ppxscal/embedding_network huggingface dataset:
On universal linear prediction of Gaussian data
Authors: S. S. Kozat
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

In this paper, we derive some of the stochastic properties of a universal linear predictor, through analyses similar to those generally made in the adaptive signal processing literature. A. C. Singer et al. (see IEEE Trans. Signal Proc., vol.47, no.10, p.2685-2700, Oct. 1999) introduced a predictor whose sequentially accumulated mean squared error for any bounded individual sequence was shown to be as small as that for any linear predictor of order less than some maximum order m. For stationary Gaussian time series, we generalize these results, and remove the boundedness restriction. In this paper we show that the learning curve of this universal linear predictor is dominated by the learning curve of the best order predictor used in the algorithm.
The Probabilistic Relationship Between the Assignment and Asymmetric Traveling Salesman Problems
Authors: Alan Frieze, Gregory B. Sorkin
Venue: SIAM Journal on Computing (2006)

We consider the gap between the cost of an optimal assignment in a complete bipartite graph with random edge weights, and the cost of an optimal traveling salesman tour in a complete directed graph with the same edge weights. Using an improved &ldquo;patching&rdquo; heuristic, we show that with high probability the gap is $O((\ln n)^2/n)$, and that its expectation is $\Omega(1/n)$. One of the underpinnings of this result is that the largest edge weight in an optimal assignment has expectation $\Theta(\ln n / n)$. A consequence of the small assignment-TSP gap is an $e^{\tilde{O}(\sqrt{n})}$-time algorithm which, with high probability, exactly solves a random asymmetric traveling salesman instance. In addition to the assignment-TSP gap, we also consider the expected gap between the optimal and second-best assignments; it is at least $\Omega(1/n^2)$ and at most $O(\ln n/n^2)$.
Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting
Authors: Paolo Buono, Catherine Plaisant, Adalberto Simeone, Aleks Aris, Galit Shmueli, Wolfgang Jank
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called Similarity-Based Forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.
Optimization in companion search spaces: the case of cross-entropy and the Levenberg-Marquardt algorithm
Authors: C. L. Fancourt
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02 (2001)

We present a new learning algorithm for the supervised training of multilayer perceptions for classification that is significantly faster than any previously known method. Like existing methods, the algorithm assumes a multilayer perceptron with a normalized exponential (softmax) output trained under a cross-entropy criterion. However, this output-criteria pairing turns out to have poor properties for existing optimization methods (backpropagation and its second order extensions) because second-order expansion of the network weights about the optimal solution is not a good approximation. The proposed algorithm overcomes this limitation by defining a new search space for which a second-order expansion is valid and such that the optimal solution in the new space coincides with the original criterion. This allows the application of the Levenberg-Marquardt search procedure to the cross-entropy criterion, which was previously thought applicable only to a mean square error criteria.
The Challenges of Building Advanced Mechatronic Systems
Authors: Wilhelm Schafer, Heike Wehrheim
Venue: 2007 Future of Software Engineering (2007)

Mechatronics is an engineering discipline integrating the fields of mechanical engineering, electrical engineering and computer science. While the word "mechatronics" already has a long history, it is only the last ten years that we see their application all around us. Cars, CD players, washing machines, railways are all examples of mechatronic systems. The main characteristic (and driving force) of recent advances is the progressively tighter coupling of mechanic and electronic components with software. This makes software engineering (together with network technology) the main computer science discipline involved in mechatronics. In this paper we survey current developments and discuss future trends in mechatronics, in particular from a software engineering point of view. The future of mechatronics will specifically see a move towards a high degree of adaptibility and self-organisation. This poses new challenges on software engineering, especially on modelling, code generation and analysis. We exemplify existing as well as future strands by a collaborative research and development project of a mechatronic rail system from the University of Paderborn.
An algorithm to minimize leakage through simultaneous input vector control and circuit modification
Authors: Nikhil Jayakumar, Sunil P Khatri
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

