimation: two-dimensional sharpening by predictive bandwidth extrapolation and fast algorithms for three-dimensional autoregressive estimation
Authors: S. Lawrence Marple,  Jr.
Venue:  (2006)

Achieving sharpened (enhanced detail) features of a multi-dimensional data source using the linear prediction (LP) bandwidth extrapolation (BWE) technique in the transform domain is the main objective of this research. The evolution of sensor technology has provided acquisition scenarios in which the data format is inherently multi-dimensional, including hyperspectral imaging (HSI), interferometric synthetic aperture radar (IF-SAR) imaging, and radar space-time adaptive processing (STAP). In all these applications, fully multi-dimensional signal processing that has the capability (1) to enhance the resolution of the final multi-dimensional analysis result, and (2) to provide reduced-dimension parametric features of the multi-dimensional data for purposes of data encoding/compression is highly desirable. This thesis provides algorithmic techniques that achieve both capabilities using a novel 2-D BWE approach and its associated fast computational algorithms. Furthermore, 3-D LP fast algorithms were developed, as part of this research, that estimate 3-D AR parameters of the original 3-D sensor domain data, which are then used to produce high resolution 3-D AR spectral estimates. These new 3-D algorithms will become components of future research in 3-D BWE algorithms that sharpen features in 3-D data sources.
Performance Analysis of Optical Switched Networks with Two-Way Reservation
Authors: Hozumi Kawanami, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi
Venue: Proceedings of the Second International Conference on Informatics Research for Development of Knowledge Society Infrastructure (2007)

Performance of an optical burst switching with two-way reservation is analyzed using queueing theoretical approach in this paper. The switching considered hereafter exploits both the resource sharing nature of packet switching and the highspeed transmission capability of wavelength division multiplexing in optical networks. Under the swiching architecture, when a group of packets to be addressed to the same edge node of an optical network and thus to be put in an optical burst gets ready for transmission at an edge node, the node sends a request for service to a centralized control node to get wavelength allocated for the duration of a burst transmission. After an offset interval, a designated wavelength is allocated to the request if available. The delay might cause the degradation of QoS, especially for real time applications. One of possible improvements in the above mechanism is for an edge node to send a request for a slot in advance before it counts an enough number of packets to fill up a slot and to expect that the remaining space in a slot is filled with packets arriving in an offset delay. We first describe a queueing model of a finite buffer where a batch of packets are served in a burst and then derive the probability distribution of the number of packets in the buffer. As for performance measures, we obtain the packet loss probability, the average transmission delay and the utilization factor of bursts.
Interactive presentation: Automatic hardware synthesis from specifications: a case study
Authors: Roderick Bloem, Stefan Galler, Barbara Jobstmann, Nir Piterman, Amir Pnueli, Martin Weiglhofer
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

We propose to use a formal specification language as a high-level hardware description language. Formal languages allow for compact, unambiguous representations and yield designs that are correct by construction. The idea of automatic synthesis from specifications is old, but used to be completely impractical. Recently, great strides towards efficient synthesis from specifications have been made. In this paper we extend these recent methods to generate compact circuits and we show their practicality by synthesizing an arbiter for ARM's AMBA AHB bus and a generalized-buffer from specifications given in PSL. These are the first industrial examples that have been synthesized automatically from their specifications.
Generative Programming Approach for Building Pervasive Computing Applications
Authors: Devdatta Kulkarni, Anand Tripathi
Venue: Proceedings of the 1st International Workshop on Software Engineering for Pervasive Computing Applications, Systems, and Environments (2007)

In this paper we present a generative programming approach for building context-aware applications. In this approach, a context-aware application is programmed using high-level specification constructs provided in our programming framework. The runtime environment of the application is generated from this specification by a middleware. We demonstrate the utility of this approach by presenting an example case-study.
A quantitative version of Trotter's approximation theorem
Authors: Elisabetta M. Mangino, Ioan Rasa
Venue: Journal of Approximation Theory (2007)

A quantitative version, based on modified K-functionals, of the classical Trotter's theorem concerning the approximation of C"0-semigroups is presented. The result is applied to the study of the degree of convergence of the iterated Bernstein operators on the N-dimensional simplex to their limiting semigroup.
Experimental perspectives on learning from imbalanced data
Authors: Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
Venue: Proceedings of the 24th international conference on Machine learning (2007)

We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the objective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.
Design of cascaded controllers for DC drives using evolutionary algorithms
Authors: F. Cupertino, D. Naso, L. Salvatore, B. Turchiano
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

In this paper, we describe a new design procedure for cascaded controller for electrical drives based on evolutionary algorithms. Most electric drives have two separate controllers for current and speed control, which are in general designed in two consecutive steps (firstly the current controller and then the speed one). Using a hybrid evolutionary algorithm designed to test and compare controllers of different orders, we search simultaneously for the couple of discrete anti-windup controllers achieving the optimal compromise of cost and performance indices related to both current and speed responses. Simulation comparison with conventional design techniques on a non-linear DC drive with variable load are presented to show the effectiveness of the approach.
Towards Bridging the Gap between Goal-Oriented Requirements Engineering and Compositional Architecture Development
Authors: Sebastian Herold, Andreas Metzger, Andreas Rausch, Heiko Stallbaum
Venue: Proceedings of the Second Workshop on SHAring and Reusing architectural Knowledge Architecture, Rationale, and Design Intent (2007)

Requirements engineering and architectural design are key activities for the successful development of software-intensive systems. Although both activities are strongly intertwined and interrelated, many steps to date are driven solely by the intuition and the architectural knowledge of individuals. Thus, systematic approaches are needed which could minimize the risks of wrong early requirements and architectural decisions and foster the explicit reuse of architectural knowledge especially for supporting early design decisions are. In this paper, we present our vision of supporting the early requirements and architectural decisions by making explicit the interactions between the early steps and artifacts in requirements engineering and architectural design and thereby reusing architectural knowledge. To this end, we propose to couple goal-oriented requirements engineering and compositional architecture development by means of a repository of reusable, generic architectural drivers.
Induction of M-convex functions by linking systems
Authors: Yusuke Kobayashi, Kazuo Murota
Venue: Discrete Applied Mathematics (2007)

Induction (or transformation) by bipartite graphs is one of the most important operations on matroids, and it is well known that the induction of a matroid by a bipartite graph is again a matroid. As an abstract form of this fact, the induction of a matroid by a linking system is known to be a matroid. M-convex functions are quantitative extensions of matroidal structures, and they are known as discrete convex functions. As with matroids, it is known that the induction of an M-convex function by networks generates an M-convex function. As an abstract form of this fact, this paper shows that the induction of an M-convex function by linking systems generates an M-convex function. Furthermore, we show that this result also holds for M-convex functions on constant-parity jump systems. Previously known operations such as aggregation, splitting, and induction by networks can be understood as special cases of this construction.