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81
Exploiting process lifetime distributions for dynamic load balancing
 ACM Transactions on Computer Systems
, 1997
"... We consider policies for CPU load balancing in networks of workstations. We address the question of whether preemptive migration (migrating active processes) is necessary, or whether remote execution (migrating processes only at the time of birth) is sufficient for load balancing. We show that resol ..."
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Cited by 313 (31 self)
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We consider policies for CPU load balancing in networks of workstations. We address the question of whether preemptive migration (migrating active processes) is necessary, or whether remote execution (migrating processes only at the time of birth) is sufficient for load balancing. We show that resolving this issue is strongly tied to understanding the process lifetime distribution. Our measurements indicate that the distribution of lifetimes for a UNIX process is Pareto (heavytailed), with a consistent functional form over a variety of workloads. We show how to apply this distribution to derive a preemptive migration policy that requires no handtuned parameters. We used a tracedriven simulation to show that our preemptive migration strategy is far more effective than remote execution, even when the memory transfer cost is high.
Maximum Entropy Models for Natural Language Ambiguity Resolution
, 1998
"... The best aspect of a research environment, in my opinion, is the abundance of bright people with whom you argue, discuss, and nurture your ideas. I thank all of the people at Penn and elsewhere who have given me the feedback that has helped me to separate the good ideas from the bad ideas. I hope th ..."
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Cited by 202 (1 self)
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The best aspect of a research environment, in my opinion, is the abundance of bright people with whom you argue, discuss, and nurture your ideas. I thank all of the people at Penn and elsewhere who have given me the feedback that has helped me to separate the good ideas from the bad ideas. I hope that Ihave kept the good ideas in this thesis, and left the bad ideas out! Iwould like toacknowledge the following people for their contribution to my education: I thank my advisor Mitch Marcus, who gave me the intellectual freedom to pursue what I believed to be the best way to approach natural language processing, and also gave me direction when necessary. I also thank Mitch for many fascinating conversations, both personal and professional, over the last four years at Penn. I thank all of my thesis committee members: John La erty from Carnegie Mellon University, Aravind Joshi, Lyle Ungar, and Mark Liberman, for their extremely valuable suggestions and comments about my thesis research. I thank Mike Collins, Jason Eisner, and Dan Melamed, with whom I've had many stimulating and impromptu discussions in the LINC lab. Iowe them much gratitude for their valuable feedback onnumerous rough drafts of papers and thesis chapters.
Enhancing Supervised Learning with Unlabeled Data
, 2000
"... In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new "cotraining" strategy for using unlabeled data to improve the performance ..."
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Cited by 120 (0 self)
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In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new "cotraining" strategy for using unlabeled data to improve the performance of standard supervised learning algorithms. Unlike much of the prior work, such as the cotraining procedure of Blum and Mitchell (1998), we do not assume there are two redundant views both of which are sufficient for perfect classification. The only requirement our cotraining strategy places on each supervised learning algorithm is that its hypothesis partitions the example space into a set of equivalence classes (e.g. for a decision tree each leaf defines an equivalence class). We evaluate our cotraining strategy via experiments using data from the UCI repository. 1. Introduction In many practical learning scenarios, there is a small amount of labeled data along with a lar...
Better prediction of protein cellular localization sites with the k nearest neighbors classifier
, 1997
"... We have compared four classifiers on the problem of predicting the cellular localization sites of proteins in yeast and E.coli. A set of sequence derived features, such as regions of high hydrophobicity, were used for each classifier. The methods compared were a structured probabilistic model specif ..."
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Cited by 83 (5 self)
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We have compared four classifiers on the problem of predicting the cellular localization sites of proteins in yeast and E.coli. A set of sequence derived features, such as regions of high hydrophobicity, were used for each classifier. The methods compared were a structured probabilistic model specifically designed for the localization problem, the k nearest neighbors classitier, the binary decision tree classifier, and the naive Bayes classifier. The result of tests using stratified cross validation shows the k nearest neighbors classifier to perform better than the other methods. In the case of yeast this difference was statistically significant using a crossvalidated paired t test. The result is an accuracy of approximately 60°/o for 10 yeast classes and 86 % for 8 E.coli classes. The best previously reported accuracies for these datasets were 55 % and 81% respectively.