Leakage power currently comprises a large fraction of the total power consumption of an IC. Techniques to minimize leakage have been researched widely. In this paper, we present an approach which minimizes leakage by simultaneously modifying the circuit while deriving the input vector that minimizes leakage. In our approach, we selectively modify a gate so that its output (in sleep mode) is in a state which helps minimize the leakage of other gates in its transitive fanout. Gate replacement is performed in a slack-aware manner, to minimize the resulting delay penalty.
Models of selected problems in mathematical finance and numerical methods for stochastic differential equations
Authors: Harbir Lamba
Venue:  (2007)

The research conducted for this dissertation addresses two different problems from computational science and mathematical modeling.The first problem concerns the relaxation of the Efficient Market Hypothesis and the development of agent-based models that can help explain the consistent, but poorly understood, non-Gaussian statistical properties of real financial markets. A simple yet robust class of models is introduced in which the agents are driven both by rational considerations and also by less rational, but psychologically plausible, factors. It is shown that these models are capable of reproducing many of the important features of real markets. Furthermore, the results suggest that the two most significant deviations from Gaussian behavior in the price returns, namely 'fat tails' and volatility clustering, may have different causes allowing for future models that can more accurately reproduce them.The second area of research is the analysis and development of efficient variable timestepping algorithms for the numerical solution of stochastic differential equations. The starting point is a previously introduced algorithm that uses a dual-error-control strategy. One local error estimate corresponds to the error in the drift and the other to the diffusion. It is proved that under certain modes of operation the algorithm is mean-square stable for a class of test problems with multiplicative noise. Additional error controls are then introduced to determine whether they result in improved performance on different test problems. The research demonstrates that algorithms based upon such error controls are feasible and can result in large efficiency and stability improvements over their fixed timestepping counterparts.
Fault-tolerant typed assembly language
Authors: Frances Perry, Lester Mackey, George A. Reis, Jay Ligatti, David I. August, David Walker
Venue: Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation (2007)

A transient hardware fault occurs when an energetic particle strikes a transistor, causing it to change state. Although transient faults do not permanently damage the hardware, they may corrupt computations by altering stored values and signal transfers. In this paper, we propose a new scheme for provably safe and reliable computing in the presence of transient hardware faults. In our scheme, software computations are replicated to provide redundancy while special instructions compare the independently computed results to detect errors before writing critical data. In stark contrast to any previous efforts in this area, we have analyzed our fault tolerance scheme from a formal, theoretical perspective. To be specific, first, we provide an operational semantics for our assembly language, which includes a precise formal definition of our fault model. Second, we develop an assembly-level type system designed to detect reliability problems in compiled code. Third, we provide a formal specification for program fault tolerance under the given fault model and prove that all well-typed programs are indeed fault tolerant. In addition to the formal analysis, we evaluate our detection scheme and show that it only takes 34% longer to execute than the unreliable version.
The effect of a computer-based, spectrometer tutorial on chemistry students' learning in a uv/vis spectroscopy laboratory experiment
Authors: Frances Lawrenz
Venue:  (2006)

It is common for fairly sophisticated instruments to be used in undergraduate, general chemistry, laboratory courses. Typically, these instruments are treated as incidental to the experiment: students are given extensive operating instructions, but told little or nothing about how they work, because understanding the instruments themselves is not an objective of the course. The implicit assumption is that chemical principles can be deduced simply from accurate data. However, cognitive load theory (Sweller, 1988, 2005) predicts it would be more difficult for students with limited prior knowledge to make sense of their data if they do not know how measurements made with the instruments are actually derived from their physical sample. Therefore, treating laboratory instruments as incidental may actually make it more difficult for students to learn the chemical concepts that underlie the data they collect. This experimental study was intended to determine whether a multimedia tutorial, designed to help students understand how a UV/vis spectrophotometer works, brings about any changes in performance on a laboratory experiment about food dye solutions. Working in pairs, 750 students were randomly assigned to receive either the tutorial (treatment) or an alternative task (comparison) as an introduction to an experiment that was a regular part of an undergraduate, general chemistry, laboratory course. Students' responses to all laboratory questions were collected and scored. The amount of time students spent on each laboratory task was collected as well. On average, treatment students completed many of the laboratory tasks significantly more quickly than comparison students. Treatment students typically also provided more concise responses to many of the laboratory questions. Unfortunately, no differences were found in scores on laboratory questions. Therefore, while there is evidence the tutorial helped students learn more efficiently, evidence could not be found that they learned more deeply. Potential explanations for these results and their implications are discussed.
Progressive ranking of range aggregates
Authors: Hua-Gang Li, Hailing Yu, Divyakant Agrawal, Amr El Abbadi
Venue: Data Knowledge Engineering (2007)

Ranking-aware queries have been gaining much attention recently in many applications such as multimedia databases, search engines and data streams. They are, however, not only restricted to such applications but are also very useful in On-Line Analytical Processing (OLAP) applications. In this paper, we introduce aggregation ranking queries in OLAP data cubes motivated by an online advertisement tracking data warehouse application. These queries aggregate information over a specified range and then return the ranked order of the aggregated values. For instance, an advertiser might be interested in the top-k publishers over the last three months in terms of sales obtained through the online advertisements placed on the publishers. They differ from range aggregate queries in that range aggregate queries are mainly concerned with an aggregate operator such as SUM and MIN/MAX over the selected ranges of all dimensions in the data cubes. Existing techniques for range aggregate queries are not able to process aggregation ranking queries efficiently. Hence, in this paper we propose new algorithms to handle this problem. The essence of the proposed algorithms is based on both ranking and cumulative information to progressively rank aggregation results. Furthermore we empirically evaluate our techniques and the experimental results show that the query cost is improved significantly.
Meta-Regression: A Framework for Robust Reactive Optimization
Authors: Daniel W. McClary, Violet R. Syrotiuk, Murat Kulahci
Venue: Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems (2007)

Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in which system components self-organize to changing conditions in a manner that is robust, or affected minimally by other sources of variability. Meta-regression extends profiling, providing a methodology for model-building when there is incomplete knowledge of the mechanisms and interactions of a nonlinear system.
On Improving the Extended Aloha Protocol for Broadband Powerline Communications Networks with Light and Heavy Disturbances
Authors: T. Chiras, P. Koutsakis, M. Paterakis
Venue: Proceedings of the Third International Conference on Networking and Services (2007)

Powerline Communications (PLC) are currently being considered as an alternative for high-speed data communications and Internet access. With multiple outlets in almost every room, power lines are already the most pervasive network in the home or small office. This work presents a new Medium Access Control (MAC) protocol for the "last mile" access PLC networks. Via an extensive simulation study, our protocol is compared to a well-known protocol from the literature in terms of the efficiency of the transmission of short messages; our protocol is shown to excel both in network utilization and in the average signaling delay required for the completion of the transmission request procedure, in both the cases of a lightly and heavily disturbed PLC network.
Unmodified device driver reuse and improved system dependability via virtual machines
Authors: Joshua LeVasseur, Volkmar Uhlig, Jan Stoess, Stefan Götz
Venue: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (2004)

We propose a method to reuse unmodified device drivers and to improve system dependability using virtual machines. We run the unmodified device driver, with its original operating system, in a virtual machine. This approach enables extensive reuse of existing and unmodified drivers, independent of the OS or device vendor, significantly reducing the barrier to building new OS endeavors. By allowing distinct device drivers to reside in separate virtual machines, this technique isolates faults caused by defective or malicious drivers, thus improving a system's dependability. We show that our technique requires minimal support infrastructure and provides strong fault isolation. Our prototype's network performance is within 3-8% of a native Linux system. Each additional virtual machine increases the CPU utilization by about 0.12%. We have successfully reused a wide variety of unmodified Linux network, disk, and PCI device drivers.
Real-time high accurate cell loss recovery technique for speech over ATM networks
Authors: K. Matsumoto
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01 (1996)

In speech transmission over asynchronous transfer mode (ATM) networks, ATM cell loss cannot be avoided. Over 0.1 percent of cell loss is detected by the human ear, and over 1 percent of cell loss degrades speech articulation. Therefore, the recovery of cell loss is necessary for the improvement of speech quality. For this improvement, several methods have been studied. However, they have problems of speech quality, the delay acceptable for ATM, or the computational and implementational complexity. Moreover, most of them were not evaluated in a realistic telecommunication environment. For speech quality improvement over ATM networks, a real-time high accurate cell loss recovery method is proposed, where a great simplification and large data reduction are achieved by introducing trinary waveforms and encoding. Evaluations in a realistic telecommunication environment show that the method sufficiently improves the speech quality (90-100 percent correct recovery of ATM cell loss, and 20-40 dB improvement of signal-to-noise ratio). The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Parametric spectral estimation on a single FPGA
Authors: S. Bellis
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04 (1999)