Experiencing SAX: A Novel Symbolic Representation of Time Series. Data Mining and Knowledge Discovery Journal
, 2007
"... Abstract Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would pote ..."
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Cited by 51 (13 self)
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Abstract Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many symbolic representations of time series have been introduced over the past decades, they all suffer from two fatal flaws. First, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Second, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. In this work we formulate a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction,
Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter
, 2005
"... Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For su ..."
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Cited by 48 (7 self)
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Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications we developed a realtime system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear Gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear Gaussian model. Decoding was performed using a Kalman filter which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and Gaussian. In offline experiments, the Kalmanfilter reconstructions of hand trajectory were more accurate than previously reported results. The resulting decoding algorithm provides a principled probabilistic model of motorcortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightfor3 ward to implement. Additionally the formulation unifies and extends previous models of neural coding while prov...
Performance Prediction in Production Environments
 PROCEEDINGS OF THE IPPS/SPDP CONFERENCE
, 1998
"... Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional pointvalued performance parameters and prediction models are often inaccurate since they can only represent one point in a range of possible behaviors. We ad ..."
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Cited by 46 (8 self)
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Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional pointvalued performance parameters and prediction models are often inaccurate since they can only represent one point in a range of possible behaviors. We address this problem by allowing characteristic application and system data to be represented by a set of possible values and their probabilities, which we call stochastic values. In this paper, we give a practical methodology for using stochastic values as parameters to adaptable performance prediction models. We demonstrate their usefulness for a distributed SOR application, showing stochastic values to be more effective than single (point) values in predicting the range of application behavior that can occur during execution in production environments. 1 Introduction Parallel and distributed production platforms provide a challenging environment in which to achieve performance. The impa...
Exploration of MultiState Environments: Local Measures and BackPropagation of Uncertainty
, 1998
"... . This paper presents an action selection technique for reinforcement learning in stationary Markovian environments. This technique may be used in direct algorithms such as Qlearning, or in indirect algorithms such as adaptive dynamic programming. It is based on two principles. The rst is to dene a ..."
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Cited by 42 (1 self)
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. This paper presents an action selection technique for reinforcement learning in stationary Markovian environments. This technique may be used in direct algorithms such as Qlearning, or in indirect algorithms such as adaptive dynamic programming. It is based on two principles. The rst is to dene a local measure of the uncertainty using the theory of bandit problems. We show that such a measure suers from several drawbacks. In particular, a direct application of it leads to algorithms of low quality that can be easily misled by particular congurations of the environment. The second basic principle was introduced to eliminate this drawback. It consists of assimilating the local measures of uncertainty to rewards, and backpropagating them with the dynamic programming or temporal dierence mechanisms. This allows reproducing globalscale reasoning about the uncertainty, using only local measures of it. Numerical simulations clearly show the eciency of these propositions. Keywords: ...
More Accurate Tests for the Statistical Significance of Result Differences
, 2000
"... Sl,ai,isl,ica,1 significance, kest,ing of diflkn'ences in values of metrics like recall, i)rccision mM batmined Fs(x)re is a ne(:cssm'y tmrt of eml)iri(:a.l ua.t;ural bmguage 1)ro(;easing. Unfi)rtunat,ely we inertly used tests of'ten ulnlerestimake i,he significm ce mM so a.re less likely to detect ..."
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Cited by 42 (0 self)
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Sl,ai,isl,ica,1 significance, kest,ing of diflkn'ences in values of metrics like recall, i)rccision mM batmined Fs(x)re is a ne(:cssm'y tmrt of eml)iri(:a.l ua.t;ural bmguage 1)ro(;easing. Unfi)rtunat,ely we inertly used tests of'ten ulnlerestimake i,he significm ce mM so a.re less likely to detect, difihrences l,hat exist between difM'eni techniques. This 1111deresl;illla(;ioll comes from an independcn(;e asSmnl)tion that is offten violated. Wc l)oint, out sonic ltse]'Hl l.es(,s (,]mL do nol, make lhis assmnl) tion, including contput;a, tionallyiltcnsive domizai,ion tests.