Parametric, model based, spectral estimation techniques can offer increased frequency resolution over conventional short-term fast Fourier transform methods, overcoming limitations caused by the windowing of sampled, time domain, input data. However, parametric techniques are significantly more computationally demanding than the Fourier based methods and require a wider range of arithmetic functionality; for example, operations such as division and square-root are often necessary. These arithmetic processes exhibit communication bottleneck and their hardware implementation can be inefficient when used in conjunction with multipliers. A programmable, bit-serial, multiplier/divider, which overcomes the bottleneck problems by using a data interleaving scheme, is introduced. This interleaved processor is used to show how the parametric modified covariance spectral estimator can be efficiently routed on a field programmable gate array for real-time applications.
ATLAS: a new text alignment architecture
Authors: Bettina Schrader
Venue: Proceedings of the COLING/ACL on Main conference poster sessions (2006)

We are presenting a new, hybrid alignment architecture for aligning bilingual, linguistically annotated parallel corpora. It is able to align simultaneously at paragraph, sentence, phrase and word level, using statistical and heuristic cues, along with linguistics-based rules. The system currently aligns English and German texts, and the linguistic annotation used covers POS-tags, lemmas and syntactic constitutents. However, as the system is highly modular, we can easily adapt it to new language pairs and other types of annotation. The hybrid nature of the system allows experiments with a variety of alignment cues to find solutions to word alignment problems like the correct alignment of rare words and multiwords, or how to align despite syntactic differences between two languages. First performance tests are promising, and we are setting up a gold standard for a thorough evaluation of the system.
Learning to combine distances for complex representations
Authors: Adam Woznica, Alexandros Kalousis, Melanie Hilario
Venue: Proceedings of the 24th international conference on Machine learning (2007)

The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Both the representation of the learning instances and the dissimilarity employed on that representation should be determined on the basis of domain knowledge. However, even in the presence of domain knowledge, it can be far from obvious which complex representation should be used or which dissimilarity should be applied on the chosen representation. In this paper we present a framework that allows to combine different complex representations of a given learning problem and/or different dissimilarities defined on these representations. We build on ideas developed previously on metric learning for vectorial data. We demonstrate the utility of our method in domains in which the learning instances are represented as sets of vectors by learning how to combine different set distance measures.
Scheduling deadline-constrained bulk data transfers to minimize network congestion
Authors: Bin Bin Chen, Pascale Vicat-Blanc Primet
Venue: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (2007)

Tight coordination of resource allocation among end points in Grid networks often requires a data mover service to transfer a voluminous dataset from one site to another in a specified time interval. With flexibility at its best, the transfer can start from any time after its arrival, use any and even time variant bandwidth value, as long as it is completed before its deadline. Given a set of such tasks, we study the Bulk Data Transfer Scheduling (BDTS) problem, which searches for the optimal bandwidth allocation profile for each task to minimize the overall network congestion. We show that the multi-interval scheduling, which divides the active window of a task into multiple intervals and assigns bandwidth value independently in each of them, is both sufficient and necessary to attain the optimality in BDTS. Specifically, we show that BDTS can be solved in polynomial time as a Maximum Concurrent Flow Problem. The optimal solution attained is in the form of multi-interval scheduling with the number of intervals upper-bounded. Simulations are conducted over several representative topologies to demonstrate the significant advantage of optimal solutions.
A classification for the implementations of heterogeneous strong migration of computations
Authors: Anolan Milanes, Noemi Rodriguez, Bruno Schulze
Venue: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (2007)

The Grid's intrinsic characteristics of high instability and unpredictability, makes adaptation a must-have requirement. The capability of moving a running computation to another available host has the desirable property of allowing the topology of distributed applications to adapt dynamically, reacting to changes in the environment. An outstanding example is that of Opportunistic Computing, where a mechanism must exist in order to evict remote computations in favor of the local owner. This work studies the problem of heterogeneous strong migration, identifying its structure and the taxonomy of the methods currently used for its implementation.